Cosmological cross-correlations and nearest neighbour distributions
Abstract
Cross-correlations between data sets are used in many different contexts in cosmological analyses. Recently, k-nearest neighbour cumulative distribution functions (kNN-CDF) were shown to be sensitive probes of cosmological (auto) clustering. In this paper, we extend the framework of NN measurements to describe joint distributions of, and correlations between, two data sets. We describe the measurement of joint kNN-CDFs, and show that these measurements are sensitive to all possible connected N-point functions that can be defined in terms of the two data sets. We describe how the cross-correlations can be isolated by combining measurements of the joint kNN-CDFs and those measured from individual data sets. We demonstrate the application of these measurements in the context of Gaussian density fields, as well as for fully non-linear cosmological data sets. Using a Fisher analysis, we show that measurements of the halo-matter cross-correlations, as measured through NN measurements are more sensitive to the underlying cosmological parameters, compared to traditional two-point cross-correlation measurements over the same range of scales. Finally, we demonstrate how the NN cross-correlations can robustly detect cross-correlations between sparse samples – the same regime where the two-point cross-correlation measurements are dominated by noise.
- Authors:
-
- Fermi National Accelerator Laboratory, Cosmic Physics Center, Batavia, IL 60510, USA, Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 452 Lomita Mall, Stanford, CA 94305, USA, Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
- Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 452 Lomita Mall, Stanford, CA 94305, USA, Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
- Publication Date:
- Research Org.:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1781444
- Alternate Identifier(s):
- OSTI ID: 1779478; OSTI ID: 1818470
- Report Number(s):
- FERMILAB-PUB-21-029-T; arXiv:2102.01184
Journal ID: ISSN 0035-8711
- Grant/Contract Number:
- AC02-76SF00515; AC02-07CH11359
- Resource Type:
- Published Article
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 504 Journal Issue: 2; Journal ID: ISSN 0035-8711
- Publisher:
- Oxford University Press
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; cosmology; large-scale structure of the Universe
Citation Formats
Banerjee, Arka, and Abel, Tom. Cosmological cross-correlations and nearest neighbour distributions. United Kingdom: N. p., 2021.
Web. doi:10.1093/mnras/stab961.
Banerjee, Arka, & Abel, Tom. Cosmological cross-correlations and nearest neighbour distributions. United Kingdom. https://doi.org/10.1093/mnras/stab961
Banerjee, Arka, and Abel, Tom. Thu .
"Cosmological cross-correlations and nearest neighbour distributions". United Kingdom. https://doi.org/10.1093/mnras/stab961.
@article{osti_1781444,
title = {Cosmological cross-correlations and nearest neighbour distributions},
author = {Banerjee, Arka and Abel, Tom},
abstractNote = {Cross-correlations between data sets are used in many different contexts in cosmological analyses. Recently, k-nearest neighbour cumulative distribution functions (kNN-CDF) were shown to be sensitive probes of cosmological (auto) clustering. In this paper, we extend the framework of NN measurements to describe joint distributions of, and correlations between, two data sets. We describe the measurement of joint kNN-CDFs, and show that these measurements are sensitive to all possible connected N-point functions that can be defined in terms of the two data sets. We describe how the cross-correlations can be isolated by combining measurements of the joint kNN-CDFs and those measured from individual data sets. We demonstrate the application of these measurements in the context of Gaussian density fields, as well as for fully non-linear cosmological data sets. Using a Fisher analysis, we show that measurements of the halo-matter cross-correlations, as measured through NN measurements are more sensitive to the underlying cosmological parameters, compared to traditional two-point cross-correlation measurements over the same range of scales. Finally, we demonstrate how the NN cross-correlations can robustly detect cross-correlations between sparse samples – the same regime where the two-point cross-correlation measurements are dominated by noise.},
doi = {10.1093/mnras/stab961},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 504,
place = {United Kingdom},
year = {Thu Apr 22 00:00:00 EDT 2021},
month = {Thu Apr 22 00:00:00 EDT 2021}
}
https://doi.org/10.1093/mnras/stab961
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