Publ. Astron. Soc. Japan (2014) 00(0), 1–25
doi: 10.1093/pasj/xxx000

Third Data Release of the Hyper Suprime-Cam
Subaru Strategic Program
Hiroaki Aihara1,2 , Yusra AlSayyad3 , Makoto Ando4 , Robert Armstrong5 ,James Bosch3 , Eiichi Egami6 , Hisanori Furusawa7 , Junko Furusawa7 ,Sumiko Harasawa7 , Yuichi Harikane8,9 , Bau-Ching Hsieh10 , Hiroyuki
Ikeda11 , Kei Ito12,7 , Ikuru Iwata7,12 , Tadayuki Kodama13 , Michitaro Koike7 ,Mitsuru Kokubo3 , Yutaka Komiyama7,12 , Xiangchong Li2,1 , Yongming
Liang12,7 , Yen-Ting Lin10 , Robert H. Lupton3 , Nate B Lust3 , Lauren A.
MacArthur3 , Ken Mawatari7,8 , Sogo Mineo7 , Hironao Miyatake14,2 , Satoshi
Miyazaki7 , Surhud More15,2 , Takahiro Morishima7 , Hitoshi Murayama2,16,17 ,Kimihiko Nakajima7 , Fumiaki Nakata7 , Atsushi J. Nishizawa18,19 ,Masamune Oguri20,1,2 , Nobuhiro Okabe21 , Yuki Okura7 , Yoshiaki Ono8 , Ken
Osato22,23 , Masami Ouchi7,8,2 , Yen-Chen Pan24 , Andrés A. Plazas Malagón3 ,Paul A. Price3 , Sophie L Reed3 , Eli S. Rykoff25,26 , Takatoshi Shibuya27 ,Mirko Simunovic28 , Michael A. Strauss3 , Kanako Sugimori12,7 , Yasushi
Suto1,20 , Nao Suzuki2 , Masahiro Takada2 , Yuhei Takagi28 , Tadafumi
Takata7,12 , Satoshi Takita29 , Masayuki Tanaka7,12,* , Shenli Tang1,2,8 , Dan S.
Taranu3 , Tsuyoshi Terai28 , Yoshiki Toba30,10 , Edwin L. Turner3,2 , Hisakazu
Uchiyama31 , Bovornpratch Vijarnwannaluk13 , Christopher Z Waters3 ,Yoshihiko Yamada32,7 , Naoaki Yamamoto13 , Takuji Yamashita7,31
1

Department of Physics, 7-3-1 Hongo, Bunkyo-ku, The University of Tokyo, Tokyo 113-0033,
Japan
2
Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI), The
University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8583, Japan3
Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ
08544, USA
4
Department of Astronomy, Graduate School of Science, The University of Tokyo, 7-3-1
Hongo, Bunkyo, Tokyo, 113-0033, Japan
5
Lawrence Livermore National Laboratory, Livermore, CA 94551, USA
6
Steward Observatory, The University of Arizona, 933 North Cherry Avenue, Tucson, AZ
85721-0065, USA
7
National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan
8
Institute for Cosmic Ray Research, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa,
Chiba 277-8582, Japan
9
Department of Physics and Astronomy University College London Gower Street London
WC1E 6BT UK
10
Institute of Astronomy and Astrophysics, Academia Sinica, 11F of Astronomy-Mathematics
Building, AS/NTU No.1, Sec. 4, Roosevelt Rd, Taipei 10617, Taiwan, R.O.C.
11
National Institute of Technology, Wakayama College, 77 Noshima, Nada-cho, Gobo,
Wakayama 644-0023, Japan
12
Department of Astronomical Science, The Graduate University for Advanced Studies,© 2014. Astronomical Society of Japan.

1

2

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

SOKENDAI, 2-21-1 Osawa, Mitaka, Tokyo, 181-8588, Japan
Astronomical Institute, Tohoku University, 6-3, Aramaki, Aoba-ku, Sendai, Miyagi, 980-8578,
Japan
14
Kobayashi-Maskawa Institute for the Origin of Particles and the Universe (KMI), Nagoya
University, Nagoya, 464-8602, Japan
15
Inter University Centre for Astronomy and Astrophysics, Ganeshkhind, Pune 411007, India
16
Department of Physics and Center for Japanese Studies, University of California, Berkeley,
CA 94720, USA
17
Theoretical Physics Group, Lawrence Berkeley National Laboratory, MS 50A-5104,
Berkeley, CA 94720
18
Institute for Advanced Research, Nagoya University, Furocho, Chikusa-ku, Nagoya,464-8602 Japan
19
Division of Particle and Astrophysical Science, Graduate School of Science, Nagoya
University, Furo-cho, Nagoya 464-8602, Japan
20
Research Center for the Early Universe, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,
Tokyo 113-0033, Japan
21
Physics Program, Graduate School of Advanced Science and Engineering, Hiroshima
University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima 739-8526 Japan
22
Center for Gravitational Physics, Yukawa Institute for Theoretical Physics, Kyoto University,
Kyoto 606-8502, Japan23
Départment de Physique École Normale Supérieure Université PSL CNRS Sorbonne
Université Université de Paris 75005 Paris France
24
Graduate Institute of Astronomy, National Central University, 300 Jhongda Road, 32001
Jhongli, Taiwan
25
Kavli Institute for Particle Astrophysics and Cosmology, P. O. Box 2450 Stanford University
Stanford CA 94305 USA
26
Kavli Institute for Particle Astrophysics and Cosmology, SLAC National Accelerator
Laboratory, Stanford University, 2575 Sand Hill Road, M/S 29, Menlo Park, CA 94025
27
Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan
28
Subaru Telescope, National Astronomical Observatory of Japan, 650 North A‘ohoku Pl, Hilo,
HI 96720, USA
29
Institute of Astronomy, The University of Tokyo, 2-21-1 Osawa, Mitaka, Tokyo
181-0015,Japan30
Department of Astronomy, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto
606-8502, Japan
31
Research Center for Space and Cosmic Evolution, Ehime University, 2-5 Bunkyo-cho,
Matsuyama, Ehime 790-8577, Japan
32
Japan Space Forum, Shin-Ochanomizu Urban Trinity Bldg. 3F, 3-2-1, Kandasurugadai,
Chiyoda, Tokyo 101-0062, Japan13*

The corresponding author is Masayuki Tanaka.

∗ E-mail: masayuki.tanaka@nao.ac.jp

Received ; Accepted

Abstract
The paper presents the third data release of Hyper Suprime-Cam Subaru Strategic Program(HSC-SSP), a wide-ﬁeld multi-band imaging survey with the Subaru 8.2m telescope. HSC-SSP
has three survey layers (Wide, Deep, and UltraDeep) with different area coverages and depths,
designed to address a wide array of astrophysical questions. This third release from HSC-SSP
includes data from 278 nights of observing time and covers about 670 square degrees in all

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

3ﬁve broad-band ﬁlters at the full depth (∼ 26 mag at 5σ) in the Wide layer. If we include
partially observed area, the release covers 1,470 square degrees. The Deep and UltraDeep
layers have ∼ 80% of the originally planned integration times, and are considered done, as wehave slightly changed the observing strategy in order to compensate for various time losses.
There are a number of updates in the image processing pipeline. Of particular importance isthe change in the sky subtraction algorithm; we subtract the sky on small scales before thedetection and measurement stages, which has signiﬁcantly reduced false detections. Thanks
to this and other updates, the overall quality of the processed data has improved since theprevious release. However, there are limitations in the data (for example, the pipeline is not
optimized for crowded ﬁelds), and we encourage the user to check the quality assurance plots
as well as a list of known issues before exploiting the data. The data release website is https://hsc-release.mtk.nao.ac.jp.
Key words: Surveys, Astronomical databases, Galaxies: general, Cosmology: observations

1 Introduction
The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP;
Aihara et al. 2018a) is a three-tiered imaging survey aimed toaddress a wide range of astrophysical questions ranging fromcosmology to solar system bodies. The survey uses Hyper
Suprime-Cam (HSC; Miyazaki et al. 2018), a wide-ﬁeld imaging camera installed at the prime focus of the Subaru 8.2m
telescope on the summit of Maunakea, Hawaii. In its widest
component (the Wide layer), we cover about 1200 deg2 of thesky mostly around the celestial equator in ﬁve broad-band ﬁlters (grizy ; Kawanomoto et al. 2018) with integration times of
10-20 min. A particular emphasis is put on the i-band, withwhich we measure precise shapes of galaxies for weak-lensing
cosmology. The second component, the Deep layer, covers four
separate ﬁelds, each of which is ∼ 7 deg2 , both in the broadband ﬁlters and three narrow-band ﬁlters. The last component,
UltraDeep, is two ﬁelds centered at the COSMOS and Subaru
XMM-Newton Deep Field (SXDS). We take very long integrations (∼ 5 − 10 hours in each band) to peer deep into the distant Universe. These components are designed to be used inconjunction with each other in order to enable a wide array ofscientiﬁc explorations.
The survey was originally awarded 300 nights of observing
time. This is the largest observing program ever approved at
Subaru. While we have made good progress with the observations, we have suffered from various issues such as bad weather,telescope trouble, and seismic activities in Hawaii during thecourse of the survey. In order to compensate for the time lost
due to these issues, an additional 30 nights were recently allocated to the survey, giving a total of 330 nights. We have also
made changes to the observing strategy as we discuss in Section
2.2 to catch up with the original survey plan.
HSC-SSP has made two public releases so far (Aihara et al.
2018b; Aihara et al. 2019). In addition to these major releases, we have made several incremental releases to add fur-ther value. The public data release 2 (PDR2) has accompanied ﬁve incremental releases and the following products weremade available in each release: (1) revised bright star masks andshape catalog (Mandelbaum et al. 2018), (2) photometric redshifts (Tanaka et al. 2018; Nishizawa et al. 2020), (3) emissionline object catalog (Hayashi et al. 2020) and deblended images (heavyFootprint), (4) narrow-band data from CHORUS(Cosmic HydrOgen Reionization Unveilled with Subaru; Inoueet al. 2020), and (5) galaxy density map from Shimakawa et al.
(2021). This paper presents a new major release, PDR3. PDR3
was originally anticipated to be the ﬁnal data release, but due
to the additional time awarded to the survey, PDR4 will be theﬁnal release as we discuss later in Section 7.
The structure of the paper is as follows. We ﬁrst give a brief
overview of the release in Section 2, followed by updates in theprocessing pipeline in Section 3. We then summarize data products included in the release in Section 4. Section 5 evaluates thequality of the data and discusses issues that the user should beaware of. Section 6 gives updates on our collaborating surveys.
Finally, we discuss prospects for the ﬁnal data release, and conclude the paper in Section 7. All magnitudes quoted in the paper
are AB magnitudes (Oke & Gunn 1983).

2 Overview of the Release
2.1 Updates from PDR2This release includes data taken between March 2014 andJanuary 2020 from 278 nights in total (including nights lost
to weather and other reasons). The release includes data froman additional ∼ 100 nights since PDR2 and hence represents amajor increase in terms of the data volume and area coverage.
There are also changes in the data products. Major updates fromPDR2 can be summarized as follows.
• The Deep/UltraDeep (D/UD for short) layers are now ∼ 80%
complete and we choose to stop there (i.e., we do not take4

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

any further data in D/UD). Most of the Deep ﬁelds have
∼ 1.5 times longer integration times compared to PDR2. UDSXDS is much deeper (×3 times longer integration times)
than PDR2 due to a transient survey carried out in that ﬁeld.
The overall increase in D/UD-COSMOS is relatively minor
because the transient survey in that ﬁeld was performed earlier in the survey(Yasuda et al. 2019). NB1010 data are newly
available in UD.
• There is also major progress in the Wide layer. The full-color
full-depth (FCFD, i.e., observed to the required depth in allﬁve ﬁlters) area increased from 300 deg2 to 670 deg2 . The
area coverage is shown in Fig. 1. Statistical properties of thedata are summarized in Table 1.
• We have changed the observing strategy as we detail in
Section 2.2. In short, we have relaxed the seeing constraint
in the i-band, reduced the exposure times in the izy bands inthe Wide layer, and declared the D/UD layers done at 80% of
the planned exposure times.
• In order to astrometrically calibrate many visits available
in the UD ﬁelds, we switched from the meas mosaic algorithm (Bosch et al. 2018a), which was used until PDR2, tojointcal. jointcal uses sparse linear algebra for efﬁcient
memory usage. The astrometric accuracy remains similar tothe previous releases.
• We use a new photometric calibration algorithm, the Forward
Global Calibration Method (FGCM; Burke et al. 2018),
which forward-models the atmosphere and system response
as a function of time and position (Section 3.2). There is,however, a small error in the implementation, which resulted
in spatially varying offsets in the photometric zero-point at
a few percent level across the survey ﬁeld. These offsets
are corrected for using the location of the stellar sequence
in color-color diagrams (Section 3.9).
• The pipeline performs global sky subtraction for extended
object science as in the previous release. We have improved
the global sky subtraction algorithm (Section 3.5), but extended wings of stars and bright galaxies still introduce false
detections and measurement failures. Thus, we introduce asecond, local sky subtraction just before object detection anddetailed measurements (Section 3.6). While there is a single
object catalog, there are two types of coadd images (local sky
vs. global sky) and the user should choose which one to use
depending on their science goals.
• We use the ﬁfth order Lanczos kernel, as opposed to the thirdorder we have used before, to warp images for coaddition.
This improves the PSF model on the coadds and reduces thefractional PSF size residual deﬁned in Section 2.3 to about
0.1% (Li et al. 2021).
• Utilizing the effective ﬁlter transmission introduced in Aihara
et al. (2019), we have estimated corrections to translate r/iband magnitudes into r2/i2 band magnitudes (Section 3.8).

• As in PDR2, we do not include detailed shape measurements
as well as deblended images (heavyFootprint) in the release. The shape measurements in PDR2 are still withheld,
but as we discuss in Section 2.3, the shapes are not useful,
and we instead plan to release a shape catalog based on anewer internal data release to the community. The deblended
images from PDR3 will be made available in August 2022.

2.2 Survey Progress and Changes in the Survey
Strategy
Here, we brieﬂy summarize where we stand in terms of the survey progress. Fig. 2 compares the expected survey speed andactual survey progress. This is for the Wide layer, but as we
spend 2/3 of the observing time for Wide, it is a good proxy
for the overall survey progress. There is a big plateau in 2018
when survey progress was very slow. This is for a combinationof reasons; bad weather, telescope troubles, and earthquakes
triggered by volcanic activity in Hawaii. The progress in 2019
was approximately at the expected survey speed, but overall weare signiﬁcantly behind the original survey plan. We have been
awarded an additional 30 nights to compensate for the loss, but
in order to further catch up with the plan, (1) we decided to stopat ∼ 80% of the original integration time in the D/UD ﬁelds, (2)we relaxed the seeing constraint in the i-band in the Wide layerfrom ∼ 0.7 to ∼ 0.9 arcsec, although we still try to observe in thei-band under good seeing conditions whenever possible, and (3)we reduce the exposure time from 20 min to 16.7 min (from 6 to
5 dithers) in the izy bands in Wide. We applied these changes
in observation strategy after November 2018. We made major
progress in the i-band in 2019 thanks to the relaxed seeing constraint, but the median seeing is still as good as ∼ 0.7 arcsec forthe data taken after November 2018. With these changes, we
expect to cover ∼ 1, 200 deg2 in all the bands at the full depth
by the end of the survey.
This change in the dithering strategy is illustrated in Fig. 3.
Due to the reduced exposure time, the 5-dither region is shallower than the 6-dither region by ∼ 0.1 mag. This is not a major effect and the seeing may be more important for the depth,
especially for compact sources. The reduced dithers also meanthat there is less spatial averaging of the PSF, which may have a
non-negligible effect on the PSF model, especially from weaklensing perspectives. Li et al. (2021) carry out PSF model tests
using the i-band data in the Wide area around the region in
Fig. 3, which has large enough areas of both 5-dither and 6dither regions to provide sufﬁcient statistical power to examine the differences. The fractional PSF size residual deﬁned
as fδσ = (σPSF − σ∗ )/σ∗ , where σ∗ is the measured size ofa star and σPSF is the size of the PSF model evaluated at theposition of the star, is computed as a function of the i-band
magnitude. Fig. 20 of Li et al. (2021) shows that small dif-Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

5

Fig. 1. The area covered in this release. The blue and green areas show the Wide and Deep/UltraDeep layers, respectively. For the Wide layer, the shading
indicates the number of ﬁlters in which data are available; the darkest blue corresponds to all ﬁve ﬁlters. The red boxes indicate the approximate boundariesof the three disjoint regions that will make up the ﬁnal Wide survey. Note that AEGIS is a calibration ﬁeld observed at the Wide depth. The Galactic extinction
map from Schlegel et al. (1998) is shown in the background.

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Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Wide
exposure (min)seeing (arcsec)
depth (mag)saturation (mag)area (deg2 )Deep/UltraDeep
exposure (min)seeing (arcsec)
depth (mag)saturation (mag)area (deg2 )

g
10+2
−2
0.79+0.09−0.08
26.5+0.2−0.2
17.4+0.6
−0.4
1332
g
70+21
−21
0.83+0.05−0.12
27.4+0.2−0.2
18.0+0.4
−0.5
36

r
10+2
−2
0.75+0.13−0.09
26.5+0.2−0.2
18.1+0.5
−0.5
1298
r
66+17
−17
0.77+0.04−0.04
27.1+0.1−0.2
18.2+0.4
−0.4
36

i
20+3
−6
0.61+0.05−0.05
26.2+0.2−0.3
18.3+0.5
−0.3
1264
i
98+46
−32
0.66+0.07−0.06
26.9+0.2−0.3
18.6+0.3
−0.4
36

z
20+3
−10
0.68+0.08−0.06
25.2+0.2−0.3
17.5+0.5
−0.4
1299
z
177+130−46
0.78+0.02−0.03
26.3+0.1−0.3
17.7+0.3
−0.3
37

y
20+3
−10
0.68+0.10−0.08
24.4+0.2−0.3
17.0+0.5
−0.7
1209
y
93+23
−23
0.70+0.04−0.05
25.3+0.2−0.2
17.4+0.3−0.3
36

N B38768+13−13
0.82+0.07−0.08
25.0+0.2−0.2
14.8+0.4−0.330

N B816
120+30−15
0.70+0.07−0.08
26.0+0.2−0.2
16.8+0.4−0.4
33

N B921
168+14−28
0.67+0.04−0.04
25.9+0.2−0.2
16.9+0.4−0.3
33

N B1010705+45−345
0.77+0.02−0.02
24.2+0.2−0.5
14.8+0.2−0.2
5

Table 1. Approximate exposure time, seeing, 5σ depth, and saturation magnitudes for each ﬁlter and survey layer, averaged over thearea included in this release. The depth and saturation magnitudes are for point sources. The numbers are the median and the quartilesof the distribution, except for the area, which shows the total area covered in at least one exposure. The NB1010 coverage is small
but that is because it is used only in the UD ﬁelds. The numbers for the Wide layer are close to the full-depth values, while those forthe D/UD are closer to the Deep depth due to the spatial averaging (Deep is wider than UD). UD is roughly 0.8 magnitude deeper than
Deep. The user is referred to quality assurance (QA) plots available at the data release site for the spatial variation of some of these

201

5

201

6

150
Allocated nights

7
201
Date

200

201

8

250

201

9

202

0

4
100

2

50

+7

0

0

+10

+5

-2

4000
3500
3000
2500
2000
1500
1000
500
0

HSC-g
HSC-r
HSC-i
HSC-z
HSC-Y

Dec. [deg]Number of visits

4000
3500
3000
2500
2000
1500
1000
500
0

Number of visits

6

numbers.

+2

184

182

180

178

176

+0

R.A. [deg]Fig. 2. Top: Cumulative number of visits for the Wide layer as a functionof the number of allocated nights. The upper dotted line is for the g andr-bands and it indicates the mean survey speed required to complete thesurvey in 300 nights. The lower dotted line is for the other bands, but notethat 6 dithers per pointing is assumed. The solid lines are the achieved
survey speed for each ﬁlter as shown in the legend. Bottom: Same plot as
above but as a function of date.

ferences between the two dithering strategies as a function ofmagnitude. However, these differences seem to be driven by a
few regions with extreme size residuals. Once these areas are
excised out, the fractional size residuals are similar irrespectiveof the dithering strategy. These tests were carried out using theinternal S19A release, but the same conclusion should hold inthis release, as there is no change in the way we model the PSF.

Fig. 3. A portion of the Wide area in this release. The color coding shows
the number of visits in the i-band. The bottom half of this area was observedwith 6 dithers, while the top half was with 5 dithers.

2.3 Shape Measurements
As we did in PDR2, we withhold detailed shape measurements
as well as deblended images from this release. We plan to release the shapes once their characteristics are fully understoodand ready to be used for weak-lensing science. We will makethe deblended images available to the community in August2022.
The galaxy shapes from PDR2 are still withheld. Detailed
investigations have shown that the PDR2 shapes are problematic; this is likely due to the global sky subtraction algorithmPublications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

introduced in PDR2. Although the modiﬁcations to the sky
subtraction vastly improved the over-subtraction of backgroundaround bright nearby galaxies, it resulted in a considerable population of faint objects with anomalously large size measurements. We discuss this issue further in Section 5.2.2. We did not
see this effect in image simulations performed to calibrate theweak lensing shape measurements in PDR2, meaning that the
PDR2 shapes were unusable for precision weak lensing studies. As we discuss in Section 3.6, we have gone back to the
local sky subtraction scheme for detections and measurements
after PDR2, and the weak lensing working group is utilizing
the shapes from the S19A internal data release (Li et al. 2021),
which will eventually be released to the community.

2.4 Previous Internal Releases
A public data release from HSC-SSP is based on an internal data
release made ∼ 1 year ago. We brieﬂy summarize our internal
releases since PDR2 in Table 2. We changed the observing strategy between S18A and S19A (Section 2.2). That means that thedeﬁnition of FCFD changed and the increase in the FCFD areain S19A is partially due to the change. We also note that the decrease in the number of objects in S19A, particularly in D/UD,
is because we changed the sky subtraction scheme and the number of spurious sources dropped signiﬁcantly (Section 3.6).

3 Pipeline Updates
The PDR3 data have been processed with hscPipe v8, a customization of the LSST Science Pipelines (Jurić et al. 2017;
Bosch et al. 2018a; Bosch et al. 2019; Ivezić et al. 2019). This is
an updated version of the pipeline used in PDR2. This section
summarizes the improvements in the order of the processing
ﬂow.
It is useful to remind the reader of important notions in ourimage processing at this point. visit is an single exposure with
HSC and has an integer number uniquely assigned. A visit thus
has 112 CCD images. In the joint calibration stage onwards,
we use tracts, which are equi-area rectangular regions on thesky, each of which is ∼ 1.7 degree on a side with ∼ 1 arcmin
overlap with adjacent tracts. A tract is split into 9 × 9 patches,
each of which is 4200 pixels on a side (∼ 12 arcmin) with anoverlap of 100 pixels (∼ 17 arcsec) on the edges. The tracts andpatches are introduced to parallize the processing and are themost fundamental areal units.

3.1 Jointcal
The ﬁrst is an update in the astrometric calibration. We used touse an algorithm termed meas_mosaic (Bosch et al. 2018a) tosolve for astrometry in the joint calibration step, but its memory usage becomes prohibitively expensive when we try to ﬁt

7

all visits in the UD ﬁelds. In order to improve the memory
efﬁciency, we switch to using the LSST jointcal1 package,
which uses sparse linear algebra for efﬁcient memory usage.
This is an older version of the code used in Léget et al. (2021),
with considerable customization to integrate it with the rest of
the LSST/HSC pipelines, and a less sophisticated model chosen(conservatively) to mimic what we have used in the past (Boschet al. 2018a): a single per-visit, full-focal-plane, high-orderpolynomial to capture both optical distortions and atmosphericeffects, composed with an afﬁne transform for each CCD, ﬁt
to each band separately. The order of the per-visit polynomial
has been reduced from 9th order to 7th order, and the objectivefunction of the ﬁt is updated via an efﬁcient Cholesky rank-1
update each time an outlier is rejected, instead of in batches.
The quality of the astrometry is unfortunately not competitive
with Léget et al. (2021) (our astrometry has a 2-3 times larger
scatter), but it is still similar to the previous releases.

3.2 FGCM
Photometric calibration is performed with the Forward Global
Calibration Method (FGCM; Burke et al. 2018). FGCM calibrates the full PDR3 dataset with a forward model approachthat uses atmospheric model parameters in conjunction withscans of the instrument throughput as a function of wavelength.
FGCM was originally developed for use with the Dark Energy
Survey data (Burke et al. 2018; Sevilla-Noarbe et al. 2021), andhas been incorporated into the LSST/HSC pipeline for use with
HSC and LSST data.
The FGCM model begins with measurements of the instrument throughput, including the mirrors, ﬁlters, and detectors.
Filter scans are taken from Kawanomoto et al. (2018), and weaverage in the azimuthal direction prior to use in the model ﬁt.
We use the ﬁducial mirror and detector throughput measurements as reported at the Subaru website2 . The chromatic response of the mirror changes with time as the surface oxidizes.
The FGCM model approximately accounts for this variation by
allowing for a different rate of throughput decline in each band,
such that the bluer bands have an overall throughput that degrades more rapidly than the redder bands. In the PDR3 calibration run, no adjustments are made for the intra-band throughput
variation. Finally, we note that the ﬁducial detector response
does not account for the differing anti-reﬂection (AR) coatings
across the CCD surface (Kamata et al. 2014) and therefore different chromatic response in the g-band. The effect of the different AR coatings is visible in the chromatic residuals from theﬁt and it is about ±2% between the ﬁrst and third quartiles of
the color distribution of the stars.
The atmospheric model of FGCM is provided by1
2

https://github.com/lsst/jointcal/
https://subarutelescope.org/Observing/Instruments/HSC/
sensitivity.html

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Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Release

Date

Layer

N
ﬁlter

Area
(deg2 )

N
objectPublic Data Release 3(=S20A)
S19A

2020-08-03

Public Data Release 2(=S18A)

2019-05-31

Deep/UltraDeep
Wide
Deep/UltraDeep
Wide
Deep/UltraDeep
Wide

9
5
9
5
8
5

37
1470 (670)371289 (560)371114 (305)

19,051,243
507,215,729
18,090,313
433,472,409
20,451,226
436,333,410

2019-09-25

hscPipeversion
8
8
7
7
6
6

Table 2. Summary of this public release and previous internal data releases. The ﬁfth column gives the survey area in deg2 covered
in at least in one ﬁlter and one exposure. The full-color full-depth area in the Wide survey is shown in parentheses. The sixth column
shows the number of primary objects.

MODTRAN (Berk et al. 1999), which has been run to create alook-up table with the atmospheric throughput as a function of
zenith distance at the elevation of the Subaru telescope. Light is
attenuated as it travels through the atmosphere due to absorption
and Rayleigh scattering by molecular constituents (particularly
O2 and O3 ), absorption by precipitable water vapor (PWV),
Mie scattering by airborne particular aerosols, and ﬁnally gray(achromatic) scattering by larger ice crystals and water dropletsin clouds. We allow the aerosols and PWV to vary linearly overeach night, with a constant aerosol optical index and O3 contribution per night. See Section 3 of Burke et al. (2018) for detailsof the FGCM atmospheric model.
The FGCM ﬁt minimizes the variance in repeated observations of stars, and we use stars with a signal-to-noise ratio larger
than 10 within the 2 arcsec aperture, which roughly corresponds
to i < 23. As conﬁgured, we calibrate the 2 arcsecond aperture ﬂuxes. Due to signiﬁcant residuals in the background of
the processed CCD images, we additionally subtract off a perstar local background estimate. Tests with a subset of the data
show that this reduces residuals as a function of magnitude, as
well as tightening the repeatability in the photometry of a given
star. The local background subtraction introduces a few percent
ﬂux change at faint magnitudes of i = 22 − 23 and is smaller
at brighter magnitudes of i ∼ 20 (but still up to 0.5% depending on the local source density) due to the over-subtraction of
the sky. Unfortunately, we did not notice this effect until after
the calibrations were produced, and therefore the ﬁnal output
zero-points used in the coaddition do not account for this effect,
resulting in spatially varying photometric zero-points at a 0.5%level, which we correct for at a later stage (Section 3.9).
As an additional constraint on the ﬁt, we use the
PanSTARRS1 (PS1; Schlaﬂy et al. 2012; Tonry et al. 2012;
Magnier et al. 2013; Chambers et al. 2016) network of calibrated stars to ensure uniformity across the disconnected regions of the PDR3 footprint. We use the updated color terms
from Section 4.8 of the PDR2 paper. Only stars detected with asignal-to-noise ratio greater than 50 in the PS1 i-band (roughlyi < 20) are used to constrain the ﬁt. The details of how a networkof standard stars is incorporated into FGCM will be describedin detail in Rykoff et al. (in prep).
For good photometric results, we need to accurately constrain the degradation of the mirror surface over time. The default model in FGCM is for a piecewise-linear decay function,with discontinuous changes when the instrument/telescope underwent changes. We set October 2017, at the date of the Subaru
primary mirror recoating as such a discontinuous point. Fig. 4
illustrates the throughput degradation from the ﬁt. After the
mirror recoating, the system throughput improves by ∼ 5% (g )and ∼ 2% (z ). As expected, bluer bands show a larger change.
Interestingly, we notice that the rate of decay increased between
22 April 2016 and 08 November 2016 in all bands. These dates
coincide with increased activity of the Maunaloa volcano, andsuch increased decay may be due to volcanic fog.
One particular challenge of performing a global calibrationof the PDR3 dataset is that we replaced the r and i ﬁlters withthe r2 and i2 ﬁlters with improved uniformity, respectively.
Although these ﬁlters cover similar wavelengths, they have different chromatic response (with much smaller focal plane variations in the replacement ﬁlters), as well as different throughputs. The FGCM model includes additional ﬁt terms to constrain the relative offset of the r and r2 (and i and i2) ﬁlters,
using reference stars and the limited number of stars with observations in multiple ﬁlters. Unfortunately, these terms are
somewhat degenerate with the temporal variation of the systemthroughput. Therefore, after the processing is complete we noticed that there are signiﬁcant differences in the stellar locus incolor-color space, corresponding to regions with i and i2 observations. These observations and the mitigations we applied tocorrect for them are described in Sections 3.8 and 3.9.
After ﬁve cycles of ﬁtting the atmospheric parameters
and rejecting outliers and non-photometric observations, weachieve fairly good repeatability in the photometric calibration. This is demonstrated in Fig. 5. We ﬁnd rms values of
5.92, 5.88, 6.17, 5.29, and 5.11 mmag for the g , r, i, z , ybands respectively for a sample of 10% of stars that were reserved from the ﬁt. For the narrow-bands, the repeatability

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

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Fig. 4. Comparison of individual star observations in the g (left) and z-bands (right) between the observed HSC magnitude and the PS1 reference magnitude.
We show these two ﬁlters as an example, and the other ﬁlters show the same behavior. Each point contributing to the heat map corresponds to a single
observation of a single star at a moment in time (on the x axis). The value mobs
g/z is the observed magnitude, corrected for atmospheric transmission andabsolute throughput, but excluding any ﬁt terms from relative system throughput. The value mref
g/z is the PS1 reference star magnitude, with the default color
terms applied. The red dashed line shows the date that the mirror was recoated. The cyan dotted lines show the approximate time period that the rapid decay
is observed in all bands, due perhaps to volcanic fog.

rms values are 42.7, 8.74, 6.13, and 9.05 mmag for NB387,
NB816, NB921, and NB1010 respectively. NB387 is not calibrated very well as we discuss in Section 5.2.1; it is a difﬁcult
ﬁlter to calibrate.

3.3 Increased Lanczos Kernel Order for Warping
After the astrometric and photometric joint calibrations, wewarp individual CCD images for coaddition. We used to use
the third order Lanczos kernel for warping, but as we discussed
in the PDR2 paper, there is an about 0.3 − 0.4% bias in our PSF
model on the coadds, in the sense that the model PSF is larger
than the observed PSF. This bias level is close to the required accuracy for the ﬁrst year HSC shape catalog (Mandelbaum et al.
2018), but exceeds the accuracy needed for successive weaklensing analyses over a wider area. We found that using a ﬁfth
order Lanczos kernel improves the size residual to ∼ 0.1%, sufﬁcient for year-3 weak-lensing analyses (Li et al. 2021). For
this reason, we adopt the ﬁfth order kernel in PDR3.

3.4 Artifact Rejection
CCD images have various artifacts such as cosmic rays, satellite
trails, and optical ghosts around bright stars. Some of them
are identiﬁed and interpolated over in the CCD processing, but
the remainder are left in the processed image. The processing
pipeline makes an attempt to identify those remaining artifacts
and remove them in the coaddition stage.
The current artifact rejection algorithm, CompareWarp, detects artifacts in the image differences, produced by subtracting
a PSF-matched 2-sigma-clipped coadded image as a template of
the static sky from each warped and PSF-matched visit. As de-scribed in AlSayyad (2018) and the PDR2 paper, artifact candidates are then detected on these image differences and labelled
as follows. If an artifact candidate is seen in a small percentageof visits, it is labeled transient, and is masked during coaddition,
or “clipped”. If seen in many visits, it is considered part of thestatic sky, labeled persistent and is not clipped. The algorithm
works better when more visits are available, but several visits
available in the Wide layer are still effective in identifying artifacts. There are, however, a few typical failure modes, which
we will discuss in Section 5.2.1.
If footprint of an artifact candidate falls entirely within thefootprint of a source in the template, it is not clipped. This feature is designed to avoid the introduction of a visit discontinuity
within a source footprint (e.g., part of a footprint falls in a CCD
gap in a visit). In particular, point sources with visit discontinuities within their footprints are not well modeled by the PSF
models, which do not take into account clipped pixels. Two conﬁguration parameters control the size of the artifact footprints
on the difference image and the size of the source footprints on
the template. The parameters adopted in PDR2 introduce increased scatter in the PSF photometry with respect to PS1 inregions where all but one visit had good seeing (Section 6.6.6
in the PDR2 paper). This scatter is particularly apparent (> 50
mmag) in several tracts in the VVDS ﬁeld, however we estimate that this misconﬁguration in PDR2 contributed an extra
1-4 mmag scatter in the stellar locus to other ﬁelds.
In this data release, we change these conﬁguration parameters to reduce the effect on photometry. With respect to theprevious data release, the new conﬁguration shrinks the artifact
candidate footprints relative to the footprints of sources detectedon the static template coadd. These parameters are empirically10

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Fig. 6. Count of visits contributing to each pixel in PDR2 (left), PDR3
(center), and the i coadded image (right) of a region centered at
R.A.=22h 31m 57s .3, Dec.=+00◦ 30′ 34′′ .8 and 35” across. Artifact detections are not grown as large in this data release, leading to less total areaclipped and more conservative preservation of the wings of stars. The brightstar in the ﬁgure is i ∼ 15.

chosen to minimize false positives—as measured the width of
the stellar locus in color space, an independent metric of the ﬁdelity of the PSF photometry—without increasing the false negatives. False negatives, the number of compact artifacts leaking
into the coadd, are estimated by the source density of detectedsources in the CompareWarp coadd that do not have a corresponding match in the 2-sigma-clipped template. Shrinking thesize of the artifacts also provides the added beneﬁt of reducing
the total number of clipped pixels (Fig. 6).

3.5 Improved Global Sky Frame

Fig. 5. The distribution of magnitude differences of repeated measurementsof reserved stars not used in the FGCM ﬁt for each band. Only stars withphotometric errors less than 0.01 mag are used. The horizontal axis is
E gray , the difference between fully calibrated individual visits and the meanmagnitude of all the visits of the same star. See, e.g., Equation 30 and
Figure 11 from Burke et al. (2018). The blue solid line is a histogram of thedata (arbitrarily normalized), and the black dashed line is a Gaussian ﬁt to
this histogram. The calibration dispersion, σFGCM (in mmag), is computedby subtracting the median photometric error of the sample in quadrature.
Overﬂow counts are accumulated in the extreme ends of the horizontal bins.
The ﬁgure includes both Wide and D/UD data.

A global sky subtraction scheme was introduced in PDR2 to
improve the deﬁciencies of the background subtraction we performed on individual CCDs in PDR1. The algorithm is described in detail in the PDR2 paper, but in short, (1) it ﬁrst puts
the subtracted sky back in to processed CCD images, (2) grids
a visit image into superpixels of 1k x 1k pixels (168 arcsec ona side) taking a clipped mean with object footprints excluded,(3) ﬁts the superpixels with 2d polynomials to construct a sky
background model, (4) subtracts it from a visit image, and then(5) subtracts a scaled sky frame, which is prepared separately bystacking many science visits with large dithers. The sky frame
contains spatial structure on a scale smaller than 1k pixels. Thisalgorithm was run just before the coaddition stage.
While the algorithm works well to preserve extended wingsof bright objects, it has a few side effects such as longer compute time, increased false detection, and poor galaxy photometry for a fraction of faint sources as we discuss later(Section 5.2.2). Also, only the spatial structure smaller than
the superpixel (168 arcsec) is included in the sky frame, and we
suffer from missing CCDs when constructing a sky frame as thesuperpixel is smaller than the size of a CCD (11.5 arcmin ×5.7
arcmin).
For PDR3, we modify the algorithm to include large-scalestatic features in the sky frame. We ﬁrst subtract the backgroundusing 8k x 8k superpixel (∼ 23′ on a side). This is meant to subtract exposure speciﬁc large-scale gradients such as scattered
light and is done on a per-visit basis. It is also important forPublications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Fig. 7. Blow-up of a visit image after the sky subtraction using the superpixels (i.e., image to be used to construct a sky frame) processed with the PDR2
(left) and PDR3 (right) algorithms. There are regions with over-subtracted
sky near the ﬁeld edge in PDR2, while those regions are much ﬂatter in
PDR3, allowing us to construct a less biased sky frame. This is the i-band,
but a similar feature can be seen in the other bands.

reducing effects of missing CCDs because the superpixel sizeis larger than the size of a single CCD. We then combine many
visits, typically over 50, to generate a normalized sky frame.
Fig. 7 demonstrates the improvement in this release. In
PDR2, a sky frame was often biased around dead CCDs or deadampliﬁers located at the ﬁeld edge because the superpixel size
is smaller than the size of a CCD or ampliﬁer and we had to
extrapolate. This propagated to the sky frame and resulted inover/under-subtracted sky near the ﬁeld edge, which then propagated to the coadded image. On the other hand, the sky background is ﬂat to the edge of the ﬁeld in PDR3, demonstrating
the improved sky frame.
For science exposures, we ﬁrst subtract the 8k x 8k background in the same manner as above, followed by the subtraction of the scaled sky frame. There is a small-scale backgroundﬂuctuation left at a very low ﬂux level at this stage. To eliminate
it, we perform additional small-scale sky subtraction using 256
x 256 superpixels (∼ 43 arcsec). Care is taken to mask objects
well; we subtract the sky, detect sources, deﬁne their footprints(which are pixels above 2.5σ grown by 2.4 times the size ofPSF) and mask them, put the sky back in, and repeat. We iterate
3 times to ensure that we have sufﬁciently large masks aroundobjects. This way, we keep the wings of bright sources, while
subtracting the sky background on a relatively small scale.

3.6 Local Sky Subtraction for Detection andMeasurements
In PDR2, the deblending in UD-COSMOS became prohibitively long and memory-intensive because too many source
footprints are connected with each other and the deblender had
to run many times. This is due to the well-preserved wingsof objects (i.e., a consequence of the global sky subtraction).
This also causes reduced detection efﬁciencies at faint magnitudes. While we have improved the global sky subtraction

11

scheme as described in the previous subsection, the extended
wings of bright objects still remain as a major issue. To mitigate it, we choose to subtract the sky on small scales and intentionally subtract wings of bright objects before the detection and measurement steps, so that object footprints are connected less frequently and the deblender does not have to run
too many times. The choice of the local sky subtraction scale
is primarily driven by object detection efﬁciency and footprint
size. After some experiments, we found that a superpixel sizeof 128 (∼ 21.5 arcsec) is a reasonable trade-off between the detection efﬁciency and over-subtraction of moderately extended
sources, and we adopt it here.
We recall that we perform the global sky subtraction in theprocessing prior to the local sky subtraction described here.
The coadd images with the global sky subtraction are stored as
patch images (under deepCoadd/), so that the user interested inlarge extended objects can exploit the coadds for image analyses. Then, the object detection and measurements are performedon coadds with the local sky subtraction applied; these coadd
images are stored as calexp under deepCoadd-results/.
Fig. 8 illustrates the difference between the two sky subtraction
schemes. As can be seen, only bright sources lose their wings
with local sky subtracted and sources with <∼ 30 arcsec extent
remain unaffected.

3.7 Algorithm ﬁx in detection and peak merge
After the object detection in each band, we merge footprints
and peak positions within them across the bands in order for us
to perform consistent photometry. As described in Section 3.4
in Bosch et al. (2018b), peaks detected in different bands are
merged sequentially in so-called “priority order,” which we deﬁne as irzyg for the broadband ﬁlters, followed by the narrowband ﬁlters. We merge a peak from the current band into the setof peaks detected in the higher-priority bands (found in source
footprints that overlap) per the following rules:• If a peak in the new band is at least 1” from all peaks fromthe previous step, we add a new peak.
• If a peak in the new band is less than 0.3” from the nearest
peak from the previous step, we mark the peak as having been
detected in the new band while maintaining the position fromthe previous step.
• If a peak in the new band is between 0.3” and 1” from thenearest peak from the previous step, it is ignored. We cannot
conclusively identify the peak as either the same source or a
distinct source.
In rare cases, a software bug prevented this prescription frombeing followed. If the new peak is in a footprint that connected two previously disconnected footprints in the higherpriority bands, the new lower-priority peak could replace theset of higher-priority peaks if it were between 0.3” and 1” away.

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Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Fig. 8. gri color composite of a portion of the UD-COSMOS ﬁeld (8′ × 6′ ). The left is the coadd with the global sky subtraction applied and the right is withthe local sky subtraction. The main differences are seen around bright stars, and galaxies are largely unaffected.

For PDR3, the routine has been corrected to always chose thehigher-priority peak set for the merged catalog.

3.8 Effective Filter Response
The i-band ﬁlter has strong radial dependence in its transmission curve (Kawanomoto et al. 2018). This spatially varying
transmission function is the primary cause of the donut-likebackground structure seen in a visit image in that ﬁlter. A new
ﬁlter, i2, was manufactured and has been in use since the middle of the survey in 2016. We thus have images taken with bothi and i2 ﬁlters and the processing pipeline combines them together in the coaddition process. These ﬁlters have similar but
not identical transmission curves, and they introduce two issues;(1) spatially varying response function in regions observed in
the i-band, and (2) a fraction of the survey area is observed in i,some in i2 and the rest in both, further introducing spatial variation of the response function. As we discussed in Section 6.6.4
of the PDR2 paper, colors of objects differ between the i and i2
regions, which illustrates the latter problem here.
To mitigate these problems, we use the effective response
function introduced in PDR2 (Section 4.5 of the PDR2 paper)to correct for the spatially varying ﬁlter transmissions. We use
an effective response function of an object given where it fell in
the focal plane in the individual visits and estimate the expecteddifference between that response and the ﬁducial i2 response as
a function of object color.
We need two ingredients here; the underlying spectral energy distribution (SED) of an object and its observed broadband color. We want to use a wide enough range of SEDs tocompute the magnitude offset for various objects. Here, we use
a set of SED templates from the photometric redshift code of
Tanaka (2015), which has a large number of objects covering awide range of SED types over a wide redshift range. We have
conﬁrmed that the corrections are very similar if we use the

Pickles (1998) stellar library instead. We then convolve these
SEDs with a set of ’target’ ﬁlters and the ﬁducial i2 ﬁlter. A target ﬁlter is either i or i+i2 combined. The latter just a weighted
average of i and i2 ﬁlters. We pre-compute the target ﬁlters in
the i-only and i/i2-mixed regions and estimate the magnitude
corrections to the ﬁducial ﬁlter (i2). We also convolve these
SEDs with the ﬁducial grizy ﬁlter set so that we can computebroad-band colors for each SED. Each SED gives a slightly different amount of correction, and we average the correction as afunction of the g − i, r − i, i − z and i − y colors. At this point,
we have a look-up table for the correction as a function of thetarget ﬁlter and observed color.

For each object, we have its observed color from the HSC
data and also can compute the effective response function (=target ﬁlter) because we know where on the focal plane the object
is located in each visit. We can then estimate the magnitude offset from the look-up table and apply it to each object to translate
the i-band (or mixture of i and i2-bands) photometry into theﬁducial i2 photometry. We perform exactly the same analysis
for the r vs. r2 bands as well.

Fig.9 illustrates the correction, showing the riz color-color
diagram of stars. In the top panel, there is a clear difference
between the stellar sequences of stars measured with the i and
i2 ﬁlters. If we apply the correction and translate the i band
photometry into i2, the two stellar sequences agree well (bottom panel). We provide the correction table for each object inthis data release. Note that the correction is not available forobjects observed in a single ﬁlter because we cannot compute
their color and hence cannot infer their intrinsic SEDs. Noteas well that the correction is for objects with typical SEDs andshould be used with care for objects with exotic colors.

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

13

the correction derived in the previous subsection to the r andi band photometry. We then draw color-color diagrams of theobserved stars and estimate average offsets from the expected
location for each ﬁlter. In this step, we assume that there is nooffset in the i-band. After correcting for the offsets, there arestill spatially varying stellar sequence offsets at a level of ∼ 2%
remaining, suggesting that our correction was imperfect. The
next step is to attribute the remaining offsets to the i-band only.
We have conﬁrmed that the correction to the i-band is small(∼ 1%) but it does reduce the spatial variation.
The bottom panel of Fig.10 shows the improvement. The
variation observed in the top panel is reduced to ∼ 0.015 mag
after the correction. We encourage the user to apply these magnitude corrections to the cataloged magnitudes3 .

4 Data4.1 Value-added Products

Fig. 9. r − i plotted against i − z for stars with i < 23. The ﬁlled points arestars observed with the i2 ﬁlter, while the open squares are observed with
the i ﬁlter. The top panel is the original data and the bottom panel is after
the correction for the ﬁlter response difference.

3.9 Stellar Sequence Regression
We mentioned in Section 3.2 that FGCM resulted in a small inhomogeneity of photometric zero-points across the survey ﬁeld.
While it should be possible to correct for it in a principled way,
we adopt an empirical approach and correct for it using the location of the stellar sequence in color-color diagrams. We use
stars with i < 22.5 mag and estimate the zero-point offsets ineach ﬁlter in each patch. We correct for the Galactic extinction
in all bands using Schlegel et al. (1998) and assuming that thestars we use are all behind the dust curtain. This is a reasonableassumption because the stars we use are typically i ∼ 22 andthus likely are halo stars, but this could potentially be a sourceof systematic uncertainty. We estimate the expected locationof the stellar sequence in the grizy multi-color space using the
Pickles (1998) stellar library. The top panel of Fig. 10 shows
the observed offsets of the stellar sequence measured in gri.
There is a clear spatial structure with a peak-to-peak variationof ∼ 0.05 mag. Note that some of the variation is due to the r/i
vs. r2/i2 difference.
In order to estimate the zero-point correction, we ﬁrst apply

In addition to the data products from the pipeline, we serve
value-added products as in our previous releases, including the
COSMOS Wide-depth stacks, a collection of public spectroscopic redshifts, and random points. The COSMOS Wide-depth
stacks and the random points are generated in the same way asin the previous release; the reader is referred to the PDR2 paper.
In what follows, we brieﬂy discuss the updated list of spectroscopic redshifts, and photometric redshifts based on the spectroscopic catalog. There is also a major update to the mask aroundbright stars, which we discuss in Section 4.2.
• Public spectroscopic redshifts: We have updated the listof public spectroscopic redshifts from the literature. The
list includes redshifts from 2dFGRS (Colless et al. 2003),
3D-HST (Skelton et al. 2014; Momcheva et al. 2016), 6dFGRS (Jones et al. 2004; Jones et al. 2009), C3R2 DR2(Masters et al. 2017; Masters et al. 2019), DEEP2 DR4(Davis et al. 2003; Newman et al. 2013), DEEP3 (Cooper
et al. 2011; Cooper et al. 2012), DEIMOS 10k sample(Hasinger et al. 2018), FMOS-COSMOS (Silverman et al.
2015; Kashino et al. 2019), GAMA DR2 (Liske et al. 2015),
LEGA-C DR2 (Straatman et al. 2018), PRIMUS DR1 (Coilet al. 2011; Cool et al. 2013), SDSS DR16 (Ahumada et al.
2020), SDSS IV QSO catalog (Pâris et al. 2018), UDSz
(Bradshaw et al. 2013; McLure et al. 2013), VANDELS DR1(Pentericci et al. 2018), VIPERS PDR1 (Garilli et al. 2014),
VVDS (Le Fèvre et al. 2013), WiggleZ DR1 (Drinkwater
et al. 2010), and zCOSMOS DR3 (Lilly et al. 2009). As oneto-one correspondence between the spectroscopic objects andphotometric objects is not always obvious, we match objects
3

Note that the cataloged magnitudes in the pipeline outputs and database
are not corrected for the offsets. There are database tables that serve
the offsets and the offsets have to be subtracted from the cataloged magnitudes.

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Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Fig. 10. Stellar sequence offset in gri around the R.A.=12h region before the zero-point correction (top) and after the correction (bottom). The color-coding
indicates the amount of offset as shown in the color bar, and the offset is computed for each patch. The gray rectangles are the tract boarders, and the tract
IDs are also shown.

within 1 arcsec and all matched objects are stored in the
database. In most cases, the most likely match will be the object with the smallest matching distance. Each spectroscopic
survey has its own ﬂagging scheme to indicate the reliabilityof a redshift measurement. We have homogenized the ﬂags
to make it easy for the user to make a clean redshift catalog.
See the online documentation for the deﬁnition of the homogenized ﬂag. We emphasize that the user should acknowledge
the original data source(s) when using this product.
• Photometric Redshifts: Photometric redshifts for objects in
PDR3 have been computed using a few independent codes(Tanaka et al. 2018; Nishizawa et al. 2020). The performanceof the codes are overall similar to the previous release. There
has been a signiﬁcant delay in the delivery of the photometricredshift products to the internal team. We thus anticipate a future incremental release to make these photometric redshifts
available to the community in mid to late 2022.

4.2 Bright Star Mask
The vicinity of a bright star suffers from optical ghosts and otherartifacts, and object detection and measurements there are unlikely reliable. We generate a mask around a bright star to indicate such a problematic region since PDR1. Given that wechanged the way we subtract the sky (Section 3.6), we have updated the bright star mask accordingly. We have changed themask size around stars and made a few improvements in how
we treat various artifacts around bright stars as we detail in thissubsection.
4.2.1 Reference catalog
We use Gaia DR2 (Gaia Collaboration et al. 2018) as a reference
catalog of bright stars. To ensure good photometric quality, we
require that the S/N in the G-band is greater than 50, and thosein the Bp and Rp bands are both greater than 20. In addition,
we apply cuts on the Bp + Rp ﬂux excess as discussed in Gaia
Collaboration et al. (2018) to reduce contamination of nearby
sources in the prism photometry. These cuts leave us with stars
down to G ∼ 19.
In our previous releases, the size of the mask around brightstars was made independent of ﬁlter for simplicity. However,this resulted in sub-optimal masks around very blue/red stars.
We now account for the magnitudes of bright stars in each of
the HSC bands. As bright stars are often saturated in the HSC
images, we use the Gaia prism photometry (Bp and Rp ) to infer magnitudes of stars in the HSC bands. We use the Pickles(1998) stellar library to make a mapping between the Gaia Gband and each of the HSC broad-bands as a function of Bp −Rp .

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

We apply a magnitude cut of < 18 to the Gaia stars in each of
the HSC ﬁlters. For instance, we deﬁne the i-band mask forstars with i < 18. The same applies in all HSC ﬁlters.

4.2.2 Artifacts
There are several different types of optical/detector artifacts,
each of which has its own shape and size. We consider the following features:• halo: A star has an extended smooth halo around it, which
can be considered as part of PSF. The size of this halo depends on the brightness of a star and also on the algorithm
that we use to subtract the sky.
• ghost: In addition to a halo, a very bright star is surroundedby larger optical ghosts due to reﬂection inside the camera.
As there are multiple paths of reﬂection, each ghost feature
is out of focus by a different amount, and thus has a different
angular size on the focal plane. A ghost feature is roughly
constant in surface brightness and it is partly subtracted by
the sky subtraction. However, its edge can be sharp and thus
remains clearly on the coadded images. We wish to mask theoutermost edge to avoid it affecting object detection.
• blooming (bleeding): A common CCD feature. When a pixel
exceeds its full-well capacity, electrons overﬂow to adjacentpixels along the column. The maximum length of the blooming feature is set by the detector size, which is 4k in case of
HSC.
• channel-stop: At long wavelengths (∼ 1µm), a CCD becomes optically thin and a fraction of incident photons go
through the silicon layer and get reﬂected by elements in the
top layer of the underlying circuitry, probably by the channelstop. This causes a diffraction pattern around a bright starperpendicular to the blooming direction. In HSC, this feature
is seen only in the y -band and N B1010. Fig. 11 illustrates
the ghost, blooming, and channel-stop features on 5th magnitude stars.
• dip: Although we account for the object footprint when we
estimate the sky background, a portion of a bright star is still
subtracted as part of the sky, resulting in slightly negative
background around the vicinity of a star. Because of the negative sky, the object detection there is slightly inefﬁcient andthe density of detected objects drops. We call this feature adip.

We brieﬂy discuss how we determine the mask size for eachcomponent in what follows. Our goal here is to deﬁne a mask
around bright stars to minimize effects of these artifacts, in particular to avoid spurious sources. At the same time, we want tomake the masks no larger than they need to be to maximize theusable area of the survey.

15

4.2.3 Halo and ghost
We carry out two-way analyses; image-based and catalogbased. For the image analysis, we normalize the radial proﬁleof each bright star using the coadd image with the local sky
subtraction algorithm applied. We then take the median of the
proﬁles of many bright stars with similar magnitudes. This isdone for various magnitude bins. The top panel of Fig.12 shows
the averaged radial proﬁle of 5th magnitude stars in the i-band.
The ghost extends roughly to 300 arcsec and settles down tothe background level at a larger distance. The background level
does not reach zero; this is because we do not mask objects
around the bright stars. We perform the same analysis in theother ﬁlters, but we ﬁnd that the dependence on ﬁlter at ﬁxed
magnitude is small.
We move on to examine the effect of the ghosts more carefully using the coadd catalog. We here look at the object detection; the source density increases where there are optical artifacts and hence it is a sensitive probe of artifacts. The bottom
panel of Fig.12 shows the mean source density as a function of
distance from 5th magnitude stars. As can be seen, there aredetection spikes at speciﬁc radii. These spikes correspond to
the ghost edges, whose radii are independent of stellar brightness. The outermost ghost is located at 320 arcsec. This is themost signiﬁcant component for stars brighter than ∼7th mag. It
gets fainter for fainter stars and the inner ghost at 160 arcsec is
dominant at 7 − 9th mag. We note that the exact size and shapeof the reﬂection ghost depends on position of the star relative tothe telescope boresight. Thus, there are spurious sources outside
the mask discussed here in some cases because of that effect. In
order to deal with such cases, we make two additional masksthat are 20 and 50 % larger than the ghost sizes of 160 and 320
arcsec, and they are assigned separate mask bits.
The reﬂection ghosts are not prominent for stars fainter than
9th mag and the halo component becomes the dominant component. We measure the halo size using the same image andcatalog based methods over a range of magnitudes and ﬁt an
exponential function to represent the relationship between thehalo radius and stellar brightness:rhalo = 1.105 × 103 × exp(−0.347mag) + 4.950 arcsec.

(1)

We ﬁnd that the halo size does not strongly depend on ﬁlter at
ﬁxed magnitude. We thus adopt this relation in all ﬁlters.
4.2.4 Blooming
Blooming, or bleed trail, is a common phenomenon in CCDs
and appears along the columns. The blooming length depends
on the source brightness and seeing as well as the source position. It can be up to 11 arcmin long, which corresponds to thedetector size. As the blooming feature appears at the same location (albeit with differences in length) with respect to a star inall visits, the pipeline cannot completely remove it. Recall that

16

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

stellar brightness:

imag=5 (Nstar=97)

100

sblooming = −8.5714 × mag2 + 40.857 × mag + 778.57

10−1

10−3
10−4
0.4
0.3
0.2
0.1
0.0

(2)

The width of the blooming feature is primarily driven by theseeing, but it can be larger than the seeing FWHM at very brightmagnitudes because a large number of photons may saturate notjust the core of a star but also its outer parts. Visual inspectionsof the stacked images of bright stars reveal that the width of 20
pixels is sufﬁcient and we adopt it here for all stars.

10−2mean source density

normalized median profile

Fig. 11. Left: Image of a 5th magnitude star in the i-band. This is a coadd image with the local sky subtraction applied (i.e., image used for object detection
and measurement). The inner and outer circles have radii of 160 and 320 arcsec, respectively. These circles correspond to the ghost edges as discussed
in the text. The horizontal rectangle indicates the blooming feature. Right: Stacked y-band image of 5th magnitude stars. The vertical box encloses thechannel-stop feature. The horizontal stripe is blooming and the ghost edges at 160 and 320 arcsec are also clearly seen.

0

100

200
300
distance [arcsec]

400

500

Fig. 12. Top: Normalized intensity proﬁle of 5th magnitude stars in the iband. The intensity is normalized at the center and the vertical lines show
radii of 160, 265, and 320 arcsec, respectively, where we observe sharpedge of the ghost feature. Bottom: Number density of detected sources asa function of distance from 5th magnitude stars. The increased number ofsources at the ghost edges can be clearly seen.

4.2.5 Channel-stop
As discussed above, a CCD can be optically thin at wavelengths
close to the Silicon band-gap. The front structure, probably
the channel-stop, introduces a diffraction pattern at long wavelengths, which is aligned perpendicular to the blooming feature.
The length of this feature depends on the stellar brightness. We
parameterize the half-length as a function of magnitude in asimilar fashion to the blooming feature.
schannel−stop = 5.982 × 102 × exp(−1.640mag) − 17.66 (3)We ﬁt a similar function to the width of this feature:
we always observe at the same position angle on the sky (i.e.,
we do not perform rotational dithers). We carry out the samecatalog analysis as for the halos and ghosts above and compare
the numbers of objects detected along the horizontal and vertical directions. A detection excess along the horizontal directionis due to spurious detections along the blooming feature, andfor a bright star, this excess is seen over as large as 600 arcsec.
We parameterize the half-length of the excess as a function ofwchannel−stop = 28.3 × 102 × exp(−0.1640mag) − 2.03

(4)

4.2.6 Dip
A small region around a star suffers from over-subtracted background. The source density is again a useful probe of its effect. We ﬁnd that, interestingly, the reduced source density
around the vicinity of a star is limited to within 40 arcsec regardless of the stellar brightness. This is probably due to ourPublications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

17photometric calibration by FGCM; Sections 3.1 and 3.2). But
our primary calibration source has not changed; we still use the
PanSTARRS1 DR2 catalog (Schlaﬂy et al. 2012; Tonry et al.
2012; Magnier et al. 2013; Chambers et al. 2016) astrometrically calibrated against Gaia DR1 (Gaia Collaboration et al.
2016a; Gaia Collaboration et al. 2016b). We plan to change theastrometric reference catalog to a newer Gaia release in our future public release. As in the two previous releases, we present a
full set of quality assurance (QA) plots on the data release website, and the user should check these QA plots before utilizing
the data for science. Here we present some of the key example
diagnostics.

Fig. 13. Mask size as a function of stellar magnitude for each component.
The points show the sizes of the halo feature in each magnitude bin andthe solid curve is a ﬁt to the points. The dotted, dashed, dot-dashed, dotdot-dashed curves are for ghost, channel-stop, blooming and dip features,
respectively. Using the union of these masks (perhaps except for the dipmask) is the most conservative approach.

sky subtraction scheme; we adopt a superpixel size of 128 pixels
to estimate the background, which corresponds to 21.5 arcsec.
Depending on where a bright star is located inside a superpixel,
up to 2 superpixels can be affected by the extended stellar halo,
which can explain our observation here. The dip mask is ﬁxed
to 40 arcsec regardless of brightness for all stars brighter than
18th magnitude.
4.2.7 Summary of masks
Fig. 13 summarizes the sizes of masks discussed here as a function of stellar magnitude. The reﬂection ghost is the largest
component at very bright magnitudes and the dip feature is
the dominant feature for stars fainter than 10th magnitude.
However, the dip mask introduces a signiﬁcant loss in the survey area; about 80% of the Wide area is left after applying thehalo, ghost, and blooming masks, but the fraction reduces to
50% when the dip mask is also applied because faint stars are
numerous. If one does not care about a ∼ 10% detection loss
around the detection limit around bright stars, the dip mask canbe ignored. All these mask ﬂags are available for each object ineach ﬁlter at the database. At this point, the masks are deﬁned
only for broad-band ﬁlters. For narrow-band ﬁlters, one can use
the broad-band mask that is closest in wavelength (e.g., y -bandis a good proxy for NB1010).

5 Data Quality and Known Issues
5.1 Photometry and Astrometry
The overall astrometric and photometric accuracy of this release is similar to that of PDR2. There are changes in the way
we calibrate the data (astrometric calibration by jointcal andFig. 14 shows the astrometric calibration accuracy in a portion of the Wide ﬁeld. We compute an offset in R.A. by comparing the HSC and Gaia DR1 coordinates of common stars
brighter than G = 20. Overall, our astrometric calibrations are
good, but there are multiple small regions with rather large negative offsets. We have visually inspected some of these regions,but there are nothing obviously wrong in the images. It turns
out that these are regions in which the astrometry in our own
reference catalog (PS1 calibrated against Gaia) is problematic.
The bad astrometry in the reference data unfortunately propagated to HSC. We hope to ﬁx it in our future release. Fig. 14
shows only the offset, but we also estimate rms of the astrometric residual, which is a good indicator of overall astrometricaccuracy. We ﬁnd that our astrometry is rms∼ 13 mas.
Moving on to photometry, Fig. 15 shows a comparison between HSC and PS1 for bright point sources. Here we focus
on stars 1.5 mag or more brighter than the typical 5σ magnitude limits of PS1 in each ﬁlter (23.3, 23.2, 23.1, 22.3, 21.3 ingrizy , respectively). There is a systematic bias in our photometry with respect to PS1 at a 0.5 − 1.5 % level depending onﬁlter. A similar trend is seen in other regions of the Wide survey, although it is not seen in the D/UD ﬁelds. The reason why
we see the offset only in Wide is not understood yet. There arepatches that show a signiﬁcant offset, especially in the y -band.
These patches seem to coincide with regions that show a somewhat large difference between the size of model PSF and thesize of observed PSF (plot not shown), which suggests that theroot cause of the problem is the PSF modeling in HSC. Further
investigations are in progress as of this writing.
Finally, Fig.16 shows the 5σ depths for point sources in the
D/UD-COSMOS ﬁeld. This is based on the ﬂux uncertaintyof the PSF photometry as quoted by the pipeline and could beslightly optimistic. The central UD region reaches down to 27
to 28th magnitude in the broad-bands; this is one of the deepest imaging data sets of the COSMOS ﬁeld. It is even deeper
than the ﬁnal 10-year depth of Rubin LSST (Ivezić et al. 2019).
Because the ﬁeld includes both Deep and UltraDeep imaging,
there is a signiﬁcant spatial variation of the depths, which theuser should be aware of when utilizing the D/UD data.

18

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Wide-Autumn: XMM-LSS
40

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10707 10706 10705 10704 10703 10702 10701 10700 10699 10698 10697 10696 10695 10694 10693 10692 10691 10690 10689 10688 10687 10686 10685 10684 10683 10682 10681 10680 10679

10466 10465 10464 10463 10462 10461 10460 10459 10458 10457 10456 10455 10454 10453 10452 10451 10450 10449 10448 10447 10446 10445 10444 10443 10442 10441 10440 10439 10438

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10224 10223 10222 10221 10220 10219 10218 10217 10216 10215 10214 10213 10212 10211 10210 10209 10208 10207 10206 10205 10204 10203 10202 10201 10200 10199 10198 10197 10196

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9239

9238

9237

9236

9235

9234

9233

9232

9231

9230

9229

9228

9227

9226

9011

9496

9010

9495

9009

9494

9008

9493

9007

9492

9006

9491

9005

9490

9004

9489

9003

9488

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9000

9485

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9480

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9479

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9477

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8989

9474

8988

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8984

9012

9497

9011

9496

9010

9495

9009

9494

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9493

9007

9492

9006

9491

9005

9490

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9489

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9488

9002

9487

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9486

9000

9485

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9484

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9483

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9481

8995

9480

8994

9479

8993

9478

8992

8991

8990

9475

8989

8988

8987

8986

8985

8984

0

9476

9254

9012

8770

8747

8746

8745

8744

8743

8742

8749

8748

8747

8744

8743

8742

8527

8526

8525

8524

8523

8522

8521

8520

8519

8518

8517

8516

8515

8514

8513

8512

8511

8510

8509

8508

8507

8506

8505

8504

8503

8502

8501

8500

8528

8527

8526

8525

8524

8523

8522

8521

8520

8519

8518

8517

8516

8515

8514

8513

8512

8511

8510

8509

8508

8507

8506

8505

8504

8503

8502

8501

8500

8286

8769

8285

8768

8284

8767

8283

8766

8282

8765

8281

8764

8280

8763

8279

8762

8278

8761

8277

8760

8276

8759

8275

8758

8274

8757

8273

8756

8272

8755

8271

8754

8270

8753

8269

8752

8268

8751

8267

8750

8266

8749

8265

8264

8263

8262

8261

8260

8259

8287

8286

8285

8284

8283

8282

8281

8280

8279

8278

8277

8276

8275

8274

8273

8272

8271

8270

8269

8268

8267

8266

8265

8264

8263

8262

8261

8260

8259

8047

8046

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8025

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8023

8770

8769

8768

8767

8766

8765

8764

8763

8762

8761

8760

8759

8758

8757

8756

8755

8754

8753

8752

8751

8750

8022

8021

8020

8019

8047

8046

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8042

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8040

8039

8038

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8035

8034

8033

8032

8031

8030

8029

8028

8027

8026

8025

8024

8023

8022

8021

8020

8019

z

-10

-5

8748

8528

8287

8746

8745

y

+30
+10
-10
-30
-50

40

30

20

10

40

30

20

R.A. [deg]10

R.A. offset[milli arcsec]

Fig. 14. Astrometric offset in the R.A. direction for a portion of the Wide survey. We ﬁrst match objects between HSC and Gaia in a patch and then computethe mean offset. We show the mean offset for each patch using the color-coding shown in the bottom-right panel. The panels are for the grizy ﬁlters from
top-left to bottom-right. The number in each panel is the median R.A. offset in milli arcsec over the area shown for each ﬁlter (the numbers are small due tospatial averaging). The gray squares are the tract borders.

5.2 Known Issues
As we have discussed above, the overall quality of the data inthis release is good. In this section, we summarize a few issues
that the user should be aware of.
5.2.1 Issues that persist from PDR2
While we have put efforts in improving the data processing
pipeline, there are issues persisting from our previous releases,
some of which are fundamentally difﬁcult problems. One suchfundamental problem is the over-shredding of nearby galaxies.
For instance, star-forming regions and arms of a spiral galaxy
can be deblended into separate pieces. It is difﬁcult to distinguish multiple galaxies blended with each other from a single galaxy with structure. Another fundamental difﬁculty withthe deblending is that it occasionally fails to cleanly deblend
sources in crowded regions such as the dense core of galaxy
clusters. There is no easy solution to this problem. There is amulti-color deblender (Melchior et al. 2018), which we do notuse in this release. We hope it will help, although it remains
to be seen how subtle color differences the algorithm can exploit (cluster galaxies have similar colors). We also note thatthe pipeline is not optimized for very crowded ﬁelds such as starclusters or dwarf galaxies around the Milky Way Galaxy. The

user should carefully check the images and catalogs in crowded
regions before using them.
The NB387 data should also be used with caution because
this is a rather difﬁcult ﬁlter to calibrate. We infer NB387
magnitudes by applying color-terms to the PS1 gr photometry when we calibrate its zero-point. We use the stellar libraryfrom Pickles (1998) to derive the color-terms. Most of the stars
in this library are close to solar metallicity, but the stars that
we use for calibration are faint halo stars, which are likely tohave subsolar metallicities. The NB387 ﬁlter is quite sensitive
to the metallicity of stars and the PS1 gr photometry does not
fully capture the metallicity variation. As we have reported on
the known issue page for PDR2 at the data release website, thephotometric zero-point can be off as much as 0.45 mag. This isfrom a comparison with SDSS spectroscopic stars and is still atentative number. Further investigations are needed.
There are artifacts left in the coadd images. Most obvious
artifacts are optical ghosts around bright stars. We do make an
attempt to remove them (Section 3.4), but we are not always
successful, especially in regions where only a few visits havebeen taken. Also, our algorithm cannot remove artifacts if they
stay at similar positions on the sky. This is often the case in the
D/UD ﬁelds, where the dithers are relatively small (several arcmin) and optical ghosts stay at similar positions. An additional

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

19

Wide-Autumn: VVDS
350

350

10

median=-4.2

340

330

350

median=-12.0

340

330

median=-5.5

10679

10918

10917

10916

10915

10914

10913

10912

10911

10910

10909

10908

10907

10906

10905

10904

10903

10902

10901

10900

10899

10898

10679

10918

10917

10916

10915

10914

10913

10912

10911

10910

10909

10908

10907

10906

10905

10904

10903

10902

10901

10900

10899

10898

10679

10918

10917

10916

10915

10914

10913

10912

10911

10910

10909

10908

10907

10906

10905

10904

10903

10902

10901

10900

10899

10898

10438

10678

10677

10676

10675

10674

10673

10672

10671

10670

10669

10668

10667

10666

10665

10664

10663

10662

10661

10660

10659

10658

10438

10678

10677

10676

10675

10674

10673

10672

10671

10670

10669

10668

10667

10666

10665

10664

10663

10662

10661

10660

10659

10658

10438

10678

10677

10676

10675

10674

10673

10672

10671

10670

10669

10668

10667

10666

10665

10664

10663

10662

10661

10660

10659

10658

10196

10437

10436

10435

10434

10433

10432

10431

10430

10429

10428

10427

10426

10425

10424

10423

10422

10421

10420

10419

10418

10417

10196

10437

10436

10435

10434

10433

10432

10431

10430

10429

10428

10427

10426

10425

10424

10423

10422

10421

10420

10419

10418

10417

10196

10437

10436

10435

10434

10433

10432

10431

10430

10429

10428

10427

10426

10425

10424

10423

10422

10421

10420

10419

10418

10417

9954

10195

10194

10193

10192

10191

10190

10189

10188

10187

10186

10185

10184

10183

10182

10181

10180

10179

10178

10177

10176

10175

9954

10195

10194

10193

10192

10191

10190

10189

10188

10187

10186

10185

10184

10183

10182

10181

10180

10179

10178

10177

10176

10175

9954

10195

10194

10193

10192

10191

10190

10189

10188

10187

10186

10185

10184

10183

10182

10181

10180

10179

10178

10177

10176

10175

9712

9953

9952

9951

9950

9949

9948

9947

9946

9945

9944

9943

9942

9941

9940

9939

9938

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9935

9934

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9712

9953

9952

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9947

9946

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9944

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9942

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9938

9937

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9934

9933

9712

9953

9952

9951

9950

9949

9948

9947

9946

9945

9944

9943

9942

9941

9940

9939

9938

9937

9936

9935

9934

9933

9469

9711

9710

9709

9708

9707

9706

9705

9704

9703

9702

9701

9700

9699

9698

9697

9696

9695

9694

9693

9692

9691

9469

9711

9710

9709

9708

9707

9706

9705

9704

9703

9702

9701

9700

9699

9698

9697

9696

9695

9694

9693

9692

9691

9469

9711

9710

9709

9708

9707

9706

9705

9704

9703

9702

9701

9700

9699

9698

9697

9696

9695

9694

9693

9692

9691

9226

9468

9467

9466

9465

9464

9463

9462

9461

9460

9459

9458

9457

9456

9455

9454

9453

9452

9451

9450

9449

9448

9448

8984

9225

9224

9223

9222

9221

9220

9219

9218

9217

9216

9215

9214

9213

9212

9211

9210

9209

9208

9207

9206

8742

8983

8982

8981

8980

8979

8978

8977

8976

8975

8974

8973

8972

8971

8970

8969

8968

8967

8966

8965

8964

0

5

330

9226

9468

9467

9466

9465

9464

9463

9462

9461

9460

9459

9458

9457

9456

9455

9454

9453

9452

9451

9450

9449

9205

8984

9225

9224

9223

9222

9221

9220

9219

9218

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9215

9214

9213

9212

9211

9210

9209

9208

9207

9206

8963

8742

8983

8982

8981

8980

8979

8978

8977

8976

8975

8974

8973

8972

8971

8970

8969

8968

8967

8966

8965

8964

g

10

median=-11.8

9226

9468

9467

9466

9465

9464

9463

9462

9461

9460

9459

9458

9457

9456

9455

9454

9453

9452

9451

9450

9449

9448

9205

8984

9225

9224

9223

9222

9221

9220

9219

9218

9217

9216

9215

9214

9213

9212

9211

9210

9209

9208

9207

9206

9205

8963

8742

8983

8982

8981

8980

8979

8978

8977

8976

8975

8974

8973

8972

8971

8970

8969

8968

8967

8966

8965

8964

8963

r

i

+100

median=-13.2

10679

10918

10917

10916

10915

10914

10913

10912

10911

10910

10909

10908

10907

10906

10905

10904

10903

10902

10901

10900

10899

10898

10679

10918

10917

10916

10915

10914

10913

10912

10911

10910

10909

10908

10907

10906

10905

10904

10903

10902

10901

10900

10899

10898

10438

10678

10677

10676

10675

10674

10673

10672

10671

10670

10669

10668

10667

10666

10665

10664

10663

10662

10661

10660

10659

10658

10438

10678

10677

10676

10675

10674

10673

10672

10671

10670

10669

10668

10667

10666

10665

10664

10663

10662

10661

10660

10659

10658

10196

10437

10436

10435

10434

10433

10432

10431

10430

10429

10428

10427

10426

10425

10424

10423

10422

10421

10420

10419

10418

10417

10196

10437

10436

10435

10434

10433

10432

10431

10430

10429

10428

10427

10426

10425

10424

10423

10422

10421

10420

10419

10418

10417

9954

10195

10194

10193

10192

10191

10190

10189

10188

10187

10186

10185

10184

10183

10182

10181

10180

10179

10178

10177

10176

10175

9954

10195

10194

10193

10192

10191

10190

10189

10188

10187

10186

10185

10184

10183

10182

10181

10180

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10178

10177

10176

10175

9712

9953

9952

9951

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9949

9948

9947

9946

9945

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9942

9941

9940

9939

9938

9937

9936

9935

9934

9933

9712

9953

9952

9951

9950

9949

9948

9947

9946

9945

9944

9943

9942

9941

9940

9939

9938

9937

9936

9935

9934

9933

9469

9711

9710

9709

9708

9707

9706

9705

9704

9703

9702

9701

9700

9699

9698

9697

9696

9695

9694

9693

9692

9691

9469

9711

9710

9709

9708

9707

9706

9705

9704

9703

9702

9701

9700

9699

9698

9697

9696

9695

9694

9693

9692

9691

9226

9468

9467

9466

9465

9464

9463

9462

9461

9460

9459

9458

9457

9456

9455

9454

9453

9452

9451

9450

9449

9448

9226

9468

9467

9466

9465

9464

9463

9462

9461

9460

9459

9458

9457

9456

9455

9454

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9450

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8984

9225

9224

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9221

9220

9219

9218

9217

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9215

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9212

9211

9210

9209

9208

9207

9206

9205

8984

9225

9224

9223

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9221

9220

9219

9218

9217

9216

9215

9214

9213

9212

9211

9210

9209

9208

9207

9206

8742

8983

8982

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8978

8977

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8742

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8966

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8964

+60
+20
-20

0

5

Dec. [deg]

340

z

-60

y

9205

8963

-100
350

340

330

350

340

330

HSC-PS1

R.A. [deg][milli mag]Fig. 15. The mean offset between the HSC PSF photometry and PS1 photometry for bright point sources in ﬁve broad-band ﬁlters as labeled in each panel.
This is for a portion of the Wide survey. As in Fig. 14, the offset is computed for each patch and the color coding shows the amount of offset as shown in thebottom-right panel. The numbers in the panels show the median offset over the area shown.

D/UD-COSMOS

5152 151 150 149 152 151 150 149 152 151 150 149 152 151 150 149 152 151 150 149
10298

10297

10296

10298

10297

10296

10298

median=27.2

10297

10296

10298

median=27.1

10297

10296

10298

median=26.7

10297

10296

median=25.5

4

median=27.5
10055

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1
9571

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10298 9328

10297 9327

9569

9571

9570

10296 9326
10298 9328

10297 9327

9571

9570

10296 9326
10298 9328

10297 9327

g

0
5

9569

9569

9571

9570

9569

9571

9570

10296 9326
10298 9328

10297 9327

10296 9326

9328

9327

r

median=25.1

imedian=25.9

zmedian=25.9

9569

y
9326

median=24.4
234

Dec. [deg]

2

3

10056

10056

10055

10054

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10055

10054

10056

10055

10054

10056

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10054

3

24
9813

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25

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2

9814

1

26
9571

9570

9569

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0

NB387
9328

9327

9570

9569

9571

NB816
9326

9328

9327

9570

9569

9571

NB921
9326

9328

9327

9570

27

NB1010
9326

9328

9327

152 151 150 149 152 151 150 149 152 151 150 149 152 151 150 149

R.A. [deg]

9569

9326

28
29
5σ depth

Fig. 16. As in Fig. 14 but the color represents the 5σ depth for point sources estimated using the PSF photometry. This is the D/UD-COSMOS region with
one UD pointing at the center surrounded by four D pointings. In addition to the broad-bands, 4 narrow-band ﬁlters are also shown.

20

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

Fig. 17. Blow-up of UD-COSMOS in the i-band. The ellipses show the
CModel ellipses with sizes scaled to twice the half-light radii for illustrative
purposes. The red ellipses are objects with a magnitude difference larger
than 1 mag between CModel and 2 arcsec aperture.

algorithm would be needed to fully eliminate the artifacts.
5.2.2 Overestimated CModel ﬂuxes
We perform CModel photometry in our measurements (Section
4.9.9 ofBosch et al. 2018a). This is for galaxy photometry
and is expected to deliver good colors of extended sources. In
PDR2, there was a population of apparently faint objects withlarge CModel ﬂuxes. Visual inspections of those objects on
the coadds indicate that their CModel ﬂuxes are likely overestimated. We suspect that the over-estimated ﬂuxes are at least
partly due to the global sky subtraction applied in PDR2; it preserves wings of bright objects and it could contaminate the outerparts of nearby objects’ footprints. It could also be due to thedeblender; HSC images are very deep and the deblender often
faces a difﬁcult problem of deblending multiple sources simultaneously (especially when the extended wings are preserved),
and the deblender may leave residual ﬂuxes in crowded regions,which then contaminate the outskirts of nearby objects. As
CModel is relatively sensitive to ﬂuxes in the outer parts, theﬂux can be over-estimated. The fact that the Kron photometry,
which is also sensitive to ﬂuxes in the outskirts, shows similar
over-estimated ﬂuxes supports this hypothesis.
To further explore this, we show in Fig. 17 a small piece of
the UD-COSMOS ﬁeld. The ellipses illustrate inferred object
sizes from CModel, and as can be seen, most objects have ellipses with reasonable sizes. However, there are ellipses thatappear much too large given the visual sizes of objects. They
are indicated as the red ellipses and they are the problematic
objects with over-estimated ﬂuxes. A close inspection of theseobjects shows that some of them are even false detections. In
this release (PDR3), we switch back to the local sky subtraction
scheme and the fraction of these objects decreases compared to
PDR2, but they are still there.
Let us be quantitative about these objects. A way to identify

them is to compare the ﬁxed-aperture (e.g., 2 arcsec) photometry and CModel photometry because the ﬁxed-aperture photometry is insensitive to ﬂuxes in the outer parts of footprints andis thus relatively robust against ﬂux contamination from nearby
sources. Fig. 18 makes this comparison in D/UD. The ﬁgures
are for the i-band, but the same trends can be seen in all the ﬁlters. In PDR1, there is a reasonable agreement between the two
photometric measurements and the difference between them isa smooth function of magnitude. This is expected because thesize of objects is a function of magnitude and the amount ofﬂux missed from the ﬁxed-aperture changes with magnitude. In
contrast, in PDR2, there are many objects around 25-26th magnitudes with large magnitude differences. At this faint level,
most sources are compact and the aperture photometry should
be a reasonable proxy for total ﬂux. Thus, CModel is likely too
bright by ∼ 3 magnitudes or more for many of these sources.
The fraction of objects with a magnitude difference of morethan 1 mag and detected at S/N > 5 is about 6% in D/UD and
5% in Wide4 . In this release (PDR3), the number of such objects is signiﬁcantly reduced as shown in the right panel and thefraction decreases to 3% and 2% in D/UD and Wide, respectively. Care must be taken when comparing these numbers, but
it is clear that PDR3 is better behaved than PDR2. The fraction is only 0.5% in PDR1 UD, although the data were rather
shallow at that time.
We encourage the user to check the consistency between
CModel and aperture photometry for faint sources. If the user
is interested in relatively faint sources (e.g., >∼ 22 mag), thePSF-matched aperture photometry is more reliable; it is ﬁxedaperture photometry performed on images smoothed to a common seeing size across bands and is a robust measure of colors.
It does not capture the total ﬂux, but it is fairly robust against
deblending effects and residual ﬂux in the outskirts.

5.2.3 FGCM and r/i vs. r2/i2
As already discussed in Section 3.2, there is an issue with thephotometric zero-point introduced by the local sky subtraction
performed internally in FGCM. This has been mitigated by thestellar sequence regression (Section 3.9) and we encourage theuser to apply the zero-point offsets as well as the r/i into r2/i2
offsets computed in Section 3.8. Note that these offsets have notbeen applied to the photometry in the database (it is as quoted
by the pipeline). The user can apply these correction by joining
the magnitude offset tables. See the data release website fordetails.

4

These numbers should be regarded as rough numbers because we apply only a minimal set of ﬂag cuts here: only primary objects with no
CModel/aperture measurement failure.

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

21

Fig. 18. Magnitude differences in i-band between 2 arcsec aperture and CModel as a function of the 2 arcsec aperture magnitude in three data releases,
PDR1, PDR2, and PDR3 from left to right. Objects with aperture photometry with S/N > 5 in UD (PDR1) and D/UD (PDR2 and 3) are plotted. For comparison,
50,000 objects are randomly chosen and plotted in each panel. The dotted horizontal lines indicate ±1 mag. The ﬁgures for Wide look very similar and theoutlier fraction is only slightly reduced (see text).

✗
✻

✥

✥

✥

✥

✖

✥

✥

✕

✥
◆

✙

✘

♦
✁

✶

✂

✶

☎

✄

✶

✶

✄

✶

✆

✄

✶

However, doing so can be problematic in the UD ﬁelds,
which include 100 or more visits, any one of which could be
affected by a cosmic ray, giving a substantial chance of the object being excluded. Fig.19 illustrates the effect. The ﬁgure

✌

☞

☛

✡

✠

✞

✟

✞

A ﬂag, pixelflags crcenter is set for an object if a cosmic
ray is detected within 3 pixels from the center of that object inany of the individual visits going into the coadd. In the CCD
processing, cosmic rays are identiﬁed and interpolated, but because interpolation does not normally work well at object centers, we recommend excluding objects with this ﬂag set to selectobjects with clean photometry in the Wide area.

❘

5.2.6 Pixel ﬂags in the UltraDeep ﬁelds

Fig. 19. Number of detected objects on a 0.05 deg grid after rejecting objects with the cosmic ray pixel ﬂag set in the D/UD-COSMOS ﬁeld. The
color-coding shows the number of objects. Recall the central pointing is the
UD pointing and the surrounding 4 pointings are D pointings.

6 Status of Collaborating Surveys
The HSC-SSP survey has a number of collaborating surveys inother wavebands. Here we give a brief update on each of them.

✥

✥

✥
✥

✥

✽

✹
✸
✷
✵

The joint ﬂux calibration is performed using FGCM, but a tiny
fraction of the CCD images have incorrect calibrations applied
due to bad outlier rejection (25 CCD images out of 2,822,678
CCD images used for Wide). We identify these CCDs as an image with a strong spatial gradient in the ﬂux calibration, with atleast one pixel having an inverted ﬂux (i.e., the calibration applied to that pixel is negative). There are probably more CCDs
that are less problematic but still bad. This problem is visible particularly in warps because these are the images that are
ﬂux-calibrated by FGCM. The warps are then added together togenerate coadds. We have visually inspected the potentially affected coadds, but we see no obvious artifact. It may be that theartifact rejection algorithm (Section 3.4) rejected those problematic warps during the coaddition. It is unlikely that the coadd
images as well as catalogs are signiﬁcantly affected, but warps
are affected. We provide a list of problematic warp images atthe data release site.

✝

5.2.5 Catastrophic ﬂux calibration failures

shows the number of objects in the COSMOS ﬁeld, on a grid of
0.05 deg, after excluding objects with pixelflags crcenter
applied. The number density in the UD region is only 2/3 thatin the surrounding D pointings, even though the UD data areconsiderably deeper. The ﬁgure is for the i-band, but the otherﬁlters show exactly the same trend. If we do not apply the ﬂag
cut, the UD region shows a considerably larger source density
than the Deep pointings, as expected. We therefore suggest not
to apply the cosmic ray and interpolated pixel ﬂags in D/UD,
where there is a large number of visits and effects of cosmic
rays are minor on coadds.

✑ ✒✍
✓✔

Due to a processing error, seven i-band visits (31.5 min in total)
are excluded from tract=9463 (DEEP2-F3) from the coaddition
stage and the tract has i2-band data only. The coadd is thus
shallower than it should be by ∼ 0.2 mag. This will be ﬁxed in
our next release.

❉ ✍✎✏5.2.4 Missing data in DEEP2-F3

22

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

They all target the D/UD ﬁelds, where multi-wavelength data
enable new galaxy evolution science.

6.1 U -band
The CFHT Large Area U-band Deep Survey (CLAUDS5 ;Sawicki et al. 2019) used the MegaCam imager on the CanadaFrance-Hawaii 3.6m telescope to obtain very deep U -band images that overlap the D/UD ﬁelds. These new data, together
with archival MegaCam images in some of the ﬁelds, have beenprocessed, resampled, and stacked to match the tract/patch grid,
astrometric solution (updated to match the Gaia-based astrometry of HSC-SSP PDR2/3), and pixel scale. The CLAUDS data
cover 18.60 deg2 with median seeing of FWHM=0.92 arcsec
and to a median depth of U = 27.1 AB (5σ in 2” apertures);selected areas in the COSMOS and SXDS ﬁelds that total 1.36
deg2 reach a median depth of U = 27.7 AB (5σ in 2” apertures).
Altogether, the CLAUDS images represent the equivalent of
113 classical-mode CFHT nights and are the deepest U -banddata ever taken over this combination of depth and area.
Multiband (U + grizy as well as NIR, where publicly available) photometry is carried out by the CLAUDS team using SExtractor (Bertin & Arnouts 1996) and an adaptation of
hscPipe that has been adapted to handle CFHT U -band images. The combination of CLAUDS and HSC-SSP data enables
research in many broad science areas by signiﬁcantly enhancing
photometric redshift performance (particularly at z <∼ 0.8 and
z>
2
)
and
permitting
theselection
of
z
∼
3
Lyman
break
galax∼
ies and quasars. Several science projects have already been
carried out with the joint CLAUDS+HSC-SSP datasets (Halevi
et al. 2019; Moutard et al. 2020; Huang et al. 2020; Golob et al.
2021; Thibert et al. 2021; Harikane et al. 2021), while others
are underway. The CLAUDS team anticipates releasing theirU + grizy catalogs and processed images to the public in late
2021 or early 2022 (G. Desprez et al. in prep.).

6.2 near-IR

ration) is ﬁlling the missing part. With a total time allocation of∼270 hours, DUNES2 observed the E-COSMOS to J ∼ 23.6,
H ∼ 23.2, and K ∼ 23.2 mag at 5σ within 2 arcsec aperture,
DEEP2-F3 to J ∼ 23.3, and K ∼ 23.1 mag, and ELAIS-N1 to
H ∼ 23.2 mag under seeing conditions of FWHM = 0.8–1.0
arcsec. The actual ﬁeld coverage can be found at the DUNES2
project website6 .
The DUNES2 team has internally produced a U -to-K bandmerged catalog by combining the DUNES2 and other existingnear-infrared source catalogs with the CLAUDS U -band and
HSC optical (grizy ) catalogs. This internal catalog is primarily being used to improve photometric redshifts, derive galaxy
stellar mass functions (with a special focus on constraining
the massive end), and identify massive high-redshift (z ∼ 3)
galaxy candidates, which are being actively followed up spectroscopically using the Large Binocular Telescope (LBT) andMultiple Mirror Telescope (MMT) in Arizona (Y.-H. Huang etal. in preparation). This catalog has also been used to crosscheck the quality/accuracy of the hscPipe-based multi-band
catalog, which will be the ofﬁcial catalog from HSC-SSP (seebelow). The DUNES2 team plans to publicly release the processed UKIRT/WFCAM images and associated source catalogs
in early 2022.
A complementary survey, DeepCos (PI: Y.-T. Lin), is also
being carried out. The survey originally aimed to bring the
DUNES2 footprint to the depth of VIDEO and used WFCAM
on UKIRT to further observe E-COSMOS and DEEP2-F3 in J
and K . Due to operation issues with UKIRT, we have switched
to using WIRCam on CFHT in 2020 and continued the survey.
The survey now aims to provide a uniform JAB = 23.7 coverage over ∼ 11 deg2 area in three of the Deep ﬁelds (XMM-LSS,
E-COSMOS, and DEEP2-F3). Such a data set will not only
improve the photo-z accuracy of the D/UD ﬁelds, but will also
play a critical role in the target selection for an upcoming survey
with Prime Focus Spectrograph (PFS; Tamura et al. 2018). The
DeepCos survey is approximately 70% done at this point and is
expected to deliver coadded WFCAM and WIRCam images by2022.

Turning to near-infrared, the D/UD ﬁelds overlap with someof the major near-infrared imaging surveys including the
Deep eXtragalactic Survey of the UKIRT Infrared Deep Sky
Survey (UKIDSS/DXS; Kim et al. 2011), the UKIDSS UltraDeep Survey (UDS; Lawrence et al. 2007), VISTA Deep
Extragalactic Observations Survey (VIDEO; Jarvis et al. 2013),
and Ultra Deep Survey with the VISTA Telescope (UltraVISTA;
McCracken et al. 2012). We designed the D/UD ﬁelds to maximize the overlap with these surveys, but a portion of the D/UD
ﬁelds, especially the ﬂanking ﬁelds of the E-COSMOS region
and DEEP2-F3, are missing the near-IR coverage. Deep UKIRT
Near-infrared Steward Survey (DUNES2 ; Egami et al., in prepa-

Spitzer IRAC 3.6 and 4.5 µm observations exist for a fraction of the D/UD ﬁelds. These include the ∼2 deg2 , Spitzer
COSMOS survey (S-COSMOS) (Sanders et al. 2007) as well as
multiple subsequent deeper surveys within the same footprint.
There is also the warm mission SERVS survey (Mauduit et al.
2012), which covered ∼2 deg2 in ELAIS-N1 and ∼4.5 deg2
in XMM-LSS. The SERVS coverage is fairly uniform with exposure times of ≈1200s, translating to ∼23.1 mag at 5σ forpoint sources. The entire XMM-LSS ﬁeld was later covered to56https://www.ap.smu.ca/~sawicki/sawicki/CLAUDS.html

6.3 IR

http://gxn.as.arizona.edu/DUNES

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

the same depth with the Spitzer DeepDrill survey (Lacy et al.
2021). However, some of the D/UD ﬁelds are missing the IR
coverage and the depths are shallow in some areas.
We have two collaborating surveys that increase the areaand depth in the IR. One is Spitzer Large Area Survey with
Hyper Suprime-Cam (SPLASH; Mehta et al. 2018). SPLASH
focuses on the two UD ﬁelds and it goes down to ∼ 25.2
at 5σ in 3.6µm and 4.5µm. The survey is complete and amulti-band catalog in SXDS has been released to the community (Mehta et al. 2018). The other collaborating survey is theSpitzer Coverage of the HSC-Deep with IRAC for Z-studies(SHIRAZ; Annunziatella et al. in prep). Nearly 500 hours
have been awarded to SHIRAZ and it covers the missing portions of the E-COSMOS and ELAIS-N1 ﬁelds as well as thewhole DEEP-F3 ﬁeld, where only very shallow data existed previously (Timlin et al. 2016), increasing the Spitzer coverage of
the Deep ﬁelds by ∼ 7 deg2 . The depth now matches that of the
SERVS and Spitzer DeepDrill surveys (∼ 23 mag in 3.6µm and4.5µm). By combining with the existing data, we have newly
produced IRAC mosaics over ∼17 deg2 for all the Deep ﬁelds,
except for XMM-LSS (which is published in Lacy et al. 2021).
Details of the survey and the data processing can be found in
Annunziatella et al. (in prep.).
In order to fully exploit the data from the collaborating surveys, we have been putting efforts to combine the HSC datawith data from other wavelengths. Instead of processing theraw data from the collaborating/external surveys, we start fromcoadds from each survey and run object detection and measurements using hscPipe to perform consistent object detection andphotometry. We have constructed a combined catalog with deep
multi-band photometry from u through K and it is being exploited by the collaboration, mostly for distant galaxy studies.
The next step is to incorporate the Spitzer photometry into thecatalog, extending the wavelength coverage out to 8µm, which
will further enhance our science. We plan to release the combined data set to the community in the future.

7 Towards the Final Data Release
We have presented the third data release from HSC-SSP in thispaper. PDR3 covers about 670 square degrees of FCFD Widearea as well as four separate D/UD ﬁelds from 278 nights of observing time. The dynamic range of the area and depth spannedin this survey is a unique aspect of the survey. The data areserved at the same data release site as the previous releases,
https://hsc-release.mtk.nao.ac.jp/, where the user can
ﬁnd both the data and extensive metadata, such as a comprehensive set of QA plots. The list of known issues given in this paper
and listed on the website should be referred to before exploiting
the data for science.
We have executed 95% of the allocated observing time as of

23

this writing and we expect to complete the survey by the end of
2021. The remaining observing time will be spent on the Widelayer and ﬁnal data set will include ∼ 1, 200 square degrees of
FCFD area. We will not take additional D/UD data. We anticipate that the next data release will be the ﬁnal data release from
HSC-SSP. We do not have a speciﬁc release date yet, but we willkeep the community informed on our website. Following theimaging survey, we plan to carry out a massive spectroscopic
survey using PFS on the Subaru Telescope. The PFS survey
will be based largely on the imaging data from HSC-SSP, andthe combination of imaging and spectroscopy will allow us to
explore deeper into the Universe.

Acknowledgments
The Hyper Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan and Taiwan, and Princeton
University. The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan(NAOJ), the Kavli Institute for the Physics and Mathematicsof the Universe (Kavli IPMU), the University of Tokyo, theHigh Energy Accelerator Research Organization (KEK), theAcademia Sinica Institute for Astronomy and Astrophysics
in Taiwan (ASIAA), and Princeton University. Funding wascontributed by the FIRST program from Japanese Cabinet
Ofﬁce, the Ministry of Education, Culture, Sports, Science andTechnology (MEXT), the Japan Society for the Promotion of
Science (JSPS), Japan Science and Technology Agency (JST),the Toray Science Foundation, NAOJ, Kavli IPMU, KEK,ASIAA, and Princeton University.
This paper is based on data collected at the Subaru Telescopeand retrieved from the HSC data archive system, which is operated by Subaru Telescope and Astronomy Data Center at NAOJ.
Data analysis was in part carried out with the cooperation ofCenter for Computational Astrophysics at NAOJ. We are honored and grateful for the opportunity of observing the Universe
from Maunakea, which has the cultural, historical and natural
signiﬁcance in Hawaii.
This paper makes use of software developed for Vera
C. Rubin Observatory. We thank the Rubin Observatory
for making their code available as free software at
http://pipelines.lsst.io/.
The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by
the Institute for Astronomy, the University of Hawaii, the PanSTARRS Project Ofﬁce, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy,
Heidelberg and the Max Planck Institute for Extraterrestrial
Physics, Garching, The Johns Hopkins University, Durham
University, the University of Edinburgh, the Queen’s University
Belfast, the Harvard-Smithsonian Center for Astrophysics,

24

Publications of the Astronomical Society of Japan, (2014), Vol. 00, No. 0

the Las Cumbres Observatory Global Telescope Network
Incorporated, the National Central University of Taiwan, theSpace Telescope Science Institute, the National Aeronautics andSpace Administration under Grant No. NNX08AR22G issued
through the Planetary Science Division of the NASA Science
Mission Directorate, the National Science Foundation Grant
No. AST-1238877, the University of Maryland, Eotvos Lorand
University (ELTE), the Los Alamos National Laboratory, and
the Gordon and Betty Moore Foundation.
This work is also based on the following data and observations: zCOSMOS observations carried out using the
Very Large Telescope at the ESO Paranal Observatory under Programme ID: LP175.A-0839, observations taken by the
3D-HST Treasury Program (GO 12177 and 12328) with theNASA/ESA HST, which is operated by the Association of
Universities for Research in Astronomy, Inc., under NASA
contract NAS5-26555, data from the VIMOS VLT Deep
Survey, obtained from the VVDS database operated by
Cesam, Laboratoire d’Astrophysique de Marseille, France,data from the VIMOS Public Extragalactic Redshift Survey(VIPERS), which has been performed using the ESO Very
Large Telescope, under the ”Large Programme” 182.A0886, and the participating institutions and funding agencies are listed at http://vipers.inaf.it. Funding for SDSS-III
has been provided by the Alfred P. Sloan Foundation, theParticipating Institutions, the National Science Foundation,and the U.S. Department of Energy Ofﬁce of Science. The
SDSS-III web site is http://www.sdss3.org/. SDSS-III is
managed by the Astrophysical Research Consortium for theParticipating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation
Group, Brookhaven National Laboratory, Carnegie Mellon
University, University of Florida, the French Participation
Group, the German Participation Group, Harvard University,
the Instituto de Astroﬁsica de Canarias, the Michigan
State/Notre Dame/JINA Participation Group, Johns Hopkins
University, Lawrence Berkeley National Laboratory, Max
Planck Institute for Astrophysics, Max Planck Institute forExtraterrestrial Physics, New Mexico State University, NewYork University, Ohio State University, Pennsylvania State
University, University of Portsmouth, Princeton University, theSpanish Participation Group, University of Tokyo, University of
Utah, Vanderbilt University, University of Virginia, Universityof Washington, and Yale University. GAMA is a joint
European-Australasian project based around a spectroscopic
campaign using the Anglo-Australian Telescope. The GAMA
input catalogue is based on data taken from the Sloan Digital
Sky Survey and the UKIRT Infrared Deep Sky Survey.
Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE,

Herschel-ATLAS, GMRT and ASKAP providing UV to radio
coverage. GAMA is funded by the STFC (UK), the ARC(Australia), the AAO, and the participating institutions. The
GAMA website is http://www.gama-survey.org/. Funding forthe DEEP2 Galaxy Redshift Survey has been provided by
NSF grants AST-95-09298, AST-0071048, AST-0507428, and
AST-0507483 as well as NASA LTSA grant NNG04GC89G.
Funding for PRIMUS is provided by NSF (AST-0607701, AST0908246, AST-0908442, AST-0908354) and NASA (Spitzer1356708, 08-ADP08-0019, NNX09AC95G). Funding for the
DEEP3 Galaxy Redshift Survey has been provided by NSF
grants AST-0808133, AST-0807630, and AST-0806732. The
LEGA-C survey is based on based on data products from observations made with ESO Telescopes at the La Silla Paranal
Observatory under programme ID 194.A-2005(A–F).

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