# WFC3 Infrared Spectroscopic Parallel (WISP) survey: photometric and emission-line data release

A.J Battisti, M.B Bagley, M Rafelski, I Baronchelli, Y.S Dai, A.L Henry, H Atek, J Colbert, M.A Malkan, P.J McCarthy, et al.

► To cite this version:

A.J Battisti, M.B Bagley, M Rafelski, I Baronchelli, Y.S Dai, et al., WFC3 Infrared Spectroscopic Parallel (WISP) survey: photometric and emission-line data release. Mon.Not.Roy.Astron.Soc., 2024, 530 (1), pp.894-928. 10.1093/mnras/stae911. hal-04584585

HAL Id: hal-04584585

https://hal.science/hal-04584585

Submitted on 23 May 2024

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# WFC3 Infrared Spectroscopic Parallel (WISP) survey: photometric and emission-line data release

A.J Battisti, M.B Bagley, M Rafelski, I Baronchelli, Y.S Dai, A.L Henry, H

Atek, J Colbert, M.A Malkan, P.J McCarthy, et al.

► To cite this version:

A.J Battisti, M.B Bagley, M Rafelski, I Baronchelli, Y.S Dai, et al., WFC3 Infrared Spectroscopic Parallel (WISP) survey: photometric and emission-line data release. Monthly Notices of the Royal Astronomical Society, 2024, 530 (1), pp.894-928. 10.1093/mnras/stae911. hal-04584585

HAL Id: hal-04584585

https://hal.science/hal-04584585v1

Submitted on 23 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# WFC3 Infrared Spectroscopic Parallel (WISP) survey: photometric and emission-line data release

A. J. Battisti<sup>1,2*</sup> M. B. Bagley<sup>3*</sup> M. Rafelski<sup>4,5</sup> I. Baronchelli<sup>6,7</sup> Y.S. Dai<sup>8</sup> A. L. Henry<sup>4,5</sup>

H. Atek<sup>9</sup> J. Colbert<sup>10</sup> M. A. Malkan<sup>11</sup> P. J. McCarthy<sup>12</sup> C. Scarlata<sup>13</sup> B. Siana<sup>14</sup>

H. I. Teplitz<sup>10</sup> A. Alavi<sup>10</sup> K. Boyett<sup>2,15</sup> A. J. Bunker<sup>16</sup> J. P. Gardner<sup>17</sup> N. P. Hathi<sup>4</sup>

D. Masters<sup>10</sup> V. Mehta<sup>10</sup> M. Rutkowski<sup>18</sup> K. Shahinyan<sup>13</sup> B. Sunnquist<sup>4</sup> and X. Wang<sup>19,20,21</sup>

*Affiliations are listed at the end of the paper*

Accepted 2024 March 22. Received 2024 March 9; in original form 2023 May 1

## ABSTRACT

We present reduced images and catalogues of photometric and emission-line data (~230 000 and ~8000 sources, respectively) for the WFC3 (Wide Field Camera 3) Infrared Spectroscopic Parallel (WISP) survey. These data are made publicly available on the *Mikulski Archive for Space Telescopes* and include reduced images from various facilities: ground-based *ugri*, *Hubble Space Telescope (HST)* WFC3, and *Spitzer IRAC* (Infrared Array Camera). Coverage in at least one additional filter beyond the WFC3/IR data are available for roughly half of the fields (227 out of 483), with ~20 per cent (86) having coverage in six or more filters from *u* band to IRAC 3.6 µm (0.35–3.6 µm). For the lower spatial resolution (and shallower) ground-based and IRAC data, we perform PSF (point spread function)-matched, prior-based, deconfusion photometry (i.e. forced-photometry) using the TPHOT software to optimally extract measurements or upper limits. We present the methodology and software used for the WISP emission-line detection and visual inspection. The former adopts a continuous wavelet transformation that significantly reduces the number of spurious sources as candidates before the visual inspection stage. We combine both WISP catalogues and perform spectral energy distribution fitting on galaxies with reliable spectroscopic redshifts and multiband photometry to measure their stellar masses. We stack WISP spectra as functions of stellar mass and redshift and measure average emission-line fluxes and ratios. We find that WISP emission-line sources are typically ‘normal’ star-forming galaxies based on the mass-excitation diagram ([O III]/Hβ versus $M_*$; 0.74 < $z_{grism}$ < 2.31), the galaxy main sequence (SFR versus $M_*$; 0.30 < $z_{grism}$ < 1.45), $S_{32}$ ratio versus $M_*$ (0.30 < $z_{grism}$ < 0.73), and $O_{32}$ and $R_{23}$ ratios versus $M_*$ (1.27 < $z_{grism}$ < 1.45).

**Key words:** catalogues – surveys – ISM: evolution – galaxies: evolution – galaxies: general – galaxies: photometry.

## 1 INTRODUCTION

Slitless spectroscopy provides simultaneous spectra for every source in an observed field of view (FOV). Such observations have the notable advantage that they can be used to efficiently perform blind spectroscopic surveys over large areas of the sky for the study of galaxy evolution, relative to slits, fibres, or integral field spectroscopy (IFS) that cover much smaller FOVs. This gain in mapping area from slitless spectroscopy usually comes at the expense of reduced sensitivity due to a higher background, more source confusion, and low to moderate spectral resolution ($R \sim 100–1000$) relative to slits or IFS ($R \gtrsim 2000$). The latter limits dynamical analyses and may result in lines blending together (e.g. $H\alpha + [N\ii]$).

Blind surveys provide a unique and unbiased view of galaxy evolution by avoiding issues of cosmic variance or photometric pre-selection. The grisms on the *Hubble Space Telescope (HST)* have been widely utilized to perform slitless spectroscopic surveys of thousands of galaxies at intermediate redshifts (0.5 $\lesssim$ $z \lesssim$ 2.5; e.g.

APPLES (ACS Pure Parallel Ly$\alpha$ Emission Survey), Pasquali et al. 2003; GRAPES (Grism ACS Program for Extragalactic Science), Pirzkal et al. 2004; WISP, Atek et al. 2010; 3D-HST, Brammer et al. 2012; PEARs (Probing Evolution And Reionization Spec-troscopically), Pirzkal et al. 2013; GLAss (Grism Lens Amplified Survey from Space), Treu et al. 2015; fIGs (Faint Infrared Grism Survey), Pirzkal et al. 2017; MAMMOTH–Grism (MApping the Most Massive Overdensity Through Hydrogen), Wang et al. 2022; cLEAR (cANDELS Ly$\alpha$ Emission at Reionization), Simons et al. 2023; MUfD (MUSE Ultra Deep Field), Revalski et al. 2023). Next generation facilities, including the *JWST*, *Euclid*, and the *Nancy Grace Roman Space Telescope (Roman)*, will revolutionize this capability by providing slitless spectroscopy for millions of galaxies that span larger areas of the sky and a wider range in cosmic time (e.g. Bagley et al. 2020).

The WFC3 (Wide Field Camera 3) Infrared Spectroscopic Parallel (WISP, Atek et al. 2010) survey was the largest, multicycle *HST* pure-parallel grism program. WISP obtained slitless spectroscopy for thousands of galaxies in 483 pointings using up to two near-infrared (NIR) grisms, *G102* (0.80–1.15 µm, $R \sim$ 210) and *G141* (1.08–1.69 µm, $R \sim$ 130). Pure-parallel surveys, such as WISP,

<footnote>* E-mail: Andrew.Battisti@anu.edu.au (AJB); mbagley@utexas.edu (MBB)</footnote>
# WFC3 Infrared Spectroscopic Parallel (WISP) survey: photometric and emission-line data release

A. J. Battisti<sup>1,2*</sup> M. B. Bagley<sup>3*</sup> M. Rafelski<sup>4,5</sup> I. Baronchelli<sup>6,7</sup> Y.S. Dai<sup>8</sup> A. L. Henry<sup>4,5</sup>

H. Atek<sup>9</sup> J. Colbert<sup>10</sup> M. A. Malkan<sup>11</sup> P. J. McCarthy<sup>12</sup> C. Scarlata<sup>13</sup> B. Siana<sup>14</sup>

H. I. Teplitz<sup>10</sup> A. Alavi<sup>10</sup> K. Boyett<sup>2,15</sup> A. J. Bunker<sup>16</sup> J. P. Gardner<sup>17</sup> N. P. Hathi<sup>4</sup>

D. Masters<sup>10</sup> V. Mehta<sup>10</sup> M. Rutkowski<sup>18</sup> K. Shahinyan<sup>13</sup> B. Sunnquist<sup>4</sup> and X. Wang<sup>19,20,21</sup>

*Affiliations are listed at the end of the paper*

Accepted 2024 March 22. Received 2024 March 9; in original form 2023 May 1

## ABSTRACT

We present reduced images and catalogues of photometric and emission-line data (~230 000 and ~8000 sources, respectively) for the WFC3 (Wide Field Camera 3) Infrared Spectroscopic Parallel (WISP) survey. These data are made publicly available on the *Mikulski Archive for Space Telescopes* and include reduced images from various facilities: ground-based *ugri*, *Hubble Space Telescope (HST)* WFC3, and *Spitzer IRAC* (Infrared Array Camera). Coverage in at least one additional filter beyond the WFC3/IR data are available for roughly half of the fields (227 out of 483), with ~20 per cent (86) having coverage in six or more filters from *u* band to IRAC 3.6 µm (0.35–3.6 µm). For the lower spatial resolution (and shallower) ground-based and IRAC data, we perform PSF (point spread function)-matched, prior-based, deconfusion photometry (i.e. forced-photometry) using the TPHOT software to optimally extract measurements or upper limits. We present the methodology and software used for the WISP emission-line detection and visual inspection. The former adopts a continuous wavelet transformation that significantly reduces the number of spurious sources as candidates before the visual inspection stage. We combine both WISP catalogues and perform spectral energy distribution fitting on galaxies with reliable spectroscopic redshifts and multiband photometry to measure their stellar masses. We stack WISP spectra as functions of stellar mass and redshift and measure average emission-line fluxes and ratios. We find that WISP emission-line sources are typically ‘normal’ star-forming galaxies based on the mass-excitation diagram ([O III]/Hβ versus $M_*$; 0.74 < $z_{grism}$ < 2.31), the galaxy main sequence (SFR versus $M_*$; 0.30 < $z_{grism}$ < 1.45), $S_{32}$ ratio versus $M_*$ (0.30 < $z_{grism}$ < 0.73), and $O_{32}$ and $R_{23}$ ratios versus $M_*$ (1.27 < $z_{grism}$ < 1.45).

**Key words:** catalogues – surveys – ISM: evolution – galaxies: evolution – galaxies: general – galaxies: photometry.

## 1 INTRODUCTION

Slitless spectroscopy provides simultaneous spectra for every source in an observed field of view (FOV). Such observations have the notable advantage that they can be used to efficiently perform blind spectroscopic surveys over large areas of the sky for the study of galaxy evolution, relative to slits, fibres, or integral field spectroscopy (IFS) that cover much smaller FOVs. This gain in mapping area from slitless spectroscopy usually comes at the expense of reduced sensitivity due to a higher background, more source confusion, and low to moderate spectral resolution ($R \sim 100–1000$) relative to slits or IFS ($R \gtrsim 2000$). The latter limits dynamical analyses and may result in lines blending together (e.g. $H\alpha + [NII]$).

Blind surveys provide a unique and unbiased view of galaxy evolution by avoiding issues of cosmic variance or photometric pre-selection. The grisms on the *Hubble Space Telescope (HST)* have been widely utilized to perform slitless spectroscopic surveys of thousands of galaxies at intermediate redshifts (0.5 $\lesssim$ $z \lesssim$ 2.5; e.g.

APPLES (ACS Pure Parallel Lyα Emission Survey), Pasquali et al. 2003; GRAPES (Grism ACS Program for Extragalactic Science), Pirzkal et al. 2004; WISP, Atek et al. 2010; 3D-HST, Brammer et al. 2012; PEARs (Probing Evolution And Reionization Spec-troscopically), Pirzkal et al. 2013; GLASS (Grism Lens Amplified Survey from Space), Treu et al. 2015; FIGS (Faint Infrared Grism Survey), Pirzkal et al. 2017; MAMMOTH–Grism (MApping the Most Massive Overdensity Through Hydrogen), Wang et al. 2022; CLEAR (CANDELS Lyα Emission at Reionization), Simons et al. 2023; MUDF (MUSE Ultra Deep Field), Revalski et al. 2023). Next generation facilities, including the JWST, Euclid, and the Nancy Grace Roman Space Telescope (Roman), will revolutionize this capability by providing slitless spectroscopy for millions of galaxies that span larger areas of the sky and a wider range in cosmic time (e.g. Bagley et al. 2020).

The WFC3 (Wide Field Camera 3) Infrared Spectroscopic Parallel (WISP, Atek et al. 2010) survey was the largest, multicycle HST pure-parallel grism program. WISP obtained slitless spectroscopy for thousands of galaxies in 483 pointings using up to two near-infrared (NIR) grisms, G102 (0.80–1.15 µm, $R \sim$ 210) and G141 (1.08–1.69 µm, $R \sim$ 130). Pure-parallel surveys, such as WISP,

<footnote>* E-mail: Andrew.Battisti@anu.edu.au (AJB); mbagley@utexas.edu (MBB)</footnote>
have the benefit that the data are obtained ‘for-free’ for numerous random fields that are independent and uncorrelated (i.e. unbiased). The versatility of large slitless surveys like WISP are demonstrated by the range of science cases that can be explored, such as: characterizing the star-forming galaxy main sequence (MS, Atek et al. 2014), characterizing dust attenuation (Domínguez et al. 2013) and dust attenuation curves (Battisti et al. 2022), characterizing the mass-metallicity relation (Henry et al. 2013, 2021), characterizing massive, quenched galaxies (Bedregal et al. 2013), identifying galaxy pairs (Dai et al. 2021), identifying single spectral lines through machine learning (Baronchelli et al. 2020, 2021), identifying Lyman-α emitters (Bagley et al. 2017), identifying bright, rare galaxies (Bagley et al. 2024), predicting emission-line galaxy number counts for future surveys (Colbert et al. 2013; Mehta et al. 2015; Bagley et al. 2020), investigating the mass-size relation of passive galaxies (Zanella et al. 2016), crowd-sourced analysis of slitless spectroscopic data (Dickinson et al. 2018), nitrogen enhancement of star-forming galaxies (Masters et al. 2014), and the discovery of very faint, distant (400 pc) brown dwarfs (Masters et al. 2012).

A challenge for pure-parallel slitless surveys is that the random fields are sporadic in position and typically do not have deep ancillary photometric data available (e.g. compared to legacy deep fields). To remedy this, the WISP team has carried out several supplementary observing programs to obtain additional photometry with a variety of facilities, with priority given to the deepest ~200 WISP fields. These include *HST* WFC3/UVIS optical, ground-based optical, and *Spitzer*/IRAC NIR data.

This paper describes the public data release of a self-consistent photometric catalogue, reduced images, and the emission-line catalogue of WISP fields. These data are hosted at the WISP website,<fnref n="1" /> on the *Mikulski Archive for Space Telescopes (MAST)*. Currently ~50 per cent of the WISP fields have additional photometric data beyond WFC3/IR. These data provide a valuable galaxy sample for legacy science and can serve as a useful reference for ongoing and future grism surveys with *JWST*, *Euclid*, and *Roman*.

This paper is organized as follows: Section 2 describes the observational data, Section 3 describes the WISP photometric pipeline, Section 4 presents the WISP photometric catalogue, Section 5 describes the WISP spectroscopic pipeline, Section 6 presents the WISP emission-line catalogue, Section 7 shows our results from combining both catalogues to study galaxies at $0.3 < z_{\text{grism}} < 2.3$, and Section 8 summarizes our main conclusions. Throughout this work, we adopt a lambda-cold dark matter cosmological model, with $H_0 = 70 \text{ km s}^{-1} \text{ Mpc}^{-1}$, $\Omega_M = 0.3$, and $\Omega_\Lambda = 0.7$. All magnitudes are in the AB magnitude system (Oke & Gunn 1983).

## 2 OBSERVATIONS AND DATA REDUCTION

### 2.1 WISP survey – *HST* WFC3/IR grism spectroscopy and imaging

The WISP survey (PI: M. Malkan; Atek et al. 2010) is a multicycle (cycles 17–23) *HST* pure-parallel program that obtained WFC3/IR observations for 483 pointings in random fields (i.e. location depended on the primary observing target and its position angle). Of the 483 parallel pointings (which we denote as ‘Par no.’; e.g. Par1, Par2, etc.), there are 40 fields with partial overlap (i.e. there are 443 unique fields). The position of the WISP pointings are shown in Fig. 1. The program IDs and details for the different *HST* cycles are summarized

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Locations of the 483 WISP fields. These fields are primarily located outside of the Galactic Plane. Most short parallel opportunities obtained WFC3/IR G141 grism data along with one imaging filter (F140W or F160W) and long opportunities obtained G102+G141 grisms and two imaging filters (F110W+F140W or F110W+F160W).</figcaption></figure>

in Table 1. The effective grism area of each WISP pointing is ~3.55 arcmin² in G102 and ~3.8 arcmin² in G141, relative to the full 4.6 arcmin² WFC3/IR FOV, due to area on both the left and right sides of each field being ‘lost’. On the left, this is because sources are not covered in the direct images necessary for source identification and wavelength calibration. On the right, this is because emission lines cannot be distinguished from contaminating zero-order images (Bagley et al. 2017). In principle, high-resolution ancillary data with a larger FOV, such as the WFC3/UVIS data available for a subset of fields, could be used to locate sources outside the WFC3/IR FOV. However, such an approach is not implemented for the current data release. Thus, the effective imaging and grism areas of the full WISP survey are ~2200 and ~1600 arcmin², respectively (this also accounts for field overlap). An overview of the imaging and grism filters that are included in this data release are shown in Fig. 2.

Due to the nature of parallel observations, the integration times for each field was set by the primary target observations. We refer the reader to Atek et al. (2010) for a complete description of the observing strategy and data reduction. In brief, short opportunities (1–3 continuous orbits; 180 fields) usually obtained G141 and one WFC3/IR filter, either F140W or F160W. However, a handful of short opportunities obtained G102 and F110W instead (4 fields). Long opportunities (four or more continuous orbits; 299 fields) obtained G102+G141 and two WFC3/IR filters, either F110W+F140W or F110W+F160W, except for Cycle 19 which used F140W-only. Cycle 20 and beyond are almost exclusively deep fields (G102 + G141; See Table 1). In general, grism integration times are ~6 × those for the direct images. For the long opportunities, the integration times in the two grisms were set to achieve approximately uniform sensitivity for an emission line of a given flux across the full wavelength range (~5:2 for G102:G141). The median $5\sigma$ detection limit for emission lines fluxes (point source) in both grisms is ~5 × $10^{-17}$ erg s$^{-1}$ cm$^{-2}$ (Atek et al. 2010), but we stress that this varies considerably with the length of the opportunity and variation in background levels (zodiacal light, Earth limb brightening, and telescope thermal emission). The line detection limits for each individual WISP fields are presented in Fig. 3 and Table 2.

Due to *HST* only having a single dispersion direction for each grism, and position/orientation being tied to the Primary observing target, all grism data are single orientation. We note that multi-orientation pure-parallel observations are possible on *JWST* due to the availability of multidirectional grisms, and that this is already being utilized (e.g. Cycle 1 PASSAGE survey with NIRISS, GO 1571; PI: M. Malkan, Malkan et al. 2021). Multi-orientation grism

<footnote n="1">¹https://archive.stsci.edu/preps/wisp/</footnote>
have the benefit that the data are obtained ‘for-free’ for numerous random fields that are independent and uncorrelated (i.e. unbiased). The versatility of large slitless surveys like WISP are demonstrated by the range of science cases that can be explored, such as: characterizing the star-forming galaxy main sequence (MS, Atek et al. 2014), characterizing dust attenuation (Domínguez et al. 2013) and dust attenuation curves (Battisti et al. 2022), characterizing the mass-metallicity relation (Henry et al. 2013, 2021), characterizing massive, quenched galaxies (Bedregal et al. 2013), identifying galaxy pairs (Dai et al. 2021), identifying single spectral lines through machine learning (Baronchelli et al. 2020, 2021), identifying Lyman-α emitters (Bagley et al. 2017), identifying bright, rare galaxies (Bagley et al. 2024), predicting emission-line galaxy number counts for future surveys (Colbert et al. 2013; Mehta et al. 2015; Bagley et al. 2020), investigating the mass-size relation of passive galaxies (Zanella et al. 2016), crowd-sourced analysis of slitless spectroscopic data (Dickinson et al. 2018), nitrogen enhancement of star-forming galaxies (Masters et al. 2014), and the discovery of very faint, distant (400 pc) brown dwarfs (Masters et al. 2012).

A challenge for pure-parallel slitless surveys is that the random fields are sporadic in position and typically do not have deep ancillary photometric data available (e.g. compared to legacy deep fields). To remedy this, the WISP team has carried out several supplementary observing programs to obtain additional photometry with a variety of facilities, with priority given to the deepest ~200 WISP fields. These include *HST* WFC3/UVIS optical, ground-based optical, and *Spitzer*/IRAC NIR data.

This paper describes the public data release of a self-consistent photometric catalogue, reduced images, and the emission-line catalogue of WISP fields. These data are hosted at the WISP website,<fnref n="1" /> on the *Mikulski Archive for Space Telescopes (MAST)*. Currently ~50 per cent of the WISP fields have additional photometric data beyond WFC3/IR. These data provide a valuable galaxy sample for legacy science and can serve as a useful reference for ongoing and future grism surveys with *JWST*, *Euclid*, and *Roman*.

This paper is organized as follows: Section 2 describes the observational data, Section 3 describes the WISP photometric pipeline, Section 4 presents the WISP photometric catalogue, Section 5 describes the WISP spectroscopic pipeline, Section 6 presents the WISP emission-line catalogue, Section 7 shows our results from combining both catalogues to study galaxies at $0.3 < z_{\text{grism}} < 2.3$, and Section 8 summarizes our main conclusions. Throughout this work, we adopt a lambda-cold dark matter cosmological model, with $H_0 = 70 \text{ km s}^{-1} \text{ Mpc}^{-1}$, $\Omega_M = 0.3$, and $\Omega_\Lambda = 0.7$. All magnitudes are in the AB magnitude system (Oke & Gunn 1983).

## 2 OBSERVATIONS AND DATA REDUCTION

### 2.1 WISP survey – *HST* WFC3/IR grism spectroscopy and imaging

The WISP survey (PI: M. Malkan; Atek et al. 2010) is a multicycle (cycles 17–23) *HST* pure-parallel program that obtained WFC3/IR observations for 483 pointings in random fields (i.e. location depended on the primary observing target and its position angle). Of the 483 parallel pointings (which we denote as ‘Par no.’; e.g. Par1, Par2, etc.), there are 40 fields with partial overlap (i.e. there are 443 unique fields). The position of the WISP pointings are shown in Fig. 1. The program IDs and details for the different *HST* cycles are summarized

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Locations of the 483 WISP fields. These fields are primarily located outside of the Galactic Plane. Most short parallel opportunities obtained WFC3/IR G141 grism data along with one imaging filter (F140W or F160W) and long opportunities obtained G102+G141 grisms and two imaging filters (F110W+F140W or F110W+F160W).</figcaption></figure>

in Table 1. The effective grism area of each WISP pointing is ~3.55 arcmin² in G102 and ~3.8 arcmin² in G141, relative to the full 4.6 arcmin² WFC3/IR FOV, due to area on both the left and right sides of each field being ‘lost’. On the left, this is because sources are not covered in the direct images necessary for source identification and wavelength calibration. On the right, this is because emission lines cannot be distinguished from contaminating zero-order images (Bagley et al. 2017). In principle, high-resolution ancillary data with a larger FOV, such as the WFC3/UVIS data available for a subset of fields, could be used to locate sources outside the WFC3/IR FOV. However, such an approach is not implemented for the current data release. Thus, the effective imaging and grism areas of the full WISP survey are ~2200 and ~1600 arcmin², respectively (this also accounts for field overlap). An overview of the imaging and grism filters that are included in this data release are shown in Fig. 2.

Due to the nature of parallel observations, the integration times for each field was set by the primary target observations. We refer the reader to Atek et al. (2010) for a complete description of the observing strategy and data reduction. In brief, short opportunities (1–3 continuous orbits; 180 fields) usually obtained G141 and one WFC3/IR filter, either F140W or F160W. However, a handful of short opportunities obtained G102 and F110W instead (4 fields). Long opportunities (four or more continuous orbits; 299 fields) obtained G102+G141 and two WFC3/IR filters, either F110W+F140W or F110W+F160W, except for Cycle 19 which used F140W-only. Cycle 20 and beyond are almost exclusively deep fields (G102 + G141; See Table 1). In general, grism integration times are ~6 × those for the direct images. For the long opportunities, the integration times in the two grisms were set to achieve approximately uniform sensitivity for an emission line of a given flux across the full wavelength range (~5:2 for G102:G141). The median $5\sigma$ detection limit for emission lines fluxes (point source) in both grisms is ~5 × $10^{-17}$ erg s$^{-1}$ cm$^{-2}$ (Atek et al. 2010), but we stress that this varies considerably with the length of the opportunity and variation in background levels (zodiacal light, Earth limb brightening, and telescope thermal emission). The line detection limits for each individual WISP fields are presented in Fig. 3 and Table 2.

Due to *HST* only having a single dispersion direction for each grism, and position/orientation being tied to the Primary observing target, all grism data are single orientation. We note that multi-orientation pure-parallel observations are possible on *JWST* due to the availability of multidirectional grisms, and that this is already being utilized (e.g. Cycle 1 PASSAGE survey with NIRISS, GO 1571; PI: M. Malkan, Malkan et al. 2021). Multi-orientation grism

<footnote n="1">¹https://archive.stsci.edu/preps/wisp/</footnote>
<table><caption>**Table 1.** Summary of WISP HST Survey (PI: M Malkan).</caption><thead><tr><th>GO ID</th><th>Cycle</th><th>Observing period<sup>a</sup></th><th>WFC3/UVIS<sup>b</sup></th><th>WFC3/IR</th><th>WFC3/IR grism</th><th>Orbits</th></tr></thead><tbody><tr><td>11696</td><td>17</td><td>2009 Nov 24–2011 May 1</td><td>F475X, F600LP</td><td>F110W, F140W, F160W</td><td>G102, G141</td><td>250</td></tr><tr><td>12283</td><td>18</td><td>2010 Oct 6–2012 Mar 11</td><td>F606W, F814W<sup>c</sup></td><td>F110W, F160W</td><td>G102, G141</td><td>280</td></tr><tr><td>12568</td><td>19</td><td>2011 Oct 29–2013 May 19</td><td>None</td><td>F140W</td><td>G102, G141</td><td>260</td></tr><tr><td>12902</td><td>20</td><td>2012 Oct 16–2014 Mar 20</td><td>F606W, F814W</td><td>F110W, F160W</td><td>G102, G141</td><td>260</td></tr><tr><td>13352</td><td>21</td><td>2013 Oct 31–2015 June 14</td><td>F606W, F814W</td><td>F110W, F160W</td><td>G102, G141</td><td>375</td></tr><tr><td>13517</td><td>21</td><td>2013 Dec 8–2015 Feb 12</td><td>F606W, F814W</td><td>F110W, F160W</td><td>G102, G141</td><td>200</td></tr><tr><td>14178</td><td>23</td><td>2015 Nov 30–2017 May 1</td><td>F606W, F814W</td><td>F110W, F140W, F160W</td><td>G102, G141</td><td>520</td></tr></tbody><tfoot><tr><td colspan="6">Total:</td><td>2145</td></tr></tfoot></table>

Notes. There are 184 short opportunity fields (one to three continuous orbits), 180 fields with G141 and either F140W or F160W, and 4 fields with G102 and F110W (see Section 2.1). There are 299 long opportunity fields (four or more continuous orbits); these obtained G102+G141 and either F110W+F140W or F110W + F160W, except for Cycle 19 which used F140W-only.

<sup>a</sup>Observations often extended beyond the nominal HST cycle period.

<sup>b</sup>UVIS data were only obtained for a subset of the deep fields (155 fields; see Section 2.2).

<sup>c</sup>Most UVIS data in this cycle used 2 × 2 binning in the F475X and F600LP filters and those are not included in the current data release.

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Filters used in the WISP survey and follow-up observations separated by (a) space- and (b) ground-based facilities. The WISP survey obtained WFC3/IR imaging together with the grism spectroscopy (dotted black curves) and all WISP fields have either F140W or F160W data, with deep fields also having F110W imaging. Additional photometry is inhomogeneous and comprises of WFC3/UVIS, Spitzer, and/or ground-based optical data (ugri) from Magellan, WIYN 3.5m, and Palomar. The F475X, F600LP, and IRAC ch2 curves are shown as thinner lines to highlight that they are available for <20 fields (other filters are >20 fields), with a breakdown shown in Table 5.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 3.** The 5σ emission-line sensitivities in the individual WISP fields (light grey curves). The blue dashed line indicates the median 5σ field depth. The depth varies significantly field-to-field due to variations in the exposure time and background levels in each field.</figcaption></figure>

data can be used to construct spatially resolved emission-line maps (e.g. Pirzkal et al. 2018; Wang et al. 2022).

All WISP data are reduced and calibrated with the WFC3 pipeline calwf3, together with custom scripts to improve the calibration and account for the specific challenges of undithered, pure-parallel observations. The foundation of the WISP reduction pipeline is described in Atek et al. (2010). Here, we additionally implement a multiple component fit to the sky background in the grism images. While the ‘input’ spectrum of the sky can be considered uniform across the FOV, the sky emission is not uniform on the detector. Typically, a single master sky image is scaled and fit to remove the background in each grism image, under the assumption that the relative strength of the background components remain the same exposure to exposure. However, as noted by Brammer et al. (2012), the structure in the sky of WFC3 grism images is highly variable, with zodiacal light and a helium emission line at 1.083 µm contributing. While the zodiacal component should stay relatively constant throughout a full set of exposures taken on the same date, the helium emission comes from Earth’s upper atmosphere and can vary on time-scales shorter than the length of an exposure as HST moves closer or farther from Earth and the telescope pointing changes. We model and subtract the background in each grism exposure with a linear combination of the zodiacal light and helium sky images created by Brammer, Ryan & Pirzkal (2015), calculating the amplitudes of each sky component with two iterations of a least-squares fit.

We use the AstroDrizzle and TWEAKREG routines of DRIZ-ZLEPAC (Gonzaga et al. 2012; Hoffmann et al. 2021) to combine the individual exposures, correcting for astrometric distortions and any alignment issues between exposures. The IR direct and grism images are drizzled onto a 0.08 arcsec pixel⁻¹ scale. Object detection in the IR direct images (F110W, F140W, and F160W) is performed with Source Extractor (version 2.5; Bertin & Arnouts 1996). For fields with imaging in two filters, we create a combined detection image and supplement the catalogue with sources detected individually in only one of the filters. We use the AXE software package (Kümmel et al. 2009) to extract and calibrate the spectra, using the Source Extractor catalogues as inputs.

## 2.2 HST WFC3/UVIS imaging

For 155 of the long opportunity fields, WFC3/UVIS (FOV = 2.7 arcmin×2.7 arcmin) imaging data were also obtained with some combination of F475X, F606W, F600LP, and/or F814W filters (only two of these at most for a single field). These observations were
<table><caption>**Table 1.** Summary of WISP HST Survey (PI: M Malkan).</caption><thead><tr><th>GO ID</th><th>Cycle</th><th>Observing period<sup>a</sup></th><th>WFC3/UVIS<sup>b</sup></th><th>WFC3/IR</th><th>WFC3/IR grism</th><th>Orbits</th></tr></thead><tbody><tr><td>11696</td><td>17</td><td>2009 Nov 24–2011 May 1</td><td>F475X, F600LP</td><td>F110W, F140W, F160W</td><td>G102, G141</td><td>250</td></tr><tr><td>12283</td><td>18</td><td>2010 Oct 6–2012 Mar 11</td><td>F606W, F814W<sup>c</sup></td><td>F110W, F160W</td><td>G102, G141</td><td>280</td></tr><tr><td>12568</td><td>19</td><td>2011 Oct 29–2013 May 19</td><td>None</td><td>F140W</td><td>G102, G141</td><td>260</td></tr><tr><td>12902</td><td>20</td><td>2012 Oct 16–2014 Mar 20</td><td>F606W, F814W</td><td>F110W, F160W</td><td>G102, G141</td><td>260</td></tr><tr><td>13352</td><td>21</td><td>2013 Oct 31–2015 June 14</td><td>F606W, F814W</td><td>F110W, F160W</td><td>G102, G141</td><td>375</td></tr><tr><td>13517</td><td>21</td><td>2013 Dec 8–2015 Feb 12</td><td>F606W, F814W</td><td>F110W, F160W</td><td>G102, G141</td><td>200</td></tr><tr><td>14178</td><td>23</td><td>2015 Nov 30–2017 May 1</td><td>F606W, F814W</td><td>F110W, F140W, F160W</td><td>G102, G141</td><td>520</td></tr></tbody><tfoot><tr><td colspan="6">Total:</td><td>2145</td></tr></tfoot></table>

Notes. There are 184 short opportunity fields (one to three continuous orbits), 180 fields with G141 and either F140W or F160W, and 4 fields with G102 and F110W (see Section 2.1). There are 299 long opportunity fields (four or more continuous orbits); these obtained G102+G141 and either F110W+F140W or F110W + F160W, except for Cycle 19 which used F140W-only.

<sup>a</sup>Observations often extended beyond the nominal HST cycle period.

<sup>b</sup>UVIS data were only obtained for a subset of the deep fields (155 fields; see Section 2.2).

<sup>c</sup>Most UVIS data in this cycle used 2 × 2 binning in the F475X and F600LP filters and those are not included in the current data release.

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Filters used in the WISP survey and follow-up observations separated by (a) space- and (b) ground-based facilities. The WISP survey obtained WFC3/IR imaging together with the grism spectroscopy (dotted black curves) and all WISP fields have either F140W or F160W data, with deep fields also having F110W imaging. Additional photometry is inhomogeneous and comprises of WFC3/UVIS, Spitzer, and/or ground-based optical data (ugri) from Magellan, WIYN 3.5m, and Palomar. The F475X, F600LP, and IRAC ch2 curves are shown as thinner lines to highlight that they are available for <20 fields (other filters are >20 fields), with a breakdown shown in Table 5.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 3.** The 5σ emission-line sensitivities in the individual WISP fields (light grey curves). The blue dashed line indicates the median 5σ field depth. The depth varies significantly field-to-field due to variations in the exposure time and background levels in each field.</figcaption></figure>

data can be used to construct spatially resolved emission-line maps (e.g. Pirzkal et al. 2018; Wang et al. 2022).

All WISP data are reduced and calibrated with the WFC3 pipeline calwf3, together with custom scripts to improve the calibration and account for the specific challenges of undithered, pure-parallel observations. The foundation of the WISP reduction pipeline is described in Atek et al. (2010). Here, we additionally implement a multiple component fit to the sky background in the grism images. While the ‘input’ spectrum of the sky can be considered uniform across the FOV, the sky emission is not uniform on the detector. Typically, a single master sky image is scaled and fit to remove the background in each grism image, under the assumption that the relative strength of the background components remain the same exposure to exposure. However, as noted by Brammer et al. (2012), the structure in the sky of WFC3 grism images is highly variable, with zodiacal light and a helium emission line at 1.083 µm contributing. While the zodiacal component should stay relatively constant throughout a full set of exposures taken on the same date, the helium emission comes from Earth’s upper atmosphere and can vary on time-scales shorter than the length of an exposure as HST moves closer or farther from Earth and the telescope pointing changes. We model and subtract the background in each grism exposure with a linear combination of the zodiacal light and helium sky images created by Brammer, Ryan & Pirzkal (2015), calculating the amplitudes of each sky component with two iterations of a least-squares fit.

We use the AstroDrizzle and TWEAKREG routines of DRIZ-ZLEPAC (Gonzaga et al. 2012; Hoffmann et al. 2021) to combine the individual exposures, correcting for astrometric distortions and any alignment issues between exposures. The IR direct and grism images are drizzled onto a 0.08 arcsec pixel⁻¹ scale. Object detection in the IR direct images (F110W, F140W, and F160W) is performed with Source Extractor (version 2.5; Bertin & Arnouts 1996). For fields with imaging in two filters, we create a combined detection image and supplement the catalogue with sources detected individually in only one of the filters. We use the AXE software package (Kümmel et al. 2009) to extract and calibrate the spectra, using the Source Extractor catalogues as inputs.

## 2.2 HST WFC3/UVIS imaging

For 155 of the long opportunity fields, WFC3/UVIS (FOV = 2.7 arcmin×2.7 arcmin) imaging data were also obtained with some combination of F475X, F606W, F600LP, and/or F814W filters (only two of these at most for a single field). These observations were
<table><caption>**Table 2.** WISP emission-line depths for the 419 fields in the emission-line catalogue.</caption><thead><tr><th>Par</th><th>RA</th><th>Dec.</th><th>1σ depth G102<sup>a</sup><br>(erg s<sup>-1</sup> cm<sup>-2</sup>)</th><th>1σ depth G141<sup>a</sup><br>(erg s<sup>-1</sup> cm<sup>-2</sup>)</th></tr></thead><tbody><tr><td>1</td><td>01:06:35.29</td><td>+15:08:53.8</td><td>9.67 × 10<sup>-18</sup></td><td>9.57 × 10<sup>-18</sup></td></tr><tr><td>2</td><td>01:25:10.02</td><td>+21:39:13.7</td><td>...</td><td>1.17 × 10<sup>-17</sup></td></tr><tr><td>5</td><td>14:27:06.64</td><td>+57:51:36.2</td><td>7.72 × 10<sup>-18</sup></td><td>5.30 × 10<sup>-18</sup></td></tr><tr><td>6</td><td>01:50:17.18</td><td>+13:04:12.8</td><td>1.15 × 10<sup>-17</sup></td><td>8.76 × 10<sup>-18</sup></td></tr><tr><td>7</td><td>14:27:05.90</td><td>+57:53:33.7</td><td>1.54 × 10<sup>-17</sup></td><td>8.56 × 10<sup>-18</sup></td></tr></tbody></table>

Notes. A full ASCII version of this table is available online.

<sup>a</sup>Grism flux limits depend on wavelength (see Fig. 3). Values presented here are at λ = 1.1 µm for G102 and 1.5 µm for G141.

all carried out as part of the main parallel survey (PI: M. Malkan; see Table 1 for cycles and GO IDs). The variety in UVIS photometric filters used is a result of a change in strategy between cycles. Specifically, in the first cycle of WISP observations (Cycle 17), the F475X and F600LP filters were utilized to maximize throughput. However, it was realized that the less-wide filters provided more information for spectral energy distribution (SED) fitting, and thus future observations switched to F606W and F814W. In Cycle 18, we switched to 2 × 2 binned observations for the UVIS imaging, although it was later realized that the combination of lack of binned calibrations and pixel based charge transfer efficiency corrections made the data less useful. In Cycle 19, we only obtained short parallel opportunities, and thus none included UVIS imaging. Starting with Cycle 20 and onwards, all UVIS observations were obtained in unbinned F606W and F814W data. In total, we have 70 and 43 fields with two and one unbinned UVIS filters, respectfully, for a total of 113 fields with G102 + G141 grism spectroscopy and excellent UVIS imaging.

### 2.2.1 HST data reduction and co-addition

The UVIS images are also reduced through calwf3, along with additional custom calibrations described in Rafelski et al. (2015) including custom dark calibration files to address hot pixel masking, background gradients, and blotchy background patterns. These calibrations only apply to unbinned WFC3 data and, as a result, the binned UVIS data in Cycle 18 are not included in this data release. We first applied a pixel-based Charge Transfer Efficiency (CTE) correction on all individual raw dark files. When post-flashed dark files were available, we used those. The Cycle 17 UVIS data (F475X and F600LP) had high background and minimal CTE degradation. All UVIS data since Cycle 20 (F606W and F814W) use post-flash or have sufficient background on their own based on the recommendations at the time. We then used the dark files from a 3–5 d window to create a super dark to identify the hot pixels. These hot pixels use a lower threshold to identify hot pixels otherwise lost due to CTE degradation. We also create a master superdark that stacks all dark observations within the same anneal cycle as the observations, which we subtract from the data within calwf3 to reduce the blotchy patterns and gradients. The details of all these steps are outlined in the appendix of Rafelski et al. (2015). This dark calibration methodology was partly incorporated as part of UVIS 2.0 (Bourque & Baggett 2016) utilizing post-flashed CTE-corrected darks. Then later in 2020 the dark reference files moved to contemporaneous darks. The pipeline still does not include improvements for hot pixels. For future reductions, we point to the hot pixel treatment in Prichard et al. (2022) and Realski et al. (2023),

which also includes additional improvements to artefacts caused by CTE degradation such as matched amplifier backgrounds and read out cosmic rays.

The UVIS image mosaics were created with AstroDrizzle and TWEAKREG. The images where corrected for cosmic rays and aligned to the NIR images. The final image drizzle parameters are set to have final_scale of 0.04 arcsec pixel⁻¹, final.pixfrac = 0.75, and The final.wht.type = IVM (inverse-variance weighting map). We also create NIR and UVIS image mosaics with matched plate scales (final_scale) of 0.04, 0.08, and 0.13 arcsec pixel⁻¹. The use of final_scale of 0.08 or 0.13 arcsec pixel⁻¹ for UVIS data produces significantly undersampled images, and we therefore use the 0.04 arcsec pixel⁻¹ NIR and UVIS mosaics for matched photometry. These images are then convolved with a kernel to match the point spread-function (PSF) of all the images to that of the F160W filter. We used the IRAF task PSFMATCH to calculate the matching kernels and convolve the images. Additionally, RMS images are created by taking 1/√(IVM) and cleaned images of the UVIS data are generated by masking edges, chip gaps, and bad pixels with randomly generated Gaussian noise. The pixel-matched imaging data in both WFC3/UVIS and WFC3/IR were made available in previous data releases from the WISP team via the survey website on MAST.<fnref n="2" />

## 2.3 Ground-based imaging

The ground-based data were taken between the 2010A–2019A semesters from various PIs across several independent programs. The data were taken with Palomar/LFC (Large Format Camera; 31 nights), Palomar/WaSP (Wafer-Scale camera for Prime; 2 nights), WIYN/MiniMosaic (Wisconsin-Indiana-Yale-NOIRLab; 12 nights), WIYN/ODI (One Degree Imager; 9.5 nights), and Magellan/Megacam (3.5 nights). A summary of the programs, including their IDs, PIs, dates, and the number of nights, are listed in Table 3.

The observing campaign can be broadly divided into two categories: (1) programs with Palomar (LFC or WASP) and WIYN/MiniMosaic focused on observations in the *g* and *i* bands, where *i* band was only obtained for fields without HST/UVIS F814W or F600LP data (these filters have similar effective wavelengths), with the goal of improving stellar mass estimates for WISP galaxies; (2) programs with WIYN/ODI and Magellan/Megacam focused on observations in *u*, *g*, and *r* bands, where *r* band was only obtained for cases without HST/UVIS F606W data (similar in wavelength), with the goal of providing rest-frame UV measurements to constrain reddening from dust attenuation (Battisti et al. 2022). The

<footnote n="2">²https://archive.stsci.edu/prepds/wisp/</footnote>
<table><caption>**Table 2.** WISP emission-line depths for the 419 fields in the emission-line catalogue.</caption><thead><tr><th>Par</th><th>RA</th><th>Dec.</th><th>1σ depth G102<sup>a</sup><br>(erg s<sup>-1</sup> cm<sup>-2</sup>)</th><th>1σ depth G141<sup>a</sup><br>(erg s<sup>-1</sup> cm<sup>-2</sup>)</th></tr></thead><tbody><tr><td>1</td><td>01:06:35.29</td><td>+15:08:53.8</td><td>9.67 × 10<sup>-18</sup></td><td>9.57 × 10<sup>-18</sup></td></tr><tr><td>2</td><td>01:25:10.02</td><td>+21:39:13.7</td><td>...</td><td>1.17 × 10<sup>-17</sup></td></tr><tr><td>5</td><td>14:27:06.64</td><td>+57:51:36.2</td><td>7.72 × 10<sup>-18</sup></td><td>5.30 × 10<sup>-18</sup></td></tr><tr><td>6</td><td>01:50:17.18</td><td>+13:04:12.8</td><td>1.15 × 10<sup>-17</sup></td><td>8.76 × 10<sup>-18</sup></td></tr><tr><td>7</td><td>14:27:05.90</td><td>+57:53:33.7</td><td>1.54 × 10<sup>-17</sup></td><td>8.56 × 10<sup>-18</sup></td></tr></tbody></table>

Notes. A full ASCII version of this table is available online.

<sup>a</sup>Grism flux limits depend on wavelength (see Fig. 3). Values presented here are at λ = 1.1 µm for G102 and 1.5 µm for G141.

all carried out as part of the main parallel survey (PI: M. Malkan; see Table 1 for cycles and GO IDs). The variety in UVIS photometric filters used is a result of a change in strategy between cycles. Specifically, in the first cycle of WISP observations (Cycle 17), the F475X and F600LP filters were utilized to maximize throughput. However, it was realized that the less-wide filters provided more information for spectral energy distribution (SED) fitting, and thus future observations switched to F606W and F814W. In Cycle 18, we switched to 2 × 2 binned observations for the UVIS imaging, although it was later realized that the combination of lack of binned calibrations and pixel based charge transfer efficiency corrections made the data less useful. In Cycle 19, we only obtained short parallel opportunities, and thus none included UVIS imaging. Starting with Cycle 20 and onwards, all UVIS observations were obtained in unbinned F606W and F814W data. In total, we have 70 and 43 fields with two and one unbinned UVIS filters, respectfully, for a total of 113 fields with G102 + G141 grism spectroscopy and excellent UVIS imaging.

### 2.2.1 HST data reduction and co-addition

The UVIS images are also reduced through calwf3, along with additional custom calibrations described in Rafelski et al. (2015) including custom dark calibration files to address hot pixel masking, background gradients, and blotchy background patterns. These calibrations only apply to unbinned WFC3 data and, as a result, the binned UVIS data in Cycle 18 are not included in this data release. We first applied a pixel-based Charge Transfer Efficiency (CTE) correction on all individual raw dark files. When post-flashed dark files were available, we used those. The Cycle 17 UVIS data (F475X and F600LP) had high background and minimal CTE degradation. All UVIS data since Cycle 20 (F606W and F814W) use post-flash or have sufficient background on their own based on the recommendations at the time. We then used the dark files from a 3–5 d window to create a super dark to identify the hot pixels. These hot pixels use a lower threshold to identify hot pixels otherwise lost due to CTE degradation. We also create a master superdark that stacks all dark observations within the same anneal cycle as the observations, which we subtract from the data within calwf3 to reduce the blotchy patterns and gradients. The details of all these steps are outlined in the appendix of Rafelski et al. (2015). This dark calibration methodology was partly incorporated as part of UVIS 2.0 (Bourque & Baggett 2016) utilizing post-flashed CTE-corrected darks. Then later in 2020 the dark reference files moved to contemporaneous darks. The pipeline still does not include improvements for hot pixels. For future reductions, we point to the hot pixel treatment in Prichard et al. (2022) and Revalski et al. (2023),

which also includes additional improvements to artefacts caused by CTE degradation such as matched amplifier backgrounds and read out cosmic rays.

The UVIS image mosaics were created with AstroDrizzle and TWEAKREG. The images where corrected for cosmic rays and aligned to the NIR images. The final image drizzle parameters are set to have final_scale of 0.04 arcsec pixel⁻¹, final.pixfrac = 0.75, and The final.wht.type = IVM (inverse-variance weighting map). We also create NIR and UVIS image mosaics with matched plate scales (final_scale) of 0.04, 0.08, and 0.13 arcsec pixel⁻¹. The use of final_scale of 0.08 or 0.13 arcsec pixel⁻¹ for UVIS data produces significantly undersampled images, and we therefore use the 0.04 arcsec pixel⁻¹ NIR and UVIS mosaics for matched photometry. These images are then convolved with a kernel to match the point spread-function (PSF) of all the images to that of the F160W filter. We used the IRAF task PSFMATCH to calculate the matching kernels and convolve the images. Additionally, RMS images are created by taking 1/√(IVM) and cleaned images of the UVIS data are generated by masking edges, chip gaps, and bad pixels with randomly generated Gaussian noise. The pixel-matched imaging data in both WFC3/UVIS and WFC3/IR were made available in previous data releases from the WISP team via the survey website on MAST.<fnref n="2" />

## 2.3 Ground-based imaging

The ground-based data were taken between the 2010A–2019A semesters from various PIs across several independent programs. The data were taken with Palomar/LFC (Large Format Camera; 31 nights), Palomar/WaSP (Wafer-Scale camera for Prime; 2 nights), WIYN/MiniMosaic (Wisconsin-Indiana-Yale-NOIRLab; 12 nights), WIYN/ODI (One Degree Imager; 9.5 nights), and Magellan/Megacam (3.5 nights). A summary of the programs, including their IDs, PIs, dates, and the number of nights, are listed in Table 3.

The observing campaign can be broadly divided into two categories: (1) programs with Palomar (LFC or WASP) and WIYN/MiniMosaic focused on observations in the *g* and *i* bands, where *i* band was only obtained for fields without HST/UVIS F814W or F600LP data (these filters have similar effective wavelengths), with the goal of improving stellar mass estimates for WISP galaxies; (2) programs with WIYN/ODI and Magellan/Megacam focused on observations in *u*, *g*, and *r* bands, where *r* band was only obtained for cases without HST/UVIS F606W data (similar in wavelength), with the goal of providing rest-frame UV measurements to constrain reddening from dust attenuation (Battisti et al. 2022). The

<footnote n="2">²https://archive.stsci.edu/prepds/wisp/</footnote>
<table><caption>**Table 3.** Summary of ground-based imaging campaigns on WISP fields included in this release.</caption><thead><tr><th>ID</th><th>PI(s)</th><th>Telescope/Instr.</th><th>Semester</th><th>Bands</th><th>Nights</th></tr></thead><tbody><tr><td>P-23</td><td>B. Siana</td><td>Palomar/LFC</td><td>2010A</td><td>gi</td><td>4</td></tr><tr><td>H-05</td><td>B. Siana</td><td>Palomar/LFC</td><td>2010B</td><td>gi</td><td>2</td></tr><tr><td>J-06</td><td>C. Scarlata</td><td>Palomar/LFC</td><td>2010B</td><td>gi</td><td>3</td></tr><tr><td>0438</td><td>A. Henry</td><td>WIYN/MiniMo</td><td>2010B</td><td>gri</td><td>4</td></tr><tr><td>0160</td><td>A. Henry</td><td>WIYN/MiniMo</td><td>2011A</td><td>gi</td><td>5</td></tr><tr><td>0222</td><td>A. Henry</td><td>WIYN/MiniMo</td><td>2011B</td><td>g</td><td>3</td></tr><tr><td>J-18</td><td>H. Telpitz</td><td>Palomar/LFC</td><td>2012A</td><td>gi</td><td>3</td></tr><tr><td>J-01</td><td>M. Rafelski</td><td>Palomar/LFC</td><td>2013A</td><td>gi</td><td>2</td></tr><tr><td>J-10</td><td>M. Rafelski</td><td>Palomar/LFC</td><td>2013B</td><td>gi</td><td>3</td></tr><tr><td>J-23</td><td>M. Rafelski</td><td>Palomar/LFC</td><td>2014A</td><td>gi</td><td>2</td></tr><tr><td>J-05</td><td>Y. Dai</td><td>Palomar/LFC</td><td>2014B</td><td>gi</td><td>2</td></tr><tr><td>J-16</td><td>J. Colbert</td><td>Palomar/LFC</td><td>2015A</td><td>gi</td><td>3</td></tr><tr><td>J-12</td><td>Y. Dai</td><td>Palomar/LFC</td><td>2016A</td><td>g</td><td>1</td></tr><tr><td>J-14</td><td>Y. Dai</td><td>Palomar/LFC</td><td>2016B</td><td>gi</td><td>3</td></tr><tr><td>J-14</td><td>I. Baronchelli</td><td>Palomar/LFC</td><td>2017B</td><td>gi</td><td>3</td></tr><tr><td>0298</td><td>A. Battisti</td><td>WIYN/ODI</td><td>2017B</td><td>ugr</td><td>2.5</td></tr><tr><td>...</td><td>P. McCarthy<br/>and A. Battisti</td><td>Magellan/Megacam</td><td>2017B</td><td>ugr</td><td>1.5</td></tr><tr><td>0275</td><td>A. Battisti</td><td>WIYN/ODI</td><td>2018A</td><td>ugr</td><td>2.5</td></tr><tr><td>...</td><td>A. Battisti</td><td>Magellan/Megacam</td><td>2018A</td><td>ugr</td><td>2</td></tr><tr><td>0140</td><td>A. Battisti</td><td>WIYN/ODI</td><td>2018B/19A</td><td>ugr</td><td>4.5<sup>a</sup></td></tr><tr><td>N-115</td><td>Y. Dai</td><td>Palomar/WASP</td><td>2019A</td><td>g</td><td>2</td></tr></tbody></table>

*Note*. Columns list the (1) program ID, (2) principal investigator, (3) telescope and instrument used, (4) semester observed, (5) bands observed, and (6) number of nights where data were taken. <sup>a</sup>Three of these nights were provided as additional technical time.

WIYN/ODI and Magellan/Megacam instruments have large FOVs (40 arcmin × 48 arcmin and 24 arcmin × 24 arcmin, respectively), which are significantly larger than the HST WFC3/IR footprint (FOV = 2.3 arcmin × 2.2 arcmin). Whenever possible, observations were optimized to observe multiple WISP fields simultaneously.

The desired depths of the programs varied, but for WIYN/ODI and Magellan/Megacam they were AB mag = 26, 26, and 25.3 in *u*, *g*, and *r* (5σ point source), respectively. These depths were typically achieved for the Magellan runs but not for most of the WIYN runs, due to poor weather and/or observing conditions (e.g. lunar phase). A detailed summary on the depths for each facility is given in Section 4.2.

### 2.3.1 Ground-based data reduction and co-addition

Below we describe the data reduction process for each telescope individually, but note that they follow a similar overall procedure that includes bias, flat, and dark-subtraction. All images were astrometrically aligned to the World Coordinate System (WCS) using the `astrometry.net` software package, unless otherwise specified. Flux calibrations were also performed in similar ways, unless otherwise specified, and is summarized in Section 3.1.

(i) *Palomar/LFC* – the Palomar/LFC WISP observations spanned the largest number of different observers among our data sets. All raw images were re-reduced following the same procedure for self-consistency. The LFC consists of six chips (each 6.14 arcmin × 12.29 arcmin; referred to as *chip0* through *chip5*), covering a circular area with a ~24 arcmin diameter, and the WISP field was always centred on *chip0*. Basic data reduction steps were only performed on the *chip0* data. We note that dark frames were not always available for subtraction, however, this has a minimal

impact on the final photometric quality because the LFC has very little dark current.<fnref n="3" />

(ii) *Palomar/WaSP* – basic reduction steps were identical to Palomar/LFC, except that WaSP has four large chips (square FOV: 18.4 arcmin × 18.5 arcmin) and all chips were reduced.

(iii) *WIYN/Minimosaic* – the raw archival WIYN data were obtained via the NOAO Science Archive (now known as the NOIRLab Astro Data Archive).<fnref n="4" /> Basic reduction steps were similar to Palomar, except that Minimosaic has two large chips (square FOV: 9.6 arcmin × 9.6 arcmin) and both chips were used.

(iv) *WIYN/ODI* – basic reduction, WCS-alignment, and photometric calibration was performed using the ODI Pipeline, Portal, and Archive (PPA) system.<fnref n="5" /> PPA is a service provided by the WIYN Consortium, Inc., and hosted at Indiana University. The photometric calibration in the ODI PPA is based on measurements from the SDSS (Sloan Digital Sky Survey; *ugriz*) and Pan-STARRS (Panoramic Survey Telescope And Rapid Response System; *grizy*). We note that for a few fields outside the SDSS footprint, the *u*-band ODI data required manual photometric calibration, which is discussed in Section 3.1.

(v) *Magellan/Megacam* – images with basic reduction and WCS-alignment were provided by the OIR Telescope Data Center at Harvard's Center for Astrophysics (CfA), supported by the Smithsonian Astrophysical Observatory.

The process for co-adding images was the same for all ground-based data. First, images are normalized by exposure time. Second, normalized images are combined using the SWARP software (Bertin et al. 2002; Bertin 2010). During SWARP, images are background

<footnote n="3">³https://sites.astro.caltech.edu/palomar/observer/200inchResources/lfccookbook.html#dark</footnote>

<footnote n="4">⁴https://astroarchive.noirlab.edu</footnote>

<footnote n="5">⁵https://portal.odi.iu.edu/index/front</footnote>
<table><caption>**Table 3.** Summary of ground-based imaging campaigns on WISP fields included in this release.</caption><thead><tr><th>ID</th><th>PI(s)</th><th>Telescope/Instr.</th><th>Semester</th><th>Bands</th><th>Nights</th></tr></thead><tbody><tr><td>P-23</td><td>B. Siana</td><td>Palomar/LFC</td><td>2010A</td><td>gi</td><td>4</td></tr><tr><td>H-05</td><td>B. Siana</td><td>Palomar/LFC</td><td>2010B</td><td>gi</td><td>2</td></tr><tr><td>J-06</td><td>C. Scarlata</td><td>Palomar/LFC</td><td>2010B</td><td>gi</td><td>3</td></tr><tr><td>0438</td><td>A. Henry</td><td>WIYN/MiniMo</td><td>2010B</td><td>gri</td><td>4</td></tr><tr><td>0160</td><td>A. Henry</td><td>WIYN/MiniMo</td><td>2011A</td><td>gi</td><td>5</td></tr><tr><td>0222</td><td>A. Henry</td><td>WIYN/MiniMo</td><td>2011B</td><td>g</td><td>3</td></tr><tr><td>J-18</td><td>H. Telpitz</td><td>Palomar/LFC</td><td>2012A</td><td>gi</td><td>3</td></tr><tr><td>J-01</td><td>M. Rafelski</td><td>Palomar/LFC</td><td>2013A</td><td>gi</td><td>2</td></tr><tr><td>J-10</td><td>M. Rafelski</td><td>Palomar/LFC</td><td>2013B</td><td>gi</td><td>3</td></tr><tr><td>J-23</td><td>M. Rafelski</td><td>Palomar/LFC</td><td>2014A</td><td>gi</td><td>2</td></tr><tr><td>J-05</td><td>Y. Dai</td><td>Palomar/LFC</td><td>2014B</td><td>gi</td><td>2</td></tr><tr><td>J-16</td><td>J. Colbert</td><td>Palomar/LFC</td><td>2015A</td><td>gi</td><td>3</td></tr><tr><td>J-12</td><td>Y. Dai</td><td>Palomar/LFC</td><td>2016A</td><td>g</td><td>1</td></tr><tr><td>J-14</td><td>Y. Dai</td><td>Palomar/LFC</td><td>2016B</td><td>gi</td><td>3</td></tr><tr><td>J-14</td><td>I. Baronchelli</td><td>Palomar/LFC</td><td>2017B</td><td>gi</td><td>3</td></tr><tr><td>0298</td><td>A. Battisti</td><td>WIYN/ODI</td><td>2017B</td><td>ugr</td><td>2.5</td></tr><tr><td>...</td><td>P. McCarthy<br/>and A. Battisti</td><td>Magellan/Megacam</td><td>2017B</td><td>ugr</td><td>1.5</td></tr><tr><td>0275</td><td>A. Battisti</td><td>WIYN/ODI</td><td>2018A</td><td>ugr</td><td>2.5</td></tr><tr><td>...</td><td>A. Battisti</td><td>Magellan/Megacam</td><td>2018A</td><td>ugr</td><td>2</td></tr><tr><td>0140</td><td>A. Battisti</td><td>WIYN/ODI</td><td>2018B/19A</td><td>ugr</td><td>4.5<sup>a</sup></td></tr><tr><td>N-115</td><td>Y. Dai</td><td>Palomar/WASP</td><td>2019A</td><td>g</td><td>2</td></tr></tbody></table>

*Note*. Columns list the (1) program ID, (2) principal investigator, (3) telescope and instrument used, (4) semester observed, (5) bands observed, and (6) number of nights where data were taken. <sup>a</sup>Three of these nights were provided as additional technical time.

WIYN/ODI and Magellan/Megacam instruments have large FOVs (40 arcmin × 48 arcmin and 24 arcmin × 24 arcmin, respectively), which are significantly larger than the HST WFC3/IR footprint (FOV = 2.3 arcmin × 2.2 arcmin). Whenever possible, observations were optimized to observe multiple WISP fields simultaneously.

The desired depths of the programs varied, but for WIYN/ODI and Magellan/Megacam they were AB mag = 26, 26, and 25.3 in *u*, *g*, and *r* (5σ point source), respectively. These depths were typically achieved for the Magellan runs but not for most of the WIYN runs, due to poor weather and/or observing conditions (e.g. lunar phase). A detailed summary on the depths for each facility is given in Section 4.2.

### 2.3.1 Ground-based data reduction and co-addition

Below we describe the data reduction process for each telescope individually, but note that they follow a similar overall procedure that includes bias, flat, and dark-subtraction. All images were astrometrically aligned to the World Coordinate System (WCS) using the `astrometry.net` software package, unless otherwise specified. Flux calibrations were also performed in similar ways, unless otherwise specified, and is summarized in Section 3.1.

(i) *Palomar/LFC* – the Palomar/LFC WISP observations spanned the largest number of different observers among our data sets. All raw images were re-reduced following the same procedure for self-consistency. The LFC consists of six chips (each 6.14 arcmin × 12.29 arcmin; referred to as *chip0* through *chip5*), covering a circular area with a ~24 arcmin diameter, and the WISP field was always centred on *chip0*. Basic data reduction steps were only performed on the *chip0* data. We note that dark frames were not always available for subtraction, however, this has a minimal

impact on the final photometric quality because the LFC has very little dark current.<fnref n="3" />

(ii) *Palomar/WaSP* – basic reduction steps were identical to Palomar/LFC, except that WaSP has four large chips (square FOV: 18.4 arcmin × 18.5 arcmin) and all chips were reduced.

(iii) *WIYN/Minimosaic* – the raw archival WIYN data were obtained via the NOAO Science Archive (now known as the NOIRLab Astro Data Archive).<fnref n="4" /> Basic reduction steps were similar to Palomar, except that Minimosaic has two large chips (square FOV: 9.6 arcmin × 9.6 arcmin) and both chips were used.

(iv) *WIYN/ODI* – basic reduction, WCS-alignment, and photometric calibration was performed using the ODI Pipeline, Portal, and Archive (PPA) system.<fnref n="5" /> PPA is a service provided by the WIYN Consortium, Inc., and hosted at Indiana University. The photometric calibration in the ODI PPA is based on measurements from the SDSS (Sloan Digital Sky Survey; *ugriz*) and Pan-STARRS (Panoramic Survey Telescope And Rapid Response System; *grizy*). We note that for a few fields outside the SDSS footprint, the *u*-band ODI data required manual photometric calibration, which is discussed in Section 3.1.

(v) *Magellan/Megacam* – images with basic reduction and WCS-alignment were provided by the OIR Telescope Data Center at Harvard's Center for Astrophysics (CfA), supported by the Smithsonian Astrophysical Observatory.

The process for co-adding images was the same for all ground-based data. First, images are normalized by exposure time. Second, normalized images are combined using the SWARP software (Bertin et al. 2002; Bertin 2010). During SWARP, images are background

<footnote n="3">³https://sites.astro.caltech.edu/palomar/observer/200inchResources/lfccookbook.html#dark</footnote>

<footnote n="4">⁴https://astroarchive.noirlab.edu</footnote>

<footnote n="5">⁵https://portal.odi.iu.edu/index/front</footnote>
subtracted using a 128 pixel background mesh unless saturated stars bleed onto a significant portion of field. In such cases, a 32 pixel background is adopted to minimize the area affected by saturation. During this step, we also mask out satellite trails from individual exposures if they overlap with the WISP field (satellite trails outside the WISP area are ignored). For convenience, the publicly released images are cropped into 5 arcmin × 5 arcmin regions centred on the WISP fields. For reference, the HST/UVIS FOV is 2.70 arcmin × 2.70 arcmin and the HST/IR FOV is 2.05 arcmin × 2.27 arcmin.

## 2.4 Spitzer IRAC imaging

Spitzer IRAC/channel 1, 3.6 µm imaging (warm mission; PI: J Colbert) was obtained for ~200 of the deepest WISP field over multiple cycles [GO 80 134 (Cycle 8), 90 230 (Cycle 9), 10 041 (Cycle 10), 12 093 (Cycle 12)], with the primary goal of providing accurate stellar masses for galaxies in the WISP survey. For a handful of WISP fields, IRAC/channel 2, 4.5 µm data were also obtained.

### 2.4.1 Spitzer data reduction and co-addition

Roughly half of the WISP parallels are in low background fields (~0.08 MJy sr⁻¹ at 3.6 µm), while the remainder are in fields with medium backgrounds (~0.12 MJy sr⁻¹). To achieve similar sensitivity for all the targeted fields (5σ depth of 0.9 µJy), we split the observations into two integration times, 25 versus 35 min on-source. The variation of the background with observing window does not significantly change the required exposure time. For the observations, we use a medium dither pattern with 100 s frames and either 15 or 21 exposures, depending on the total integration time. For the small subset of fields observed with the 4.5 µm filter, we always used the 21 exposure (35 min) observation.

Individual calibrated IRAC exposures are referred to as Basic Calibrated Data, or BCDs. However, IRAC BCD data contain several artefacts, including mux-bleed, mux-stripe, column droop, and bright star ghosts.<fnref n="6" /> The IRAC pipeline therefore also produces corrected BCD images (cBCD), which attempt to mitigate these artefacts. We start with the cBCDs for all of our data reduction and analysis.

All the IRAC mosaics are generated using the Spitzer MOsaicker and Point source EXtractor (MOPEX) package. Before generating mosaics, problematic cBCDs – those with unusually high noise, extreme saturation, or other unexplained large artefacts – are removed. Such problematic cBCDs only make up a small fraction (<5 per cent) of the input cBCDs and do not significantly affect total exposure times for any observations. The first frame of all IRAC Astronomical Observation Requests has decreased sensitivity and was not included in the mosaics. The MOPEX Overlap pipeline subtracts the estimated background from Zodiacal light from each cBCD and then matches the background level in all frames with an additive correction. The MOPEX Mosaic pipeline then performs outlier rejection to remove cosmic rays, moving objects, and other artefacts, before resampling the images onto a common reference frame. Finally, the images are combined to produce a weighted mean and median mosaic along with associated coverage, uncertainty, and standard-deviation maps.

<table><caption>**Table 4.** Summary of SExTRACTOR parameters used on the ground-based data for determining zero-points.</caption><thead><tr><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td>DETECT_THRESHOLD</td><td>1.5</td></tr><tr><td>ANALYSIS_THRESHOLD</td><td>1.5</td></tr><tr><td>DEBLEND_NTHRESH</td><td>32</td></tr><tr><td>DEBLEND_MINCONT</td><td>0.005</td></tr><tr><td>DETECT_MINAREA</td><td>6</td></tr><tr><td>BACK_SIZE</td><td>64</td></tr><tr><td>BACK_FILTERSIZE</td><td>3</td></tr><tr><td>FILTER</td><td>Y</td></tr><tr><td>FILTER_NAME</td><td>gauss_2.0_5 × 5 conv<sup>a</sup></td></tr></tbody></table>

Notes. The photometry from SExTRACTOR is used only for constraining magnitude zero-points for the ground-based data. We use TPHOT to measure photometry for the WISP catalogue (see Section 3.3). a5 × 5 convolution mask of a Gaussian PSF with FWHM = 2.0 pixels.

# 3 WISP PHOTOMETRIC PIPELINE

The methods to obtain zero-points and photometry differ between the HST data (high spatial resolution) and the ground-based and Spitzer data (both low spatial resolution). In this section, we describe the data processing workflow used to create the WISP photometric catalogue.

## 3.1 Photometric zero-points

### 3.1.1 HST zero-points

The HST WFC3/UVIS and WFC3/IR zero-points are taken from the Space Telescope Science Institute (STScI) calibration website.<fnref n="7" /> We note that the WFC3 zero-point solutions change slightly over time and different zero-point values are applied to data from different HST cycles over the course of the program.

### 3.1.2 Ground-based data zero-points

For all ground-based imaging, the flux zero-points were determined, in order of priority, from: (1) direct comparison to SDSS (ugri; 110° ≤ RA ≤ 265° and Dec. > 0°), (2) direct comparison to Pan-STARRS (gri; full sky for Dec. > −30°), and (3) using standard fields [i.e. fields with (1) or (2)] at different airmasses (atmospheric extinction) for individual nights of each observation. Option (3) was necessary when neither SDSS or Pan-STARRS are available for calibration. This was required for many of the southern WISP fields observed with Magellan, particularly for the u-band imaging.

First, we extract psfMag values for stars (PhotoType = 6) in the SDSS DR14 and Pan-STARRS DR2 public catalogues to use as our reference values. Magnitudes in Pan-STARRS were converted into the SDSS system using the relationships in Tonry et al. (2012). Second, we run SExTRACTOR (Bertin & Arnouts 1996) with similar parameters across facilities, for consistency, on our reduced, co-added images (Section 2.3.1). A summary of the most relevant SExTRACTOR parameters are listed in Table 4. Third, magnitude zero-points are determined by cross-matching close sources and comparing MAG_AUTO values from SExTRACTOR to the catalogue psfMag values (both represent total magnitudes). A distance threshold of ~1 arcsec was typically adopted, but this varied with seeing conditions (maximum of 3 arcsec).

<footnote n="6">⁶https://irsa.ipac.caltech.edu/data/SPITZER/docs/irac/iracinstrumenthandbook/35/</footnote>

<footnote n="7">⁷https://www.stsci.edu/hst/instrumentation/wfc3/data-analysis/photometric-calibration</footnote>
subtracted using a 128 pixel background mesh unless saturated stars bleed onto a significant portion of field. In such cases, a 32 pixel background is adopted to minimize the area affected by saturation. During this step, we also mask out satellite trails from individual exposures if they overlap with the WISP field (satellite trails outside the WISP area are ignored). For convenience, the publicly released images are cropped into 5 arcmin × 5 arcmin regions centred on the WISP fields. For reference, the HST/UVIS FOV is 2.70 arcmin × 2.70 arcmin and the HST/IR FOV is 2.05 arcmin × 2.27 arcmin.

## 2.4 Spitzer IRAC imaging

Spitzer IRAC/channel 1, 3.6 µm imaging (warm mission; PI: J Colbert) was obtained for ~200 of the deepest WISP field over multiple cycles [GO 80 134 (Cycle 8), 90 230 (Cycle 9), 10 041 (Cycle 10), 12 093 (Cycle 12)], with the primary goal of providing accurate stellar masses for galaxies in the WISP survey. For a handful of WISP fields, IRAC/channel 2, 4.5 µm data were also obtained.

### 2.4.1 Spitzer data reduction and co-addition

Roughly half of the WISP parallels are in low background fields (~0.08 MJy sr⁻¹ at 3.6 µm), while the remainder are in fields with medium backgrounds (~0.12 MJy sr⁻¹). To achieve similar sensitivity for all the targeted fields (5σ depth of 0.9 µJy), we split the observations into two integration times, 25 versus 35 min on-source. The variation of the background with observing window does not significantly change the required exposure time. For the observations, we use a medium dither pattern with 100 s frames and either 15 or 21 exposures, depending on the total integration time. For the small subset of fields observed with the 4.5 µm filter, we always used the 21 exposure (35 min) observation.

Individual calibrated IRAC exposures are referred to as Basic Calibrated Data, or BCDs. However, IRAC BCD data contain several artefacts, including mux-bleed, mux-stripe, column droop, and bright star ghosts.<fnref n="6" /> The IRAC pipeline therefore also produces corrected BCD images (cBCD), which attempt to mitigate these artefacts. We start with the cBCDs for all of our data reduction and analysis.

All the IRAC mosaics are generated using the Spitzer MOsaicker and Point source EXtractor (MOPEX) package. Before generating mosaics, problematic cBCDs – those with unusually high noise, extreme saturation, or other unexplained large artefacts – are removed. Such problematic cBCDs only make up a small fraction (<5 per cent) of the input cBCDs and do not significantly affect total exposure times for any observations. The first frame of all IRAC Astronomical Observation Requests has decreased sensitivity and was not included in the mosaics. The MOPEX Overlap pipeline subtracts the estimated background from Zodiacal light from each cBCD and then matches the background level in all frames with an additive correction. The MOPEX Mosaic pipeline then performs outlier rejection to remove cosmic rays, moving objects, and other artefacts, before resampling the images onto a common reference frame. Finally, the images are combined to produce a weighted mean and median mosaic along with associated coverage, uncertainty, and standard-deviation maps.

<table><caption>**Table 4.** Summary of SExTRACTOR parameters used on the ground-based data for determining zero-points.</caption><thead><tr><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td>DETECT_THRESHOLD</td><td>1.5</td></tr><tr><td>ANALYSIS_THRESHOLD</td><td>1.5</td></tr><tr><td>DEBLEND_NTHRESH</td><td>32</td></tr><tr><td>DEBLEND_MINCONT</td><td>0.005</td></tr><tr><td>DETECT_MINAREA</td><td>6</td></tr><tr><td>BACK_SIZE</td><td>64</td></tr><tr><td>BACK_FILTERSIZE</td><td>3</td></tr><tr><td>FILTER</td><td>Y</td></tr><tr><td>FILTER_NAME</td><td>gauss_2.0_5 × 5 conv<sup>a</sup></td></tr></tbody></table>

Notes. The photometry from SExTRACTOR is used only for constraining magnitude zero-points for the ground-based data. We use TPHOT to measure photometry for the WISP catalogue (see Section 3.3). a5 × 5 convolution mask of a Gaussian PSF with FWHM = 2.0 pixels.

# 3 WISP PHOTOMETRIC PIPELINE

The methods to obtain zero-points and photometry differ between the HST data (high spatial resolution) and the ground-based and Spitzer data (both low spatial resolution). In this section, we describe the data processing workflow used to create the WISP photometric catalogue.

## 3.1 Photometric zero-points

### 3.1.1 HST zero-points

The HST WFC3/UVIS and WFC3/IR zero-points are taken from the Space Telescope Science Institute (STScI) calibration website.<fnref n="7" /> We note that the WFC3 zero-point solutions change slightly over time and different zero-point values are applied to data from different HST cycles over the course of the program.

### 3.1.2 Ground-based data zero-points

For all ground-based imaging, the flux zero-points were determined, in order of priority, from: (1) direct comparison to SDSS (ugri; 110° ≤ RA ≤ 265° and Dec. > 0°), (2) direct comparison to Pan-STARRS (gri; full sky for Dec. > −30°), and (3) using standard fields [i.e. fields with (1) or (2)] at different airmasses (atmospheric extinction) for individual nights of each observation. Option (3) was necessary when neither SDSS or Pan-STARRS are available for calibration. This was required for many of the southern WISP fields observed with Magellan, particularly for the u-band imaging.

First, we extract psfMag values for stars (PhotoType = 6) in the SDSS DR14 and Pan-STARRS DR2 public catalogues to use as our reference values. Magnitudes in Pan-STARRS were converted into the SDSS system using the relationships in Tonry et al. (2012). Second, we run SExTRACTOR (Bertin & Arnouts 1996) with similar parameters across facilities, for consistency, on our reduced, co-added images (Section 2.3.1). A summary of the most relevant SExTRACTOR parameters are listed in Table 4. Third, magnitude zero-points are determined by cross-matching close sources and comparing MAG_AUTO values from SExTRACTOR to the catalogue psfMag values (both represent total magnitudes). A distance threshold of ~1 arcsec was typically adopted, but this varied with seeing conditions (maximum of 3 arcsec).

<footnote n="6">⁶https://irsa.ipac.caltech.edu/data/SPITZER/docs/irac/iracinstrumenthandbook/35/</footnote>

<footnote n="7">⁷https://www.stsci.edu/hst/instrumentation/wfc3/data-analysis/photometric-calibration</footnote>
The filters for all of the ground-based data (see Fig. 2b) are similar to SDSS. As a result, colour differences tend to be small, however, this was still accounted for during the zeropoint (ZP) determination. In particular, the Magellan *g* and *r* and Palomar *i* filters show the largest deviations from SDSS. These colour corrections are based on fitting the zero-point magnitudes as a function of *u* − *g*, *g* − *r*, *g* − *r*, and, *r* − *i* colours for the *u*, *g*, *r*, and *i* filters, respectively.

The typical precision of the ZPs are $\sigma_{ZP} \sim 0.05$ mag for Magellan/Megacam (ugr), WIYN/ODI (ugr), WIYN/MiniMosaic (gri), and Palomar/WaSP (g). For Palomar/LFC (gi), the typical precision is slightly worse, $\sigma_{ZP} \sim 0.1$ mag, due to the smaller FOV with fewer reference stars for calibration. We recommend adopting a minimum uncertainty of 0.1 mag for all ground-based optical data to account for potential systematic ZP offsets.

For rare instances of overlapping data (e.g. both Magellan and Palomar data are available in the *g* filter), we adopt the case with better seeing (typically also deeper), unless there is a dramatic difference in depth relative to the poorer seeing case (>$\sim$ 1 mag difference). A few comparisons between overlapping data from different telescopes with roughly similar imaging quality (depth and seeing) were possible and used as a check on zero-points and colour-corrections. We find that cases of overlapping data agree to within $1\sigma$ for ~60 per cent–70 per cent of sources, as expected. We find that the agreement gets poorer (larger scatter) when comparing data sets with larger quality differences (e.g. 1 arcsec versus 1.7 arcsec seeing), however the median ZP offsets remain consistent with zero.

### 3.1.3 Spitzer zero-points

Similar to *HST*, the *Spitzer* zero-points are taken from the IRAC instrument handbook website.<fnref n="8" /> We note that the WISP data were obtained during the *Spitzer* warm mission, which had a different zero-point solution to the cold mission.

## 3.2 HST PSF- and aperture-matched isophotal photometry
(high spatial resolution)

The methods used to obtain the *HST* photometry are described in Bagley et al. (2017) and Henry et al. (2021). In brief, a segmentation map is first generated from the *F110W* and *F160W* detections using *SEXTRACTOR* on the 0.08 arcsec pixel⁻¹ mosaics and then regridded to the 0.04 arcsec pixel⁻¹ scale mosaics. The photometry is determined using *photutils* in *ASTROPY* (Astropy Collaboration 2013, 2018) to derive isophotal fluxes in all *HST* bands using the regridded segmentation map and includes local sky subtraction. *SEXTRACTOR* photometry is also performed on WFC3/IR (0.08 arcsec pixel⁻¹) images (prior to PSF-matching), in order to obtain the total magnitudes (*MAG_AUTO*).

For the WFC3/UVIS data, we determine aperture corrections, *APCOR*, using the difference between *MAG_AUTO* and the isophotal magnitude, *MAG_ISO*, in the *F160W* *HST* filter and list these values in our catalogue (i.e. *APCOR_UVIS* = *MAG_AUTO* *F160W* - *MAG_ISO* *F160W*). This is similar to the method used in Henry et al. (2021) and Battisti et al. (2022). We recommend total magnitudes to be estimated for UVIS filters using *m*<sub>tot</sub> = *MAG* *ISO* - [UVISFILTER] + *APCOR_UVIS*. For the WISP subsample used in our results (Secton 7), the median value of these aperture corrections (*APCOR_UVIS*) is $\Delta m = -0.11$ mag.

## 3.3 Ground-based and *Spitzer* forced photometry (low spatial resolution)

We performed template-fitting or ‘forced’ photometric measurements on the lower resolution data (ground-based and IRAC; both data sets follow the same procedure) by exploiting the coordinates of the *HST*-detected sources as priors. Fig. 4 demonstrates an example of the photometric fitting from the *TPHOT* WISP pipeline on the ground-based data.

The core of this computation is performed using the *TPHOT* software (v2; Merlin et al. 2015, 2016), which we summarize below, and provide in more detail in Appendix A. In brief, *TPHOT* requires two images at different resolutions and a list of detected objects. The algorithm transforms the low-resolution image into a (simulated) high-resolution image. To do this, *TPHOT* ‘distributes’ the flux of the sources in the low-resolution image according to the flux distribution in the high-resolution image using a deconvolution kernel (provided by the user). Compared to aperture photometry (and closest-counterpart associations), this technique allows us to obtain more accurate photometric estimates, especially when multiple *HST* sources appear blended in the low-resolution images. For all of the *HST* sources undetected in the low-resolution bands, we set their fluxes as an upper limit to the $3\sigma$ detection limit.

# 4 WISP PHOTOMETRIC CATALOGUE

## 4.1 Photometric catalogue description

The WISP photometric catalogue contains ~230 000 sources in 439 WISP fields. These sources include stars, galaxies, and active galactic nuclei (AGNs). Fields that are absent are cases where the WISP photometric pipeline failed, which most commonly occurred due to overcrowding, bright stars, or persistence. We note that the filter availability is quite inhomogeneous among WISP fields and entries for fields without data in a given filter have values of ‘−99’ for their photometry. We provide a summary of the number of fields with individual filters in this data release in Table 5. The photometry in this catalogue are *not* corrected for foreground Milky Way (MW) extinction.

We present an explanation of the WISP photometric catalogue entries in Table 6. These include RA, Dec., sizes and position angles (based on *HST*), and Kron-like elliptical aperture magnitude (*MAG_AUTO*; only WFC3/IR filters) from *SEXTRACTOR*. There are two additional aperture measurements available for *HST* data: (1) isophotes (*ISO*) based on the *F110W* and *F160W* segmentation map and (2) fixed circular apertures (*APER*; radii *r* = 0.2, 0.5, 1.0, and 1.5 arcsec). A visual representation of these apertures is shown in Fig. 5. For the ground-based and *Spitzer* data, photometry based on *TPHOT* is provided. For consistent ‘total’ photometry, we recommend using *MAG_AUTO* for WFC3/IR, *MAG* *ISO* + *APCOR_UVIS* for WFC3/UVIS, and *AB MAG* (*TPHOT* values) for ground-based and *Spitzer* data.

There are also two quality flags in the photometric catalogue. A description of these flags is provided in Appendix B. These flags should be carefully considered when selecting sources from the catalogue to avoid use of unreliable photometry.

## 4.2 Summary of imaging depth and completeness

Since the segmentation maps of sources from WFC3/IR imaging data are used as priors for the shallower and/or lower resolution imaging data, we only formally characterize the depth and completeness

<footnote n="8">⁸https://irsa.ipac.caltech.edu/data/SPITZER/docs/irac/iracinstrumenthandbook/14/</footnote>
The filters for all of the ground-based data (see Fig. 2b) are similar to SDSS. As a result, colour differences tend to be small, however, this was still accounted for during the zeropoint (ZP) determination. In particular, the Magellan *g* and *r* and Palomar *i* filters show the largest deviations from SDSS. These colour corrections are based on fitting the zero-point magnitudes as a function of *u* − *g*, *g* − *r*, *g* − *r*, and, *r* − *i* colours for the *u*, *g*, *r*, and *i* filters, respectively.

The typical precision of the ZPs are $\sigma_{ZP} \sim 0.05$ mag for Magellan/Megacam (ugr), WIYN/ODI (ugr), WIYN/MiniMosaic (gri), and Palomar/WaSP (g). For Palomar/LFC (gi), the typical precision is slightly worse, $\sigma_{ZP} \sim 0.1$ mag, due to the smaller FOV with fewer reference stars for calibration. We recommend adopting a minimum uncertainty of 0.1 mag for all ground-based optical data to account for potential systematic ZP offsets.

For rare instances of overlapping data (e.g. both Magellan and Palomar data are available in the *g* filter), we adopt the case with better seeing (typically also deeper), unless there is a dramatic difference in depth relative to the poorer seeing case (>$\sim$ 1 mag difference). A few comparisons between overlapping data from different telescopes with roughly similar imaging quality (depth and seeing) were possible and used as a check on zero-points and colour-corrections. We find that cases of overlapping data agree to within $1\sigma$ for ~60 per cent–70 per cent of sources, as expected. We find that the agreement gets poorer (larger scatter) when comparing data sets with larger quality differences (e.g. 1 arcsec versus 1.7 arcsec seeing), however the median ZP offsets remain consistent with zero.

### 3.1.3 Spitzer zero-points

Similar to *HST*, the *Spitzer* zero-points are taken from the IRAC instrument handbook website.<fnref n="8" /> We note that the WISP data were obtained during the *Spitzer* warm mission, which had a different zero-point solution to the cold mission.

## 3.2 HST PSF- and aperture-matched isophotal photometry (high spatial resolution)

The methods used to obtain the *HST* photometry are described in Bagley et al. (2017) and Henry et al. (2021). In brief, a segmentation map is first generated from the *F110W* and *F160W* detections using *SEXTRACTOR* on the 0.08 arcsec pixel⁻¹ mosaics and then regridded to the 0.04 arcsec pixel⁻¹ scale mosaics. The photometry is determined using *photutils* in *ASTROPY* (Astropy Collaboration 2013, 2018) to derive isophotal fluxes in all *HST* bands using the regridded segmentation map and includes local sky subtraction. *SEXTRACTOR* photometry is also performed on WFC3/IR (0.08 arcsec pixel⁻¹) images (prior to PSF-matching), in order to obtain the total magnitudes (*MAG_AUTO*).

For the WFC3/UVIS data, we determine aperture corrections, *APCOR*, using the difference between *MAG_AUTO* and the isophotal magnitude, *MAG_ISO*, in the *F160W* *HST* filter and list these values in our catalogue (i.e. *APCOR_UVIS* = *MAG_AUTO* *F160W* - *MAG_ISO* *F160W*). This is similar to the method used in Henry et al. (2021) and Battisti et al. (2022). We recommend total magnitudes to be estimated for UVIS filters using *m*<sub>tot</sub> = *MAG_ISO* - [UVISFILTER] + *APCOR_UVIS*. For the WISP subsample used in our results (Secton 7), the median value of these aperture corrections (*APCOR_UVIS*) is $\Delta m = -0.11$ mag.

## 3.3 Ground-based and *Spitzer* forced photometry (low spatial resolution)

We performed template-fitting or ‘forced’ photometric measurements on the lower resolution data (ground-based and IRAC; both data sets follow the same procedure) by exploiting the coordinates of the *HST*-detected sources as priors. Fig. 4 demonstrates an example of the photometric fitting from the *TPHOT* WISP pipeline on the ground-based data.

The core of this computation is performed using the *TPHOT* software (v2; Merlin et al. 2015, 2016), which we summarize below, and provide in more detail in Appendix A. In brief, *TPHOT* requires two images at different resolutions and a list of detected objects. The algorithm transforms the low-resolution image into a (simulated) high-resolution image. To do this, *TPHOT* ‘distributes’ the flux of the sources in the low-resolution image according to the flux distribution in the high-resolution image using a deconvolution kernel (provided by the user). Compared to aperture photometry (and closest-counterpart associations), this technique allows us to obtain more accurate photometric estimates, especially when multiple *HST* sources appear blended in the low-resolution images. For all of the *HST* sources undetected in the low-resolution bands, we set their fluxes as an upper limit to the $3\sigma$ detection limit.

# 4 WISP PHOTOMETRIC CATALOGUE

## 4.1 Photometric catalogue description

The WISP photometric catalogue contains ~230 000 sources in 439 WISP fields. These sources include stars, galaxies, and active galactic nuclei (AGNs). Fields that are absent are cases where the WISP photometric pipeline failed, which most commonly occurred due to overcrowding, bright stars, or persistence. We note that the filter availability is quite inhomogeneous among WISP fields and entries for fields without data in a given filter have values of ‘−99’ for their photometry. We provide a summary of the number of fields with individual filters in this data release in Table 5. The photometry in this catalogue are *not* corrected for foreground Milky Way (MW) extinction.

We present an explanation of the WISP photometric catalogue entries in Table 6. These include RA, Dec., sizes and position angles (based on *HST*), and Kron-like elliptical aperture magnitude (*MAG_AUTO*; only WFC3/IR filters) from *SEXTRACTOR*. There are two additional aperture measurements available for *HST* data: (1) isophotes (*ISO*) based on the *F110W* and *F160W* segmentation map and (2) fixed circular apertures (*APER*; radii *r* = 0.2, 0.5, 1.0, and 1.5 arcsec). A visual representation of these apertures is shown in Fig. 5. For the ground-based and *Spitzer* data, photometry based on *TPHOT* is provided. For consistent ‘total’ photometry, we recommend using *MAG_AUTO* for WFC3/IR, *MAG* + *APCOR_UVIS* for WFC3/UVIS, and *AB MAG* (*TPHOT* values) for ground-based and *Spitzer* data.

There are also two quality flags in the photometric catalogue. A description of these flags is provided in Appendix B. These flags should be carefully considered when selecting sources from the catalogue to avoid use of unreliable photometry.

## 4.2 Summary of imaging depth and completeness

Since the segmentation maps of sources from WFC3/IR imaging data are used as priors for the shallower and/or lower resolution imaging data, we only formally characterize the depth and completeness

<footnote n="8">⁸https://irsa.ipac.caltech.edu/data/SPITZER/docs/irac/iracinstrumenthandbook/14/</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 4.** HST images and segmentation maps are used to perform 'forced' photometry on the lower resolution data (ground-based and *Spitzer*) by combining the PSFs of both HST and the lower resolution data (detailed in Section 3.3). As an example, we show data from Par 104: (left) the HST F110W image, (middle) the Magellan g image, and (right) the residual of the Magellan g image after running through TPHOT. Instances of oversubtracted residuals (black circles) are expected to occur for sources that are saturated in the HST image (mostly foreground stars); for these cases the HST (deeper) light-profile models are less accurate relative to the same sources in the unsaturated (shallower) low-resolution data. The HST WFC3/IR FOV (2.05 arcmin × 2.27 arcmin) is indicated by the green box in all panels.</figcaption></figure>

<table><caption>**Table 5.** Summary of the number of fields, *N*, with each filter in the WISP photometric catalogue.</caption><thead><tr><th>Filter</th><th>u</th><th>g</th><th>F475X</th><th>F606W</th><th>r</th><th>F600LP</th><th>i</th><th>F814W</th><th>F110W</th><th>F140W</th><th>F160W</th><th>I1</th><th>I2</th></tr></thead><tbody><tr><td>λ<sub>eff</sub> (μm)</td><td>0.3551</td><td>0.4686</td><td>0.4939</td><td>0.5893</td><td>0.6165</td><td>0.7444</td><td>0.7481</td><td>0.8060</td><td>1.1534</td><td>1.3923</td><td>1.5396</td><td>3.550</td><td>4.493</td></tr><tr><td>N</td><td>113</td><td>167</td><td>19</td><td>43</td><td>68</td><td>19</td><td>57</td><td>99</td><td>233</td><td>168</td><td>261</td><td>175</td><td>3</td></tr></tbody></table>

Notes. I1=IRAC1, I2 = IRAC2. The catalogue contains data for 439 out of 483 WISP fields. For details on individual fields, refer to Table 8.

for the WFC3/IR images. This is because, by design, every source detected in the WFC3/IR catalogue has an entry for each additional filter with data (either a detection or upper limit; i.e. 100 per cent 'complete'). Below we outline the method for estimating these values for the WFC3/IR images. We also provide a representative depth for each filter based on median $5\sigma$ magnitude (AB) for detected sources, which we also describe below.

For the HST imaging data, there are considerable differences in the depths and completeness achieved from field to field. This arises from the parallel nature of the program, where exposure times are set by the Primary observation. To estimate this, we use the code described in Prichard et al. (2022),<fnref n="9" /> and briefly summarize here. First, the code determines the rms background value in 1000 randomly selected empty sky regions, where the segmentation map from SExTRACTOR is used to exclude regions overlapping with sources. Next, it generates a histogram of the rms values and fits this distribution with a Gaussian profile to determine the sigma-clipped median. The median rms value is multiplied by the correlated pixel noise correction factor for that filter (for details, see Section 3.3 of the DRIZZLEPAC Handbook<fnref n="10" />). For the drizzle parameters of the WFC3/IR images, as described in Section 2.2.1, the correlated noise correction factor is 1.678. The corrected median rms is then converted into a limiting magnitude using the zero-point and a specified aperture size. We provide an estimate of depth for the WFC3/IR filters for a fixed-aperture size of 0.5 arcsec radius in Table 7.

To measure the completeness, we inject simulated sources of varying brightness into the images and estimate their recoverability with SExTRACTOR, following methodology similar to Revalski et al. (2023). First, we inject point sources modelled as the PSF for each

WFC3/IR filter. We adopt the PSFSTD models,<fnref n="11" /> taking the average across the detector (3 × 3 array), and resampling it to match the 0.08 arcsec resolution of the drizzled images. We inject 40 sources into each image and require that these do not overlap with themselves or with real sources, as determined from the SExTRACTOR segmentation map from the photometric pipeline. The magnitudes of the sources is scaled from [-3 mag, + 1 mag], in steps of 0.1 mag (40 steps), relative to the filter zero-point magnitude. This process is then repeated for a simulated galaxy, which is modelled as a Sérsic profile with $n = 2$, inclined at 45°, and an effective radii of 0.48 arcsec. This corresponds to the average size of a log [M*/M⊙] = 10 star-forming galaxy at $z = 1$ using the size-mass relation of van der Wel et al. (2014). The simulated galaxies are also convolved with each filter PSF. The resulting completeness measurements are shown in Fig. 6. The point-source completeness values show close agreement ($\lesssim 0.2$ mag) with the $5\sigma$ depth based on 0.5 arcsec apertures.

A full list of the median depth in each filter, based on median $5\sigma$ magnitude (AB; $0.15 < m_{err} < 0.25$ mag) for detected sources, are provided in Table 8 and summarized in Fig. 7. We note that this depth value is the median of the entire sample of $5\sigma$ sources in each field, which span a range of sizes/surface brightness and hence is not a uniform representation of depth. With regard to the WFC3/IR data, the depths reported here typically lie in-between the depth values for the point-source and simulated galaxies provided in Table 7. We provide some additional details on the variation in depth for the ground-based and *Spitzer* imaging data. For the ground-based data, there are considerable differences in the depths achieved from field to field. This difference is a result of the ground-based data being obtained through numerous programs, with different facilities/instruments and different science goals (see Table 3), and

<footnote n="9">⁹https://github.com/lprichard/hst_sky_rms</footnote>

<footnote n="10">¹⁰https://hst-docs.stsci.edu/drizzpac</footnote>

<footnote n="11">¹¹https://www.stsci.edu/hst/instrumentation/wfc3/data-analysis/psf</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 4.** *HST* images and segmentation maps are used to perform 'forced' photometry on the lower resolution data (ground-based and *Spitzer*) by combining the PSFs of both *HST* and the lower resolution data (detailed in Section 3.3). As an example, we show data from Par 104: (left) the *HST* F110W image, (middle) the Magellan g image, and (right) the residual of the Magellan g image after running through TPHOT. Instances of oversubtracted residuals (black circles) are expected to occur for sources that are saturated in the *HST* image (mostly foreground stars); for these cases the *HST* (deeper) light-profile models are less accurate relative to the same sources in the unsaturated (shallower) low-resolution data. The *HST* WFC3/IR FOV (2.05 arcmin × 2.27 arcmin) is indicated by the green box in all panels.</figcaption></figure>

<table><caption>**Table 5.** Summary of the number of fields, *N*, with each filter in the WISP photometric catalogue.</caption><thead><tr><th>Filter</th><th><i>u</i></th><th><i>g</i></th><th><i>F475X</i></th><th><i>F606W</i></th><th><i>r</i></th><th><i>F600LP</i></th><th><i>i</i></th><th><i>F814W</i></th><th><i>F110W</i></th><th><i>F140W</i></th><th><i>F160W</i></th><th>I1</th><th>I2</th></tr></thead><tbody><tr><td>λ<sub>eff</sub> (μm)</td><td>0.3551</td><td>0.4686</td><td>0.4939</td><td>0.5893</td><td>0.6165</td><td>0.7444</td><td>0.7481</td><td>0.8060</td><td>1.1534</td><td>1.3923</td><td>1.5396</td><td>3.550</td><td>4.493</td></tr><tr><td><i>N</i></td><td>113</td><td>167</td><td>19</td><td>43</td><td>68</td><td>19</td><td>57</td><td>99</td><td>233</td><td>168</td><td>261</td><td>175</td><td>3</td></tr></tbody></table>

Notes. I1=IRAC1, I2 = IRAC2. The catalogue contains data for 439 out of 483 WISP fields. For details on individual fields, refer to Table 8.

for the WFC3/IR images. This is because, by design, every source detected in the WFC3/IR catalogue has an entry for each additional filter with data (either a detection or upper limit; i.e. 100 per cent 'complete'). Below we outline the method for estimating these values for the WFC3/IR images. We also provide a representative depth for each filter based on median 5σ magnitude (AB) for detected sources, which we also describe below.

For the *HST* imaging data, there are considerable differences in the depths and completeness achieved from field to field. This arises from the parallel nature of the program, where exposure times are set by the Primary observation. To estimate this, we use the code described in Prichard et al. (2022),<fnref n="9" /> and briefly summarize here. First, the code determines the rms background value in 1000 randomly selected empty sky regions, where the segmentation map from SExTRACTOR is used to exclude regions overlapping with sources. Next, it generates a histogram of the rms values and fits this distribution with a Gaussian profile to determine the sigma-clipped median. The median rms value is multiplied by the correlated pixel noise correction factor for that filter (for details, see Section 3.3 of the DRIZZLEPAC Handbook<fnref n="10" />). For the drizzle parameters of the WFC3/IR images, as described in Section 2.2.1, the correlated noise correction factor is 1.678. The corrected median rms is then converted into a limiting magnitude using the zero-point and a specified aperture size. We provide an estimate of depth for the WFC3/IR filters for a fixed-aperture size of 0.5 arcsec radius in Table 7.

To measure the completeness, we inject simulated sources of varying brightness into the images and estimate their recoverability with SExTRACTOR, following methodology similar to Revalski et al. (2023). First, we inject point sources modelled as the PSF for each

WFC3/IR filter. We adopt the PSFSTD models,<fnref n="11" /> taking the average across the detector (3 × 3 array), and resampling it to match the 0.08 arcsec resolution of the drizzled images. We inject 40 sources into each image and require that these do not overlap with themselves or with real sources, as determined from the SExTRACTOR segmentation map from the photometric pipeline. The magnitudes of the sources is scaled from [-3 mag, + 1 mag], in steps of 0.1 mag (40 steps), relative to the filter zero-point magnitude. This process is then repeated for a simulated galaxy, which is modelled as a Sérsic profile with *n* = 2, inclined at 45°, and an effective radii of 0.48 arcsec. This corresponds to the average size of a log [M*/M⊙] = 10 star-forming galaxy at *z* = 1 using the size-mass relation of van der Wel et al. (2014). The simulated galaxies are also convolved with each filter PSF. The resulting completeness measurements are shown in Fig. 6. The point-source completeness values show close agreement (≤ 0.2 mag) with the 5σ depth based on 0.5 arcsec apertures.

A full list of the median depth in each filter, based on median 5σ magnitude (AB; 0.15 < *m*<sub>err</sub> < 0.25 mag) for detected sources, are provided in Table 8 and summarized in Fig. 7. We note that this depth value is the median of the entire sample of 5σ sources in each field, which span a range of sizes/surface brightness and hence is not a uniform representation of depth. With regard to the WFC3/IR data, the depths reported here typically lie in-between the depth values for the point-source and simulated galaxies provided in Table 7. We provide some additional details on the variation in depth for the ground-based and *Spitzer* imaging data. For the ground-based data, there are considerable differences in the depths achieved from field to field. This difference is a result of the ground-based data being obtained through numerous programs, with different facilities/instruments and different science goals (see Table 3), and

<footnote n="9">⁹https://github.com/lprichard/hst_sky_rms</footnote>

<footnote n="10">¹⁰https://hst-docs.stsci.edu/drizzpac</footnote>

<footnote n="11">¹¹https://www.stsci.edu/hst/instrumentation/wfc3/data-analysis/psf</footnote>
<table><caption>**Table 6.** Description of WISP photometric catalogue.</caption><thead><tr><th>Title</th><th>Description</th></tr></thead><tbody><tr><td>PAR</td><td>WISP field ID number</td></tr><tr><td>OBJ</td><td>Object ID number</td></tr><tr><td>RA</td><td>Decimal RA from HST images (deg)</td></tr><tr><td>DEC</td><td>Decimal Dec. from HST images (deg)</td></tr><tr><td>A_WORLD</td><td>SEXTRACTOR profile RMS along major axis in world coordinates, measured on HST images (arcsec)</td></tr><tr><td>B_WORLD</td><td>SEXTRACTOR profile RMS along minor axis in world coordinates, measured on HST images (arcsec)</td></tr><tr><td>THETA_WORLD</td><td>Position angle measured counterclockwise from world x-axis, measured on HST images (deg)</td></tr><tr><td>X_IMAGE.040</td><td>Object x pixel coordinate in 0.04 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>Y_IMAGE.040</td><td>Object y pixel coordinate in 0.04 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>A_IMAGE.040</td><td>SEXTRACTOR profile RMS along major axis measured on 0.04 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>B_IMAGE.040</td><td>SEXTRACTOR profile RMS along minor axis measured on 0.04 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>THETA_IMAGE.040</td><td>Position angle counterclockwise from x-axis, measured on 0.04 arcsec pixel<sup>-1</sup> HST images (deg)</td></tr><tr><td>X_IMAGE.080</td><td>Object x pixel coordinate in 0.08 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>Y_IMAGE.080</td><td>Object y pixel coordinate in 0.08 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>A_IMAGE.080</td><td>SEXTRACTOR profile RMS along major axis measured on 0.08 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>B_IMAGE.080</td><td>SEXTRACTOR profile RMS along minor axis measured on 0.08 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>THETA_IMAGE.080</td><td>Position angle counterclockwise from x-axis, measured on 0.08 arcsec pixel<sup>-1</sup> HST images (deg)</td></tr><tr><td>FLUX_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Sky-subtracted signal within aperture (µJy)</td></tr><tr><td>FLUXERR_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Aperture signal uncertainty (µJy)</td></tr><tr><td>MAG_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Magnitude for FLUX_[APTYPE]_[HSTFILTER]; -99 for undetected sources (AB mag)</td></tr><tr><td>MAGERR_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Magnitude uncertainty; -99 for undetected sources (AB mag)</td></tr><tr><td>FLUX SKY_[HSTFILTER]<sup>a</sup></td><td>Background used for sky subtraction in all apertures; use 10 arcsec rectangle with sources masked (µJy)</td></tr><tr><td>FLUXERR SKY_[HSTFILTER]<sup>a</sup></td><td>Sky aperture signal uncertainty (µJy)</td></tr><tr><td>MAG_AUTO_[NIRFILTER]<sup>b</sup></td><td>SEXTRACTOR Kron-like elliptical aperture magnitude; limit reported for undetected sources (AB mag)</td></tr><tr><td>MAGERR_AUTO_[NIRFILTER]<sup>b</sup></td><td>SEXTRACTOR RMS error for AUTO magnitude; -99 for undetected sources (AB mag)</td></tr><tr><td>FLAG_[NIRFILTER]<sup>b</sup></td><td>SEXTRACTOR extraction flags (8 bit flags; see Appendix B)</td></tr><tr><td>AB MAG_[FILTER]<sup>c</sup></td><td>Filter magnitude from TPHOT output; limiting magnitude for undetected sources (AB mag)</td></tr><tr><td>AB MAGERR_[FILTER]<sup>c</sup></td><td>Filter magnitude uncertainty from TPHOT output; -99 for undetected sources (AB mag)</td></tr><tr><td>TPHOT_FLAG_[FILTER]<sup>c</sup></td><td>TPHOT extraction flags (3 bit flags; see Appendix B)</td></tr><tr><td>INSTR FLAG_[GROUNDFILTER]<sup>d</sup></td><td>String indicating 'Telescope-instrument' for ground-based data (e.g. 'Magellan-Megacam')</td></tr><tr><td>APCOR UVIS</td><td>Aperture correction to convert WFC3/UVIS isophotal magnitudes to total magnitude (AB mag)</td></tr></tbody></table>

Notes. <sup>a</sup> [APTYPE] is one of: ISO (isophotes based on the F110W and F160W segmentation map) or APER (circular apertures of radii $r = 0.2, 0.5, 1.0$, and 1.5 arcsec; array has dimensions of ($n_{obj}, 4$)). [HSTFILTER] is one of: F475X, F475Xc, F606W, F606Wc, F600LP, F600LPc, F814W, F814Wc, F110W, F110Wc, or F160W. ‘c’ refers to photometry on images convolved to match the PSF of F160W. Only convolved fluxes are available for the ISO apertures. <sup>b</sup> [NIRFILTER] is one of: F110W, F140W, or F160W. <sup>c</sup> [FILTER] is one of: u, g, r, i, I1, or I2. <sup>d</sup> [GROUNDFILTER] is one of: u, g, r, i.

<figure><img src="image_6.png" /><figcaption>**Figure 5.** A demonstration of the three types of apertures available for HST imaging data in the photometric catalogue.</figcaption></figure>

due to variations in the observing conditions. For *Spitzer*, the programs run over the different cycles (see Section 2.4) used slightly different exposures that were intended to achieve roughly uniform depth for each targeted field (see Section 2.4.1).

## 4.3 Comparison of ground-based catalogue photometry to SDSS

For the fraction of WISP fields that overlap with SDSS, we can make a comparison of the values in our photometric catalogue to values in the SDSS photometric catalogue. Due to SDSS being much shallower ($5\sigma$ point-source depth for u, g, r, i is 22.15, 23.13, 22.70, and 22.20 mag, respectively) than our data (see Fig. 7), this comparison

is quite limited (particularly for u band) and only possible for the brightest galaxies in each WISP field. We restrict our comparison to galaxies (SDSS PhotoType = 4) and extract PetroMag values from SDSS DR14 public catalogues to use as our reference values. The PetroMag values are the most suitable for representing total galaxy flux that will be similar to our TPHOT photometry (psfMag was used to compare stars for the zero-points in Section 3.1.2). However, the TPHOT photometry is based on priors from the deeper HST data such that the aperture sizes are likely to differ with respect to SDSS. A distance threshold of ~0.5 arcsec is adopted, and we only compare sources with $S/N \ge 5$ in both data sets. A comparison for the u, g, r, and i filters is shown in Fig. 8.

We find good general agreement in the photometry, with median offsets of $\le 0.2$ mag, although a slight bias (0.2 mag) may exist for the r and i bands. However, these offsets are within the variance for each band indicating they may not be significant. These results support or previous claim that the optical zero-points have a typical accuracy of ~0.1 mag. Individual sources with large differences ($\Delta > 0.5$ mag) are likely due to source blending and/or incorrect cross-matching. We note that the sample available for this comparison is much smaller than the sample used in the zero-point determination for each image because the zero-point estimate made use of the entire instrument FOV ($> 15 \times$ the area of WFC3/IR) whereas the catalogue only includes the small region overlapping with the WFC3/IR data.
<table><caption>**Table 6.** Description of WISP photometric catalogue.</caption><thead><tr><th>Title</th><th>Description</th></tr></thead><tbody><tr><td>PAR</td><td>WISP field ID number</td></tr><tr><td>OBJ</td><td>Object ID number</td></tr><tr><td>RA</td><td>Decimal RA from HST images (deg)</td></tr><tr><td>DEC</td><td>Decimal Dec. from HST images (deg)</td></tr><tr><td>A_WORLD</td><td>SEXTRACTOR profile RMS along major axis in world coordinates, measured on HST images (arcsec)</td></tr><tr><td>B_WORLD</td><td>SEXTRACTOR profile RMS along minor axis in world coordinates, measured on HST images (arcsec)</td></tr><tr><td>THETA_WORLD</td><td>Position angle measured counterclockwise from world x-axis, measured on HST images (deg)</td></tr><tr><td>X_IMAGE.040</td><td>Object x pixel coordinate in 0.04 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>Y_IMAGE.040</td><td>Object y pixel coordinate in 0.04 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>A_IMAGE.040</td><td>SEXTRACTOR profile RMS along major axis measured on 0.04 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>B_IMAGE.040</td><td>SEXTRACTOR profile RMS along minor axis measured on 0.04 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>THETA_IMAGE.040</td><td>Position angle counterclockwise from x-axis, measured on 0.04 arcsec pixel<sup>-1</sup> HST images (deg)</td></tr><tr><td>X_IMAGE.080</td><td>Object x pixel coordinate in 0.08 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>Y_IMAGE.080</td><td>Object y pixel coordinate in 0.08 arcsec pixel<sup>-1</sup> scale image (pixels)</td></tr><tr><td>A_IMAGE.080</td><td>SEXTRACTOR profile RMS along major axis measured on 0.08 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>B_IMAGE.080</td><td>SEXTRACTOR profile RMS along minor axis measured on 0.08 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>THETA_IMAGE.080</td><td>Position angle counterclockwise from x-axis, measured on 0.08 arcsec pixel<sup>-1</sup> HST images (deg)</td></tr><tr><td>FLUX_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Sky-subtracted signal within aperture (μJy)</td></tr><tr><td>FLUXERR_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Aperture signal uncertainty (μJy)</td></tr><tr><td>MAG_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Magnitude for FLUX_[APTYPE]_[HSTFILTER]; -99 for undetected sources (AB mag)</td></tr><tr><td>MAGERR_[APTYPE]_[HSTFILTER]<sup>a</sup></td><td>Magnitude uncertainty; -99 for undetected sources (AB mag)</td></tr><tr><td>FLUX SKY_[HSTFILTER]<sup>a</sup></td><td>Background used for sky subtraction in all apertures; use 10 arcsec rectangle with sources masked (μJy)</td></tr><tr><td>FLUXERR SKY_[HSTFILTER]<sup>a</sup></td><td>Sky aperture signal uncertainty (μJy)</td></tr><tr><td>MAG_AUTO_[NIRFILTER]<sup>b</sup></td><td>SEXTRACTOR Kron-like elliptical aperture magnitude; limit reported for undetected sources (AB mag)</td></tr><tr><td>MAGERR_AUTO_[NIRFILTER]<sup>b</sup></td><td>SEXTRACTOR RMS error for AUTO magnitude; -99 for undetected sources (AB mag)</td></tr><tr><td>FLAG_[NIRFILTER]<sup>b</sup></td><td>SEXTRACTOR extraction flags (8 bit flags; see Appendix B)</td></tr><tr><td>AB MAG_[FILTER]<sup>c</sup></td><td>Filter magnitude from TPHOT output; limiting magnitude for undetected sources (AB mag)</td></tr><tr><td>AB MAGERR_[FILTER]<sup>c</sup></td><td>Filter magnitude uncertainty from TPHOT output; -99 for undetected sources (AB mag)</td></tr><tr><td>TPHOT_FLAG_[FILTER]<sup>c</sup></td><td>TPHOT extraction flags (3 bit flags; see Appendix B)</td></tr><tr><td>INSTR FLAG_[GROUNDFILTER]<sup>d</sup></td><td>String indicating 'Telescope-instrument' for ground-based data (e.g. 'Magellan-Megacam')</td></tr><tr><td>APCOR UVIS</td><td>Aperture correction to convert WFC3/UVIS isophotal magnitudes to total magnitude (AB mag)</td></tr></tbody></table>

Notes. <sup>a</sup> [APTYPE] is one of: ISO (isophotes based on the F110W and F160W segmentation map) or APER (circular apertures of radii $r = 0.2, 0.5, 1.0$, and 1.5 arcsec; array has dimensions of ($n_{obj}, 4$)). [HSTFILTER] is one of: F475X, F475Xc, F606W, F606Wc, F600LP, F600LPc, F814W, F814Wc, F110W, F110Wc, or F160W. <sup>c</sup> 'c' refers to photometry on images convolved to match the PSF of F160W. Only convolved fluxes are available for the ISO apertures. <sup>b</sup> [NIRFILTER] is one of: F110W, F140W, or F160W. <sup>c</sup> [FILTER] is one of: u, g, r, i, I1, or I2. <sup>d</sup> [GROUNDFILTER] is one of: u, g, r, i.

<figure><img src="image_6.png" /><figcaption>**Figure 5.** A demonstration of the three types of apertures available for HST imaging data in the photometric catalogue.</figcaption></figure>

due to variations in the observing conditions. For *Spitzer*, the programs run over the different cycles (see Section 2.4) used slightly different exposures that were intended to achieve roughly uniform depth for each targeted field (see Section 2.4.1).

## 4.3 Comparison of ground-based catalogue photometry to SDSS

For the fraction of WISP fields that overlap with SDSS, we can make a comparison of the values in our photometric catalogue to values in the SDSS photometric catalogue. Due to SDSS being much shallower ($5\sigma$ point-source depth for u, g, r, i is 22.15, 23.13, 22.70, and 22.20 mag, respectively) than our data (see Fig. 7), this comparison

is quite limited (particularly for u band) and only possible for the brightest galaxies in each WISP field. We restrict our comparison to galaxies (SDSS PhotoType = 4) and extract PetroMag values from SDSS DR14 public catalogues to use as our reference values. The PetroMag values are the most suitable for representing total galaxy flux that will be similar to our TPHOT photometry (psfMag was used to compare stars for the zero-points in Section 3.1.2). However, the TPHOT photometry is based on priors from the deeper HST data such that the aperture sizes are likely to differ with respect to SDSS. A distance threshold of ~0.5 arcsec is adopted, and we only compare sources with $S/N \ge 5$ in both data sets. A comparison for the u, g, r, and i filters is shown in Fig. 8.

We find good general agreement in the photometry, with median offsets of $\le 0.2$ mag, although a slight bias (0.2 mag) may exist for the r and i bands. However, these offsets are within the variance for each band indicating they may not be significant. These results support or previous claim that the optical zero-points have a typical accuracy of ~0.1 mag. Individual sources with large differences ($\Delta > 0.5$ mag) are likely due to source blending and/or incorrect cross-matching. We note that the sample available for this comparison is much smaller than the sample used in the zero-point determination for each image because the zero-point estimate made use of the entire instrument FOV ($> 15 \times$ the area of WFC3/IR) whereas the catalogue only includes the small region overlapping with the WFC3/IR data.
<table><caption>**Table 7.** Summary of WFC3/IR imaging depth and completeness for WISP fields in the photometric catalogue.</caption><thead><tr><th rowspan="2">Par</th><th colspan="4">F110W depth (AB mag)</th><th colspan="4">F140W depth (AB mag)</th><th colspan="4">F160W depth (AB mag)</th></tr><tr><th>N</th><th>Aperture</th><th>Point source</th><th>Galaxy</th><th>N</th><th>Aperture</th><th>Point source</th><th>Galaxy</th><th>N</th><th>Aperture</th><th>Point source</th><th>Galaxy</th></tr></thead><tbody><tr><td>1</td><td>425</td><td>26.28</td><td>26.26</td><td>25.25</td><td>355</td><td>25.65</td><td>25.78</td><td>24.77</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>2</td><td>--</td><td>--</td><td>--</td><td>--</td><td>354</td><td>25.52</td><td>25.58</td><td>24.59</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>3</td><td>622</td><td>26.27</td><td>26.41</td><td>25.43</td><td>413</td><td>25.65</td><td>25.68</td><td>24.65</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>5</td><td>502</td><td>26.48</td><td>26.42</td><td>25.42</td><td>520</td><td>26.23</td><td>26.28</td><td>25.28</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>6</td><td>346</td><td>25.86</td><td>25.87</td><td>24.84</td><td>415</td><td>25.78</td><td>25.86</td><td>24.89</td><td>--</td><td>--</td><td>--</td><td>--</td></tr></tbody></table>

Notes. N is the number of detected objects in WISP photometric pipeline. ‘Aperture’ is the average $5\sigma$ depth within 0.5 arcsec sky regions. ‘Point source’ and ‘Galaxy’ are the 50 per cent completeness depths for injected simulated point sources and galaxies, respectively (see Section 4.2 and Fig. 6). A full ASCII version of this table is available online.

<figure><img src="image_7.png" /><figcaption>**Figure 6.** Top row: comparison of the fraction of artificial sources that are recovered as a function of magnitude for the WISP WFC3/IR images. The thin grey and red lines show the simulated point-source and galaxy completeness, respectively, for individual fields. The thick black and red lines are the medians of the point-source and galaxy completeness, respectively, across all fields. The completeness (adopted as the 50 per cent detection fraction) varies by up to 1.8 mag across the sample due to the varying exposure length of the visits. The completeness values for each individual field are provided in Table 7. The median 50 per cent completeness for each source type is indicated at the bottom of each panel. Bottom row: histograms of SExTRACTOR MAG_AUTO values for the WFC3/IR images in the WISP catalogue. The black vertical dashed line denoted the median 50 per cent completeness of point sources for all fields (same as top panels).</figcaption></figure>

<table><caption>**Table 8.** Summary of photometric availability and depth (AB mag) for WISP fields in this data release.</caption><thead><tr><th>Par</th><th>u</th><th>g</th><th>F475X</th><th>F606W</th><th>r</th><th>F600LP</th><th>i</th><th>F814W</th><th>F110W</th><th>F140W</th><th>F160W</th><th>I1</th><th>I2</th></tr></thead><tbody><tr><td>λ<sub>eff</sub> (μm)</td><td>0.3551</td><td>0.4686</td><td>0.4939</td><td>0.5893</td><td>0.6165</td><td>0.7444</td><td>0.7481</td><td>0.8060</td><td>1.1534</td><td>1.3923</td><td>1.5396</td><td>3.550</td><td>4.493</td></tr><tr><td>1</td><td>25.06</td><td>26.29</td><td>--</td><td>--</td><td>25.82</td><td>--</td><td>26.35</td><td>--</td><td>26.02</td><td>25.59</td><td>--</td><td>23.88</td><td>--</td></tr><tr><td>2</td><td>--</td><td>27.16</td><td>--</td><td>--</td><td>--</td><td>--</td><td>26.28</td><td>--</td><td>--</td><td>24.94</td><td>--</td><td>--</td><td>--</td></tr><tr><td>3</td><td>--</td><td>26.68</td><td>--</td><td>--</td><td>--</td><td>--</td><td>26.14</td><td>--</td><td>25.91</td><td>25.42</td><td>--</td><td>23.75</td><td>--</td></tr><tr><td>5</td><td>24.48</td><td>26.90</td><td>--</td><td>--</td><td>24.64</td><td>--</td><td>26.23</td><td>--</td><td>26.36</td><td>25.97</td><td>--</td><td>23.53</td><td>--</td></tr><tr><td>6</td><td>--</td><td>26.91</td><td>--</td><td>--</td><td>--</td><td>--</td><td>26.06</td><td>--</td><td>25.68</td><td>25.38</td><td>--</td><td>23.85</td><td>--</td></tr></tbody></table>

Notes. The filters available for each field are indicated with the median $5\sigma$ magnitude (AB) for detected sources (dashes indicate no data). The g band and F475X cover roughly similar wavelengths and typically only one of these were obtained. This similarly occurs for F606W/r band, F600LP/i band/F814W, and F140W/F160W. I1=IRAC1 and I2 = IRAC2. A full ASCII and PDF version of this table is available online. In the full PDF version, fields with ‘full’ UV to NIR coverage (6 or more filters) are highlighted in orange.
<table><caption>**Table 7.** Summary of WFC3/IR imaging depth and completeness for WISP fields in the photometric catalogue.</caption><thead><tr><th rowspan="2">Par</th><th colspan="4">F110W depth (AB mag)</th><th colspan="4">F140W depth (AB mag)</th><th colspan="4">F160W depth (AB mag)</th></tr><tr><th>N</th><th>Aperture</th><th>Point source</th><th>Galaxy</th><th>N</th><th>Aperture</th><th>Point source</th><th>Galaxy</th><th>N</th><th>Aperture</th><th>Point source</th><th>Galaxy</th></tr></thead><tbody><tr><td>1</td><td>425</td><td>26.28</td><td>26.26</td><td>25.25</td><td>355</td><td>25.65</td><td>25.78</td><td>24.77</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>2</td><td>--</td><td>--</td><td>--</td><td>--</td><td>354</td><td>25.52</td><td>25.58</td><td>24.59</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>3</td><td>622</td><td>26.27</td><td>26.41</td><td>25.43</td><td>413</td><td>25.65</td><td>25.68</td><td>24.65</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>5</td><td>502</td><td>26.48</td><td>26.42</td><td>25.42</td><td>520</td><td>26.23</td><td>26.28</td><td>25.28</td><td>--</td><td>--</td><td>--</td><td>--</td></tr><tr><td>6</td><td>346</td><td>25.86</td><td>25.87</td><td>24.84</td><td>415</td><td>25.78</td><td>25.86</td><td>24.89</td><td>--</td><td>--</td><td>--</td><td>--</td></tr></tbody></table>

Notes. N is the number of detected objects in WISP photometric pipeline. ‘Aperture’ is the average 5σ depth within 0.5 arcsec sky regions. ‘Point source’ and ‘Galaxy’ are the 50 per cent completeness depths for injected simulated point sources and galaxies, respectively (see Section 4.2 and Fig. 6). A full ASCII version of this table is available online.

<figure><img src="image_7.png" /><figcaption>**Figure 6.** Top row: comparison of the fraction of artificial sources that are recovered as a function of magnitude for the WISP WFC3/IR images. The thin grey and red lines show the simulated point-source and galaxy completeness, respectively, for individual fields. The thick black and red lines are the medians of the point-source and galaxy completeness, respectively, across all fields. The completeness (adopted as the 50 per cent detection fraction) varies by up to 1.8 mag across the sample due to the varying exposure length of the visits. The completeness values for each individual field are provided in Table 7. The median 50 per cent completeness for each source type is indicated at the bottom of each panel. Bottom row: histograms of SExTRACTOR MAG_AUTO values for the WFC3/IR images in the WISP catalogue. The black vertical dashed line denoted the median 50 per cent completeness of point sources for all fields (same as top panels).</figcaption></figure>

<table><caption>**Table 8.** Summary of photometric availability and depth (AB mag) for WISP fields in this data release.</caption><thead><tr><th>Par</th><th>u</th><th>g</th><th>F475X</th><th>F606W</th><th>r</th><th>F600LP</th><th>i</th><th>F814W</th><th>F110W</th><th>F140W</th><th>F160W</th><th>I1</th><th>I2</th></tr><tr><th>λ<sub>eff</sub> (μm)</th><td>0.3551</td><td>0.4686</td><td>0.4939</td><td>0.5893</td><td>0.6165</td><td>0.7444</td><td>0.7481</td><td>0.8060</td><td>1.1534</td><td>1.3923</td><td>1.5396</td><td>3.550</td><td>4.493</td></tr></thead><tbody><tr><td>1</td><td>25.06</td><td>26.29</td><td>--</td><td>--</td><td>25.82</td><td>--</td><td>26.35</td><td>--</td><td>26.02</td><td>25.59</td><td>--</td><td>23.88</td><td>--</td></tr><tr><td>2</td><td>--</td><td>27.16</td><td>--</td><td>--</td><td>--</td><td>--</td><td>26.28</td><td>--</td><td>--</td><td>24.94</td><td>--</td><td>--</td><td>--</td></tr><tr><td>3</td><td>--</td><td>26.68</td><td>--</td><td>--</td><td>--</td><td>--</td><td>26.14</td><td>--</td><td>25.91</td><td>25.42</td><td>--</td><td>23.75</td><td>--</td></tr><tr><td>5</td><td>24.48</td><td>26.90</td><td>--</td><td>--</td><td>24.64</td><td>--</td><td>26.23</td><td>--</td><td>26.36</td><td>25.97</td><td>--</td><td>23.53</td><td>--</td></tr><tr><td>6</td><td>--</td><td>26.91</td><td>--</td><td>--</td><td>--</td><td>--</td><td>26.06</td><td>--</td><td>25.68</td><td>25.38</td><td>--</td><td>23.85</td><td>--</td></tr></tbody></table>

Notes. The filters available for each field are indicated with the median 5σ magnitude (AB) for detected sources (dashes indicate no data). The g band and F475X cover roughly similar wavelengths and typically only one of these were obtained. This similarly occurs for F606W/r band, F600LP/i band/F814W, and F140W/F160W. I1=IRAC1 and I2 = IRAC2. A full ASCII and PDF version of this table is available online. In the full PDF version, fields with ‘full’ UV to NIR coverage (6 or more filters) are highlighted in orange.
<figure><img src="image_8.png" /><figcaption>**Figure 7.** Comparison of the median magnitude for sources detected at ~5σ significance (sources with $0.15 < m_{err} < 0.25$ mag; S/N ~ 5) for all WISP fields in this data release. Panels show (left to right and top to bottom) HST WFC3/UVIS, WFC3/IR, ground-based *u*, *g*, *r*, *i*, and Spitzer/IRAC. The vertical dashed lines denote the median magnitude value of 5σ-detected sources for a given filter (i.e. image depth). The range in depths are mainly due to the varying length of exposure times, observing conditions, and/or field crowding (see Section 4.2).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 7.** Comparison of the median magnitude for sources detected at ~5σ significance (sources with $0.15 < m_{err} < 0.25$ mag; S/N ~ 5) for all WISP fields in this data release. Panels show (left to right and top to bottom) HST WFC3/UVIS, WFC3/IR, ground-based *u*, *g*, *r*, *i*, and Spitzer/IRAC. The vertical dashed lines denote the median magnitude value of 5σ-detected sources for a given filter (i.e. image depth). The range in depths are mainly due to the varying length of exposure times, observing conditions, and/or field crowding (see Section 4.2).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Comparison of the magnitude offset for galaxies detected at S/N ≥ 5 in optical bands from SDSS (PetroMag) and our WISP catalogue (AB_Mag). The vertical dashed lines denote the median 5σ point-source limit for SDSS. We note that the magnitude limit for galaxies is fainter than this (e.g. Fig. 6). The median offsets are typically ≤0.2 mag and agree within the variance of the data. There are very few *u*-band galaxies bright enough for reliable comparison. The solid and dashed lines show the median and ±1σ values combining all data in equal-number bins of 50 galaxies. For each instrument, we provide the median magnitude difference (i.e. y-axis) and its variance in the legend, with the number of available sources shown in parentheses.</figcaption></figure>

# 5 WISP SPECTROSCOPIC PIPELINE

The WISP emission-line catalogue is created in three steps. First, an automatic detection algorithm examines every extracted spectrum to identify emission-line candidates. Next, each candidate is independently inspected by two reviewers both for confirmation and to measure the source redshift and emission-line properties. These first two steps are performed by the wisp_analysis<fnref n="12" /> software package. Finally, the output from the two reviewers is combined to create one catalogue entry for each emission-line source. We describe each step in this process in the following sections.

## 5.1 Emission-line candidate identification

We identify emission-line candidates with a peak detection algorithm that uses a continuous wavelet transform (CWT) to select appropriately shaped peaks in 1-D spectra. A wavelet transform breaks a signal into its base components, each of which is a modified version of the same 'mother' wavelet function. This process is similar to Fourier analysis, but rather than sinusoidal components

of varying frequencies, the base components identified by a wavelet analysis are scaled or shifted versions of the mother wavelet. The CWT is an improvement over the previous WISP line detection method, which identified emission lines as contiguous pixels above a signal-to-noise (S/N) threshold (Colbert et al. 2013). The previous amplitude-based peak finding process was strongly dependent on the fit to the continuum and the amount of smoothing applied to the spectrum. It also resulted in many spurious detections as noise spikes can be misidentified as faint emission lines. The CWT technique identifies emission-line features in the spectrum based on their shape as well as their amplitude, and therefore reduces the number of spurious emission-line candidates that reviewers need to inspect. A detailed description of the steps in applying our CWT algorithm and using the results to identify emission-line candidates is provided in Appendix C.

Emission-line identification using the CWT algorithm is performed on the 1D spectra extracted and calibrated by AXE (Kümmel et al. 2009). We first remove from each spectrum the flux introduced by the overlapping continua from nearby objects. For this purpose we use the contamination model created by AXE during spectral extraction, where all objects detected in the direct images are approximated as 2D Gaussians defined by the size and shape measured by SEXTRACTOR. This process is performed on the 1D

<footnote n="12">¹²https://github.com/HSTWISP/wisp_analysis</footnote>
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Comparison of the magnitude offset for galaxies detected at S/N ≥ 5 in optical bands from SDSS (PetroMag) and our WISP catalogue (AB_Mag). The vertical dashed lines denote the median 5σ point-source limit for SDSS. We note that the magnitude limit for galaxies is fainter than this (e.g. Fig. 6). The median offsets are typically ≤0.2 mag and agree within the variance of the data. There are very few *u*-band galaxies bright enough for reliable comparison. The solid and dashed lines show the median and ±1σ values combining all data in equal-number bins of 50 galaxies. For each instrument, we provide the median magnitude difference (i.e. y-axis) and its variance in the legend, with the number of available sources shown in parentheses.</figcaption></figure>

# 5 WISP SPECTROSCOPIC PIPELINE

The WISP emission-line catalogue is created in three steps. First, an automatic detection algorithm examines every extracted spectrum to identify emission-line candidates. Next, each candidate is independently inspected by two reviewers both for confirmation and to measure the source redshift and emission-line properties. These first two steps are performed by the *wisp_analysis*<fnref n="12" /> software package. Finally, the output from the two reviewers is combined to create one catalogue entry for each emission-line source. We describe each step in this process in the following sections.

## 5.1 Emission-line candidate identification

We identify emission-line candidates with a peak detection algorithm that uses a continuous wavelet transform (CWT) to select appropriately shaped peaks in 1-D spectra. A wavelet transform breaks a signal into its base components, each of which is a modified version of the same 'mother' wavelet function. This process is similar to Fourier analysis, but rather than sinusoidal components

of varying frequencies, the base components identified by a wavelet analysis are scaled or shifted versions of the mother wavelet. The CWT is an improvement over the previous WISP line detection method, which identified emission lines as contiguous pixels above a signal-to-noise (S/N) threshold (Colbert et al. 2013). The previous amplitude-based peak finding process was strongly dependent on the fit to the continuum and the amount of smoothing applied to the spectrum. It also resulted in many spurious detections as noise spikes can be misidentified as faint emission lines. The CWT technique identifies emission-line features in the spectrum based on their shape as well as their amplitude, and therefore reduces the number of spurious emission-line candidates that reviewers need to inspect. A detailed description of the steps in applying our CWT algorithm and using the results to identify emission-line candidates is provided in Appendix C.

Emission-line identification using the CWT algorithm is performed on the 1D spectra extracted and calibrated by AXE (Kümmel et al. 2009). We first remove from each spectrum the flux introduced by the overlapping continua from nearby objects. For this purpose we use the contamination model created by AXE during spectral extraction, where all objects detected in the direct images are approximated as 2D Gaussians defined by the size and shape measured by SEXTRACTOR. This process is performed on the 1D

<footnote n="12">¹²https://github.com/HSTWISP/wisp_analysis</footnote>
<figure><img src="image_10.png" /><figcaption>**Figure 9.** The display from the interactive portion of the emission-line finding process. Reviewers inspect all available information for a given object. Top row: the 2D spectral stamps for *G102* (left) and *G141* (right) are displayed with a green box indicating the trace of the spectra. Red circles identify the positions of zeroth orders of bright ($m < 23$) objects that may contaminate the spectra. Middle row: the direct images in all available filters, *F110W* (left) and *F160W* (right), with the positions of all objects and their corresponding magnitudes marked. The source of interest, here object 93, is centred in the direct images. Bottom panel: the 1D spectrum is displayed in both flux units ($f_λ$, top) and S/N (bottom). In the example shown here, the emission features identified in the spectrum of object 93 are not centred in the green box because they are coming from object 77. The emission-line offset is clearest in the *G102* spectrum. Object 93 was rejected as an emission-line candidate and these emission features were instead measured for object 77.</figcaption></figure>

spectra of each grism individually, allowing for 500Å of overlap between the two grisms. We next require that at least two reviewers inspect each emission-line candidate, both to confirm the candidates and to measure the line properties.

## 5.2 Emission-line candidate inspection

The original, amplitude-based WISP line detection software resulted in a false detection rate of $\gtrsim 70$ per cent, depending on the depth of the fields. Although the improvements presented here – identifying emission lines using both amplitude and shape combined with the EW criterion – reduce this rate significantly to approximately 55 per cent, the visual inspection remains necessary for constructing a clean catalogue. This inspection is especially crucial for undithered parallel data, which cannot be properly cleaned of artefacts.

For each emission-line candidate, reviewers simultaneously inspect the direct images of the source, the 2D spectral stamps, the 1D extracted spectrum in units of $f_λ$, and a spectrum in units of S/N. An example of the full display from the interactive portion of the process is shown in Fig. 9.

We now briefly describe the inspection process and the series of checks reviewers perform for each candidate. The source displayed in Fig. 9, object 93 from WISP field Par94 (hereafter 94–93), is used as an example to illustrate the process. The two main questions reviewers must answer are whether the emission-line candidate identified by the detection algorithm is real, and whether it belongs to the object to which it is associated. In the first case, reviewers are validating the results of the detection software. In the second, they are considering and ruling out possible sources of contamination.

Candidate confirmation is required because spurious or false emission lines are occasionally identified by the detection program. False emission lines typically arise in two cases, when detector artefacts are identified as lines and when the continuum is improperly fit. The first case is a particular problem for a pure parallel survey such as WISP, since the telescope is not dithered between exposures. Combining multiple exposures therefore does not remove all cosmic rays, hot pixels, and other artefacts. Those that remain are sometimes selected as emission-line candidates. Reviewers can often reject these by comparing the shape of the emission-line candidate in the 2D spectral stamp (top row of Fig. 9) with the source shape in the direct image (middle row). Recall that an emission line in the spectral stamp is an image of the source at the given wavelength. The size, ellipticity, and position angle of the source are expected to be reflected to some degree in the emission line. This comparison is approximate, however, since the emission regions in a galaxy need not directly correlate with the broadband continuum emission detected in the imaging filters.

In the second case, false emission-line candidates are identified in objects with continua that are poorly fit by the automatic software, which uses a cubic spline to fit the spectrum at eight wavelengths nodes. A steep rise in the continuum of an object, often caused by contamination from the spectrum of a nearby object, that is not reflected in the model fit can be incorrectly selected as a spectral peak. Given the large range of object sizes, fluxes, and levels of spectral confusion, one set of parameters will not work perfectly for all objects. The software’s continuum fit is therefore treated as a first pass. The dashed blue line in Fig. 9 is the continuum model for object 94–93, which in this case represents a good fit to the observed continuum. For objects with improperly fit continua, reviewers can tweak the model by adding, removing, or changing the wavelengths of the nodes used in the spline fit (black circles).

The reviewers must next determine whether the identified emission feature belongs to the source in question. With only a single roll angle, WISP spectra are often contaminated by overlapping spectra from other sources along the dispersion direction. This check generally involves four parts. First, the comparison between source and emission-line shape described above can help identify emission lines coming from another object. We do not expect a galaxy to be much larger or much brighter in an emission line than it is in imaging.

Second, the emission should be vertically centred in the trace of the spectral stamps, indicated by the green box in the top panels of Fig. 9. We can see that the line candidates in the spectrum of 94–93 are not centred, evidence that they likely belong to the nearby, brighter object 77. We note that it is of course possible that the emitting region of a source may not be centred on the continuum emission, and by rejecting emission lines that are off the centre of the trace we may also be rejecting real emission-line galaxies.

Third, if there are multiple emission lines visible in the spectrum, their relative wavelengths should match. The wavelength solution of the grism is determined by the source position in the direct image, and will therefore only be correct for the spectrum of that source. For example, in Fig. 9, given the assumed redshift for this object, the spectral peak around $\lambda \sim 10\,500$ Å should be [O II]$\lambda$3727. However, it does not line up exactly with the expected wavelength for [O II] at this redshift (indicated by the blue vertical line), further indication that these emission lines are contaminants from object 77.

Finally, the reviewers must consider the position of zeroth orders. We consider a portion of a spectrum to suffer from ‘major’ zeroth-order contamination if it directly overlaps with a zeroth order from a bright source ($m < 23$ mag). The position of these bright zeroth orders are indicated by red circles in the grism stamps in Fig. 9. The
<figure><img src="image_10.png" /><figcaption>**Figure 9.** The display from the interactive portion of the emission-line finding process. Reviewers inspect all available information for a given object. Top row: the 2D spectral stamps for *G102* (left) and *G141* (right) are displayed with a green box indicating the trace of the spectra. Red circles identify the positions of zeroth orders of bright ($m < 23$) objects that may contaminate the spectra. Middle row: the direct images in all available filters, *F110W* (left) and *F160W* (right), with the positions of all objects and their corresponding magnitudes marked. The source of interest, here object 93, is centred in the direct images. Bottom panel: the 1D spectrum is displayed in both flux units ($f_λ$, top) and S/N (bottom). In the example shown here, the emission features identified in the spectrum of object 93 are not centred in the green box because they are coming from object 77. The emission-line offset is clearest in the *G102* spectrum. Object 93 was rejected as an emission-line candidate and these emission features were instead measured for object 77.</figcaption></figure>

spectra of each grism individually, allowing for 500Å of overlap between the two grisms. We next require that at least two reviewers inspect each emission-line candidate, both to confirm the candidates and to measure the line properties.

## 5.2 Emission-line candidate inspection

The original, amplitude-based WISP line detection software resulted in a false detection rate of $\gtrsim 70$ per cent, depending on the depth of the fields. Although the improvements presented here – identifying emission lines using both amplitude and shape combined with the EW criterion – reduce this rate significantly to approximately 55 per cent, the visual inspection remains necessary for constructing a clean catalogue. This inspection is especially crucial for undithered parallel data, which cannot be properly cleaned of artefacts.

For each emission-line candidate, reviewers simultaneously inspect the direct images of the source, the 2D spectral stamps, the 1D extracted spectrum in units of $f_λ$, and a spectrum in units of S/N. An example of the full display from the interactive portion of the process is shown in Fig. 9.

We now briefly describe the inspection process and the series of checks reviewers perform for each candidate. The source displayed in Fig. 9, object 93 from WISP field Par94 (hereafter 94–93), is used as an example to illustrate the process. The two main questions reviewers must answer are whether the emission-line candidate identified by the detection algorithm is real, and whether it belongs to the object to which it is associated. In the first case, reviewers are validating the results of the detection software. In the second, they are considering and ruling out possible sources of contamination.

Candidate confirmation is required because spurious or false emission lines are occasionally identified by the detection program. False emission lines typically arise in two cases, when detector artefacts are identified as lines and when the continuum is improperly fit. The first case is a particular problem for a pure parallel survey such as WISP, since the telescope is not dithered between exposures. Combining multiple exposures therefore does not remove all cosmic rays, hot pixels, and other artefacts. Those that remain are sometimes selected as emission-line candidates. Reviewers can often reject these by comparing the shape of the emission-line candidate in the 2D spectral stamp (top row of Fig. 9) with the source shape in the direct image (middle row). Recall that an emission line in the spectral stamp is an image of the source at the given wavelength. The size, ellipticity, and position angle of the source are expected to be reflected to some degree in the emission line. This comparison is approximate, however, since the emission regions in a galaxy need not directly correlate with the broadband continuum emission detected in the imaging filters.

In the second case, false emission-line candidates are identified in objects with continua that are poorly fit by the automatic software, which uses a cubic spline to fit the spectrum at eight wavelengths nodes. A steep rise in the continuum of an object, often caused by contamination from the spectrum of a nearby object, that is not reflected in the model fit can be incorrectly selected as a spectral peak. Given the large range of object sizes, fluxes, and levels of spectral confusion, one set of parameters will not work perfectly for all objects. The software’s continuum fit is therefore treated as a first pass. The dashed blue line in Fig. 9 is the continuum model for object 94–93, which in this case represents a good fit to the observed continuum. For objects with improperly fit continua, reviewers can tweak the model by adding, removing, or changing the wavelengths of the nodes used in the spline fit (black circles).

The reviewers must next determine whether the identified emission feature belongs to the source in question. With only a single roll angle, WISP spectra are often contaminated by overlapping spectra from other sources along the dispersion direction. This check generally involves four parts. First, the comparison between source and emission-line shape described above can help identify emission lines coming from another object. We do not expect a galaxy to be much larger or much brighter in an emission line than it is in imaging.

Second, the emission should be vertically centred in the trace of the spectral stamps, indicated by the green box in the top panels of Fig. 9. We can see that the line candidates in the spectrum of 94–93 are not centred, evidence that they likely belong to the nearby, brighter object 77. We note that it is of course possible that the emitting region of a source may not be centred on the continuum emission, and by rejecting emission lines that are off the centre of the trace we may also be rejecting real emission-line galaxies.

Third, if there are multiple emission lines visible in the spectrum, their relative wavelengths should match. The wavelength solution of the grism is determined by the source position in the direct image, and will therefore only be correct for the spectrum of that source. For example, in Fig. 9, given the assumed redshift for this object, the spectral peak around $\lambda \sim 10\,500$ Å should be [O II]$\lambda$3727. However, it does not line up exactly with the expected wavelength for [O II] at this redshift (indicated by the blue vertical line), further indication that these emission lines are contaminants from object 77.

Finally, the reviewers must consider the position of zeroth orders. We consider a portion of a spectrum to suffer from ‘major’ zeroth-order contamination if it directly overlaps with a zeroth order from a bright source ($m < 23$ mag). The position of these bright zeroth orders are indicated by red circles in the grism stamps in Fig. 9. The
<table><caption>**Table 9.** Model spectrum parameters.</caption><thead><tr><th>Parameter</th><th>Description</th><th>Initial value</th><th>Limits</th></tr></thead><tbody><tr><td>n<sub>nodes</sub></td><td>Number of spline nodes used in continuum fit</td><td>8</td><td>Fixed</td></tr><tr><td>Δλ<sub>fitting</sub></td><td>Size of region used for line fitting</td><td>1500 Å</td><td>Fixed</td></tr><tr><td>z<sub>init</sub></td><td>Input redshift</td><td>( λ<sub>obs</sub> / λ<sub>Hα</sub> − 1)<sup>a</sup></td><td>±0.02</td></tr><tr><td>Δz<sub>[O III]</sub></td><td>Shift in redshift allowed for [O III] profile fit</td><td>0</td><td>±0.02</td></tr><tr><td>Δz<sub>[O II]</sub></td><td>Shift in redshift allowed for [O II] profile fit</td><td>0</td><td>±0.02</td></tr><tr><td>Δz<sub>[S III], He I]</sub></td><td>Shift in redshift allowed for [S III] and He I fits</td><td>0</td><td>±0.02</td></tr><tr><td>FWHM<sub>init</sub></td><td>Input FWHM</td><td>2a Δλ<sub>G141</sub> Å<sup>b</sup></td><td>−0.3FWHM<sub>init</sub> +2.0FWHM<sub>init</sub></td></tr><tr><td>A<sub>line</sub></td><td>Input amplitude for each emission line</td><td>...</sup><sub>c</sub></td><td>0,1<sup>c</sup></td></tr><tr><td>r<sub>[S III],λ9532/λ9069</sub></td><td>Ratio of [S III]λ9532 to [S III]λ9069 fluxes</td><td>2.48</td><td>Fixed</td></tr><tr><td>λ<sub>T</sub></td><td>Grism transition wavelength</td><td>11 200 Å</td><td>Fixed</td></tr><tr><td>λ<sub>blue</sub></td><td>Blue wavelength cut-off for the G102 grism</td><td>8100 Å</td><td>Fixed</td></tr><tr><td>λ<sub>red</sub></td><td>Red wavelength cut-off for the G141 grism</td><td>17 000 Å</td><td>Fixed</td></tr></tbody></table>

Notes. $^a$The input redshift is that which will give $H\alpha$ for the current line, or is a redshift guess provided by the user.

$^b$FWHM<sub>init</sub> is taken as twice the source semimajor axis ($A_{\_IMAGE}$) multiplied by the dispersion in the red grism.

$^c$Emission-line amplitudes are estimated as the flux value at line centre and are constrained to be positive.

chance alignment of a bright zeroth order, especially from a compact source, can appear as a very convincing emission line. Spectral peaks that suffer from this major contamination are automatically rejected by the automatic software. Meanwhile, ‘minor’ zeroth-order contamination could be caused by (1) bright zeroth orders that are close to but not directly overlapping the spectral trace or (2) from direct overlap with zeroth orders of fainter sources (m > 23). In some cases, again especially for the most compact objects, these fainter zeroth orders can be bright enough to masquerade as emission lines, and so the reviewers must remain vigilant for this possibility. The minor zeroth-order contamination regions for object 94–93 are shown as orange bands in the spectrum of Fig. 9.

## 5.3 Emission-line measurements

Once an emission-line candidate has been confirmed, the reviewers fit a model to the spectrum. The fitting is performed via Levenberg–Marquardt least-squares minimization. It is implemented with the software MPFIT, based on the MINPACK-1 FORTRAN package (Moré 1978) and translated to PYTHON by Mark Rivers.<fnref n="13" /> With MPFIT, each parameter can be held fixed or can be constrained with upper and lower bounds. The full model includes over 20 parameters, which are described below and listed in Table 9.

The model spectrum fit to the data consists of a continuum with the following emission lines added: [O II]λλ3727 + 3729, Hγ, Hβ, [O III]λλ4959 + 5007, Hα, [S II]λλ6716 + 6730, [S III]λ9069, [S III]λ9531, and He Iλ10830. The continuum is modelled using a cubic spline fit to a series of $n_{nodes}$ spectral nodes, and emission lines are modelled as Gaussians, where the line centre is determined by the redshift guess for the source, the input amplitude is estimated separately for each line within $\pm \Delta\lambda_{fitting}$ of line centre, and the standard deviation depends on the source size and the dispersion of the grism in which the line appears. Emission lines are not fit individually. The entire spectrum – continuum plus all lines – is fit simultaneously, and all line profiles are constrained to have the same FWHM (full width at half-maximum) in pixels, not in Å. Hence, lines in the higher dispersion G102 will have smaller FWHM by a factor of two. This approach is reasonable under the assumption that all emission lines are images of the same host source. The source

redshift is determined by the centre of the profile fit to the $H\alpha$ line at $z \lesssim 1.6$ and [O III] at $z \gtrsim 1.6$ (when $H\alpha$ has redshifted out of the G141 grism). The centres of each additional line are allowed to vary up to a maximum wavelength equivalent to $\Delta z_{line} = 0.02$. In the absence of multiple emission lines, single lines are assumed to be $H\alpha$ unless the clear asymmetry of the [O III]+$H\beta$ line profile is visible.

As a consequence of the use of a full spectral model, all emission lines listed above are fit provided they fall within the grism wavelength coverage at the assigned redshift. Emission lines that were not identified by the detection algorithm will therefore be measured along with the identified lines. We refer to the lines strong enough to have been identified by the detection algorithm as ‘primary lines’, while the remaining lines are called ‘secondary lines’. This distinction is an important one for the emission-line catalogue completeness, which is discussed in the next section (Section 5.4).

Lines with a flux S/N < 1 are set at $1\sigma$ and reported as upper limits in flux and therefore EW. These limits are calculated by summing in quadrature the error array within 2 × FWHM of line centre. We find, however, that the error arrays calculated by AXE are underestimates of the spectral noise properties. The $1\sigma$ limits are all systematically lower than the sensitivity limits measured for the fields. We therefore apply a correction factor to the flux limits, correcting the amplitude of the limits while preserving the scatter in the measurements.

We note several emission lines – $H\alpha$ and [N II], the [S II]λλ6716 + 6731 doublet, the [O II]λλ3727 + 3730 doublet, and the [O III]λ4959 + 5007 doublet – are blended at the resolution of the WFC3 grisms. The fluxes measured for $H\alpha$ therefore include the contribution from [N II]λ6583 and [N II]λ6550, and those for [O II], [S II], and [O III] each include both doublet lines. The flux ratio [S III]λ9532/[S III]λ9069 is fixed to 2.48:1.

While inspecting each spectrum, reviewers can change multiple parameters in order to improve the fit to the spectrum. In addition to moving, adding, or subtracting nodes for the continuum fit, they can provide a new redshift guess for the source; modify the wavelength ranges of each grism to fit emission lines at the grism edges (changing the transition between grisms, $\lambda_T$, and their wavelength cutoffs $\lambda_{blue}$ and $\lambda_{red}$); or provide a new guess for the FWHM<sub>init</sub>, usually decreasing the default guess for sources where the dispersion axis is along the minor axis. Finally, reviewers can mask regions of the spectrum that suffer from severe contamination from either zeroth orders or nearby continua, thereby making sure they do not affect the full spectral model. The emission-line candidate vetting, spectral

<footnote n="13">¹³University of Chicago, http://cars9.uchicago.edu/software/python/mpfit.html</footnote>
<table><caption>**Table 9.** Model spectrum parameters.</caption><thead><tr><th>Parameter</th><th>Description</th><th>Initial value</th><th>Limits</th></tr></thead><tbody><tr><td>n<sub>nodes</sub></td><td>Number of spline nodes used in continuum fit</td><td>8</td><td>Fixed</td></tr><tr><td>Δλ<sub>fitting</sub></td><td>Size of region used for line fitting</td><td>1500 Å</td><td>Fixed</td></tr><tr><td>z<sub>init</sub></td><td>Input redshift</td><td>(λ<sub>obs</sub>/λ<sub>Hα</sub> - 1)<sup>a</sup></td><td>±0.02</td></tr><tr><td>Δz<sub>[O III]</sub></td><td>Shift in redshift allowed for [O III] profile fit</td><td>0</td><td>±0.02</td></tr><tr><td>Δz<sub>[O II]</sub></td><td>Shift in redshift allowed for [O II] profile fit</td><td>0</td><td>±0.02</td></tr><tr><td>Δz<sub>[S III], He I]</sub></td><td>Shift in redshift allowed for [S III] and He I fits</td><td>0</td><td>±0.02</td></tr><tr><td>FWHM<sub>init</sub></td><td>Input FWHM</td><td>2a Δλ<sub>G141</sub> Å<sup>b</sup></td><td>-0.3FWHM<sub>init</sub><br>+2.0FWHM<sub>init</sub></td></tr><tr><td>A<sub>line</sub></td><td>Input amplitude for each emission line</td><td>...</sup><sub>c</sub></td><td>0, 1<sup>c</sup></td></tr><tr><td>r<sub>[S III],λ9532/λ9069</sub></td><td>Ratio of [S III]λ9532 to [S III]λ9069 fluxes</td><td>2.48</td><td>Fixed</td></tr><tr><td>λ<sub>T</sub></td><td>Grism transition wavelength</td><td>11 200 Å</td><td>Fixed</td></tr><tr><td>λ<sub>blue</sub></td><td>Blue wavelength cut-off for the G102 grism</td><td>8100 Å</td><td>Fixed</td></tr><tr><td>λ<sub>red</sub></td><td>Red wavelength cut-off for the G141 grism</td><td>17 000 Å</td><td>Fixed</td></tr></tbody></table>

Notes. $^a$The input redshift is that which will give $H\alpha$ for the current line, or is a redshift guess provided by the user.

$^b$FWHM<sub>init</sub> is taken as twice the source semimajor axis ($A_{\_IMAGE}$) multiplied by the dispersion in the red grism.

$^c$Emission-line amplitudes are estimated as the flux value at line centre and are constrained to be positive.

chance alignment of a bright zeroth order, especially from a compact source, can appear as a very convincing emission line. Spectral peaks that suffer from this major contamination are automatically rejected by the automatic software. Meanwhile, ‘minor’ zeroth-order contamination could be caused by (1) bright zeroth orders that are close to but not directly overlapping the spectral trace or (2) from direct overlap with zeroth orders of fainter sources (m > 23). In some cases, again especially for the most compact objects, these fainter zeroth orders can be bright enough to masquerade as emission lines, and so the reviewers must remain vigilant for this possibility. The minor zeroth-order contamination regions for object 94–93 are shown as orange bands in the spectrum of Fig. 9.

## 5.3 Emission-line measurements

Once an emission-line candidate has been confirmed, the reviewers fit a model to the spectrum. The fitting is performed via Levenberg–Marquardt least-squares minimization. It is implemented with the software MPFIT, based on the MINPACK-1 FORTRAN package (Moré 1978) and translated to PYTHON by Mark Rivers.<fnref n="13" /> With MPFIT, each parameter can be held fixed or can be constrained with upper and lower bounds. The full model includes over 20 parameters, which are described below and listed in Table 9.

The model spectrum fit to the data consists of a continuum with the following emission lines added: [O II]λλ3727 + 3729, Hγ, Hβ, [O III]λλ4959 + 5007, Hα, [S II]λλ6716 + 6730, [S III]λ9069, [S III]λ9531, and He Iλ10830. The continuum is modelled using a cubic spline fit to a series of $n_{nodes}$ spectral nodes, and emission lines are modelled as Gaussians, where the line centre is determined by the redshift guess for the source, the input amplitude is estimated separately for each line within $\pm \Delta\lambda_{fitting}$ of line centre, and the standard deviation depends on the source size and the dispersion of the grism in which the line appears. Emission lines are not fit individually. The entire spectrum – continuum plus all lines – is fit simultaneously, and all line profiles are constrained to have the same FWHM (full width at half-maximum) in pixels, not in Å. Hence, lines in the higher dispersion G102 will have smaller FWHM by a factor of two. This approach is reasonable under the assumption that all emission lines are images of the same host source. The source

redshift is determined by the centre of the profile fit to the $H\alpha$ line at $z \lesssim 1.6$ and [O III] at $z \gtrsim 1.6$ (when $H\alpha$ has redshifted out of the G141 grism). The centres of each additional line are allowed to vary up to a maximum wavelength equivalent to $\Delta z_{line} = 0.02$. In the absence of multiple emission lines, single lines are assumed to be $H\alpha$ unless the clear asymmetry of the [O III]+$H\beta$ line profile is visible.

As a consequence of the use of a full spectral model, all emission lines listed above are fit provided they fall within the grism wavelength coverage at the assigned redshift. Emission lines that were not identified by the detection algorithm will therefore be measured along with the identified lines. We refer to the lines strong enough to have been identified by the detection algorithm as ‘primary lines’, while the remaining lines are called ‘secondary lines’. This distinction is an important one for the emission-line catalogue completeness, which is discussed in the next section (Section 5.4).

Lines with a flux S/N < 1 are set at $1\sigma$ and reported as upper limits in flux and therefore EW. These limits are calculated by summing in quadrature the error array within 2 × FWHM of line centre. We find, however, that the error arrays calculated by AXE are underestimates of the spectral noise properties. The $1\sigma$ limits are all systematically lower than the sensitivity limits measured for the fields. We therefore apply a correction factor to the flux limits, correcting the amplitude of the limits while preserving the scatter in the measurements.

We note several emission lines – $H\alpha$ and [N II], the [S II]λλ6716 + 6731 doublet, the [O II]λλ3727 + 3730 doublet, and the [O III]λ4959 + 5007 doublet – are blended at the resolution of the WFC3 grisms. The fluxes measured for $H\alpha$ therefore include the contribution from [N II]λ6583 and [N II]λ6550, and those for [O II], [S II], and [O III] each include both doublet lines. The flux ratio [S III]λ9532/[S III]λ9069 is fixed to 2.48:1.

While inspecting each spectrum, reviewers can change multiple parameters in order to improve the fit to the spectrum. In addition to moving, adding, or subtracting nodes for the continuum fit, they can provide a new redshift guess for the source; modify the wavelength ranges of each grism to fit emission lines at the grism edges (changing the transition between grisms, $\lambda_T$, and their wavelength cutoffs $\lambda_{blue}$ and $\lambda_{red}$); or provide a new guess for the FWHM<sub>init</sub>, usually decreasing the default guess for sources where the dispersion axis is along the minor axis. Finally, reviewers can mask regions of the spectrum that suffer from severe contamination from either zeroth orders or nearby continua, thereby making sure they do not affect the full spectral model. The emission-line candidate vetting, spectral

<footnote n="13">¹³University of Chicago, http://cars9.uchicago.edu/software/python/mpfit.html</footnote>
fitting, and cataloguing are all performed as part of one streamlined
process. The results from the two reviewers are then combined
for each object, and a series of quality flags are assigned to the
emission-line object determined by the level of redshift, flux and
EW agreement between the reviewers' classifications (see Section 6
and Appendix E).

5.4 Completeness corrections

The completeness of a survey or catalogue is a description of how
accurately the detected sample represents the true population in the
universe. Understanding a survey's completeness is necessary before
the results can be used to conclude anything about the true underlying
distribution of sources and source properties. A survey such as WISP
can suffer from incompleteness for a variety of reasons. Sources
may be lost amidst the noise in images if their fluxes are close
to the detection limit. Some sources may not be detected, or their
emission lines missed in their spectra, because they overlap or are
blended with nearby bright objects. The completeness of a survey
depends on the specific selection function used to detect sources.
In the case of the WISP emission-line catalogue, the selection
function includes the detection of sources in the direct images, the
identification of emission-line candidates via the detection algorithm,
and the acceptance during the visual inspection. We also only look
for emission lines of continuum-detected sources in the images. We
must understand the fraction of sources and emission lines that are
not detected through this full process as a function of their properties
such as size, shape, and the strength of their emission.

In quantifying the completeness corrections that must be applied
to a catalogue, we are determining the types and numbers of sources
that are missed. To do so, we create a simulated catalogue of 10 000
sources and their spectra, 5000 each for the shallow and deep portions
of the WISP Survey. The simulated sources have *H*-band magnitudes
in the range 16.8 $\le m_H$ $\le 26.2(27.6)$ and observed $H\alpha$ fluxes in the
range of $5 \times 10^{-17} (1 \times 10^{-17}) \le f \le 1 \times 10^{-15}$ erg s$^{-1}$ cm$^{-1}$
for the shallow (deep) fields. See Table D1 for the full list of input
parameters and values. We insert these simulated sources into real
WISP images, 25 sources at a time, and run them through the full
WISP pipeline and emission-line detection software. The creation
of the simulated data is described in Appendix D. In order to save
time and the effort required during the visual inspection stage, the
reviewers only inspect the spectra of simulated sources that were
identified by the line finding algorithm. Yet not all of the emission-
line candidates were real. Some were noise spikes, contamination,
or the result of poorly fit continua. We note, however, that because
of this choice, we cannot use the simulations to measure the rates of
contamination or redshift mis-identification in the catalogue.

Of the 10 000 input sources, 7721 were recovered by the WISP
reduction pipeline, with an equal number recovered in the shallow
and deep fields. This 77 per cent recovery fraction represents the
overall imaging completeness given the imaging depths and our set
of source detection parameters. The majority of the sources that
are not recovered in the imaging catalogue, and which therefore
have no extracted spectra, are faint and/or extended. In Fig. 10,
the input semimajor axis sizes (before convolution with the PSF) are
shown as a function of magnitude for the simulated sources that were
input and recovered. The distributions of real sources are shown for
reference. The sources that are not recovered in imaging mainly have
a semimajor axis of $a \ge 0.7$ arcsec and are fainter than 24.5 mag in
the *H* band.

For the simulated sources recovered in imaging, we calculate the
fraction of these that are recovered by the full line finding process.

<figure><img src="image_11.png" /><figcaption>**Figure 10.** The semimajor axis as a function of the *H*-band magnitude for all input sources (grey) and those recovered in imaging (blue contours). The real sources from the photometric and emission-line catalogues are shown in red. The imaging completeness is a function of the magnitude and size of the sources. The recovery fraction drops for large ($a \ge 0.7$ arcsec) and faint (*H* > 24.5) sources.</figcaption></figure>

We find that the completeness depends on source size and shape
and emission-line EW and S/N. The S/N dependence is essentially
a dependence on line flux, but includes the effects of the variable
depths of the WISP fields (see Fig. 3). While we can measure the
S/N of observed emission lines, there is no analogous definition of the
input S/N for the simulated lines. The input template spectra do not
include noise, and while we have added shot noise to the simulated
grism data based on the integration times of the exposures into which
they are added, this is not the only source of noise that will affect the
flux measurements. The depths reached in each field depend also on
the level of the zodiacal background for each pointing. We therefore
instead characterize the completeness as a function of emission line
'scaled flux', or the emission-line flux (input or recovered) divided
by the sensitivity limit of the field at the wavelength of the line.

As discussed in Section 5.3, a source will enter the catalogue
because of the detection of the primary lines. We consider only
one line per spectrum – both for the input simulated and the output
measured spectra – taking the line with the brightest scaled flux as
the source’s primary line. As we are not attempting to quantify the
rates of redshift misidentification, we consider a detected emission
line recovered regardless of whether the reviewers have properly
identified it (i.e. regardless of what redshift is assigned to the object).
We recover 868 of the 5000 simulated sources added to deep WISP
fields covered by both grisms and 1541 of the 5000 added to shallow,
G141-only fields. This recovery reflects the completeness due to both
the imaging and the spectroscopic selection functions, and is heavily
influenced by source size and shape as discussed below.

The object size and shape will strongly affect the completeness,
as large, low surface brightness emission lines may be missed by the
peak finder. However, the large sources that suffer from the highest
levels of incompleteness, those with $a \ge 0.7$ arcsec, constitute less
than 1 per cent of the total catalogue. We simulate sources with a
uniform distribution of sizes, but then weight the input sources by
the distribution of observed sizes in the emission-line catalogue.
This step both reflects the observed distribution and allows us
to consider a two-parameter completeness correction, maintaining
fitting, and cataloguing are all performed as part of one streamlined
process. The results from the two reviewers are then combined
for each object, and a series of quality flags are assigned to the
emission-line object determined by the level of redshift, flux and
EW agreement between the reviewers' classifications (see Section 6
and Appendix E).

## 5.4 Completeness corrections

The completeness of a survey or catalogue is a description of how
accurately the detected sample represents the true population in the
universe. Understanding a survey's completeness is necessary before
the results can be used to conclude anything about the true underlying
distribution of sources and source properties. A survey such as WISP
can suffer from incompleteness for a variety of reasons. Sources
may be lost amidst the noise in images if their fluxes are close
to the detection limit. Some sources may not be detected, or their
emission lines missed in their spectra, because they overlap or are
blended with nearby bright objects. The completeness of a survey
depends on the specific selection function used to detect sources.
In the case of the WISP emission-line catalogue, the selection
function includes the detection of sources in the direct images, the
identification of emission-line candidates via the detection algorithm,
and the acceptance during the visual inspection. We also only look
for emission lines of continuum-detected sources in the images. We
must understand the fraction of sources and emission lines that are
not detected through this full process as a function of their properties
such as size, shape, and the strength of their emission.

In quantifying the completeness corrections that must be applied
to a catalogue, we are determining the types and numbers of sources
that are missed. To do so, we create a simulated catalogue of 10 000
sources and their spectra, 5000 each for the shallow and deep portions
of the WISP Survey. The simulated sources have *H*-band magnitudes
in the range $16.8 \le m_H \le 26.2(27.6)$ and observed $H\alpha$ fluxes in the
range of $5 \times 10^{-17}(1 \times 10^{-17}) \le f \le 1 \times 10^{-15}$ erg s⁻¹ cm⁻¹
for the shallow (deep) fields. See Table D1 for the full list of input
parameters and values. We insert these simulated sources into real
WISP images, 25 sources at a time, and run them through the full
WISP pipeline and emission-line detection software. The creation
of the simulated data is described in Appendix D. In order to save
time and the effort required during the visual inspection stage, the
reviewers only inspect the spectra of simulated sources that were
identified by the line finding algorithm. Yet not all of the emission-
line candidates were real. Some were noise spikes, contamination,
or the result of poorly fit continua. We note, however, that because
of this choice, we cannot use the simulations to measure the rates of
contamination or redshift mis-identification in the catalogue.

Of the 10 000 input sources, 7721 were recovered by the WISP
reduction pipeline, with an equal number recovered in the shallow
and deep fields. This 77 per cent recovery fraction represents the
overall imaging completeness given the imaging depths and our set
of source detection parameters. The majority of the sources that
are not recovered in the imaging catalogue, and which therefore
have no extracted spectra, are faint and/or extended. In Fig. 10,
the input semimajor axis sizes (before convolution with the PSF) are
shown as a function of magnitude for the simulated sources that were
input and recovered. The distributions of real sources are shown for
reference. The sources that are not recovered in imaging mainly have
a semimajor axis of $a \ge 0.7$ arcsec and are fainter than 24.5 mag in
the *H* band.

For the simulated sources recovered in imaging, we calculate the
fraction of these that are recovered by the full line finding process.

<figure><img src="image_11.png" /><figcaption>**Figure 10.** The semimajor axis as a function of the *H*-band magnitude for all input sources (grey) and those recovered in imaging (blue contours). The real sources from the photometric and emission-line catalogues are shown in red. The imaging completeness is a function of the magnitude and size of the sources. The recovery fraction drops for large ($a \ge 0.7$ arcsec) and faint (*H* > 24.5) sources.</figcaption></figure>

We find that the completeness depends on source size and shape
and emission-line EW and S/N. The S/N dependence is essentially
a dependence on line flux, but includes the effects of the variable
depths of the WISP fields (see Fig. 3). While we can measure the
S/N of observed emission lines, there is no analogous definition of the
input S/N for the simulated lines. The input template spectra do not
include noise, and while we have added shot noise to the simulated
grism data based on the integration times of the exposures into which
they are added, this is not the only source of noise that will affect the
flux measurements. The depths reached in each field depend also on
the level of the zodiacal background for each pointing. We therefore
instead characterize the completeness as a function of emission line
'scaled flux', or the emission-line flux (input or recovered) divided
by the sensitivity limit of the field at the wavelength of the line.

As discussed in Section 5.3, a source will enter the catalogue
because of the detection of the primary lines. We consider only
one line per spectrum – both for the input simulated and the output
measured spectra – taking the line with the brightest scaled flux as
the source’s primary line. As we are not attempting to quantify the
rates of redshift misidentification, we consider a detected emission
line recovered regardless of whether the reviewers have properly
identified it (i.e. regardless of what redshift is assigned to the object).
We recover 868 of the 5000 simulated sources added to deep WISP
fields covered by both grisms and 1541 of the 5000 added to shallow,
G141-only fields. This recovery reflects the completeness due to both
the imaging and the spectroscopic selection functions, and is heavily
influenced by source size and shape as discussed below.

The object size and shape will strongly affect the completeness,
as large, low surface brightness emission lines may be missed by the
peak finder. However, the large sources that suffer from the highest
levels of incompleteness, those with $a \ge 0.7$ arcsec, constitute less
than 1 per cent of the total catalogue. We simulate sources with a
uniform distribution of sizes, but then weight the input sources by
the distribution of observed sizes in the emission-line catalogue.
This step both reflects the observed distribution and allows us
to consider a two-parameter completeness correction, maintaining
<figure><img src="image_12.png" /><figcaption>**Figure 11.** The completeness of the emission-line catalogue as a function of the scaled flux and observed EW of the strongest line for each source. The dashed horizontal line indicates the EW completeness limit 40 Å. The small, transparent points indicate the lines with EW < 40 Å and/or S/N < 5.</figcaption></figure>

sufficient number counts for the completeness analysis without requiring reviewers to visually inspect tens of thousands of sources.

The completeness is calculated in four bins of scaled flux and five bins of EW. The bin edges are determined by the distribution of sources in the real WISP emission-line catalogue such that there are an approximately equal number of real sources in each bin. The one exception is the bin of lowest EW, which we add in order to probe an area of the parameter space with low completeness (EW<sub>obs</sub> < 40 Å, see Colbert et al. 2013). We use a radial basis function to approximate the 3D surface formed by the bin centres and recovery fractions calculated in each bin. The resulting completeness corrections calculated for each source in the WISP emission-line catalogue are shown in Fig. 11. They are applied according to the scaled flux and observed EW of the strongest line in the spectrum, which is most often Hα or [O III], but is occasionally [O II] when Hα or [O III] are masked out due to overlap with a bright zeroth order or other major contaminant. These completeness corrections are applicable at the source level or for the primary lines for each source. They are not appropriate for secondary lines. As expected, very few low-EW emission lines were recovered in the simulations, making the completeness corrections calculated in bins with EW<sub>obs</sub> < 40 Å very uncertain. We therefore find that the EW criterion presented in Colbert et al. (2013) applies to the new version of the line finding process as well (see Bagley et al. 2020 for a comparison of these completeness corrections with those calculated by Colbert et al. 2013). We also recommend applying an emission-line flux S/N cut at S/N>5 when using this catalogue.

# 6 WISP EMISSION-LINE CATALOGUE

## 6.1 Emission-line catalogue description

The WISP emission-line catalogue contains 8192 sources in 419 WISP fields. These sources include only galaxies and AGN (spanning 0.137 < z<sub>grism</sub> < 2.785; no stars included). Fields that are absent, relative to the photometric catalogue, fall into one of a few categories: they were too crowded (a few WISP fields captured portions of dwarf galaxies), heavily contaminated by bright stars, or suffered from poor and uneven background subtraction in the grism data. We note

<figure><img src="image_13.png" /><figcaption>**Figure 12.** The emission-line S/N and EW of all emission lines in the catalogue. Hα is the two most commonly identified 'primary line' and therefore the majority of Hα in the catalogue has an S/N > 4. The majority of the lower-S/N lines were fit as a consequence of the redshift assigned by identification of a primary line.</figcaption></figure>

that line availability is dependent on the grism coverage and galaxy redshift and that sources without data in a given emission line have values of '-1' for their entries. The S/N and EW of all emission lines in the catalogue are shown in Fig. 12. The vast majority of the Hα emission lines (blue circles) have a S/N greater than 4 (vertical dashed line), indicating that Hα is most often the primary line in a spectrum. Emission-

line fluxes to the left of the dashed line were likely fit as secondary lines and are therefore at a lower S/N. The emission lines in this catalogue are *not* extinction-corrected (neither foreground MW nor internal extinction applied).

We present an explanation of the WISP emission-line catalogue entries in Table 10. These include RA, Dec., WFC3/IR total magnitudes, sizes (based on HST), spectroscopic redshift, and the FWHM used for all emission lines. There are also emission-line flux, flux error, EW, and observed wavelength, for nine lines ([O II], Hγ, Hβ, [O III], Hα + [N II], [S II], [S III]λ9069, [S III]λ9532, and He Iλ10830).

There are also nine quality flags in the emission-line catalogue. A description of these flags is provided in Appendix E. These flags should be carefully considered when selecting sources from the catalogue. For the most robust selection of emission-line sources, we recommend only using cases with REDSHIFT_FLAG = 0 (5054 sources) to avoid cases with ambiguous redshift determinations. Cases with REDSHIFT_FLAG > 0 indicate some type of redshift disagreement between the reviewers or redshift uncertainty for single line emitters. The majority of sources with REDSHIFT_FLAG > 0 are those that were identified by only a single reviewer (REDSHIFT_FLAG ≥ 16; 2061 sources). These cases are likely marginal detections where one reviewer did not consider the identified emission feature to be real above the noise or rejected the emission feature as some type of artefact or contamination. Sources with REDSHIFT_FLAG ≥ 16 should therefore be considered with caution.

## 6.2 Grism redshift accuracy and precision

The redshift accuracy of the grism data are primarily driven by the number of available lines, with a greater number of lines generally providing more reliable redshifts. For example, Baronchelli et al. (2020) show that the default choice of assuming WISP single-line emitters are Hα is incorrect for ~30 per cent of cases, where most of these are likely to be the [O III]λ5007 emission line. Therefore, in the absence of other independent information that can inform how to break single-emitter degeneracies, such as photometric redshifts (photo-z) and/or machine learning (e.g. Baronchelli et al.
<figure><img src="image_12.png" /><figcaption>**Figure 11.** The completeness of the emission-line catalogue as a function of the scaled flux and observed EW of the strongest line for each source. The dashed horizontal line indicates the EW completeness limit 40 Å. The small, transparent points indicate the lines with EW < 40 Å and/or S/N < 5.</figcaption></figure>

sufficient number counts for the completeness analysis without requiring reviewers to visually inspect tens of thousands of sources.

The completeness is calculated in four bins of scaled flux and five bins of EW. The bin edges are determined by the distribution of sources in the real WISP emission-line catalogue such that there are an approximately equal number of real sources in each bin. The one exception is the bin of lowest EW, which we add in order to probe an area of the parameter space with low completeness (EW<sub>obs</sub> < 40 Å, see Colbert et al. 2013). We use a radial basis function to approximate the 3D surface formed by the bin centres and recovery fractions calculated in each bin. The resulting completeness corrections calculated for each source in the WISP emission-line catalogue are shown in Fig. 11. They are applied according to the scaled flux and observed EW of the strongest line in the spectrum, which is most often Hα or [O III], but is occasionally [O II] when Hα or [O III] are masked out due to overlap with a bright zeroth order or other major contaminant. These completeness corrections are applicable at the source level or for the primary lines for each source. They are not appropriate for secondary lines. As expected, very few low-EW emission lines were recovered in the simulations, making the completeness corrections calculated in bins with EW<sub>obs</sub> < 40 Å very uncertain. We therefore find that the EW criterion presented in Colbert et al. (2013) applies to the new version of the line finding process as well (see Bagley et al. 2020 for a comparison of these completeness corrections with those calculated by Colbert et al. 2013). We also recommend applying an emission-line flux S/N cut at S/N>5 when using this catalogue.

# 6 WISP EMISSION-LINE CATALOGUE

## 6.1 Emission-line catalogue description

The WISP emission-line catalogue contains 8192 sources in 419 WISP fields. These sources include only galaxies and AGN (spanning 0.137 < z<sub>grism</sub> < 2.785; no stars included). Fields that are absent, relative to the photometric catalogue, fall into one of a few categories: they were too crowded (a few WISP fields captured portions of dwarf galaxies), heavily contaminated by bright stars, or suffered from poor and uneven background subtraction in the grism data. We note

<figure><img src="image_13.png" /><figcaption>**Figure 12.** The emission-line S/N and EW of all emission lines in the catalogue. Hα is the two most commonly identified ‘primary line’ and therefore the majority of Hα in the catalogue has an S/N > 4. The majority of the lower-S/N lines were fit as a consequence of the redshift assigned by identification of a primary line.</figcaption></figure>

that line availability is dependent on the grism coverage and galaxy redshift and that sources without data in a given emission line have values of ‘-1’ for their entries. The S/N and EW of all emission lines in the catalogue are shown in Fig. 12. The vast majority of the Hα emission lines (blue circles) have a S/N greater than 4 (vertical dashed line), indicating that Hα is most often the primary line in a spectrum. Emission-

line fluxes to the left of the dashed line were likely fit as secondary lines and are therefore at a lower S/N. The emission lines in this catalogue are *not* extinction-corrected (neither foreground MW nor internal extinction applied).

We present an explanation of the WISP emission-line catalogue entries in Table 10. These include RA, Dec., WFC3/IR total magnitudes, sizes (based on HST), spectroscopic redshift, and the FWHM used for all emission lines. There are also emission-line flux, flux error, EW, and observed wavelength, for nine lines ([O II], Hγ, Hβ, [O III], Hα + [N II], [S II], [S III]λ9069, [S III]λ9532, and He Iλ10830).

There are also nine quality flags in the emission-line catalogue. A description of these flags is provided in Appendix E. These flags should be carefully considered when selecting sources from the catalogue. For the most robust selection of emission-line sources, we recommend only using cases with REDSHIFT_FLAG = 0 (5054 sources) to avoid cases with ambiguous redshift determinations. Cases with REDSHIFT_FLAG > 0 indicate some type of redshift disagreement between the reviewers or redshift uncertainty for single line emitters. The majority of sources with REDSHIFT_FLAG > 0 are those that were identified by only a single reviewer (REDSHIFT_FLAG ≥ 16; 2061 sources). These cases are likely marginal detections where one reviewer did not consider the identified emission feature to be real above the noise or rejected the emission feature as some type of artefact or contamination. Sources with REDSHIFT_FLAG ≥ 16 should therefore be considered with caution.

## 6.2 Grism redshift accuracy and precision

The redshift accuracy of the grism data are primarily driven by the number of available lines, with a greater number of lines generally providing more reliable redshifts. For example, Baronchelli et al. (2020) show that the default choice of assuming WISP single-line emitters are Hα is incorrect for ~30 per cent of cases, where most of these are likely to be the [O III]λ5007 emission line. Therefore, in the absence of other independent information that can inform how to break single-emitter degeneracies, such as photometric redshifts (photo-z) and/or machine learning (e.g. Baronchelli et al.
<table><caption>**Table 10.** Description of WISP emission-line catalogue.</caption><thead><tr><th>Title</th><th>Description</th></tr></thead><tbody><tr><td>PAR</td><td>WISP field ID number</td></tr><tr><td>OBJ</td><td>Object ID number</td></tr><tr><td>RA</td><td>Decimal RA from HST images (deg)</td></tr><tr><td>DEC</td><td>Decimal Dec. from HST images (deg)</td></tr><tr><td>JMAG</td><td>J-band magnitude; S<sub>EXTRACTOR</sub> MAG_AUTO_F110W from WISP photometric catalogue (mag)</td></tr><tr><td>HMAG</td><td>H-band magnitude; S<sub>EXTRACTOR</sub> MAG_AUTO_[F140W or F160W] from WISP photometric catalogue (mag)</td></tr><tr><td>A_IMAGE</td><td>S<sub>EXTRACTOR</sub> profile RMS along major axis measured on 0.08 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>B_IMAGE</td><td>S<sub>EXTRACTOR</sub> profile RMS along minor axis measured on 0.08 arcsec pixel<sup>-1</sup> HST images (pixels)</td></tr><tr><td>FILTER_FLAG</td><td>Flag identifying filter coverage for field (10 bit flags; see Appendix E)</td></tr><tr><td>GRISM_FLAG</td><td>Integer flag identifying grism coverage for field; (1=G102, 2=G141, and 3 = G102 + G141)</td></tr><tr><td>[GRISM_FILTER].FLAG<sup>a</sup></td><td>Flags identifying any problems with the grism data for this field (5 bit flags; see Appendix E)</td></tr><tr><td>EDGE_FLAG</td><td>Flag identifying objects close to edges of direct image (5 bit flags; see Appendix E)</td></tr><tr><td>NNEIGHBORS</td><td>Number of sources in WISP photometric catalogue within 1 arcsec of object</td></tr><tr><td>NLINES</td><td>Number of lines detected at >2σ for object</td></tr><tr><td>REDSHIFT</td><td>Redshift from simultaneous fit to all lines in spectrum</td></tr><tr><td>REDSHIFT_ERR</td><td>Redshift 1σ uncertainty</td></tr><tr><td>dz_OIII</td><td>Shift in redshift of the [O III] profile fit compared with reported REDSHIFT for object</td></tr><tr><td>dz_OII</td><td>Shift in redshift of the [O II] profile fit compared with reported REDSHIFT for object</td></tr><tr><td>dz_SIII_HE1</td><td>Shift in redshift of the [S II] and He I profile fit compared with reported REDSHIFT for object</td></tr><tr><td>DELTA_REDSHIFT</td><td>Difference in redshift fits of the two reviewers</td></tr><tr><td>REDSHIFT_FLAG</td><td>Flag identifying quality of redshift fit (6 bit flags; see Appendix E)</td></tr><tr><td>FWHM_OBS</td><td>FWHM used for all emission-line profile fits; observed frame using the 46.5 Å pixel<sup>-1</sup> dispersion in G141 grism (Å)</td></tr><tr><td>FWHM_OBS_ERR</td><td>FWHM 1σ uncertainty (Å)</td></tr><tr><td>FWHM_FLAG</td><td>Flag identifying quality of reported FWHM (5 bit flags; see Appendix E)</td></tr><tr><td>COMPLETENESS</td><td>Source completeness reflecting the selection function of the full line-finding procedure (see Section 5.4)</td></tr><tr><td>[LINE].NREVS<sup>b</sup></td><td>Number of reviewers who measured emission line</td></tr><tr><td>[LINE].FLUX<sup>b</sup></td><td>Emission-line flux from profile fit (erg s<sup>-1</sup> cm<sup>-2</sup>)</td></tr><tr><td>[LINE].FLUX_ERR<sup>b</sup></td><td>Emission-line flux 1σ uncertainty (erg s<sup>-1</sup> cm<sup>-2</sup>)</td></tr><tr><td>[LINE].DELTA FLUX<sup>b</sup></td><td>Difference in emission-line fluxes of the two reviewers (erg s<sup>-1</sup> cm<sup>-2</sup>)</td></tr><tr><td>[LINE].EW.OBS<sup>b</sup></td><td>Emission-line EW reported in observed frame; -1 for undetected lines (Å)</td></tr><tr><td>[LINE].DELTA_EW<sup>b</sup></td><td>Difference in emission-line EWs of the two reviewers (Å)</td></tr><tr><td>[LINE].FLAG<sup>b</sup></td><td>Flag identifying quality of emission-line measurements (6 bit flags; see Appendix E)</td></tr><tr><td>[LINE].CONTAM<sup>b</sup></td><td>Flag identifying the contamination noted by each reviewer ('a' and 'b'); String has form 'a.b' (4 bit flags; see Appendix E)</td></tr><tr><td>[LINE].WAVEOBS<sup>b</sup></td><td>Observed wavelength of emission line (Å)</td></tr><tr><td>[LINE].EDGE FLAG<sup>b</sup></td><td>Flag identifying emission lines close to edges of grism where sensitivity decreases (4 bit flags; see Appendix E)</td></tr></tbody></table>

Notes.<sup>a</sup> [GRISM_FILTER] is one of: G102 or G141. <sup>b</sup> [LINE] is one of: O II, Hg, Hb, O III, HN II, S II, S III_9069, S III_9532, or HeI_10830.

2020, 2021), we urge caution in using sources with only a single emission line (NLINES ≤ 1; 3350 sources in catalogue). It is also important to consider the redshift quality flags (REDSHIFT_FLAG; see Appendix E) that indicate potential disagreements between emission-line reviewers.

For sources with multiple detected emission lines (i.e. NLINES > 1; 4842 sources in catalogue), the reliability of the redshifts is expected to higher, however this is difficult to quantify without independent metrics or follow-up higher resolution spectroscopy. We refer readers to Baronchelli et al. (2020, 2021) for a detailed discussion on the issues pertaining to contamination and purity in a grism spectroscopic sample. Masters et al. (2014) presented a subset of 26 emission-line galaxies (from 23 WISP fields) where follow-up observations with Magellan/FIRE (R ~ 6000; 0.8–2.5 µm) were made on sources with S/N ≥ 10 (from grism) in [O III] and/or Hα. They found very good agreement in line identification (close to 100 per cent) for these sources, although these cases have higher S/N than most of the WISP sources. A larger sample with follow-up observations from VLT/FORS2 (Very Large Telescope/Focal Reducer and low dispersion Spectrograph; R ~ 1200; 0.51–0.85 µm) will be presented in Boyett et al. (in preparation), which we briefly highlight here. This program observed 85 emission-line galaxies out of 138 potential emission sources in the

four targeted WISP fields. Emission lines were detected in the FORS2 data for 38/85 cases, with agreement in the line identification between the grism and FORS2 data seen for 36 of those galaxies (suggesting 95 per cent accuracy of line identification). Of the 38 galaxies with detected lines, 17 are cases where the grism redshift is based on a single line. All cases where lines were not detected with FORS2 are either cases where no lines were predicted to lie in the FORS2 window (15/85) or the grism redshift is based on a single line (32/85). In summary, these findings add further support that selecting sources with multiple emission lines is necessary to ensure robust redshift estimates.

The precision of the redshifts (relating to REDSHIFT_ERR) are a separate metric from accuracy, with the former mainly driven by the spectral resolution of the grism data. For reference, the WFC3/IR detector using the G141 grism has a sampling of 46.5 Å pixel<sup>-1</sup> and a FWHM ~ 110 Å. We characterize the precision as σ<sub>σ</sub>/(1 + z) (i.e. REDSHIFT_ERR/(1 + REDSHIFT)), which has a median value of 0.00088 (~0.09 per cent) and the 16th and 84th percentiles are 0.00045 and 0.00154, respectively. Bagley et al. (2020) performed an independent test of the precision using the 36 WISP fields that overlap to some degree with each other. These fields result in ~140 sources that were observed multiple times, often with very different exposure times (field depths) and roll angles. This comparison