arXiv subjects

Mehdi Rezaie

Publications and source records attributed to Mehdi Rezaie.

40 records · Page 3Linked to original sources

arXiv2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale Structure Catalogs for Cosmological Analysis

We present large-scale structure catalogs from the completed extended Baryon Oscillation Spectroscopic Survey (eBOSS). Derived from Sloan Digital Sky Survey (SDSS) -IV Data Release 16 (DR16), these catalogs provide the data samples, corrected for observational systematics, and random positions sampling the survey selection function. Combined, they allow large-scale clustering measurements suitable for testing cosmological models. We describe the methods used to create these catalogs for the eBOSS DR16 Luminous Red Galaxy (LRG) and Quasar samples. The quasar catalog contains 343,708 redshifts with $0.8 < z < 2.2$ over 4,808\,deg$^2$. We combine 174,816 eBOSS LRG redshifts over 4,242\,deg$^2$ in the redshift interval $0.6 < z < 1.0$ with SDSS-III BOSS LRGs in the same redshift range to produce a combined sample of 377,458 galaxy redshifts distributed over 9,493\,deg$^2$. Improved algorithms for estimating redshifts allow that 98 per cent of LRG observations result in a successful redshift, with less than one per cent catastrophic failures ($Δz > 1000$ ${\rm km~s}^{-1}$). For quasars, these rates are 95 and 2 per cent (with $Δz > 3000$ ${\rm km~s}^{-1}$). We apply corrections for trends between the number densities of our samples and the properties of the imaging and spectroscopic data. For example, the quasar catalog obtains a $χ^2$/DoF$= 776/10$ for a null test against imaging depth before corrections and a $χ^2$/DoF$=6/8$ after. The catalogs, combined with careful consideration of the details of their construction found here-in, allow companion papers to present cosmological results with negligible impact from observational systematic uncertainties.

astro-ph.CO↗

arXiv2020

Baryon Acoustic Oscillations in the projected cross-correlation function between the eBOSS DR16 quasars and photometric galaxies from the DESI Legacy Imaging Surveys

We search for the Baryon Acoustic Oscillations in the projected cross-correlation function binned into transverse comoving radius between the SDSS-IV DR16 eBOSS quasars and a dense photometric sample of galaxies selected from the DESI Legacy Imaging Surveys. We estimate the density of the photometric sample of galaxies in this redshift range to be about 2900 deg$^{-2}$, which is deeper than the official DESI ELG selection, and the density of the spectroscopic sample is about 20 deg$^{-2}$. In order to mitigate the systematics related to the use of different imaging surveys close to the detection limit, we use a neural network approach that accounts for complex dependencies between the imaging attributes and the observed galaxy density. We find that we are limited by the depth of the imaging surveys which affects the density and purity of the photometric sample and its overlap in redshift with the quasar sample, which thus affects the performance of the method. When cross-correlating the photometric galaxies with quasars in $0.6 \leq z \leq 1.2$, the cross-correlation function can provide better constraints on the comoving angular distance, $D_{\rm M}$ (6\% precision) compared to the constraint on the spherically-averaged distance $D_{\rm V}$ (9\% precision) obtained from the auto-correlation. Although not yet competitive, this technique will benefit from the arrival of deeper photometric data from upcoming surveys which will enable it to go beyond the current limitations we have identified in this work.

astro-ph.CO↗

arXiv2020

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale Structure Catalogues and Measurement of the isotropic BAO between redshift 0.6 and 1.1 for the Emission Line Galaxy Sample

We present the Emission Line Galaxy (ELG) sample of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) from the Sloan Digital Sky Survey IV Data Release 16 (DR16). After describing the observations and redshift measurement for the 269,243 observed ELG spectra over 1170 deg$^2$, we present the large-scale structure catalogues, which are used for the cosmological analysis. These catalogues contain 173,736 reliable spectroscopic redshifts between 0.6 and 1.1, along with the associated random catalogues quantifying the extent of observations, and the appropriate weights to correct for non-cosmological fluctuations. We perform a spherically averaged baryon acoustic oscillations (BAO) measurement in configuration space, with density field reconstruction: the data 2-point correlation function shows a feature consistent with that of the BAO, providing a 3.2-percent measurement of the spherically averaged BAO distance $D_V(z_{\rm eff})/r_{\rm drag} = 18.23\pm 0.58$ at the effective redshift $z_{\rm eff}=0.845$.

astro-ph.CO↗

arXiv2020

Improving Galaxy Clustering Measurements with Deep Learning: analysis of the DECaLS DR7 data

Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper we present, validate, and implement the idea of adopting the systematics mitigation method of Artificial Neural Networks for modeling the relationship between the target galaxy density field and various observational realities including but not limited to Galactic extinction, seeing, and stellar density. Our method by construction allows a wide class of models and alleviates over-training by performing k-fold cross-validation and dimensionality reduction via backward feature elimination. By permuting the choice of the training, validation, and test sets, we construct a selection mask for the entire footprint. We apply our method on the extended Baryon Oscillation Spectroscopic Survey (eBOSS) Emission Line Galaxies (ELGs) selection from the Dark Energy Camera Legacy Survey (DECaLS) Data Release 7 and show that the spurious large-scale contamination due to imaging systematics can be significantly reduced by up-weighting the observed galaxy density using the selection mask from the neural network and that our method is more effective than the conventional linear and quadratic polynomial functions. We perform extensive analyses on simulated mock datasets with and without systematic effects. Our analyses indicate that our methodology is more robust to overfitting compared to the conventional methods. This method can be utilized in the catalog generation of future spectroscopic galaxy surveys such as eBOSS and Dark Energy Spectroscopic Instrument (DESI) to better mitigate observational systematics.

astro-ph.CO↗