The effect of photometric redshift uncertainties on galaxy clustering and baryonic acoustic oscillations

Chaves-Montero, Jonás; Angulo, Raúl E.; Hernández-Monteagudo, C.

doi:10.1093/mnras/sty924

Cited by 36 publications

(25 citation statements)

References 47 publications

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“…Photo-z uncertainties make measuring ACFs in tomographic bins more challenging as the uncertainties introduce spurious cross-correlations across the redshift bins (e.g., see Bailoni et al 2017 for a study on the impacts of bin cross-correlations on cosmological parameters) and smear out valuable cosmological information, including the BAO (e.g., as in Chaves-Montero et al 2018). Since the traditional ACF estimators do not account for contamination arising from photo-z uncertainties, the standard tomographic clustering analysis entails estimating N (z), i.e., the number of galaxies as a function of redshift, in each nominal redshift bin and forward modeling the contaminated ACFs using the N (z) estimates (e.g., as in Crocce et al 2016;Balaguera-Antolínez et al 2018;Abbott et al 2018); also see e.g., Newman (2008) for a discussion on estimating N (z).…”

Section: Introductionmentioning

confidence: 99%

Angular Correlation Function Estimators Accounting for Contamination from Probabilistic Distance Measurements

Awan

Gawiser

2020

ApJ

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With the advent of surveys containing millions to billions of galaxies, it is imperative to develop analysis techniques that utilize the available statistical power. In galaxy clustering, even small sample contamination arising from distance uncertainties can lead to large artifacts, which the standard estimator does not account for. We first introduce a formalism, termed decontamination, that corrects for sample contamination by utilizing the observed cross-correlations in the contaminated samples; this corrects any correlation function estimator for contamination. Using this formalism, we present a new estimator that uses the standard estimator to measure correlation functions in the contaminated samples but then corrects for contamination. We also introduce a weighted estimator that assigns each galaxy a weight in each redshift bin based on its probability of being in that bin. We demonstrate that these estimators effectively recover the true correlation functions and their covariance matrices. Our estimators can correct for sample contamination caused by misclassification between object types as well as photometric redshifts; they should be particularly helpful for studies of galaxy evolution and baryonic acoustic oscillations, where forward-modeling the clustering signal using the contaminated redshift distribution is undesirable.

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Section: Introductionmentioning

confidence: 99%

Angular Correlation Function Estimators Accounting for Contamination from Probabilistic Distance Measurements

Awan

Gawiser

2020

ApJ

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“…Photometric imaging surveys, on the other hand, provide a less accurate estimation of the redshifts of galaxies (photometric redshifts or photo-zs) from the color information obtained through multi-band photometry using a small number of filters (e.g., Salvato, Ilbert & Hoyle 2019). This method allows one to estimate the redshifts of a significantly larger number of galaxies (at least an order of magnitude more), at the expense of losing most of the radial information (Benítez, et al 2009;Asorey, Crocce, Gaztañaga & Lewis 2012;Chaves-Montero, Angulo & Hernández-Monteagudo 2018). For that reason, in photometric surveys, instead of the three dimensional galaxy distribution, one usually considers its projection in a number of redshift bins and measures the angular 2-point correlation function or the angular power spectrum (e.g., Crocce, Cabré & Gaztañaga 2011;Asorey, Carrasco Kind, Sevilla-Noarbe, Brunner & Thaler 2016;Budavári, et al 2003;Elvin-Poole, et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Optimizing galaxy samples for clustering measurements in photometric surveys

Tanoglidis

Chang

Frieman

2019

Monthly Notices of the Royal Astronomical Society

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When analyzing galaxy clustering in multi-band imaging surveys, there is a trade-off between selecting the largest galaxy samples (to minimize the shot noise) and selecting samples with the best photometric redshift (photo-z) precision, which generally include only a small subset of galaxies. In this paper, we systematically explore this trade-off. Our analysis is targeted towards the third year data of the Dark Energy Survey (DES), but our methods hold generally for other data sets. Using a simple Gaussian model for the redshift uncertainties, we carry out a Fisher matrix forecast for cosmological constraints from angular clustering in the redshift range z = 0.2−0.95. We quantify the cosmological constraints using a Figure of Merit (FoM) that measures the combined constraints on Ω m and σ 8 in the context of ΛCDM cosmology. We find that the trade-off between sample size and photo-z precision is sensitive to 1) whether cross-correlations between redshift bins are included or not, and 2) the ratio of the redshift bin width δ z and the photo-z precision σ z . When cross-correlations are included and the redshift bin width is allowed to vary, the highest FoM is achieved when δ z ∼ σ z . We find that for the typical case of 5 − 10 redshift bins, optimal results are reached when we use larger, less precise photo-z samples, provided that we include cross-correlations. For samples with higher σ z , the overlap between redshift bins is larger, leading to higher cross-correlation amplitudes. This leads to the self-calibration of the photo-z parameters and therefore tighter cosmological constraints. These results can be used to help guide galaxy sample selection for clustering analysis in ongoing and future photometric surveys.

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“…Determining the angular and physical separations of all galaxy pairs and weighting the information should allow more precise BAO measurements. Estrada et al (2009) present an analysis to do so in the context of photometric galaxy clusters and Chaves- Montero et al (2016) analyze the results one expects for surveys that obtain redshifts from narrow-band filters. Etherington & Thomas (2015); Etherington et al (2017) have conducted similar studies on the effect of DES redshift uncertainties on measures of galaxy environment.…”

Section: Introductionmentioning

confidence: 99%

Optimized clustering estimators for BAO measurements accounting for significant redshift uncertainty

Ross

Banik

Ávila

et al. 2017

Monthly Notices of the Royal Astronomical Society

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We determine an optimized clustering statistic to be used for galaxy samples with significant redshift uncertainty, such as those that rely on photometric redshifts. To do so, we study the baryon acoustic oscillation (BAO) information content as a function of the orientation of galaxy clustering modes with respect to their angle to the line-of-sight (LOS). The clustering along the LOS, as observed in a redshift-space with significant redshift uncertainty, has contributions from clustering modes with a range of orientations with respect to the true LOS. For redshift uncertainty σ z 0.02(1 + z) we find that while the BAO information is confined to transverse clustering modes in the true space, it is spread nearly evenly in the observed space. Thus, measuring clustering in terms of the projected separation (regardless of the LOS) is an efficient and nearly lossless compression of the signal for σ z 0.02(1 + z). For reduced redshift uncertainty, a more careful consideration is required. We then use more than 1700 realizations (combining two separate sets) of galaxy simulations mimicking the Dark Energy Survey Year 1 sample to validate our analytic results and optimized analysis procedure. We find that using the correlation function binned in projected separation, we can achieve uncertainties that are within 10 per cent of those predicted by Fisher matrix forecasts. We predict that DES Y1 should achieve a 5 per cent distance measurement using our optimized methods. We expect the results presented here to be important for any future BAO measurements made using photometric redshift data.

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The effect of photometric redshift uncertainties on galaxy clustering and baryonic acoustic oscillations

Cited by 36 publications

References 47 publications

Angular Correlation Function Estimators Accounting for Contamination from Probabilistic Distance Measurements

Angular Correlation Function Estimators Accounting for Contamination from Probabilistic Distance Measurements

Optimizing galaxy samples for clustering measurements in photometric surveys

Optimized clustering estimators for BAO measurements accounting for significant redshift uncertainty

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