Galaxy Correlation Function and Local Density from Photometric Redshifts Using the Stochastic Order Redshift Technique (SORT)

Abstract

The stochastic order redshift technique (SORT) is a simple, efficient, and robust method to improve cosmological redshift measurements. The method relies upon having a small ($\sim$10 per cent) reference sample of high-quality redshifts. Within pencil-beam-like sub-volumes surrounding each galaxy, we use the precise dN/d$z$ distribution of the reference sample to recover new redshifts and assign them one-to-one to galaxies such that the original rank order of redshifts is preserved. Preserving the rank order is motivated by the fact that random variables drawn from Gaussian probability density functions with different means but equal standard deviations satisfy stochastic ordering. The process is repeated for sub-volumes surrounding each galaxy in the survey. This results in every galaxy with an uncertain redshift being assigned multiple "recovered" redshifts from which a new redshift estimate can be determined. An earlier paper applied SORT to a mock Sloan Digital Sky Survey at $z\lesssim$ 0.2 and accurately recovered the two-point correlation function on scales $\gtrsim$4 $h^{-1}$Mpc. In this paper, we test the performance of SORT in surveys spanning the redshift range 0.75$<z<$2.25. We used two mock surveys extracted from the Small MultiDark-Planck and Bolshoi-Planck N-body simulations with dark matter haloes that were populated by the Santa Cruz semi-analytic model. We find that SORT is able to improve redshift estimates and recover distinctive large-scale features of the cosmic web. Further, it provides unbiased estimates of the redshift-space two-point correlation function $\xi (s)$ on scales $\gtrsim$2.5 $h^{-1}$Mpc, as well as local densities in regions of average or higher density. This may allow improved understanding of how galaxy properties relate to their local environments.

arXiv commentMNRAS submitted, 24 pages

References in corpus (56)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• Stellar population synthesis at the resolution of 2003
• A Universal Density Profile from Hierarchical Clustering
• The Structure of Cold Dark Matter Halos
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• Mass and environment as drivers of galaxy evolution in SDSS and zCOSMOS and the origin of the Schechter function
• Halo Models of Large Scale Structure
• The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets of Cluster Cores
• Physical Models of Galaxy Formation in a Cosmological Framework
• UniverseMachine: The Correlation between Galaxy Growth and Dark Matter Halo Assembly from z=0-10
• A Semi-Analytic Model for the Co-evolution of Galaxies, Black Holes, and Active Galactic Nuclei
• Semi-Analytic Modelling of Galaxy Formation: The Local Universe
• MultiDark simulations: the story of dark matter halo concentrations and density profiles
• Halos and galaxies in the standard cosmological model: results from the Bolshoi simulation
• The Halo Occupation Distribution: Towards an Empirical Determination of the Relation Between Galaxies and Mass
• The Connection between Galaxies and their Dark Matter Halos
• Galaxy Clustering in the Completed SDSS Redshift Survey: The Dependence on Color and Luminosity
• The persistent cosmic web and its filamentary structure I: Theory and implementation
• COSMOS2020: A panchromatic view of the Universe to $z\sim 10$ from two complementary catalogs
• How to Plant a Merger Tree
• The dependence of clustering on galaxy properties
• Galaxy And Mass Assembly (GAMA): the G02 field, Herschel-ATLAS target selection and Data Release 3
• Halo and Subhalo Demographics with Planck Cosmological Parameters: Bolshoi-Planck and MultiDark-Planck Simulations
• Galaxy Properties from the Ultra-violet to the Far-Infrared: Lambda-CDM models confront observations
• Dependence of Galaxy Quenching on Halo Mass and Distance from its Centre
• Star Formation in Semi-Analytic Galaxy Formation Models with Multiphase Gas
• Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU
• Semi-analytic forecasts for JWST - I. UV luminosity functions at z = 4 - 10
• KiDS-1000 catalogue: Redshift distributions and their calibration
• Semi-analytic forecasts for JWST -- II. physical properties and scaling relations for galaxies at z = 4-10
• Recovering Redshift Distributions with Cross-Correlations: Pushing The Boundaries
• SatGen: a semi-analytical satellite galaxy generator -- I. The model and its application to Local-Group satellite statistics
• Dark Energy Survey Year 3 Results: Clustering Redshifts -- Calibration of the Weak Lensing Source Redshift Distributions with redMaGiC and BOSS/eBOSS
• The-wiZZ: Clustering redshift estimation for everyone
• Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts
• Large Scale Structures in the CANDELS Fields: The Role of the Environment in Star Formation Activity
• Properties of Dark Matter Halos as a Function of Local Environment Density
• The Extreme Small Scales: Do Satellite Galaxies Trace Dark Matter?
• Mock Lightcones and Theory Friendly Catalogs for the CANDELS Survey
• PRIMUS+DEEP2: The Dependence of Galaxy Clustering on Stellar Mass and Specific Star Formation Rate at 0.2 < z < 1.2
• Clustering-based redshift estimation: application to VIPERS/CFHTLS
• Updated Results on the Galaxy-Halo Connection from Satellite Kinematics in SDSS
• Multitracer cosmological line intensity mapping mock lightcone simulation
• Sub-Megaparsec Individual Photometric Redshift Estimation from Cosmic Web Constraints
• SatGen -- II. Assessing the impact of a disc potential on subhalo populations
• Testing the accuracy of clustering redshift with simulations
• Strong Angular Clustering of Very Blue Galaxies: Evidence of a Low Redshift Population
• Main Sequence Scatter is Real: The Joint Dependence of Galaxy Clustering on Star Formation and Stellar Mass
• PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys
• Galaxy And Mass Assembly: Group and field galaxy morphologies in the star-formation rate - stellar mass plane
• BASILISK: Bayesian Hierarchical Inference of the Galaxy-Halo Connection using Satellite Kinematics--I. Method and Validation
• PRIMUS: Clustering of Star-forming and Quiescent Central Galaxies at $0.2<z<0.9$
• Stochastic Order Redshift Technique (SORT): a simple, efficient and robust method to improve cosmological redshift measurements

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• Semi-analytic forecasts for JWST -- VI. Simulated lightcones and galaxy clustering predictions
• Semi-analytic forecasts for Roman -- the beginning of a new era of deep-wide galaxy surveys