光⾚天連シンポジウム：2030年代にどのような戦略的中型計画を推進するのか

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中島 王彦
国⽴天⽂台

Big Bang

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Young dwarf galaxies in early universe as building blocks
Epoch of
Reionization

Cosmic Dawn

Cosmic Dark Ages

Peak of Cosmic
Star-Formation

CosmicFirst

HII

EvolvedMicrowave

Galaxies / Quasars

bubbleGalaxies / AGNs

Background
Metal enrichment

Reionization

Neutral
Universe

Ionized
Universe

380,000 yr-- Age of the Universe --

1 Gyr

3 Gyr

z=1,100-- Redshift --

z=6

z=2

© Subaru/TMT Science Book 2020

Big Bang

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

Young dwarf galaxies in early universe as building blocks
Epoch of
Reionization

Cosmic Dawn

Cosmic Dark Ages

Peak of Cosmic
Star-Formation

CosmicFirst

HII

EvolvedMicrowave

Galaxies / Quasars

bubbleGalaxies / AGNs

Background
Metal enrichment

Reionization

Neutral
Universe

Ionized
Universe

380,000 yr-- Age of the Universe --

1 Gyr

3 Gyr

z=1,100-- Redshift --

z=6

© ESO
z=2

© Subaru/TMT Science Book 2020

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Downloaded from https://academic.oup.com/mnras/article/484/4/5587/5299581
by guest on 13 March 2022

5598

P. Torrey et al.

Figure 6. Gas-phase mass–metallicity relation at redshifts z = {0, 1, 2, 3, 4, 6}, as labelle
background histogram indicates the distribution of central galaxies from the TNG100 si
the median MZR and the 1σ scatter, respectively. Observational data from Tremonti ethave been included in the z = 0, z = 1, and z = 2 panels, respectively. We do not plac
uncertainties in the observed metallicity diagnostics, but instead focus on the slope andshape of the MZR and we find there is a gradual decline in the normalization with increa
mass bin (right) we find the continued presence of a correlation
between metallicity and ISM gas-mass. However, the slope grad−0.1
for the highest mass bin. This slope
ually flattens to Z ∝ MISM
flattening is consistent with the observed mass-dependent trend.
We emphasize again, however, that some caution should be used
when interpreting the exact quantitative slope comparisons between
these two datasets owing to non-trivial conversions from MISM
to MH I .

gure 10. The average metallicity is shown as a function of MISM for three thin mass bins (corresponding to the three panels) at several redshifts as indicatedthin the legend. There is a clear correlation between galactic metallicity and the ISM mass where galaxies with higher ISM masses have lower metallicities.
e steepness of the correlation decreases for higher galaxy mass samples. The dashed red lines indicate observational data from Bothwell et al. (2013) which
oadly agrees with the simulated trends. The dot–dashed black lines indicate a leaky box model with a yield of y = 0.05 and an assumed outflow mass loadingη = 4). The dotted red lines indicate the equilibrium model predictions (see text for further details).

Higher
Gas fraction

gure 9. The median gas fraction, defined as the ratio of ISM mass to stellar mass, is shown with respect to the mass metallicity relation for six distinct
shifts as labelled in the upper left corner of each panel. As in Fig. 6, the median MZR is indicated with a solid black line, with the 1σ variation indicatedth dashed black lines. A trend can be identified where galaxies with high (low) gas fractions have low (high) metallicities, when compared against the MZR.
is trend holds across a wide redshift and mass range.

Extremely metal-poor galaxies as representative populationin early universe

redsh
2 ZsunIn general, the slope between metallicity and H I gas-mass foundthe Bothwell et al. (2013) data is very similar to the slope
und in the majority of the IllustrisTNG data between metallicityd ISM mass. In more detail, the redshift z = 0, low-mass
ustrisTNG galaxy metallicity (black line in the left-hand panel,−0.3, which is very similar to theFig. 10) scales as Z ∝ MISM
thwell et al. (2013) trend. Moving from the lowest mass bin
ft-hand panel) to the intermediate-mass bin (centre) and highest

Downloaded from https://academic.oup.com/mnras/article/484

lower
Za
data
1 Zsunsaturaother(or in0.5 Zsimul
sun
(2014
strongsatura
redsh
(2014
obser
redsh
0.1 Zevolu
sun
1 Zsunwill bExtremely metal-poor
expec
years
Figure 7. Evolution of galactic metallicity as a function of redshift forgalaxies(EMPGs)Th
several mass bins, as indicated in the legend. Solid curves indicate the
MZR
metallicity as determined from the simulation, while dashed lines indicate
trend
at Z<0.1Z
sun
best fits and extrapolations to the data. We find a best-fitting
slope ofZ∝−
scena
0.1 Z0.064z
sun which applies reasonably well for z > 2 across the full mass rangechang
resolved in our simulations.
the IS
notsixreFigure 9. The median gas fraction, defined as the ratio of ISM mass to stellar mass, is shown with respect to the mass metallicity relation forminimization
on
the
full
dataset
for
z
>
1
of
Z
=
Z
−
0.064z.
ss, isshownet
with
to
the
mass
metallicity
relationforsix
distinct
Torreyal. respect
2019
based
on
IllustrisTNG
0
While
redshifts as labelled in the upper left corner of each panel. As in Fig. 6, the median MZR is indicated with a solid black line, with the 1σ variationin
MZR is indicated witha solidblack
line,
withAthe1σ canvariation
indicated
Predictions
for(low)
metallicity
of galaxies
pastz metallicities,> 6 are dependentonwithdashedblacklines.
trendbeidentified
wheregalaxies
with
high
gas
fractionshavelow(high)
when
comparedagainstth
MZR
See also: Jones et al.
2020, Sanders
et al. 2021
basedMZR.
on obs.
fractions have low (high)when acomparedagainstThis metallicities,
trend holds acrosswide redshiftandthemass range. the nature of our adopted extrapolation, but clearly point towards

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Hα[OIII]5007gr@z=0.03izy

The Astrophysical Journal, 898:142 (30pp), 2020 August 1

Kojima et al.

Obs. wavelength (Ang)

Figure 8. Schematic illustration of the structure of our ML classiﬁer based on the DNN. The nodes (open circles) and branches (solid lines) represent a linear
combination. The green circles are ReLU activation functions.

Deep neural network ML classifier
to efficiently/reliably select EMPG candidates
from Subaru HSC photometric data

@ Subaru HSC SSP

not use the HSC y-band photometry, which is shallower thanthe other bands, to reach as faint magnitudes as possible. Aftercalculations, the classiﬁer outputs four probabilities of the
EMPG, non-EMPG galaxy, star, and QSO and chooses only
one type whose probability is the highest of the four. Here we
obtain probabilities with the softmax function. The softmax function is a mathematical function that normalizes avector with an exponential function.
The structure of the neural network is optimized so that thesum of the output errors is minimized. We optimize ourclassiﬁer with a training sample, in which object types are
already known beforehand. The optimization process is usuallycalled “training.” We use the cross-entropy and Adam

Kojima et al. 2020

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

The Astrophysical Journal, 898:142 (30pp), 2020 August 1

The Astrophysical Journal, 898:142 (30pp), 2020 August 1

12+log(O/H) = 6.90

Kojimaal.
The etAstrophysicalJournal, 898:142 (30pp), 2020 August 1

KojimaThe etAstrophysicalal.
Journal, 898:142 (30p

The Astrophysical Journal, 925:111 (18pp), 2022 February 1

Isobe etFigure 13. The HSC gri-band images of the four HSC-EMPG candidates, HSC
FigureJ1429−0110,
13. The HSC
HSC
gri-band
J2314+0154,
images HSCof theJ1142−0038,
four HSC-EMPG
and HSC
candidates,
J1631+4426.
HSC
J1429−0110,
HSC
J2314+0154,
and HSCJ1631+4426.
Figure13. The HSC
gri-bandimages HSC
of t
Figure13. The HSC
gri-bandimages HSCof theJ1142−0038,
four HSC-EMPG
candidates,
HSC
J1429−0110,
HSC
J2314+0154,

catalogs are called “HSC-EMPG candidates” and “SDSS-catalogsepoch
are andcalledcalculate“HSC-EMPG
candidates”
and “SDSScatalogs
are andcalledcalculate“HSC-EMPG
an average
and a standard
deviation
ofepochcalculatean average
and a standard
deviation
ofepochcatalogs
are andcalled“HSC-EMPG
candidates”
and “SDSSan aver
EMPG candidates,” respectively.
EMPG thesecandidates,”
respectively.
EMPG
candidates,”
respectively.
ﬂux values.
If the standard deviation becomes larger
thanthese
ﬂux values.
If the standard deviation becomes larger
thanthese
EMPG
candidates,”
respectively.
ﬂux values.
If the stand
25% of the average value, we regard the source as a transient25% of the average value, we regard the source as a transient25% of the average value, w
4.1. EMPG Candidates from the HSC Data
4.1. EMPG
Candidates
from
HSC Removing
Data
4.1. EMPG
Candidates
froobjectand eliminate
it fromthethesample.
10 and 15object
eliminate
it fromthe thesample.
10 and 15object
4.1. and
EMPG
Candidates
from
HSC Removing
Dataand eliminate
it fromsources, we obtain 22 and 42 sources after the third step for thesources, we obtain 22 and 42 sources after the third step for thesources, we obtain 22 and 42
In Section 2.2, we created the HSC-SSP source catalog, In Section 2.2, we created the HSC-SSP source catalog, In Section 2.2, we created the HSC-SSP source catalog, In Section 2.2, we created the
S17A and S18A data, respectively.
S17A and S18A data, respectively.
S17A and S18A data, respec
which consists of 17,912,612 and 40,407,765 sources in thewhich consists of 17,912,612 and 40,407,765 sources in thewhich consists of 17,912,612 and 40,407,765 sources in thewhich consists of 17,912,612 andIn the last step, we inspect a gri-composite image. Here we In the last step, we inspect a gri-composite image. Here we In the last step, we inspect
S17A and S18A source catalogs, respectively. As noted inS17A and S18A source catalogs, respectively. As noted inS17A and S18A source catalogs, respectively. As noted inS17A and S18A source catalogs,
remove apparent H II regions inside a large SFG, sourcesremove apparent H II regions inside a large SFG, sourcesremove apparent H II regio
Section 2.2, the sources selected from S17A and S18A data atSection 2.2, the sources selected from S17A and S18A data atSection 2.2, the sources selected from S17A and S18A data atSection 2.2, the sources selected fro
affected by a surrounding bright star, and apparently redaffected by a surrounding bright star, and apparently redaffected by a surrounding
this point are partly duplicated, but the duplication will bethis point are partly duplicated, but the duplication will bethis point are partly duplicated, but the duplication will bethis point are partly duplicated, bu
sources. The apparently red sources are mistakenly selected duesources. The apparently red sources are mistakenly selected duesources. The apparently red soremoved in the last step of the selection. In this section, weremoved in the last step of the selection. In this section, weremoved in the last step of the selection. In this section, weremoved in the last step of the sele
to an issue in the cmodel photometry. Indeed, they show redto an issue in the cmodel photometry. Indeed, they show redto an issue in the cmodel phselect EMPG candidates from the HSC-SSP source catalog inselect EMPG candidates from the HSC-SSP source catalog inselect EMPG candidates from the HSC-SSP source catalog inselect EMPG candidates from the Hcolors (r−i>0.0) in the 1 0 aperture photometry, while theycolors (r−i>0.0) in the 1 0 aperture photometry, while theycolors (r−i>0.0) in the 1
four steps, described below.
four steps, described below.
four steps, described below.
four steps, described below.
become blue in the cmodel photometry. In the inspection ofbecome blue in the cmodel photometry. In the inspection ofbecome blue in the cmodel
In the ﬁrst step, we coarsely remove sources based on In the ﬁrst step, we coarsely remove sources based on In the ﬁrst step, we coarsely remove sources based on In the ﬁrst step, we coarsely r
multi-epoch and gri-composite images, we removed 10 and 21multi-epoch and gri-composite images, we removed 10 and 21multi-epoch and gri-composi
blending, extendedness, and color before we apply our MLblending, extendedness, and color before we apply our MLblending, extendedness, and color before we apply our MLblending, extendedness, and color
sources from the S17A and S18A data, respectively.
sources from the S17A and S18A data, respectively.
sources from the S17A and S
classiﬁer. We remove sources whose photometry is stronglyclassiﬁer. We remove sources whose photometry is stronglyclassiﬁer. We remove sources whose photometry is stronglyclassiﬁer. We remove sources who
Eventually, we thus obtain 12 and 21 HSC-EMPG Eventually, we thus obtain 12 and 21 HSC-EMPG Eventually, we thus obaffected by back/foreground objects as follows. Fluxes of aaffected by back/foreground objects as follows. Fluxes of aaffected by back/foreground objects as follows. Fluxes of aaffected by back/foreground object
candidates from the S17A and S18A catalogs, respectively.candidates from the S17A and S18A catalogs, respectively.candidates from the S17A asource and a back/foreground object are measured at thesource and a back/foreground object are measured at thesource and a back/foreground object are measured at thesource and a back/foreground ob
We ﬁnd that six of the HSC-EMPG candidates are duplicatedWe ﬁnd that six of the HSC-EMPG candidates are duplicatedWe ﬁnd that six of the HSCcentral position of the source, and when a ﬂux of the back/central position of the source, and when a ﬂux of the back/central position of the source, and when a ﬂux of the back/central position of the source, andbetween the S17A and S18A catalogs. Thus, the number ofbetween the S17A and S18A catalogs. Thus, the number ofbetween the S17A and S18A
foreground object exceeds 50% of the source ﬂux, the source isforeground object exceeds 50% of the source ﬂux, the source isforeground object exceeds 50% of the source ﬂux, the source isforeground object exceeds 50% of th
independent HSC-EMPG candidates is 27 (=12 + 21 − 6).independent HSC-EMPG candidates is 27 (=12 + 21 − 6).independent HSC-EMPG canremoved. We only select extended sources whose exten-removed. We only select extended sources whose exten-removed. We only select extended sources whose exten-removed. We only select extended
The magnitude range of the 27 HSC-EMPG candidates isThe magnitude range of the 27 HSC-EMPG candidates isThe magnitude range of th
dedness_value ﬂags are 1 in all of the griz bands. Thededness_value ﬂags are 1 in all of the griz bands. Thededness_value ﬂags are 1 in all of the griz bands. Thededness_value ﬂags are 1 in ai = 19.3–24.3 mag.
i = 19.3–24.3 mag.
i = 19.3–24.3 mag.
hscPipe labels a point source and an extended source ashscPipe labels a point source and an extended source ashscPipe labels a point source and an extended source ashscPipe labels a point source an
Out of the 27 candidates, we ﬁnd six candidates that are Out of the 27 candidates, we ﬁnd six candidates that are Out of the 27 candidates,
extendedness_value = 0 and 1, respectively. Theextendedness_value = 0 and 1, respectively. Theextendedness_value = 0 and 1, respectively. Theextendedness_value = 0 a
selected in S17A but not selected again in S18A. Four out ofselected in S17A but not selected again in S18A. Four out ofselected in S17A but not sele
hscPipe deﬁnes a point source as a source whose PSFhscPipe deﬁnes a point source as a source whose PSFhscPipe deﬁnes a point source as a source whose PSFhscPipe deﬁnes a point source
the six candidates are slightly redder in S18A than in S17A andthe six candidates are slightly redder in S18A than in S17A andthe six candidates are slightly
magnitude (mpsf) and cmodel magnitude (mcmodel) matchmagnitude (mpsf) and cmodel magnitude (mcmodel) matchmagnitude (mpsf) and cmodel magnitude (mcmodel) matchmagnitude (mpsf) and cmodel mthus not selected in S18A. The other two are removed in S18Athus not selected in S18A. The other two are removed in S18Athus not selected in S18A. Th(Boschetgri-composite
al. 2018). To
save− mcmodel< 0.0164(B
within mpsf − mcmodel < 0.0164 (Bosch et al. 2018). To savewithin mpsf − mcmodel < 0.0164
withini bands
mpsf correspond− mcmodelEMPGs< 0.0164we(Boschet al.LRIS.
2018). To
saver,
withinmpsfphotometric
2. HSC
imagesstar.
of theSuch
13 HSCobserved
HSC g,
to the blue,
due to ﬂags related to aFigure
cosmicray ornearby brightdue
to ﬂags
related tothata cosmic
raywithor nearbyThebrightstar.and
Suchdue
to ﬂags
relatedto agreen,cosma
calculation time, we remove part of the sources before wecalculation time, we remove red
partof inthebefore wewe shown
remove thepartcalculation
time,between
we removepart barsof thebeforewecalculation
colorsthe sources
ﬁgure,
respectively.
EMPG
is locatedthe twowhiteeachsources
panel. The
cutout size
is 20″ ×differences
20″. time,
The number
differences probably arise dueto thedifferent
pipelineEachversions
differences
probablyarise
due toofthe
different
pipelineversions
probably atarise topdue
apply the classiﬁer. To roughly remove sources whose colorsapply the classiﬁer. To roughly
remove sources
whose colors
theTo roughly remove sources whose colorsapply the classiﬁer. To roughly rem
corresponds
to theapply
numberin classiﬁer.
Table 2.
between S17A and corner
S18A.of each
We panelcheck
the images
andbetween
S17A and S18A. We check the images andbetween S17A and S18A
are apparently different from EMPGs, we apply
are apparently different from EMPGs, we apply
are apparently different from EMPG
are apparently different from EMPGs, we apply
photometry of these six
candidates
individually.
Thenwephotometryofthese
six candidates
individually.
Thenwephotometry of these six caof EMPG-tails potentially contaminate those of EMPGs
by atIII]λλ 4959,5007/Hβ with electr
3727,3729/Hβand [Or  i   0.3,
(2 )r sixi candidates
 0.3,(2 )r sixi candid
 0.3
r

i


0.3,
(
2
conﬁrm that these
seem
to
have
no
problemasconﬁrm
that
these
six
candidates
seem
to
have
no
problemthat thesemost 50%, which implies that the EW0(Hβ) of EMPGs would
temperatures of Te(O II)) and asTconﬁrme(O III), respectively, and t
an EMPG candidate.
candidate.
EMPG
i  z  0.2.
(3 )i  z be0.2.
(3) errorsanofEMPGi  zrelation
 0.2.
i  z  0.2. derived n . We calculate(3)T (O IIan
including) usingan candidate.
empirical
underestimated. We calculate
EW (Hβ)Hα[OIII]5007gr

6@z=0.03izyNakajima et al.

Obs. wavelength (Ang)

0

e

e

the uncertainties of stellar continua as well as those of Hβ
To remove possible contamination from normal galaxies, weTo remove possible
contamination
normalgalaxies,contamination
contamination
fromfrom
normalgalaxies,
Te (ODataii) weTo
0.7remove
Te (O IIIpossible) 4.2.
3000EMPG
4.2. EMPG
Candidates
the SDSS
DataweTo remove possible
4.2. EMPG
Candidates
the SDSS
Candida(ﬂuxes.fromfrom
also apply
also apply
also apply
also apply+ In +Section2+2.3,+ we const
In Section 2.3, we constructed the SDSS source catalog In Section 2.3, we constructed
theSDSS
sourcecatalog(Garnett 1992). Just adding O /H to O /H , we ﬁna
Chemical
Propertyg  r   0.3125 (r  i) 0.1375.
(4 )g  r of
0.3125 (r sources.
i) 0.1375.
(we4) selectgr
0.3125 (r soi
g  r of
0.3125 (r sources.
i) 0.1375.
(4) selectconsisting
consisting
31,658,307
In this 4.2.
section,consisting
31,658,307
this section,
31,658,307
log (O Hwe). We note that ofneutral
obtainIn 12
oxygenEMPG candidates from theSDSSsourcecatalogsimilarly
toEMPG
candidates
from
the
SDSS
source catalog similarly toEMPG candidates from the S]λ4363sourcesnegligible
All ofthat
the satisfy
LRIS Equations
EMPG In
candidates
have we[O IIIin HEquations
II regions
the ionization
potential
In other words, we choose sources that satisfy EquationsIn other words, we choose sources
In because
other words,
we choose
sourc
other
words,
choosethatsatisfy
the HSC source catalogdetections,in Section which
4.1. allows us to derivethemetallicities
HSC sourceand
cataloginSection
4.1.
the
HSC
sourcecatalog
in Se
other (2)–(4)neutraloxygenatoms
is(2)–(4)13.6 eV,
theWe
same
as
that
ofsource
neut((2)–(4) here. We show Equations (2)–(4) in Figure 12. After(2)–(4) here.
Equations(2)–(4)inhaveFigurecolors
12. After
here.
show
Equations(2)–(4) here.
WeweshowEquationsinhaveFigurecolors
12.
After
First,Weweshow
removesourcesthatapparently
First,
removesourcesthatapparently
First,
weremove3+(e.g.,
Izoelement
abundanceswiththedirect-T
e methodhydrogenatoms.
We
also
ignoreOandhigher-orderoxyg
these selection criteria, 680 and 2494 sources remain from thethese selection
criteria,
680
and
2494
sourcesremain
from
the
these
selection
criteria,
680
and
249E
these
selection
criteria,
680
and
2494
sourcesremainfromthedifferent from EMPGs with
Equations(2)–(4). below.
Then we applydifferent from EMPGs with Equations (2)–(4). Then we applydifferent from EMPGs with
tov
2006)
as
described
ionssource
for consistency
previous
works(e.g.,
200
S17A and S18A data, respectively. The source removal in theS17A anddata, respectively.
The source
removal
S17A
and
data,
respectively.
S17A and
S18A
data, respectively.
The
removaltheour S18A
ML classiﬁer
to the SDSS
catalog,and inourtheclassiﬁer
MLclassiﬁer
and inourwithclassiﬁer
our S18A
ML
classiﬁer
toIzotov
the SDSS
calculated
fromto the
twoSDSS source catalog,
The source
electrontemperature
Te is ourPaperI)28time.
ﬁrst step effectively reduces the calculation time in the MLﬁrst step
effectively
reduces thecalculation
time in the MLﬁrst step
effectively
reduces thecaeffectively
reduces2+thecalculationin theimages,
MLﬁrst step
selects
107 sources.
Checking
gri-composite
selects
gri-composite
weselects
107 sources.
Check,collisional
excitation linesimages,of the wesame
ion107suchsources.
as O Checking
Using the latest atomic
dataeliminate
andtemperature
relationshi
classiﬁer in the second step below.
classiﬁer
in the second
stepHbelow.
in thesecondstep
II regions in a spiral galaxy, classiﬁer
sourcessourceseliminateapparent
eliminateapparentinsecondstep
II regiapparent
Hbelow.
because the collisional excitation
rateis thedeterminedbyHbelow.
TIIe. regions in a spiral galaxy, classiﬁer
listedin Table
we incanIn red
also
derivestep,
gas-phasei
In the second step, we apply the ML classiﬁer constructed in In theaffected
second bystep,a weapplyUsing
the ML
constructed
in In redtheaffected
secondapply the M
surrounding
brightstar, package
and apparently
affecteda wesurrounding
brightstar,
and 3,
apparently
thesecond by
step,
apply
classiﬁer
constructedbyothera wesurrounding
theclassiﬁer
PyNeb
getCrossTemDenwiththelatestthe ML
+
+
+
, to
Ar2+
/Hsources
,also
N+/H
, a
abundances
such
as Section
Ne2+/H
Section 3.2 to the sources selected above. The ML classiﬁerSectionsources.
3.2 to the
Theif
ML
3.2
the
selected
Wesources
also selected
removesourcesthe classiﬁer
corresponding
WeTablealso3, selected
removesourcesTheif ML
thecorresponding
Sectionsources.
3.2listed
to thesources
above.
classiﬁer
sources.
We
removeatomicabove.
data andtemperature
relationship
in
we
2+
+
with opticalemissionlines[NeIII]λ3869,
Fe680/H(S17A)
selects 32 and 57 sources out of the 680 (S17A) and 2494selects composite
32 and 57image
sourcesshows
outderive
ofanthe2494
selects32
andof57
sourcesout [Ar
ofantI2+ and thatapparent
may
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apparent
problem
may becomposite
selects
32 and57imagesourcesshowsout ofanthe
andthat
2494
image
shows
Oproblem(Te(O III)) andnebefromemission-line
ratiosthe680Te of(S17A)
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respective
λ7136,magnitude.
[N II]λλ 6548,6584,(S18A), respectively.
(S18A),caused
respectively.
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causedby]λ4658,
an incorrectzeroIII]λλ 4959,5007 and [S II]λ6731/[S II][O III]λ4363/[O5"

Kojima et al. 2020
Isobe et al. 2022
Nakajima et al. 2022
More based on latest DRGREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

EMPGs explored by Subaru: EMPRESS

ESS
R
P
EM

al Journal, 898:142 (30pp), 2020 August 1

ces
r
u
so

Kojima et al.

Prev. known EMPGs
e.g. I Zw 18, SBS0335-052,
DDO68 (see e.g. Izotov+18)
I Zw 18

HSC gri-band images of the four HSC-EMPG candidates, HSC J1429−0110, HSC J2314+0154, HSC J1142−0038, and HSC J1631+4426.

called “HSC-EMPG candidates” and “SDSSates,” respectively.

EMPG Candidates from the HSC Dataepoch and calculate an average and a standard deviation ofthese ﬂux values. If the standard deviation becomes larger than
25% of the average value, we regard the source as a transient
object and eliminate it from the sample. Removing 10 and 15
sources, we obtain 22 and 42 sources after the third step for the
S17A and S18A data, respectively.
In the last step, we inspect a gri-composite image. Here weremove apparent H II regions inside a large SFG, sources
affected by a surrounding bright star, and apparently redsources. The apparently red sources are mistakenly selected due
to an issue in the cmodel photometry. Indeed, they show redcolors (r−i>0.0) in the 1 0 aperture photometry, while they
become blue in the cmodel photometry. In the inspection ofmulti-epoch and gri-composite images, we removed 10 and 21

Kojima et al. 2020
Isobe et al. 2022
Nishigaki et al. in prep.

2.2, we created the HSC-SSP source catalog,of 17,912,612 and 40,407,765 sources in the
8A source catalogs, respectively. As noted ine sources selected from S17A and S18A data at
partly duplicated, but the duplication will bee last step of the selection. In this section, we
candidates from the HSC-SSP source catalog incribed below.
step, we coarsely remove sources based on©NASA/ESA/STScIGREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

EMPG exploration at higher-redshift:
Feasible with GREX-PLUS @z=2-8
Redshift of Hα
0

1

2

3

4

Roman
F062

F106
F158
F087
F129 F184

Obs. wavelength (µm)

5 6 7GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

EMPG exploration at higher-redshift:
Feasible with GREX-PLUS @z=2-8
Redshift of Hα
0

1

2

Roman
F062

F106
F158
F087
F129 F184

3

4

5 6 7

GREX-PLUS
F397 F520 F680 F890

Obs. wavelength (µm)
Based on GREX-PLUS factsheet v0.2

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

1

2

3

4

5 6 7

[OIII] Hα
F062

F106 F158
F087
F129 F184

GPs

1

EMPGs

Redshift of Hα0F184 − F232

EMPG exploration at higher-redshift:2
Feasible with GREX-PLUS@z~2.2-2.8

F397 F520 F680 F890

0

−1
−1

0
1
F232 − F303

2

2

Obs. wavelength (µm)

F232 − F303

Lyα

1
0
GPs

EMPGs

−1
−1

0
1
F303 − F397

2

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

1

2

3

4

5 6 7

[OIII] Hα
F062

F106 F158
F087
F129 F184

GPs

1

EMPGs

Redshift of Hα0F232 − F303

EMPG exploration at higher-redshift:2
Feasible with GREX-PLUS@z~3.1-4.1

F397 F520 F680 F890

0

−1
−1

0
1
F303 − F397

2

2

Obs. wavelength (µm)

F303 − F397

Lyα

1
0
GPs

EMPGs

−1
−1

0
1
F397 − F520

2

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

1

2

3

4

5 6 7

[OIII] Hα
F106 F158
F087
F129 F184

1

F397 F520 F680 F890

0

−1
−1

0
1
F397 − F520

2

2

Lyα

Obs. wavelength (µm)

F397 − F520

F062

GPs

EMPGs

Redshift of Hα0F303 − F397

EMPG exploration at higher-redshift:2
Feasible with GREX-PLUS@z~4.3-5.6

1
0
GPs

EMPGs

−1
−1

0
1
F520 − F680

2

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

1

2

3

4

5 6 7

[OIII] Hα
F106 F158
F087
F129 F184

1

F397 F520 F680 F890

0

−1
−1

0
1
F520 − F680

2

2

Lyα

Obs. wavelength (µm)

F520 − F680

F062

GPs

EMPGs

Redshift of Hα0F397 − F520

EMPG exploration at higher-redshift:2
Feasible with GREX-PLUS@z~6.0-7.7

1
0
GPs

EMPGs

−1
−1

0
1
F680 − F890

2

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

EMPG exploration at higher-redshift:
Feasible with GREX-PLUS and Roman@beyond z=8
Redshift of Hα
0

1

2

3

5 6 7 89

4

[OIII] Hα
F062

F087

F106 F158
F129 F184

F397 F520 F680 F890

Obs. wavelength (µm)

Lyman-break/Lyα available w/ Roman→ GREX-PLUS color excess for LBGs
→ Lower-mass (primordial?) EMPG searches at further high-redshift (z=8-10.5)

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

EMPG exploration with current GREX-PLUS wide survey plan
Redshift

BBcontDepthArea(deg2)>Mstar(Msun)

Exp. # of
EMPGs

2.5

F303

27.7

1.3

2.4e7

30

27

40

4.2e7

600

26

200

1.0e8

2000

24.5

2000

4.0e8

10000

27.7

1.3

4.0e7

30

27

40

6.8e7

600

26

200

1.6e8

2000

+ Evolution of mass-metallicity relation(Torrey+19, Sanders+21)

24.5

2000

6.6e8

10000

+ Evolution of stellar mass function(Baldry+12, Song+16, Davidzon+17, Stefanon+21)

25.5

1

4.3e8

5

24.4

40

1.3e9

100

23

200

3.1e9

300

22.5

500

5.0e9

700

24

1

2.3e9

2

23

40

5.0e9

50

22

200

1.0e10

200

21

500

2.2e10

90

3.5

5.0

6.8

F397

F520

F680

Assuming:+ Number density of EMPGs at z~0 (Kojima+20)
as a function of survey depth (minimum Mstar)

Confirmed to work:+ @z=0.6 using HST from GOODS-N CANDELS catalog+ @z=1.5 using JHK from COSMOS2016 catalog

Based on GREX-PLUS factsheet v0.2

GREX-PLUS：遠⽅の若い低⾦属量銀河の探査 ‒ 中島王彦 @ 光⾚天連シンポジウム

Summary: Systematic exploration of young, metal-poor galaxies
at high-redshift will become possible with GREX-PLUS
Redshift of Hα124F106 F158
F087
F129 F184

2

5 6 7

[OIII] Hα@z=6.8F062

3F397 F520 F680 F890

F397 − F520

0

EMPGs

1
0

−1

Lyα

−1
2

0
1
F520 − F680

2Photometrically-selecting EMPGs(e.g. ML classifier exploiting characteristic colors)

F520 − F680

Obs. wavelength (µm)

10GREX-PLUS to innovatively offer wide & deep−12-10µm imaging data
→ EMPG searches at z=2-8 (and beyond w/ Roman)→ Early-phase structure formation studies

EMPGs
−1

0
1
F680 − F890

2