Estimating redshift distributions using hierarchical logistic Gaussian processes

Rau, Markus; Wilson, Simon; Mandelbaum, Rachel

doi:10.1093/mnras/stz3295

Cited by 19 publications

(17 citation statements)

References 59 publications

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“…Newman (2008) presented a method to determine the redshift distribution for a set of objects precisely and accurately by measuring their spatial cross-correlation with a smaller set of objects with known spectroscopic redshifts. More recent techniques (McQuinn & White 2013;Rahman et al 2015;Sánchez & Bernstein 2019;Rau et al 2020) have combined photometric and clustering information into a single redshift estimator. While powerful, these methods are dependent on complementary estimates of both the weak lensing magnification signal and the evolving galaxy bias of the samples.…”

Section: Scientific Drivers and Critical Propertiesmentioning

confidence: 99%

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Kovacs¹,

Mao²,

Aguena³

et al. 2022

TheOJA

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Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend

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Section: Scientific Drivers and Critical Propertiesmentioning

confidence: 99%

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Kovacs¹,

Mao²,

Aguena³

et al. 2022

TheOJA

Self Cite

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“…Lima et al 2008;Hildebrandt et al 2017Hildebrandt et al , 2020Buchs et al 2019;Wright et al 2020). More recently, however, steps have been taken towards constructing a fourth, hybrid category which leverages both cross-correlation and direct calibration (Rau et al 2019;Sánchez & Bernstein 2018;Alarcon et al 2019).…”

Section: Introductionmentioning

confidence: 99%

KiDS+VIKING-450: Improved cosmological parameter constraints from redshift calibration with self-organising maps

Wright

Hildebrandt

Busch

et al. 2020

A&A

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We present updated cosmological constraints for the KiDS+VIKING-450 cosmic shear data set (KV450) estimated through redshift distributions and photometric samples defined using self-organising maps (SOMs). Our fiducial analysis finds marginal posterior constraints of S8 ≡ σ8 Ωm/0.3 = 0.716−0.038+0.043, which are smaller but otherwise consistent with previous works that have applied this data set (|ΔS8| = 0.023). We analysed additional samples and redshift distributions set up in three ways: (1) by excluding certain spectroscopic surveys during redshift calibration; (2) by excluding lower-confidence spectroscopic redshifts in redshift calibration; and (3) by considering only those photometric sources which are jointly calibrated by at least three spectroscopic surveys. In all cases, the method utilised here has been proven to be robust: we find a maximal deviation from our fiducial analysis of |ΔS8| ≤ 0.011 for all samples defined and analysed using our SOM. To demonstrate the reduction in systematic biases found within our analysis, we highlight our results when performing redshift calibration without the DEEP2 spectroscopic data set. In this case, we find marginal posterior constraints of S8 = 0.707−0.042+0.046; this is a difference, with respect to the fiducial, that is both significantly smaller and in the opposite direction with regard to the equivalent shift from previous works. These results suggest that our improved cosmological parameter estimates are not sensitive to pathological misrepresentations of photometric sources by the spectroscopy used for direct redshift calibration and, therefore, that this systematic effect cannot be responsible for the observed difference between S8 estimates made with KV450 and Planck CMB probes.

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“…Benjamin et al 2013;Stölzner et al 2020). There also exists a considerable literature in how photometric redshift uncertainty can be treated in the individual cosmological probes (McLeod et al 2017;Hoyle & Rau 2019) or how one can combine template fitting and cross correlation measurements (Alarcon et al 2020b;Sánchez & Bernstein 2019;Jones & Heavens 2019;Rau et al 2020). Shortly before this paper was submitted for publication Myles et al (2020); Gatti et al (2020); Cawthon et al (2020) presented the redshift inference scheme for the DES Y3 analyses, that combines a crosscorrelation and shear ratio data vector with redshift information derived using an empirical mapping of broad band 'Wide field' photometry to spatially smaller calibration fields with narrow-band photometric and spectroscopic redshift information.…”

Section: Introductionmentioning

confidence: 99%

A Composite Likelihood Approach for Inference under Photometric Redshift Uncertainty

Rau,

Morrison,

Schmidt

et al. 2021

Preprint

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Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We combine the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the Cos-moDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + 𝑧), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

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Estimating redshift distributions using hierarchical logistic Gaussian processes

Cited by 19 publications

References 59 publications

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

KiDS+VIKING-450: Improved cosmological parameter constraints from redshift calibration with self-organising maps

A Composite Likelihood Approach for Inference under Photometric Redshift Uncertainty

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