# A Comprehensive Study of Hα Emitters at z ~ 0.62 in the DAWN Survey: The Need for Deep and Wide Regions

Santosh Harish, Alicia Coughlin, James E. Rhoads, Sangeeta Malhotra,

Steven L. Finkelstein, Matthew Stevans, Vithal S. Tilvi, Ali Ahmad

Khostovan, Sylvain Veilleux, Junxian Wang, et al.

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## To cite this version:

Santosh Harish, Alicia Coughlin, James E. Rhoads, Sangeeta Malhotra, Steven L. Finkelstein, et al. A Comprehensive Study of Hα Emitters at z ~ 0.62 in the DAWN Survey: The Need for Deep and Wide Regions. The Astrophysical Journal, 2020, 892, 10.3847/1538-4357/ab7015. insu-03711477

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# A Comprehensive Study of Hα Emitters at $z \sim 0.62$ in the DAWN Survey: The Need for Deep and Wide Regions

Santosh Harish¹, Alicia Coughlin², James E. Rhoads¹,³, Sangeeta Malhotra¹,³, Steven L. Finkelstein⁴,⁵,⁶

Matthew Stevans⁴, Vithal S. Tilvi¹, Ali Ahmad Khostovan³, Sylvain Veilleux⁵,⁶, Junxian Wang⁷, Pascale Hibon⁸,⁹

Johnnes Zabl¹, Bhavin Joshi¹, John Pharo¹, Isak Wold³, Lucia A. Perez¹, Zhen-Ya Zheng¹⁰, Ronald Probst¹¹,

Rob Swaters¹²,¹³, Bahram Mobasher¹⁴, Tianxing Jiang¹, and Huan Yang¹⁵

¹School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287, USA; santosh.harish@asu.edu

²Chandler-Gilbert Community College, 2626 East Pecos Road, Chandler, AZ 85225-2499, USA

³NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA

⁴Department of Astronomy, University of Texas at Austin, Austin, TX 78712, USA

⁵Department of Astronomy and Joint Space-Science Institute, University of Maryland, College Park, MD 20742 USA

⁶Institute of Astronomy and Kavli Institute for Cosmology Cambridge, University of Cambridge, Cambridge CB3 0HA, UK

⁷CAS Key Laboratory for Research in Galaxies and Cosmology, Department of Astronomy, University of Science and Technology of China, People's Republic of China

⁸European Southern Observatory, Alonso de Cordova 3107, Vitacura, Casilla 19001, Santiago, Chile

⁹Univ Lyon, Univ Lyon1, Ens de Lyon, CNRS, Centre de Recherche Astrophysique de Lyon UMR5574, F-69230 Saint-Genis-Laval, France

¹⁰Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030, People's Republic of China

¹¹NOAO, 950 N. Cherry Avenue, Tucson, AZ 85719, USA

¹²Department of Astronomy, University of Maryland, College Park, MD 20742, USA

¹³Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

¹⁴Department of Physics & Astronomy, University of California, Riverside, CA, USA

¹⁵Las Campanas Observatory, Carnegie Institution for Science, Chile

Received 2019 September 18; revised 2020 January 14; accepted 2020 January 23; published 2020 March 23

## Abstract

We present new estimates of the luminosity function (LF) and star formation rate density (SFRD) for an Hα-selected sample at $z \sim 0.62$ from the Deep And Wide Narrow-band (DAWN) survey. Our results are based on a new Hα sample in the extended COSMOS region (compared to Coughlin et al.) with the inclusion of flanking fields, resulting in a total area coverage of $\sim 1.5$ deg$^2$. A total of 241 Hα emitters were selected based on robust selection criteria using spectrophotometric redshifts and broadband color-color classification. Given that dust extinction is a dominant uncertainty in the estimation of LF and SFRD, we explore the effect of different dust-correction prescriptions by calculating the LF and SFRD using a constant dust extinction correction, $A_{H\alpha} = 1$ mag, a luminosity-dependent correction, and a stellar-mass-dependent correction. The resulting Hα LFs are well fitted using Schechter functions with best-fit parameters: $L^* = 10^{42.24}$ erg s$^{-1}$, $\phi^* = 10^{-2.85}$ Mpc$^{-3}$, $\alpha = -1.62$ for constant dust correction, $L^* = 10^{42.31}$ erg s$^{-1}$, $\phi^* = 10^{-2.8}$ Mpc$^{-3}$, $\alpha = -1.39$ for luminosity-dependent dust correction, and $L^* = 10^{42.36}$ erg s$^{-1}$, $\phi^* = 10^{-2.91}$ Mpc$^{-3}$, $\alpha = -1.48$, for stellar-mass-dependent dust correction. The deep and wide nature of the DAWN survey effectively samples Hα emitters over a wide range of luminosities, thereby providing better constraints on both the faint and bright ends of the LF. Also, the SFRD estimates $\rho_{SFR} = 10^{-1.39} M_{\odot} \text{yr}^{-1} \text{Mpc}^{-3}$ (constant dust correction), $\rho_{SFR} = 10^{-1.47} M_{\odot} \text{yr}^{-1} \text{Mpc}^{-3}$ (luminosity-dependent dust correction), and $\rho_{SFR} = 10^{-1.46} M_{\odot} \text{yr}^{-1} \text{Mpc}^{-3}$ (stellar-mass-dependent dust correction) are in good agreement with the evolution of SFRD across redshifts ($0 < z < 2$) seen from previous Hα surveys.

Unified Astronomy Thesaurus concepts: Emission line galaxies (459); Star formation (1569); Star forming regions (1565); Luminosity function (942)

## 1. Introduction

Mapping the rate at which gas is transformed into stars in galaxies is a key process for understanding galaxy evolution. The spectrum of a galaxy contains emission features that indicate the underlying stellar populations' mass, age, and metallicity (Madau & Dickinson 2014). Young and massive stars contribute most of the light emitted from a galaxy, whereas older and fainter stellar populations make up most of the total stellar mass in a galaxy.

Measuring the rate of star formation (SFR) in galaxies at different epochs is essential for understanding the star formation history of our universe. Several observational tracers of SFR exist, including rest-frame UV, IR, radio, and prominent nebular emission lines such as Hα (Kennicutt & Evans 2012; Madau & Dickinson 2014). Young and short-lived

massive stars produce copious amounts of UV emission that is absorbed by surrounding gas and dust in the galaxy; the ionized gas re-emits this in the form of nebular emission lines (such as Lyα, Hα, [O III]$_{\lambda44959,5007}$), whereas the heated dust produces continuum emission in the infrared.

Among the tracers, the Hα emission line is considered one of the best indicators of SFR given that (1) the emission arises primarily due to photoionization of H II regions by young, massive stars, (2) Hα is less affected by dust extinction than UV continuum or bluer lines, and (3) Hα is readily observed in the optical and near-IR up to $z \sim 2$. Also, the relation between Hα luminosity and SFR is relatively well calibrated (Kennicutt 1998; Kennicutt & Evans 2012). Like any other SFR indicator, the Hα line is also affected by systematics, mainly due to dust attenuation, which can significantly impact
the accuracy of SFR density (SFRD) estimates; however, dust extinction can be corrected to a reasonable extent. $H\alpha$ is also a valuable redshift tracer, and therefore of great interest for future space-based missions such as *Euclid* (Laureijs et al. 2011), WFIRST (Spergel et al. 2015) and other ground-based surveys.

Early $H\alpha$ surveys have measured the luminosity function (LF) and SFRD in the local universe, $z < 0.5$ (e.g., Gallego et al. 1995; Brinchmann et al. 2004; Nakamura et al. 2004; Hanish et al. 2006), but most of these surveys used a relatively smaller sample of emission-line galaxies. However, with the advent of better instrumentation in the optical and near-IR regimes, many surveys have been able to detect larger samples of $H\alpha$ emitters (at least by an order of magnitude) and have extended $H\alpha$ studies to earlier cosmic times (e.g., Geach et al. 2008; Hayes et al. 2010; Sobral et al. 2013). Most of these studies are mainly based on spectroscopic observations using continuum-selected galaxies from large surveys such as the Sloan Digital Sky Survey (e.g., Brinchmann et al. 2004; Nakamura et al. 2004), grism spectroscopy (e.g., Pirzkal et al. 2004, 2018; Xu et al. 2007; Straughn et al. 2009; Brammer et al. 2012; Colbert et al. 2013; Pirzkal et al. 2013; Malhotra & the FIGS Team 2015), or narrowband (NB) imaging (e.g., Ly et al. 2007; Shioya et al. 2008; Villar et al. 2008; Dale et al. 2010; Sobral et al. 2013).

NB imaging surveys have been able to study large samples of emission-line galaxies thanks to the wide-format optical and near-IR cameras. This technique has several advantages: (1) NB filters are able to detect emission-line galaxies preferentially, (2) they exhibit weak dependence on continuum luminosity, and (3) they probe sources of multiple emission-line types, each across a fairly narrow range of redshifts. NB surveys for $H\alpha$ have been carried out at various redshifts between $0 < z < 2.5$, where the $H\alpha$ line shifts from optical to near-IR regime with increasing redshift. However, it is particularly challenging to conduct surveys in the near-IR domain because the night sky at these wavelengths is dominated by narrow OH emission lines. NB surveys in the recent past that have probed large samples of $H\alpha$ emitters at redshifts $z > 0.4$ include the High-redshift(Z) Emission Line Survey (HiZELS) (Sobral et al. 2013; $z \sim 0.4, 0.84, 1.47, 2.23$), NEWFIRM $H\alpha$ (Ly et al. 2011; $z \sim 0.8$), and Villar et al. (2008) at $z \sim 0.84$.

The Deep And Wide Narrow-band (DAWN) survey is a near-infrared imaging survey that was carried out using the 4 m Mayall telescope at KPNO in Arizona, USA. Three deep fields (COSMOS, UDS, EGS) were observed with a total exposure time of over 65 hr each and two other fields (CFHTLS-D4 and MACS0717) were observed for a total exposure time of over 20 hr each, using a NB filter at 1.06 µm on the NOAO Extremely Wide-Field InfraRed Imager (NEWFIRM; Probst et al. 2004, 2008). In addition, shallow exposures (~1–3 hr) of eight flanking regions around the deep COSMOS region were also obtained with the aim of detecting larger numbers of bright emission-line sources across the field.

Using DAWN, various types of emission-line galaxies at different epochs can be selected and studied. In this paper, we have used DAWN primarily to study $H\alpha$ emitters at $z \sim 0.62$. Complementing previous NB surveys of $H\alpha$ at nearby redshifts, this survey fills the void between $0.5 < z < 0.8$ by adding new measurements of the $H\alpha$ LF and SFRD at $z \sim 0.62$, thereby helping us better understand the evolution of star formation across cosmic timescales. The previous

DAWN $H\alpha$ result (Coughlin et al. 2018, hereafter C18) emphasized extending the LF to fainter luminosities and providing tighter constraints compared to other LFs at $z > 0.5$ from previous surveys. However, since the area covered was relatively small (~0.25 deg²), the bright end of the LF was not sufficiently constrained. The inclusion of flanking regions surrounding the deep COSMOS region extends the area coverage to ~1.5 deg², with a comoving volume of ~3.5 × 10⁴ Mpc³; this work improves upon C18 by providing robust constraints on the bright and faint ends of the $H\alpha$ LF as well as the SFRD estimate at $z \sim 0.62$.

The paper is organized as follows. Section 2 describes the DAWN observations and data reduction process, including photometric calibration and source extraction. In Section 3, we discuss the selection criteria for our emission-line galaxy sample, and the selection of $H\alpha$ emitters using spectrophotometric redshift and color-color criteria. In Section 4, we calculate $H\alpha$ luminosities, taking into account [N II] contamination and dust attenuation; we also determine the incompleteness arising due to selection effects and compute relevant correction factors for LF calculations. Results are presented in Section 5, including the $H\alpha$ LF and SFRD estimate at $z \sim 0.62$. The main conclusions of this work are summarized in Section 6.

Throughout the paper, we have assumed Λ-CDM cosmology: $\Omega_M = 0.3$, $\Omega_\Lambda = 0.7$, and $H_0 = 70$ km s⁻¹ Mpc⁻¹, and Salpeter IMF in our calculations. All magnitudes reported in this paper are based on the AB magnitude system.

## 2. Observations and Data

The DAWN observations were carried out using a custom NB filter installed on the NEWFIRM instrument with the 4 m Mayall telescope at the KPNO. NEWFIRM houses a mosaic of four 2K × 2K InSb detectors with a chip gap of 35" and an overall field of view of ~28' × 28' at 0.4 arcsec pixel⁻¹. The NB filter, NB1066, is a custom designed filter centered at 1.066 µm with an FWHM of 35 Å. With a target 5σ limiting line flux ~6 × 10⁻¹⁸ erg cm⁻² s⁻¹, the DAWN survey was optimized for high sensitivity and large area coverage. Using the 1.06 µm NB filter, this survey is able to detect galaxies showing prominent emission associated with any of the strong emission lines (Ly$\alpha$, $H\alpha$, [O III]$\lambda4959,5007$, [O II]$\lambda3727$), each at different redshift. In this work, we focus on $H\alpha$ emitters at redshift $z \sim 0.62$, which represent star-forming galaxies at a time when the universe was roughly half its current age.

### 2.1. Near-IR Imaging with NEWFIRM

In order to better constrain the bright end of the $H\alpha$ LF, medium-deep images in eight pointings flanking the deep COSMOS region were obtained as part of the NOAO survey program 2013B-0236 (PI: Finkelstein; Stevens et al. 2020). We present an overview of these eight fields as well as the deep field in Table 1 and Figure 1. The dithering strategy and readout patterns followed were similar to that of C18. Full details regarding the DAWN survey will be presented in an upcoming paper (J. E. Rhoads et al. 2020, in preparation).

The data reduction was performed using the NEWFIRM pipeline (Swaters et al. 2009), which produced images that were calibrated, sky-subtracted, re-projected, and resampled along with their corresponding bad-pixel masks. The seeing FWHM for each of the pointings varied due to changing
<table><caption>**Table 1**
Observation Summary of DAWN-COSMOS Fields</caption><thead><tr><th>Pointing</th><th>R.A.<br>(J2000)</th><th>Decl.<br>(J2000)</th><th>Int. Time<br>(hr)</th><th>FWHM<br>(arcsec)</th><th>Depth<sup>a</sup><br>(5σ, AB)</th></tr></thead><tbody><tr><td>Deep</td><td>10:00:30</td><td>+02:14:45</td><td>81</td><td>1.4</td><td>23.6</td></tr><tr><td>P1</td><td>10:02:13</td><td>+01:49:27</td><td>3</td><td>1.4</td><td>22.1</td></tr><tr><td>P2</td><td>09:58:45</td><td>+02:14:37</td><td>1</td><td>1.5</td><td>20.1</td></tr><tr><td>P3</td><td>09:58:46</td><td>+02:41:17</td><td>1.5</td><td>1.4</td><td>20.5</td></tr><tr><td>P4</td><td>10:00:30</td><td>+02:41:15</td><td>1.67</td><td>1.2</td><td>20.3</td></tr><tr><td>P5</td><td>10:02:14</td><td>+02:41:02</td><td>2</td><td>1.3</td><td>21.0</td></tr><tr><td>P6</td><td>10:02:10</td><td>+02:15:06</td><td>2.5</td><td>1.4</td><td>21.6</td></tr><tr><td>P7</td><td>09:58:46</td><td>+01:49:47</td><td>2</td><td>1.3</td><td>21.9</td></tr><tr><td>P8</td><td>10:00:30</td><td>+01:50:25</td><td>2</td><td>1.2</td><td>22.0</td></tr></tbody></table>

**Note.**

a Depth measurements were based on 2" apertures.

weather conditions across different observing nights. The final stacked images had slightly different total integration times across different flanking regions (see Table 1).

## 2.2. Archival Data and Photometry

We used publicly available Y- and J-band images from the UltraVISTA survey DR3 (McCracken et al. 2012) because they are substantially deeper than the NEWFIRM broadband images that were obtained along with our NB data. These images are one of the deepest near-infrared observations of the COSMOS region covering a total area of ~1.5 deg², reaching 5σ (2" aperture, AB) depths of ~25 mag in Y and ~24 mag in the JHKs bands. Since VIRCAM/UltraVISTA images have a higher spatial resolution of 0.15 arcsec pixel⁻¹ compared to NEWFIRM, the broadband images were downgraded to a pixel resolution of 0.4 arcsec pixel⁻¹, using SWARP (Bertin et al. 2002) software, to match the NEWFIRM observations. In each case, the resulting image was inspected for any evidence of astrometric mismatch by overlaying center coordinates of known bright point sources from the Two Micron All Sky Survey catalog (Skrutskie et al. 2006) and blinking between images to check for misalignment. The astrometric alignment between images matched well within a single pixel offset (≤0.01").

## 2.3. DAWN Survey

### 2.3.1. Photometric Calibration

In order to facilitate an accurate comparison between the NB and broadband images, all images were calibrated using an artificial 1.066 µm continuum magnitude based on the interpolation between the Y- and J-band magnitudes as presented in C18. For this purpose, the UltraVISTA K-selected Catalog v4.1 (Muzzin et al. 2013) was used, which provides photometry for sources in YJHKs broad bands. The image calibration was performed using only sources with NB magnitudes fainter than 15, which are bright but not saturated, and brighter than 19, which includes those detected with a high signal-to-noise ratio (S/N > 20). Thereafter, the zero-points of all images were set to a magnitude of 30 (AB). This ensured that the median NB-excess (Y-NB1066) color for unsaturated and bright sources (typically between 17 and 21 mag) is around zero. In order to maintain uniformity throughout, flux measurements were made using a 2" diameter aperture across

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Coverage of the COSMOS field in the DAWN survey, including the deep and flanking (P1–P8) regions. The dimensions of each pointing are ~28' × 28'. The combined NB1066 image for the field is shown in the background.</figcaption></figure>

all images. An aperture correction of −0.35 mag was applied to account for the differences in seeing.

### 2.3.2. Source Detection and Multi-wavelength Photometry

For all images, detection and extraction of sources was performed using SourceExtractor (also known as SExTRACTOR) (Bertin & Arnouts 1996). Photometry was measured using 2" diameter apertures with SExTRACTOR run in dual-mode, where an NB image (NB1066) was used as the detection image in each case, while photometry was measured for Y and NB1066. The SExTRACTOR parameter configurations used for source detection and extraction were similar to those used by C18.

We measured the 5σ depth (as mentioned in Table 1) using random empty aperture (2" diameter) measurements of the background for each NB1066 image. Care was taken to avoid positions where sources with S/N ≥ 3σ, are detected as well as the masked regions. A depth measurement of this kind takes into account the correlated background noise, which provides a robust estimate of the noise compared to those given by SExTRACTOR. However, this is also a conservative upper limit of the noise, since occasionally some measurements might include faint sources below the survey detection thresholds.

## 3. Sample Selection

In order to select emission-line objects from our source catalog, three main criteria were employed. First, in each NB1066 image, only sources with S/N ≥ 5 were considered for further analysis. Considering SExTRACTOR errors to be a lower estimate of the noise, given that they do not account for the correlated background noise, we scaled up SExTRACTOR errors by 20% based on the noise derived from the random empty aperture measurements in Section 2.3.2. This ensures that the selected candidate emission-line sources are robust detections.
Potential emitters were selected based on their (Y—NB1066) color and their significance relative to the general scatter of non-emitters with positive colors, similar to the methods employed in previous studies (Villar et al. 2008; Ly et al. 2011; Sobral et al. 2013). For any source to be considered a line emitter, it should be considerably brighter in the NB image compared to the broadband image. Quantitatively, our requirement was that the flux ratio of NB1066 and Y-band detections should be

$$ \frac{f(\text{NB1066})}{f(Y)} \ge 1.5. \qquad (1) $$

This corresponds to an observer-frame equivalent width (EWobs) of 18 Å at z ~ 0.6.

In addition, the (Y—NB1066) color excess should be significant so that the sample is not dominated by errors in the photometry. Adhering to typical thresholds used in previous surveys (e.g., Ly et al. 2011; Sobral et al. 2013), the color excess significance for true emitters should be

$$ \frac{f(\text{NB1066}) - f(Y)}{\sqrt{\sigma_{\text{NB1066}}^2 + \sigma_Y^2}} \ge 3, \qquad (2) $$

where f(NB1066) and f(Y) are the flux densities and σ²NB1066 and σ²Y are the flux errors in NB1066 and the Y band, respectively.

Upon applying all of the above criteria, we found 389 emission-line sources across all flanking regions put together and 774 sources in the deep region (Figure 2). These candidate line emitters were visually inspected in NB1066 as well as the Y band to remove artifacts/spurious objects or sources with artificially boosted fluxes due to the presence of halos of bright stars or neighboring noisy regions. With efficient masking of bad regions including instrument chip gaps, only ~2% of the sources had to be excluded. The final sample of candidate line emitters includes 1163 sources.

### 3.1. Selection of Hα Emitters

The sample of candidate line emitters includes various kinds of line emitters such as Hα, Hβ/[O III]λλ4959,5007, and [O II]λ3727. The nature of each source, in terms of their line emission, can be determined using several methods. A robust confirmation would be a match with available spectroscopic-redshift catalogs. However, because of a lack of large number of spectroscopic confirmations, a match with photometric-redshift catalogs would be the next best means to categorize these emission-line sources. NB filters are designed such that they are expected to detect line emitters exquisitely, which have strong and narrow emission lines, potentially with little to no continuum detected in the NB. For sources with faint continuum, it is possible that photometric redshifts might be unreliable or even nonexistent. Therefore, for such sources, a color–color calibration based on spectroscopically confirmed sources (and their broadband photometry) can be used for the classification.

For spectroscopic matches, we use a master catalog of spectroscopic redshifts compiled from various past surveys covering the COSMOS region: zCOSMOS (Lilly et al. 2009), 10K-Deep Imaging Multi-Object Spectrograph (Hasinger et al. 2018), 3D-Hubble Space Telescope (Brammer et al. 2012; Momcheva et al. 2016), Very Large Telescope /FORS2 observations (Comparat et al. 2015), Complete Calibration of

the Color-Redshift Relation (Masters et al. 2017), Fiber-Multi Object Spectrograph–COSMOS (Silverman et al. 2015), Galaxy Environment Evolution Collaboration 2 (Balogh et al. 2014), COSMOS-[O II] (Kaasinen et al. 2017), Large Early Galaxy Astrophysics Census (Straatman et al. 2018), MOSFIRE Deep Evolution Field (Kriek et al. 2015), PRIsma Multi-object Survey (Coil et al. 2011; Cool et al. 2013), MMT/Hectospec observations (Prescott et al. 2006), and Magellan/IMACS observations (Trump et al. 2009). All sources with redshifts in the range 0.6 ≤ zspec ≤ 0.65 were selected as Hα emitters irrespective of their quality flag, since NB-excess-selected sources are a reaffirmation of the measured spectroscopic redshifts.

Using multiwavelength observations from UV to near-IR, the COSMOS2015 (Laigle et al. 2016) catalog contains one of the largest compilations of photometric redshifts in the ~2 deg² COSMOS field. Given the uncertainties associated with photometric redshifts, all sources with redshifts in the range 0.57 ≤ zphot ≤ 0.67 were selected as Hα emitters. Figure 3 shows the spectroscopic and photometric-redshift distribution for all NB-excess selected sources. In both the distributions, there are well-defined peaks at z ~ 0.62, 1.13, and 1.86, corresponding to the line emitters Hα, Hβ/[O III], and [O II] detected by our NB1066 filter, respectively. Since the COSMOS2015 catalog is based on a stacked zYJKs image, the catalog is highly complete relative to our Hα sample, given that our NB1066 filter overlaps with the Y band. Out of the 1163 candidate line emitters, ~98% of the sample contained redshift estimates, either photometric or spectroscopic, and in some cases, both.

Apart from the redshift-based characterization, we also used broadband photometry available from Laigle et al. (2016) to categorize sources based on color–color selection criteria. For our sample, we used the following color–color criteria to select Hα emitters (Figure 4):

$$ (r - i^+) < 0.75 \text{ and } (r - i^+) < (B - V) - 0.45 \\ \text{and } (r - i^+) > -1.1(B - V) + 0.95, \qquad (3) $$

$$ (V - i^+) > 0.5 \text{ and } (r - z^{++}) < 1.5 \\ \text{and } (r - z^{++}) < 1.2(V - i^+) - 0.15. \qquad (4) $$

The BVri+ criteria separates low-redshift sources (z < 0.5) from all other higher-redshift sources in the sample. After excluding these low-redshift sources, the Vi+rz++ criteria is used to separate z ~ 0.62 sources, which are mostly Hα, from other high-redshift sources (mostly, [O III] and [O II]) in the sample. A drawback of this method is that some interlopers might get wrongly selected as Hα emitters and some genuine Hα sources might lie outside our color–color selection region. However, we can measure the fraction of contaminating as well as missed sources using spectroscopically confirmed sources, and the total contamination fraction remains relatively low (<10%). Finally, 241 sources were selected as Hα emitters, with 111 sources selected based on spectroscopic redshifts, 110 sources selected using photometric redshifts, and 20 unique sources selected using the broadband color–color criteria.

## 4. Analysis

### 4.1. Hα Luminosities

Using NB1066 and Y-band flux densities, the emission-line fluxes (FL) and observed equivalent width (EWobs) for our Hα
<figure><img src="image_5.png" /><figcaption>**Figure 2.** NB excess (Y—NB1066) as a function of NB1066 magnitude used to select emission-line candidates in the deep and flanking regions. In each case, all NB detections (gray), as well as the selected emission-line candidates (orange) are shown. The selection criteria employed (as detailed in Section 3) includes a detection limit of S/N ≈ 6 (vertical green dashed line), NB excess ≥0.44 mag (horizontal black dashed line), and a minimum color significance of 3 (purple line).</figcaption></figure>

sample were calculated as follows:

$$F_{L} = \Delta NB \left( \frac{f_{NB} - f_{Y}}{1 - (\Delta NB / \Delta Y)} \right), \quad (5)$$

$$EW_{\text{obs}} = \Delta NB \left( \frac{f_{\text{NB}} - f_{Y}}{f_{Y} - f_{\text{NB}}(\Delta \text{NB} / \Delta Y)} \right), \quad (6)$$

where ΔNB and ΔY are the filter widths (FWHM in Å), and $f_{NB}$ and $f_{Y}$ are the flux densities (erg s⁻¹ cm⁻² Å⁻¹) for NB1066 and the Y band, respectively. The corresponding line

luminosities are derived assuming z = 0.62, which is the median redshift of our Hα sample.

The intrinsic Hα luminosity can be derived using the observed line luminosity after correcting for contamination due to adjacent [N II]λλ6548,6584 lines, as well as attenuation due to dust.

## 4.2. [N II] Contamination

For typical L* galaxies in the nearby universe, past surveys have adopted corrections based on the typical Hα/[N II] flux
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Redshift distribution for NB1066-excess-selected sources using photometric redshifts (gray shaded histogram) from Laigle et al. (2016) and spectroscopic redshifts (black histogram) from a compilation of various surveys (mentioned in Section 3.1). Peaks in the distribution correspond to the redshifts at which prominent emission-line sources are detected in our survey and are labeled accordingly (dashed lines).</figcaption></figure>

ratio of 2.3 (Kennicutt 1992; Gallego et al. 1997). However, recent NB surveys (Villar et al. 2008; Ly et al. 2011; Sobral et al. 2013) have adopted EW-dependent corrections based on the mean relationship between the rest-frame EW of Hα+[N II] λ6583 and the Hα/[N II] ratio.

Unlike previous surveys, the NB filter, NB1066, in DAWN is relatively narrow such that, for any Hα source at z ~ 0.62, only one of the [N II] lines is expected to be contaminating the Hα line flux. For example, when Hα is detected on the bluer side of the filter, the redder [N II]λ6584 line is expected at the wings of the filter (<50% transmission). However, if Hα is detected on the redder part, the bluer [N II]λ6548 line should fall in a reasonably transmissive portion of the filter. Owing to a lack of spectroscopic redshift for each source in our Hα sample, deriving individual correction is fairly difficult given that it is highly impractical to determine which one of the [N II] lines is responsible for contamination in each source. Therefore, the necessary corrections in this case are intrinsically different compared to other surveys.

For these aforementioned reasons, we derive a luminosity-dependent [N II] correction as follows: we generate a mock galaxy sample with luminosities and redshifts based on the observed luminosity distribution and a uniform redshift distribution comprising redshifts probed by our NB1066 filter. Assuming a fixed [N II]/Hα value of 0.43 (Kennicutt 1992; Gallego et al. 1997), we derive Hα luminosities for all sources in the mock sample after convolving them through our NB1066 filter curve (case “A”). In a similar way, we also derive Hα luminosities assuming [N II]/Hα ~0 for the mock sample (case “B”). Using the Hα luminosity distribution from case “A” and “B,” we calculate their ratio as a function of luminosity, which provides an estimate of the factor by which objects have been overcounted per luminosity bin. The resulting correction factor is applied to the LFs in Section 5.1 which corrects for the presence of [N II] within our Hα sample.

### 4.3. Dust Attenuation

Dust obscuration is a significant source of uncertainty in UV and optical measurements of galaxy properties including SFR. Although Hα emission is less affected by dust compared to the UV continuum, correcting Hα luminosities for dust is necessary to accurately measure SFR. Ideally, dust corrections applied to galaxies should be measured individually, for example, based on Balmer decrements (Hα/Hβ), but that requires rest-frame optical/near-IR spectra for each galaxy (e.g., Reddy et al. 2015), which is practically infeasible for large samples.

In the past, some studies (e.g., Sobral et al. 2013) have adopted a simple dust correction of A$_{Hα}$ = 1 assuming that dust affects all sources in the sample equally, whereas some others (e.g., Ly et al. 2011) have assumed that dust extinction in galaxies depends on their SFR/luminosity (Hopkins et al. 2001) or stellar-mass (Garn & Best 2010) and hence apply corrections accordingly. Sobral et al. (2013) believe that the typical extinction in a galaxy need not necessarily depend on its SFR/luminosity in an absolute manner, but rather depends on the nature of the source (meaning the extent to which it is star-forming or luminous) relative to the normal star-forming galaxy at a particular epoch.

In order to explore the effects of different dust-extinction correction on LF and SFRD, we correct our Hα luminosities following all aforementioned prescriptions and analyze them separately hereafter. For luminosity-dependent extinction correction, we adopted the following relation given by Ly et al. (2012):

$$ \log(L_{\text{obs}}) = \log(L_{\text{int}}) - 2.36 \log\left[\frac{0.797 \log(L_{\text{int}}) - 29.1}{2.86}\right], \quad (7) $$

where L$_{obs}$ and L$_{int}$ are the observed and intrinsic Hα luminosities (erg s$^{-1}$), respectively. As mentioned earlier, this extinction correction is based on the SFR-dependent formalism derived by Hopkins et al. (2001), which demonstrates that Hα luminosity directly correlates with SFR, meaning dust redening will be higher for sources with higher Hα luminosity. In case of stellar-mass-dependent correction, the dust extinction is computed according to the following relation given by Garn & Best (2010):

$$ A_{H\alpha} = 0.91X + 0.77X^2 + 0.11X^3 - 0.09X^4, \quad (8) $$

where X = log$_{10}$(M$_*/$10$^{10}$ M$_{⊙}$). For our Hα sample, the stellar-mass estimates available from COSMOS2015 (Laigle et al. 2016) are used to derive the extinction corrections. Since several sources in our sample have stellar masses log(M$_*/$M$_{⊙}$) < 8.5, where the parameterization does not account for such low stellar-mass sources, we assume a fixed dust correction, A$_{Hα}$ = 0.3 mag, corresponding to the extinction correction derived for a source with log(M$_*/$M$_{⊙}$) ~ 8.5, for all such sources.

### 4.4. Completeness Corrections

Given our methods of detection and selection of Hα emitters, we have to estimate the incompleteness arising out of this process and apply an appropriate correction for each
<figure><img src="image_7.png" /><figcaption>Figure 4. Color–color criteria used in the selection of Hα emitters from the sample of candidate line emitters. On the left, (B − V ) vs. ( r − i $^{+}$) colors help separate low-redshift sources ( z < 0 . 5) from the rest of the sample, whereas ( V − i $^{+}$) vs. ( r − z $^{++}$) colors on the right, provide clear separation between z ∼ 0 . 62 and the high- redshift sources. H α -selected sources using all the criteria given in Section 3.1 are also indicated.</figcaption></figure>

source in our sample. Based on the procedure suggested by C18, we estimate the completeness fraction of our sample. Briefly put, artificial sources are randomly superimposed on the science image. The standard detection and selection methods are followed to determine the number of sources recovered. A comparison between the number of emission-line detected sources ( N$_{detected}$ ) and the number of artificial ( N$_{artificial}$ ) plus real ( N$_{real}$ ) sources present in the image provides us with a recovery fraction for the sample:

$$
\kappa = \frac{N_{\text{detected}} - N_{\text{real}}}{N_{\text{artificial}}} \qquad (9)
$$

This procedure is repeated once for each bin across a range of luminosities and EW$_{obs}$ .

Owing to different image depths across the deep and flanking regions, the completeness simulation was performed individually for each region. Simulations were performed for each of the 600 bins across a luminosity range 10 39 . 7 − 10 42 . 7 L ⊙ (Δ ∼ 0 . 1 dex), and an EW$_{obs}$ range 0–200 Å (Δ ∼ 10 Å). Since the total number of sources detected in the flanking regions is less than half the number detected in the deep region, the completeness simulation for flanking regions included 5000 artificial sources, whereas the simulation for the deep region included 10,000 artificial sources. The completeness correction thereby computed was applied to each source, depending on its luminosity and EW$_{obs}$ , within each region.

Figure 5 shows completeness fractions for the deep and flanking regions. We adopt a 20% completeness limit for each luminosity–EW$_{obs}$ bin while applying corrections for sources in a particular region.

5. Results

5.1. H α LF at z ∼ 0 . 62

H α LFs for this survey are derived using the V/V$_{max}$ method (Schmidt 1968). In this work, since the sample of H α emitters are selected from regions of varying imaging depths, we construct and analyze LFs separately for the deep region, the shallow flanking regions, and the full DAWN–COSMOS region ( ∼ 1 . 5 deg $^{2}$). Following Section 4.3, the LFs presented

hereafter are derived based on both prescriptions of dust correction, for comparison purposes. Unless otherwise speci- fied, the errors for each LF bin are Poissonian, with an additional error of 20% added in quadrature to account for the uncertainty in completeness corrections.

Each LF presented in this work can be modeled based on the typical Schechter profile (Schechter 1976) defined as follows:

$$
\phi(L)dL = \phi^* \left(\frac{L}{L^*}\right)^{\alpha} \exp\left(-\frac{L}{L^*}\right) \left(\frac{dL}{L^*}\right). \quad (10)
$$

In the log form, this function can be defined as

$$
\phi(L)dL = \ln(10) \phi^* \left(\frac{L}{L^*}\right)^{\alpha+1} \exp\left(-\frac{L}{L^*}\right) d(\log L). \quad (11)
$$

Many H α surveys in the past have been able to successfully model their LF using the Schechter function; in further sections, we show that this holds good for our H α LF as well. We adopt a 20% completeness limit in terms of the H α LF, for all calculations hereafter.

The best-fit Schechter parameters and their associated 1 σ uncertainties are determined using MCMC simulations based on the Metropolis–Hastings algorithm. The simulation involves the following steps: (1) an initial guess for the Schechter parameters is validated against a uniform prior ( − 4 < log φ ∗ Mpc − 3 < − 1, 40 < log L ∗ erg s − 1 < 44 and − 2 < α < 0). (2) Each iteration determines the goodness of the Schechter fit to the given H α LF based on the χ 2 statistic. (3) The entire parameter space for all Schechter parameters is explored over 500,000 iterations and their probability distributions are derived. The median and 1 σ estimates from these distributions correspond to the best-fit Schechter parameters and their errors for our H α LF, respectively.

The LFs derived using the H α subsample from the deep region alone are presented in Figure 6 (left), which are completeness- and dust-corrected. We derive three different LFs (and their respective Schechter fits) based on the three prescriptions of dust-extinction correction used in this work. In either of the LFs, it is seen that the faint-end slopes are steep and consistent with the canonical value of α = − 1 . 6, which is
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Completeness fraction as a function of luminosity and EW for deep and flanking regions. The dashed line represents the EW-cut adopted in this survey.</figcaption></figure>

observed among most Hα LFs from recent NB surveys at $z \sim 0-2$ (e.g., Ly et al. 2011; Sobral et al. 2013; Gómez-Guijarro et al. 2016). However, since the volume probed by this region is relatively small ($\sim 0.25$ deg$^2$), the brighter Hα population ($L_{H\alpha} > 10^{42}$ erg s$^{-1}$) is sparsely sampled, therefore the bright end of the LF is weakly constrained.

Using the Hα subsample from just the flanking regions, the derived LFs are shown in Figure 6 (right). In contrast to the deep region, the flanking regions contain sample-significant numbers of bright Hα sources ($L_{H\alpha} \ge 10^{42}$ erg s$^{-1}$). Unlike in the case of deep region, the bright end of the Schechter fit, which essentially depicts the break from the power-law form of the Schechter function, is better constrained for these flanking-region LFs. On the contrary, the faint end is hardly constrained owing to the lack of faint-luminosity sources, which is expected given the shallow exposures. Therefore, the

Schechter parameters for these LFs, especially the faint-end slope ($\alpha$), should not be viewed as significant implications of this work.

For the full Hα sample (deep and flanking regions combined), the resulting LFs and their Schechter fits are shown in Figure 7 in comparison with Hα LFs from previous surveys at various redshifts, $z \sim 0-2$ (Ly et al. 2007, 2011; Sobral et al. 2013). The best-fit Schechter parameters are given in Table 2. Considering the empirical relation given for $L^*$ and $\phi^*$ as a function of redshift from Sobral et al. (2013), our values are consistent with the expected value from this relation at $z \sim 0.62$. We find that the characteristic luminosity, as well as the normalization parameters, are higher compared to those at lower redshifts. However, the faint-end slope is significantly steeper in the case of the LF based on constant dust correction compared to the LF with luminosity-dependent or stellar-mass-dependent dust
<figure><img src="image_9.png" /><figcaption>Figure 6. Hα LF with their respective Schechter fits for the deep (left) and flanking regions (right). These LFs are corrected for [N II] contamination, incompleteness, and dust extinction (luminosity-dependent correction in blue, constant correction in red, and stellar-mass-dependent correction in green).</figcaption></figure>

correction. Using Hopkins et al. (2001) dust correction, previous studies have suggested that the faint-end slope tends to flatten out due to an increase in the number density on the bright end resulting from a higher correction applied for brighter sources (e.g., Villar et al. 2008; An et al. 2014). Our faint-end slope using the same dust correction is consistent with those values. On the other hand, studies employing constant dust correction, $A_{H\alpha} = 1$, observe a steeper faint-end slope, $\alpha = -1.6$, in their LFs (e.g., Ly et al. 2007; Sobral et al. 2009, 2013, who argue that there is mild dependence of extinction on observed luminosity (Sobral et al. 2012) and a median correction of ~1 mag holds good on average for large samples. Assuming constant dust correction, the best-fit faint-end slope of our LF, $\alpha = -1.62$, is in agreement with these studies.

## 5.2. SFRD at $z \sim 0.62$

Using the best-fit Schechter parameters, the total Hα luminosity density can be calculated as follows:

$$ \mathcal{L} = L^*\phi^*\Gamma(2 + \alpha). \quad (12) $$

Following this, the SFRD can be calculated using the standard calibration of Kennicutt (1998): $\rho_{SFR} = 7.9 \times 10^{-42} \mathcal{L}$, where $\mathcal{L}$ is calculated by fully integrating down the LFs.

Although most of the Hα luminosity density can be due to active star formation in galaxies, some contribution is usually attributed to active galactic nucleus (AGN) activity as well. Studies in the past have found AGN contamination to be ~10%–15% for Hα samples at redshifts $z < 2$ (e.g., Villar et al. 2008; Ly et al. 2011; Sobral et al. 2013). One way to account for AGN contamination is to look for X-ray-identified sources matching with our Hα sample. The COSMOS2015 catalog contains X-ray sources drawn from XMM-COSMOS (Cappelluti et al. 2007; Hasinger et al. 2007; Brusa et al. 2010) and Chandra-COSMOS (Elvis et al. 2009; Civano et al. 2012, 2016) surveys. The X-ray luminosity limit is ~$10^{42}$ erg s⁻¹ at $z \sim 0.6$ (Marchesi et al. 2016) and assuming a typical X-ray to Hα ratio, $\log(L_X/L_{H\alpha}) \sim 1-2$ (Ho et al. 2001; Panessa et al. 2006; Shi et al. 2010), any AGN-powered Hα emitter from either flanking or deep region should be detected in

X-rays. We find five X-ray matches for our Hα sample, which suggests that AGN contamination is ~2% of the total sample and ~4% of the flanking-region subsample (where it is expected to be complete for X-ray-luminous AGNs). However, since these X-ray surveys are flux-limited, these matches alone are not a representative of the AGN contamination in our sample.

Another method to assess AGN contamination is to use the mid-IR color criterion based on the differing spectral energy distributions (SEDs) of star-forming galaxies and AGNs around the rest-frame 1.6 µm bump. For AGNs, the SED is a rising power law after the bump due to the presence of emission from polycyclic aromatic hydrocarbons and silicate grains. Using Ks-band and IRAC CH1 (3.6 µm) photometry from COSMOS2015, we measure [Ks – 3.6] color for our sample, where redder colors ([Ks – 3.6] > 0.27) represent AGNs and bluer colors are mostly star-forming galaxies. Following this criterion, we find 11% of our sample to be AGN-contaminated.

After correcting for AGN contamination, we estimate the SFRD at $z \sim 0.62$ to be $\log \rho_{SFR} = -1.47$ for luminosity-dependent dust correction, $\log \rho_{SFR} = -1.46$ for stellar-mass-dependent dust correction, and $\log \rho_{SFR} = -1.39$ for constant dust correction (Table 2). For DAWN-COSMOS, the cosmic variance uncertainties were estimated to be around 23% based on calculations from Driver & Robotham (2010). Figure 8 shows a comparison of SFRD as a function of redshift for DAWN and other Hα based surveys (e.g., Ly et al. 2007, 2011; Morioka et al. 2008; Shioya et al. 2008; Villar et al. 2008; Sobral et al. 2009, 2013; Westra et al. 2010; Stroe & Sobral 2015; Gómez-Guijarro et al. 2016, Khostovan et al. 2020). We also compare our SFRD estimate to the empirical fits given by Sobral et al. (2013) and Madau & Dickinson (2014). Using Hα samples at $z \sim 0.4, 0.8, 1.47, 2.23$ from HiZELS, Sobral et al. (2013) provide an empirical fit for SFRD as a function of redshift, $\log \rho_{SFR} = -2.1/(z + 1)$. In Madau & Dickinson (2014), an empirical fit for SFRD is derived based on measurements from a host of recent UV and IR galaxy surveys. Within $1\sigma$ uncertainties, our SFRD estimates are consistent with these fits, as shown in Figure 8. The $\rho_{SFR}$ based on luminosity-dependent dust
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Hα LFs with their corresponding Schechter fits for the full COSMOS region surveyed by DAWN and its comparison with previous Hα surveys (Ly et al. 2007, 2011; Sobral et al. 2013; Coughlin et al. 2018). All LFs shown here are corrected for [N II] contamination, incompleteness, and dust extinction. In case of DAWN, the LFs based on three different dust-extinction corrections are shown (top left: luminosity-dependent A<sub>Hα</sub>; top right: stellar-mass-dependent A<sub>Hα</sub>; bottom: constant A<sub>Hα</sub>). In previous Hα surveys, the LFs from Ly et al. (2007, 2011), Coughlin et al. (2018) are luminosity-dependent and dust-corrected, whereas LFs from Sobral et al. (2013) are dust-corrected, assuming A<sub>Hα</sub> = 1. The DAWN LFs at z ~ 0.62 are consistent with the LF evolution observed between redshifts, 0.4 < z < 0.84.</figcaption></figure>

<table><caption>**Table 2**
Schechter Parameters of the LF and SFR Density Estimate for z ~ 0.62 Hα Emitters in DAWN–COSMOS</caption><thead><tr><th>Region</th><th>Dust correction<br>(A<sub>Hα</sub>)</th><th>L<sup>*</sup><br>(erg s<sup>-1</sup>)</th><th>φ<sup>*</sup><br>(Mpc<sup>-3</sup>)</th><th>α</th><th>log L<br>(erg s<sup>-1</sup> Mpc<sup>-3</sup>)</th><th>log ρ<sub>SFR</sub><br>(M<sub>☉</sub> yr<sup>-1</sup> Mpc<sup>-3</sup>)</th></tr></thead><tbody><tr><td rowspan="3">COSMOS</td><td>Luminosity-dependent</td><td>42.31<sup>+0.27</sup><sub>-0.18</sub></td><td>-2.80<sup>+0.22</sup><sub>-0.34</sub></td><td>-1.39<sup>+0.13</sup><sub>-0.14</sub></td><td>39.68<sup>+0.06</sup><sub>-0.06</sub></td><td>-1.47<sup>+0.06</sup><sub>-0.06</sub></td></tr><tr><td>Stellar-mass-dependent</td><td>42.36<sup>+0.35</sup><sub>-0.13</sub></td><td>-2.91<sup>+0.28</sup><sub>-0.43</sub></td><td>-1.48<sup>+0.16</sup><sub>-0.14</sub></td><td>39.69<sup>+0.08</sup><sub>-0.07</sub></td><td>-1.46<sup>+0.08</sup><sub>-0.07</sub></td></tr><tr><td>Constant</td><td>42.24<sup>+0.39</sup><sub>-0.21</sub></td><td>-2.85<sup>+0.31</sup><sub>-0.42</sub></td><td>-1.62<sup>+0.18</sup><sub>-0.16</sub></td><td>39.76<sup>+0.08</sup><sub>-0.09</sub></td><td>-1.39<sup>+0.08</sup><sub>-0.09</sub></td></tr></tbody></table>

correction is slightly lower, mostly due to the fact that the correction is unequal across the sample given the luminosity-dependent relation and also that some of the faintest Hα emitters in the sample require no correction, according to this dust-correction method.

## 6. Summary and Conclusions

In this work, we have presented new measurements of the Hα LF and SFRD for NB-selected galaxies at z ~ 0.62 from the DAWN survey. Compared to C18, an additional area of 1.23 deg² was surveyed in the COSMOS region with a
<figure><img src="image_11.png" /><figcaption>**Figure 8.** SFRD as a function of redshift. The estimates from DAWN observations (for the sake of clarity, different dust-corrected SFRDs have been artificially displaced in redshift) are compared with those from Hα surveys from the recent past (see Section 5.2 for more details) and empirical fits for SFRD evolution from Sobral et al. (2013) and Madau & Dickinson (2014). Within 1σ uncertainties, our SFRD estimates are consistent with the observed evolution in SFRD with increasing redshift between z ~ 0 and z ~ 2 (cosmic variance errors are shown in black).</figcaption></figure>

resulting total area coverage of ~1.5 deg² and comoving volume of ~3.5 × 10⁴ Mpc³ at z ~ 0.62. In the deepest COSMOS region, the survey reaches a 5σ emission-line flux depth of ~7.7 × 10⁻¹⁸ erg s⁻¹ cm⁻². The main findings of this work are as follows:

(1) A total of 1163 sources were selected as NB-excess emitters with EWobs ≥ 18Å and color significance ≥3σ. Among them, 241 were classified as Hα emitters at z ~ 0.62 based on a combination of spectrophotometric and color-color criteria with up to 111 confirmations from previous spectroscopic surveys.

(2) Hα LFs were constructed after accounting for [N II] contamination, completeness correction, and dust attenuation. Given the ambiguity surrounding different methods of dust correction, the three most popular methods were used: luminosity-dependent correction following Hopkins et al. (2001), stellar-mass-dependent correction following Garn & Best (2010), and a constant correction of A_Hα = 1. All three LFs are well described by a Schechter function with best-fit values of L* = 10⁴.²¹ erg s⁻¹, φ* = 10⁻².⁸ Mpc⁻³, α = -1.39 (luminosity-dependent dust correction), L* = 10⁴.²⁶ erg s⁻¹, φ* = 10⁻².⁹¹ Mpc⁻³, α = -1.48 (stellar-mass-dependent dust correction), and L* = 10⁴.²⁴ erg s⁻¹, φ* = 10⁻².⁸⁵ Mpc⁻³, α = -1.62 (constant dust correction). At z ~ 0.62, the LFs as well as the Schechter parameters are in good agreement with the expected evolution in comparison to those at other redshifts (0 < z < 2). Within the 1σ uncertainties, the Schechter parameters are also in good agreement with the empirical relation given by Sobral et al. (2013). However, the derived faint-end slope is shallowest for luminosity-dependent dust correction (α = -1.39), and steepest for constant dust correction (α = -1.62).

(3) On fully integrating the Hα LF, we obtain a total Hα luminosity density of $\mathcal{L}$ = 10³⁹.⁶⁸ erg s⁻¹ Mpc⁻³, in case of luminosity-dependent dust correction, $\mathcal{L}$ = 10³⁹.⁶⁹ erg s⁻¹ Mpc⁻³ for stellar-mass-dependent dust correction and $\mathcal{L}$ = 10³⁹.⁷⁶ erg s⁻¹ Mpc⁻³ for constant dust correction. Following the standard calibration from Kennicutt (1998), the SFRD at z ~ 0.62 is estimated to be, $\rho_{SFR}$ = 10⁻¹.⁴⁷ M☉ yr⁻¹ Mpc⁻³ for luminosity-dependent dust correction, $\rho_{SFR}$ = 10⁻¹.⁴⁶ M☉ yr⁻¹ Mpc⁻³ for stellar-mass-dependent dust correction, and $\rho_{SFR}$ = 10⁻¹.³⁹ M☉ yr⁻¹ Mpc⁻³ for constant dust correction, which are highly consistent with the evolution of SFR densities across the redshift range, 0 < z < 2, as seen from previous Hα surveys.

Among Hα studies at low redshifts (0 < z < 1), this survey fills the gap that exists at z ~ 0.62, and it is the only survey to comprehensively study both the faint and bright ends of the LF at this redshift. Moreover, this work illustrates the importance of combining observations that are significantly deep (compared to L*) with observations covering a substantial volume (compared to 1/φ*), in order to better constrain the entire LF.

We thank NOAO for the generous allocation of observing time for the DAWN survey, and the Kitt Peak staff for their expert support of DAWN observations. We thank Kimberly Emig, Raviteja Nallapu, Emily Neel, Mark Smith, Stephanie Stawinski, Jacob Trahan, Nicholas Valverde, Trevor Van Engelhoven, Sherman Florez, Amy Robertson, Cristian Soto, Jonathan Florez, Dave Bell, Sofia Rajas, and Karen Butler for their help with the DAWN survey observing runs. We thank the anonymous referee for their helpful comments and suggestions that improved the manuscript. We thank the US National Science Foundation for its financial support through NSF grant AST-0808165, which supported our custom narrowband filter purchase, and AST-1518057, which supported our data analysis. We also thank NASA for financial support via WFIRST Preparatory Science
grant NNX15AJ79G and WFIRST Science Investigation Team contract NNG16PJ33C, which provided additional support for our scientific analysis. S.V. acknowledges support from a Raymond and Beverley Sackler Distinguished Visitor Fellowship and thanks the host institute, the Institute of Astronomy, where this work was concluded. S.V. also acknowledges support by the Science and Technology Facilities Council (STFC) and by the Kavli Institute for Cosmology, Cambridge. J.X.W. is thankful for support from NSFC 11421303 and 11890693. This work is based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under ESO programme ID 179. A-2005 and on data products produced by TERAPIX and the Cambridge Astronomy Survey Unit on behalf of the UltraVISTA consortium. This work has made use of the following open-source softwares: SciPy, NumPy, Matplotlib (Virtanen et al. 2020; van der Walt 2011; Hunter 2007), Astropy (Robitaille et al. 2013), corner.py (Foreman-Mackey 2016), DS9 (Joye & Mandel 2003), and TOPCAT (Taylor 2005).

## ORCID iDs

Santosh Harish https://orcid.org/0000-0003-0129-2079

Alicia Coughlin https://orcid.org/0000-0002-8147-0834

James E. Rhoads https://orcid.org/0000-0002-1501-454X

Sangeeta Malhotra https://orcid.org/0000-0002-9226-5350

Steven L. Finkelstein https://orcid.org/0000-0001-8519-1130

Matthew Stevans https://orcid.org/0000-0001-8379-7606

Vithal S. Tilvi https://orcid.org/0000-0001-8514-7105

Ali Ahmad Khostovan https://orcid.org/0000-0002-0101-336X

Sylvain Veilleux https://orcid.org/0000-0002-3158-6820

Junxian Wang https://orcid.org/0000-0002-4419-6434

Pascale Hibon https://orcid.org/0000-0003-3726-5494

Johnnes Zabl https://orcid.org/0000-0002-9842-6354

Bhavin Joshi https://orcid.org/0000-0002-7593-8584

John Pharo https://orcid.org/0000-0003-2249-2539

Isak Wold https://orcid.org/0000-0002-0784-1852

Lucia A. Perez https://orcid.org/0000-0002-8449-1956

Tianxing Jiang https://orcid.org/0000-0002-2222-6129

Huan Yang https://orcid.org/0000-0003-2260-7420

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