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Register a new userImpact of photometric redshifts on the galaxy power spectrum and BAO scale in the LSST survey
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Imaging billions of galaxies every few nights during ten years, LSST should be a major contributor to precision cosmology in the 2020 decade. High precision photometric data will be available in six bands, from near-infrared to near-ultraviolet. The computation of precise, unbiased, photometric redshifts up to z = 2, at least, is one of the main LSST challenges and its performance will have major impact on all extragalactic LSST sciences. We evaluate the efficiency of our photometric redshift reconstruction on mock galaxy catalogs up to z=2.45 and estimate the impact of realistic photometric redshift (hereafter photo-z) reconstruction on the large-scale structures (LSS) power spectrum and the baryonic acoustic oscillation (BAO) scale determination for a LSST-like photometric survey. We study the effectiveness of the BAO scale as a cosmological probe in the LSST survey. We have performed a detailed modelling of the photo-z distribution as a function of galaxy type, redshift and absolute magnitude using our photo-z reconstruction code with a quality selection cut based on a Boosted decision tree (BDT). We have computed the fractional error on the recovered power spectrum which is dominated by the shot-noise at z>1 for scales k>0.1, due to the photo-z damping. The BAO scale can be recovered with a percent or better accuracy level from z = 0.5 to z = 1.5 using realistic photo-z reconstruction. Outliers can represent a significant fraction of galaxies at z>2, causing bias and errors on LSS power spectrum measurement. Although the BAO scale is not the most powerful cosmological probe in LSST, it can be used to check the consistency of the LSS measurement. Moreover we show that the impact of photo-z smearing on the recovered isotropic BAO scale in LSST should stay limited up to z=1.5, so as long as the galaxy number density balances the photo-z smoothing.
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In this paper we present and characterize a nearest-neighbors color-matching photometric redshift estimator that features a direct relationship between the precision and accuracy of the input magnitudes and the output photometric redshifts. This aspect makes our estimator an ideal tool for evaluating the impact of changes to LSST survey parameters that affect the measurement errors of the photometry, which is the main motivation of our work (i.e., it is not intended to provide the best photometric redshifts for LSST data). We show how the photometric redshifts will improve with time over the 10-year LSST survey and confirm that the nominal distribution of visits per filter provides the most accurate photo-$z$ results. The LSST survey strategy naturally produces observations over a range of airmass, which offers the opportunity of using an SED- and $z$-dependent atmospheric affect on the observed photometry as a color-independent redshift indicator. We show that measuring this airmass effect and including it as a prior has the potential to improve the photometric redshifts and can ameliorate extreme outliers, but that it will only be adequately measured for the brightest galaxies, which limits its overall impact on LSST photometric redshifts. We furthermore demonstrate how this airmass effect can induce a bias in the photo-$z$ results, and caution against survey strategies that prioritize high-airmass observations for the purpose of improving this prior. Ultimately, we intend for this work to serve as a guide for the expectations and preparations of the LSST science community with regards to the minimum quality of photo-$z$ as the survey progresses.
Accurate photometric redshift (photo-$z$) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this work we use simulated photometry for mock galaxy catalogs to explore how LSST photo-$z$ estimates can be improved by the addition of near-infrared (NIR) and/or ultraviolet (UV) photometry from the Euclid, WFIRST, and/or CASTOR space telescopes. Generally, we find that deeper optical photometry can reduce the standard deviation of the photo-$z$ estimates more than adding NIR or UV filters, but that additional filters are the only way to significantly lower the fraction of galaxies with catastrophically under- or over-estimated photo-$z$. For Euclid, we find that the addition of ${JH}$ $5{sigma}$ photometric detections can reduce the standard deviation for galaxies with $z>1$ ($z>0.3$) by ${sim}20%$ (${sim}10%$), and the fraction of outliers by ${sim}40%$ (${sim}25%$). For WFIRST, we show how the addition of deep ${YJHK}$ photometry could reduce the standard deviation by ${gtrsim}50%$ at $z>1.5$ and drastically reduce the fraction of outliers to just ${sim}2%$ overall. For CASTOR, we find that the addition of its ${UV}$ and $u$-band photometry could reduce the standard deviation by ${sim}30%$ and the fraction of outliers by ${sim}50%$ for galaxies with $z<0.5$. We also evaluate the photo-$z$ results within sky areas that overlap with both the NIR and UV surveys, and when spectroscopic training sets built from the surveys small-area deep fields are used.
We analyse the clustering of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 16 luminous red galaxy sample (DR16 eBOSS LRG) in combination with the high redshift tail of the Sloan Digital Sky Survey III Baryon Oscillation Spectroscopic Survey Data Release 12 (DR12 BOSS CMASS). We measure the redshift space distortions (RSD) and also extract the longitudinal and transverse baryonic acoustic oscillation (BAO) scale from the anisotropic power spectrum signal inferred from 377,458 galaxies between redshifts 0.6 and 1.0, with effective redshift of $z_{rm eff}=0.698$ and effective comoving volume of $2.72,{rm Gpc}^3$. After applying reconstruction we measure the BAO scale and infer $D_H(z_{rm eff})/r_{rm drag} = 19.30pm 0.56$ and $D_M(z_{rm eff})/r_{rm drag} =17.86 pm 0.37$. When we perform a redshift space distortions analysis on the pre-reconstructed catalogue on the monopole, quadrupole and hexadecapole we find, $D_H(z_{rm eff})/r_{rm drag} = 20.18pm 0.78$, $D_M(z_{rm eff})/r_{rm drag} =17.49 pm 0.52$ and $fsigma_8(z_{rm eff})=0.454pm0.046$. We combine both sets of results along with the measurements in configuration space of cite{LRG_corr} and report the following consensus values: $D_H(z_{rm eff})/r_{rm drag} = 19.77pm 0.47$, $D_M(z_{rm eff})/r_{rm drag} = 17.65pm 0.30$ and $fsigma_8(z_{rm eff})=0.473pm 0.044$, which are in full agreement with the standard $Lambda$CDM and GR predictions. These results represent the most precise measurements within the redshift range $0.6leq z leq 1.0$ and are the culmination of more than 8 years of SDSS observations.
Photometric galaxy surveys probe the late-time Universe where the density field is highly non-Gaussian. A consequence is the emergence of the super-sample covariance (SSC), a non-Gaussian covariance term that is sensitive to fluctuations on scales larger than the survey window. In this work, we study the impact of the survey geometry on the SSC and, subsequently, on cosmological parameter inference. We devise a fast SSC approximation that accounts for the survey geometry and compare its performance to the common approximation of rescaling the results by the fraction of the sky covered by the survey, $f_mathrm{SKY}$, dubbed full-sky approximation. To gauge the impact of our new SSC recipe, dubbed partial-sky, we perform Fisher forecasts on the parameters of the $(w_0,w_a)$-CDM model in a 3x2 points analysis, varying the survey area, the geometry of the mask and the galaxy distribution inside our redshift bins. The differences in the marginalised forecast errors, with the full-sky approximation performing poorly for small survey areas but excellently for stage-IV-like areas, are found to be absorbed by the marginalisation on galaxy bias nuisance parameters. For large survey areas, the unmarginalised errors are underestimated by about 10% for all probes considered. This is a hint that, even for stage-IV-like surveys, the partial-sky method introduced in this work will be necessary if tight priors are applied on these nuisance parameters.
In order to enlarge publicly available optical cluster catalogs, in particular at high redshift, we have performed a systematic search for clusters of galaxies in the CFHTLS. We used the Le Phare photometric redshifts for the galaxies detected with magnitude limits of i=25 and 23 for the Deep and Wide fields respectively. We then constructed galaxy density maps in photometric redshift bins of 0.1 based on an adaptive kernel technique and detected structures with SExtractor. In order to assess the validity of our cluster detection rates, we applied a similar procedure to galaxies in Millennium simulations. We measured the correlation function of our cluster candidates. We analyzed large scale properties and substructures by applying a minimal spanning tree algorithm both to our data and to the Millennium simulations. We have detected 1200 candidate clusters with various masses (minimal masses between 1.0 10$^{13}$ and 5.5 10$^{13}$ and mean masses between 1.3 10$^{14}$ and 12.6 10$^{14}$ M$_odot$), thus notably increasing the number of known high redshift cluster candidates. We found a correlation function for these objects comparable to that obtained for high redshift cluster surveys. We also show that the CFHTLS deep survey is able to trace the large scale structure of the universe up to z$geq$1. Our detections are fully consistent with those made in various CFHTLS analyses with other methods. We now need accurate mass determinations of these structures to constrain cosmological parameters.
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