Towards robust LSS cosmology:
how to mitigate uncertainties insmall-scale nonlinearities
Masahiro Takada (Kavli IPMU)

PTchat @ YITP, April 2019

Hyper Suprime-Cam• largest camera• 3m high• weigh 3 ton• 104 CCDs(~0.9G pixels)

Subaru HSC = superb image quality
6 fields (~140 sq. deg. in total): HSC-Y1 data
HECTOMAP

VVDS
2◦ 00’◦

0.9

1◦ 30’Dec [deg]◦

43 30’
00’
42◦ 30’00’
0◦ 30’
00’-0◦ 30’

248◦

246◦

244◦

242◦
RA [deg]

240◦

238◦

WIDE12H336◦
RA [deg]

334◦

332◦
0.8

GAMA15H

1◦ 00’

1◦ 00’

0◦ 30’

0◦ 30’Dec [deg]

Dec [deg]

338◦

340◦

00’
-0◦ 30’-1◦ 00’00’
0.7-0◦ 30’-1◦ 00’
30’

30’
182◦

180◦
RA [deg]

178◦

176◦

225◦

220◦

215◦
RA [deg]

Subaru HSC typically 0.6” seeing FWHM (spatial resolution)⇔ DES: ~0.9”
GAMA09H

0.6

XMM

4

-2◦ 00’◦2◦

Dec [deg]

Dec [deg]-3◦ 00’0.5-4◦ 00’-5◦ 00’0◦

0.4-6◦ 00’140◦135◦RA [deg]

130◦

38◦

36◦

34◦
RA [deg]

32◦

30◦

seeing [arcsec]

Dec [deg]

44 00’

HSC-Y1 cosmic shear cosmology
Hikage, Oguri+ 19

Planck CMB

Chiaki Hikage(Kavli IPMU)

HSC (140 deg2)
DES (~1300 deg2)

KiDS (400 deg2)

4

.37

VVDS
2◦ 00’Dec [deg]

Dec [deg]43◦ 30’00’
42◦ 30’
248

246

◦

244

◦

242
RA [deg]

◦

240

◦

238

Huang et al. 18336◦
RA [deg]

334◦

332◦
0.8

1◦ 00’

1◦ 00’Dec [deg]

Dec [deg]

338◦

340◦

GAMA15H

0◦ 30’
00’-0◦ 30’-1◦ 00’0◦ 30’
00’
0.7-0◦ 30’-1◦ 00’30’

180◦RA [deg]

178◦

176◦

225◦

220◦

215◦
RA [deg]

0.6

GAMA09H

XMM-2◦ 00’4◦

Dec [deg]

Dec [deg]-3◦ 00’2◦

0.5-4◦ 00’-5◦ 00’0◦

0.4-6◦ 00’140◦

Subaru HSC (8.2m)

z~0.4400’◦

WIDE12H

182◦

SDSS (2.5m)

00’
0◦ 30’-0◦ 30’◦30’• HSC (140 sq. deg.): backgrd gals,
g-g lensing• SDSS (~10000 sq. deg.): spec-z
gals (3D distribution) + RSD

0.9

1◦ 30’

135◦
RA [deg]

130◦

38◦

36◦

34◦
RA [deg]

32◦

30◦

seeing [arcsec]

HSC-SDSS joint probe
cosmology

HECTOMAP
44◦ 00’HSC-SDSS joint probe cosmology• Use a single population of source HSC
galaxies for 3 lens samples of SDSS galaxies
at 3 z-bins → this allows us to calibrate
photo-z uncertainties (Oguri & Takada 11)• S/N~50 in total for g-g lensing• We are performing the blind analysis

Hironao Miyatake(Nagoya/IPMU)

Lensing + Clustering
complementarity
IF ⇠gm ⇠ b 82 , ⇠gg ⇠ b2 82 , b ⇠ b(M )<latexit sha1_base64="uRmNSUFAvYFAciHULXYOxQPBu4g=">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</latexit>

4.0⌦m : 0.25⌦m : 0.30⌦m : 0.35

8 : 0.80
8 : 0.80

b [ 8 /0.8] [D(z)/0.76]

z ⇠ 0.55

3.0<latexit sha1_base64="F/8TWlCSJPWxGykBRLy1cylsE/I=">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</latexit>

2.5

Clustering amplitude
2.0
1.5
1.0
0.5

decreasing ⌦m0<latexit sha1_base64="78dmxQhEOzDr98J5YvGZucrVx9U=">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</latexit>

0.0
1012

⌦m : 0.30⌦m : 0.30⌦m : 0.30

3.5

Weak lensingb [ 8 /0.8] [D(z)/0.76]

3.5

8 : 0.80

1013

M200b (h 1 M )8 : 0.80
8 : 0.85

3.0
2.5
Clustering amplitude
2.0
1.5
1.0
0.5

1014

8 : 0.75

decreasing<latexit sha1_base64="cMOfdWk1IoGs19hGMzqUZxcBZgU=">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</latexit>

0.0
1012

8

Weak lensing

4.0

Hironao Miyatake(Nagoya)Surhud More
(IUCAA/IPMU)

1013

M200b (h 1 M )

1014

7

More, Miyatake, Mandelbaum, MT+15

How to extract cosmology against galaxy bias?
• Linear theory or PT: DES approach (see Elisabeth’s talk)– Pros: robust, simple theory– Cons: has to be restricted to large scales
NL
NL
Pgm (k) ' bPmm(k), Pgg (k) ' b2 Pmm(k)<latexit sha1_base64="iyLsRt2mK6wK59HM9mt/1bPUHnk=">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</latexit>• Method filtering out the small-scale information (SDSS;
Mandelbaum et al. 2013)– Pros: robust against small-scale NL’s
– Cons: conservativeR02⌥gm (R) = ⌃(R)⌃(R0 )
2
R• Halo Emulator approach (HSC; Miyatake+ in prep.)<latexit sha1_base64="4ZfjNY0UCe0dObd60Y7GYQGVEiU=">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</latexit>– Pros: could use the small-scale information– Cons: need to introduce a sufficient number of paras to model 1-haloterm uncertainties, assembly bias ….

Dark Quest & Dark Emulator
Nishimichi, MT, Takahashi et al.
Takahiro Nishiichi(IPMU→YITP)

R. Takahashi (B03)

M. Shirasaki(NAOJ: solicited)

Y. Kobayashi(IPMU: D2)

M. Oguri (B02)

H. Miyatake(Nagoya: solicited)Dark Quest

Nishimichi et al.

• Towards an accurate modeling of nonlinear structure formation
specify wCDM model<latexit sha1_base64="hEzYvBlbjo9r+SGpZxfkGI02Cko=">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</latexit>p↵ = {!c , !b , ⌦de , ln(1010 As ), ns , wde }a measurement fromthe mock~a few days
make a mock ofgalaxy survey (gals,lensing fields, …)

run N-body sim.
post processing (identify halos ~ place where galaxies form)

Dark Emulator•••

Stored all data (~200TB)
Gauss process & ML
Make an “emulator” from the tabulated
data
0.13

1.2

ωc

0.125

1.1

0.12
1

σ8

0.115
0.11

0.9

0.8

0.8

0.75

0.7

ΩΛ

0.7

0.6

0.65
0.5
0.15

0.6

0.2

0.25

0.3

0.35

0.4

0.45

Ωm

0.55
3.6
ln(1010As)

3.4
3.2
3
2.8
2.6
1

ns

0.98
0.96
0.94

w

0.92
−0.8
−0.85
−0.9
−0.95
−1
−1.05
−1.1
−1.15
0.02150.0220.02250.023 0.11 0.115 0.12 0.125 0.130.55
ωbωcLinear Modules

Halo Modules

PCA coeffs.

PCA coeffs.

PCA coeffs.

Linear RMS displacement

Linear matter power spectrum

PCA coeffs.

Halo Mass Function
Utility Modules
Off-centering of centrals
Satellite radial distribution

PCA coeffs.

Propagators(Small scale)

PCA coeffs.

Large scale correlation signals

Halo-mass cross CF
Galaxy-mass cross CF

Basic quantities

PCA coeffs.
(Small scale)

Halo auto CF
FFTlogCluster-galaxy lensing profile

FFTlogGalaxy-galaxy lensing profile

Baryonic effects
Redshift-space distortions

1

Linear mass variance

Sheth-Tormen type form

HOD

0.65 0.7 0.75 0.8 2.6 2.8 3 3.2 3.4 3.6 0.92 0.94 0.96 0.98ΩΛ
ln(1010As)
ns

Galaxy auto CF

FFTlogGalaxy projected CF

Observables

Nishimichi et al. +18
Allow a fast computationof galaxy clusteringobservables (<1sec)

PC1 (500GB memory,
20+400TB)

PC1 (1.5TB memory,
200+80TB)

For me12

my office is very hot…

validation test⇠hm (r; M, z) Nishimichi+<latexit sha1_base64="hJ6mouRQ92AC7MVZ9rPXd0iyJS0=">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</latexit>~a few % at x<30Mpc/h!!

13validation test ⇠hh (r; M, M 0 , z) Nishimichi+<latexit sha1_base64="AFoHaT4cwLjQNszeaPUr+zL46Es=">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</latexit>

14

Clustering observables form Halo Emulator• The abundance of SDSS galaxies; we don’t use forcosmology though (sensitive to masks, obs. systematics, …)n̄g =<latexit sha1_base64="pwgDxsrBb1xqIeWAINAVuG0mP1Y=">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</latexit>• g-g lensing

X dn
M

dM

[hN ic (M ) + hN is (M )]1 X dnPgm (k) =[hN ic Ho↵ (k) + hN is us (k)] Phm (k; M )n̄gdMMnuisanceemulator! ⌃(R)<latexit sha1_base64="C3mTFLwlQtrt5aQQW+M/NyiPMuM=">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</latexit>

FFTlog• g-g clustering
1h
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(k) +

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FFTlog

HSC cosmology challenges
Yosuke Kobayashi(Kavli IPMU)• Populating sim halos with galaxies
using different gal recipes with many
variants–––––

Diff HOD paras
Sat gals distribution
Off-centering
Baryonic effects
Assembly bias

Hironao Miyatake• MCMC parameter inference– Use the fiducial HOD/gal paras– Address whether to recover theunderlying cosmo paras

Stochasticity ingalaxy clustering• Scale-dependent bias
• Stochasticity
1)i =6 hNg i2
⇠gm (r)r(r) ⌘ p
6= 1⇠gg (r)⇠mm (r)hNg (Ng<latexit sha1_base64="JFpvFXBt+hlYlITRz9uwIRClm60=">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</latexit>

Seljak 00
Scoccimarro + 01

conservation laws for baryonic effects• (red) galaxy bias not changed: galaxies form from the same initial peaks• mass conservation around each galaxy -- baryonic effects causes aredistribution of total matter around each galaxy

Van Daalen et al. 13

Schneider & Teyssier 15; Leautahud+17

Results: HSC cosmology challenges
fid
Miyatake, Kobayashi,+in prep
sat_dm_dist
nonfid_nsat
2 cosmo off-centparas+ ~8 galaxy parameters (for eachsample)+ ..

Used HSC+BOSS mocks for covariance
We expect ~3% accuracy in S8

Miyatake, Kobayashi+galaxy parameters are nuisancewe don’t care any bias in galaxy parameters
Miyatake, Kobayashi+

Against baryon effects …

gals parameters can absorb
baryon effects

Miyatake, Kobayashi+

Mocks very useful!
b2 PT
2 NL
PT
testing/validation Pgm (k) = (b1 ) Pmm (k) + 2b1 b2 A (k) + 2 B (k)of PT-based approach
NL
Pgm (k) = b1 Pmm(k) + b2 APT (k)<latexit sha1_base64="SAf6Vln/RL3W3UCDhPE1BSA0/00=">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</latexit>

See Sunao’s poster<latexit sha1_base64="U0X8RZOIwQoiH/UYF3rdcxLuwIQ=">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</latexit>⌃

wgg<latexit sha1_base64="EDpWpYZBrosAg1vdTUswlhhwAWg=">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</latexit>

summary• Galaxy bias so annoying (except for Nick’s linear bias!)• The community NEEDS a good, practical method (valuable
data there)– Give us a good method– Linear theory: are you satisfied? Loose a lot of small-small info
– PT: looks great, but useful?
– Halo-model approach (our HSC approach): are you convinced?

• Want to impose conservation laws
– Mass/momentum conservation laws
– Small-scale stochasticity backreaction, P(k)~k^4 (Peebles 1980)