Guidelines for the Use of the Projected Density Map Catalogue in the HSC-SSP PDR3
Rhythm Shimakawa, Yuichi Higuchi, Masato Shirasaki, Masayuki Tanaka (NAOJ), et al., 2021, MNRAS, 503, 3896

We here offer the use of the projected density map catalogue in Shimakawa et al. (2021) with incremental updates
based on the Third Public Data Release of Subaru Strategic Program with the Hyper Suprime-Cam (HSC-SSP PDR3).
1

Table 1: Survey areas.

Catalogue overview

ID R.A.
Dec.
N grids
Area deg2
This updated catalogue based on the HSC-SSP PDR3
αmin , αmax δmin , δmax X × Y(effective)(Aihara et al., 2022, PASJ, tmp, 31) extends the survey
1212,251+42,+451132×12085(59)2area from 360 to 700 deg , which are split into 3 fields
2128,
226
−2,+53920×280686(480)since 3 fields in the original PDR2 catalogue (GAMA3330,
40−2,+32800×200350(245)
09H,12H,15H) are combined into 1 field (Table 1). The
sample selection and methodology are nearly the sameas the original paper (Shimakawa et al., 2021, MNRAS, 2 How to download galaxy members through the SQL?
503, 3896, hereafter RS21) but some modifications on theBasically, we adopted the same sampling selection as
sample selection as written in section 2. The catalogue in the original version (RS21, section 2) as below,
contents are almost the same as in the original catalogue(figure 1), but n* ** r*0 (e.g. #16–24) were revised to – isprimary=True,
underdense structures at z < 13911
the original numbers before the mask correction.Colossal over-–& (grizy)
inputcount count value>0,
Table A1. Contents of the density map catalogue.
NameDescription

1234
56789radec
fieldixiyskymap id
eff r10
eff r30
nd3 r10

R.A. (degree) of the centre of aperture
Dec. (degree) of the centre of aperture
Field id (see Table 1)
Pixel id in each field on the R.A. axis
Pixel id in each field on the Dec. axis
Nearest tract and patch in HSC-SSP
Fraction of effective area in r = 10 arcmin
Fraction of effective area in r = 30 arcmin
The number of z = [0.3:0.4) sources in r =10 arcminThe number of z = [0.3:0.4) sources in r =30arcmin
Contrast of nc3 r10 in standard deviation
Contrast of nc3 r30 in standard deviation
Contrast of nc3 r10 in varianceContrast of nc3 r30 in variance
The number of z = [0.30:0.32) sources in r =10 arcmin
The number of z = [0.30:0.32) sources in r =30 arcmin
The number of z = [0.32:0.34) sources in r =10 arcmin
The number of z = [0.32:0.34) sources in r =30 arcmin
The number of z = [0.34:0.36) sources in r =10 arcmin
The number of z = [0.34:0.36) sources in r =30 arcmin
The number of z = [0.36:0.38) sources in r =10 arcmin
The number of z = [0.36:0.38) sources in r =30 arcmin
The number of z = [0.38:0.40) sources in r =10 arcmin
The number of z = [0.38:0.40) sources in r =30 arcmin
Same as #9–24 but for z = [0.4:0.5)
Same as #9–24 but for z = [0.5:0.6)
Same as #9–24 but for z = [0.6:0.7)
Same as #9–24 but for z = [0.7:0.8)
Same as #9–24 but for z = [0.8:0.9)
Same as #9–24 but for z = [0.9:1.0)
Same as #7 but for r = 10 cMpc at z = [0.3:0.4)
Same as #7 but for r = 10 cMpc at z = [0.4:0.5)
Same as #7 but for r = 10 cMpc at z = [0.5:0.6)
Same as #7 but for r = 10 cMpc at z = [0.6:0.7)
Same as #7 but for r = 10 cMpc at z = [0.7:0.8)
Same as #7 but for r = 10 cMpc at z = [0.8:0.9)
Same as #7 but for r = 10 cMpc at z = [0.9:1.0)
Same as #11 but for r = 10 cMpc at z = [0.3:0.4)
Same as #13 but for r = 10 cMpc at z = [0.3:0.4)
Same as #11 but for r = 10 cMpc at z = [0.4:0.5)
Same as #13 but for r = 10 cMpc at z = [0.4:0.5)
Same as #11 but for r = 10 cMpc at z = [0.5:0.6)
Same as #13 but for r = 10 cMpc at z = [0.5:0.6)
Same as #11 but for r = 10 cMpc at z = [0.6:0.7)
Same as #13 but for r = 10 cMpc at z = [0.6:0.7)
Same as #11 but for r = 10 cMpc at z = [0.7:0.8)
Same as #13 but for r = 10 cMpc at z = [0.7:0.8)
Same as #11 but for r = 10 cMpc at z = [0.8:0.9)
Same as #13 but for r = 10 cMpc at z = [0.8:0.9)
Same as #11 but for r = 10 cMpc at z = [0.9:1.0)
Same as #13 but for r = 10 cMpc at z = [0.9:1.0)

10

nd3 r30

1112131415

sgm3 r10
sgm3 r30
dlt3 r10
dlt3 r30
n3 02 r10

16n3 02 r30

17n3 24 r10

18n3 24 r30

19

n3 46 r1020

n3 46 r30

21

n3 68 r10

22

n3 68 r30

23

n3 80 r1024

n3 80 r30

25–40
41–56
57–72
73–88
89–104105–120121122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141

eff z3eff z4eff z5eff z6
eff z7
eff z8eff z9sgm3 c10dlt3 c10sgm4 c10dlt4 c10sgm5 c10dlt5 c10
sgm6 c10dlt6 c10
sgm7 c10dlt7 c10sgm8 c10dlt8 c10sgm9 c10dlt9 c10

Figure 1: Contents of the density map catalogue.

– (grizy) pixelflags edge=False,– (grizy) pixelflags bad=False,– (grizy) pixelflags interpolatedcenter=False,
Downloaded from https://academic.oup.com/mnras/article/503/3/3896/6169724 by National AstronomicalObservatory, Japan user on 03 April 2021#– (grizy) pixelflags saturatedcenter=False,– (grizy) pixelflags crcenter=False,– (grizy) mask brightstar halo=False,– (grizy) mask brightstar ghost=False,

Figure B1. The upper and the lower panels, respectively, show variance andstandard deviation of number densities within (left-hand panel) r = 10 arcminand (right-hand panel) r = 30 arcmin apertures at z = [0.6: 0.7). The grey filled
histogram is the normalized density distribution from the HSC-SSP PDR2
data (Fig. 4). The cyan curve (: original-z) and the purple dashed curves (:smoothed-z) indicate those KDE from the mock catalogue with and without
the redshift variations, respectively (see the text about the methodology).

– (grizy) mask brightstar blooming=False,

where the last three criteria are further bright-object
mask flags available since PDR3. In the updated version, additionally, we excluded some point sources byA P P E N D I X B : I M PAC T O F R E D S H I F Tcomparing
PSF magnitudes with CModel magnitudes
U N C E RTA
INTIES(ipsfflux
mag−imag>0.2; see also Strauss etThis paper discusses the masscontents cmodelof various environments
traced by thebased 1810;
on the mockcatalogueet al., 2010, MNRAS,al.,projected
2002,density
AJ,map124,
Baldryfrom the cosmological simulation. While we employ a sufficiently404,86),
althoughtheyhavenegligible impacts on ourwide redshift bin of !z = 0.1 relative to typical photo-z uncertainties(σ = 0.04),
ideally, we
have to incorporate
errors andare further criteria fordensityestimation.
Thephoto-z
followings
contamination effects from outliers into the analyses. We therefore
the magnitude cuts, detection levels (5σ), and the removal
carry out a quick test to see how photo-z uncertainties makes animpact on ourin this paper bybuilding photo-z variationsof discussions
point-sources,
respectively,
into redshifts of the mock samples.
In order–to i
testcmodel
the impactsmag−aof photo-zi<23,errors on our analyses,
we choose the intermediate redshift bin z = [0.6: 0.7) out of sevenredshift slices in z = 0.3–1 that we used in this paper, which allows us
magerr<0.217,– (grizy)to add irrelevant
sources fromcmodel
distant foreground(z = 0.3–0.5) andbackground (z = 0.8–1) redshifts into the sample. First, we convolve
redshifts of–all imocksources at z mag−i= 0.3–1 witha Gaussian mag>0.2.
filter withpsfflux
cmodelσ = 0.04 according to the typical redshift dispersion of photometricredshifts from
Mizuki for i-band(i < 23) selected
at z <
Additional
thresholds
for sources
photometricredshifts (Tanaka
1 (Tanaka et al. 2018). After that, we measure the number densities
etal.
2016,
ApJ,801,20;
2018,
PASJ,70,9) were applied
within r = 10 or r = 30 arcmin apertures at z = [0.6: 0.7) in thesame way as
as forthe HSC sample and the mock data (Sections 3.2
follows,
and 4.1). In this process, we mix the randomly selected outliers in
the foreground (z = 0.3–0.5) or the background (z = 0.8–1) in with– prob star=0,
the sample at z = [0.6: 0.7). The number of outliers are arranged
to 10 per cent the outlier rate, fout ≡ Noutlier /Ntotal = 0.1, which is
chisq<5,consistent –withreduced
that of our sample(Tanaka et al. 2018). It should
be noted that our simplified test would not produce an exact photoz error although
this tells howirrelevant
sources affectthe density– photoz
best
BETWEEN
0.299
AND 0.999.
estimation: photometric redshift errors of galaxies are not random
distribution, rather, the errors should be biased to certain redshiftThat’s it! If you adopt all the above selection criteria
ranges depending on those SEDs and redshifts.
in theSQL
query,
will
obtainThe resultant
density
distribution
in the youvarianceand thestandard ∼ 20 million sources,
deviation are presented in Fig. B1. Those in consideration ofand
14.4
millions
outofthemwere used in this work (cf.
photometric redshift errors show a broad agreement with the density
weadditionallyrestricted
thesurvey areas as in table 1
distributions of the observed data, suggesting that the assumptionof the large-scale
galaxy bias shallow(equations 4.11
and 4.12,
and removed
areaswithNicola
imag psf depth<26, see

RS21 for the details of the density estimation).
MNRAS 503, 3896–3912 (2021)

3

4 How to cross-match the catalogue with your sample?

How to reproduce the density map figures?

For example, the following sample scripts will allow
The easiest solution seems to be to use TOPCAT (theyou to produce the projected density map in the Hec- Tool for OPerations on Catalogues And Tables; Taylor,toMAP (Field ID = 1) at z = [0.8, 0.9).
2005, ASPC, 347, 29). TOPCAT has a useful match selection,”Best match for each Table row”, which allows you
im po rt pandas a s pd
tofindthe nearest grid points to your individual samim po rt numpy a s np
im po rt m a t p l o t l i b . p y p l o t a s p l tples (figure 3). Also, the max error should be ∼64 arcsec,because the grid xy size of the density map catalogue isd f = pd . r e a d c s v ( ” d e n s i t y m a p p d r 3 . c s v ” )∼0.025 deg.
d f = d f [ ( d f [ ” f i e l d ”]==1) & ( d f [ ” e f f r 1 0 ” ] > 0 . 5 ) ]xmin , xmax = 2 1 2 , 251
ymin , ymax = 4 2 , 45
nx , ny = 1 1 3 2 , 120
x0 = np . l i n s p a c e ( xmin , xmax , nx , e n d p o i n t=F a l s e )y0 = np . l i n s p a c e ( ymin , ymax , ny , e n d p o i n t=F a l s e )X, Y = np . m e s h g r i d ( x0 , y0 )Z = np . z e r o s ( [ ny , nx ] )Z [ : , : ] = np . nan
f o r i x , i y , od i n z i p (d f [ ” i x ” ] , d f [ ” i y ” ] , d f [ ” sgm8 r10 ” ]):Z [ i y ] [ i x ] = odl = np . a r a n g e ( − 2 , 4 , 1 )p l t . f i g u r e ( f i g s i z e =(10 ,1))
p l t . c o n t o u r f (X, Y, Z , l e v e l s=l , cmap=” v i r i d i s ” )p l t . x l i m ( xmax , xmin )p l t . y l i m ( ymin , ymax )plt . tight layout ()p l t . show ( )This is just a simple example, and thus you may want tomodify the script to make it much nicer. If you want tochange the field to ID = 2 or 3, please change xmin, xmax
and nx, ny parameters according to table 1.
4544
43
42250

245

240

235

230

225

220

Figure 2: Output figure from the sample script.

215

Figure 3: Example setting in the cross-match with TOPCAT.