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Record W4225694764 · doi:10.1103/physrevd.106.083520

Cosmological inference from an emulator based halo model. II. Joint analysis of galaxy-galaxy weak lensing and galaxy clustering from HSC-Y1 and SDSS

2022· preprint· en· W4225694764 on OpenAlexafffund
Hironao Miyatake, Sunao Sugiyama, Masahiro Takada, Takahiro Nishimichi, Masato Shirasaki, Rachel Mandelbaum, Surhud More, Masamune Oguri, Ken Osato, Youngsoo Park, Ryuichi Takahashi, Jean Coupon, Chiaki Hikage, Bau-Ching Hsieh, Alexie Leauthaud, Xiangchong Li, Wentao Luo, Robert H. Lupton, Satoshi Miyazaki, Hitoshi Murayama, Atsushi J. Nishizawa, P. A. Price, Melanie Simet, Joshua S. Speagle, Michael A. Strauss, Masayuki Tanaka, Naoki Yoshida

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersLawrence Berkeley National LaboratoryPlanetary Science DivisionHigh Energy PhysicsJapan Society for the Promotion of ScienceSmithsonian Astrophysical ObservatoryJet Propulsion LaboratoryOffice of ScienceMax-Planck-Institut für AstronomieEötvös Loránd TudományegyetemNational Central UniversityBrookhaven National LaboratoryCore Research for Evolutional Science and TechnologyCabinet Office, Government of JapanAcademia SinicaQueen's University BelfastDurham UniversityYork UniversitySpace Telescope Science InstituteToray Science FoundationHigh Energy Accelerator Research OrganizationUniversity of TokyoOhio State UniversityIndonesia Toray Science FoundationCarnegie Mellon UniversityLos Alamos National LaboratoryCollege of Engineering, Michigan State UniversityUniversity of WashingtonPrinceton UniversityAlfred P. Sloan FoundationJohns Hopkins UniversityQueen's UniversityMinistry of Education, Culture, Sports, Science and TechnologyHarvard UniversitySimons FoundationUniversity of MarylandJapan Science and Technology AgencySmithsonian InstitutionU.S. Department of EnergyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNational Astronomical Observatory of JapanNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityScience Mission DirectorateYale UniversityNational Science Foundation
KeywordsPhysicsAstrophysicsGalaxyRedshiftHaloCosmologySigmaWeak gravitational lensingLuminosityAstronomy

Abstract

fetched live from OpenAlex

We present high-fidelity cosmology results from a blinded joint analysis of galaxy-galaxy weak lensing ($\mathrm{\ensuremath{\Delta}}\mathrm{\ensuremath{\Sigma}}$) and projected galaxy clustering (${w}_{\mathrm{p}}$) measured from the Hyper Suprime-Cam Year-1 (HSC-Y1) data and spectroscopic Sloan Digital Sky Survey (SDSS) galaxy catalogs in the redshift range $0.15<z<0.7$. We define luminosity-limited samples of SDSS galaxies to serve as the tracers of ${w}_{\mathrm{p}}$ in three spectroscopic redshift bins, and as the lens samples for $\mathrm{\ensuremath{\Delta}}\mathrm{\ensuremath{\Sigma}}$. For the $\mathrm{\ensuremath{\Delta}}\mathrm{\ensuremath{\Sigma}}$ measurements, we select a single sample of $4\ifmmode\times\else\texttimes\fi{}{10}^{6}$ source galaxies over $140\text{ }\text{ }{\mathrm{deg}}^{2}$ from HSC-Y1 with photometric redshifts (photo $z$) greater than 0.75, enabling a better handle of photo-$z$ errors by comparing the $\mathrm{\ensuremath{\Delta}}\mathrm{\ensuremath{\Sigma}}$ amplitudes for the three lens redshift bins. The deep, high-quality HSC-Y1 data enable significant detections of the $\mathrm{\ensuremath{\Delta}}\mathrm{\ensuremath{\Sigma}}$ signals, with integrated signal-to-noise ratio $S/N\ensuremath{\sim}15$ in the range $3\ensuremath{\le}R/[{h}^{\ensuremath{-}1}\text{ }\mathrm{Mpc}]\ensuremath{\le}30$ for the three lens samples, despite the small area coverage. For cosmological parameter inference, we use an input galaxy-halo connection model built on the dark emulator package (which uses an ensemble set of high-resolution $N$-body simulations and enables fast, accurate computation of the clustering observables) with a halo occupation distribution that includes nuisance parameters to marginalize over modeling uncertainties. We model the $\mathrm{\ensuremath{\Delta}}\mathrm{\ensuremath{\Sigma}}$ and ${w}_{\mathrm{p}}$ measurements on scales from $R\ensuremath{\simeq}3$ and $2\text{ }\text{ }{h}^{\ensuremath{-}1}\text{ }\mathrm{Mpc}$, respectively, up to $30\text{ }\text{ }{h}^{\ensuremath{-}1}\text{ }\mathrm{Mpc}$ (therefore excluding the baryon acoustic oscillations information) assuming a flat $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ cosmology, marginalizing over about 20 nuisance parameters and demonstrating the robustness of our results to them. With various tests using mock catalogs described in Miyatake et al. [preceding paper, Phys. Rev. D 106, 083519 (2022)], we show that any bias in the clustering amplitude ${S}_{8}\ensuremath{\equiv}{\ensuremath{\sigma}}_{8}({\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}/0.3{)}^{0.5}$ due to uncertainties in the galaxy-halo connection is less than $\ensuremath{\sim}50%$ of the statistical uncertainty of ${S}_{8}$, unless the assembly biaseffect is unexpectedly large. Our best-fit models have ${S}_{8}=0.79{5}_{\ensuremath{-}0.042}^{+0.049}$ (mode and 68% credible interval) for the flat $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model; we find tighter constraints on the quantity ${S}_{8}(\ensuremath{\alpha}=0.17)\ensuremath{\equiv}{\ensuremath{\sigma}}_{8}({\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}/0.3{)}^{0.17}=0.74{5}_{\ensuremath{-}0.031}^{+0.039}$.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.368
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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