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Title: Controlling and leveraging small-scale information in tomographic galaxy–galaxy lensing

Abstract

ABSTRACT The tangential shear signal receives contributions from physical scales in the galaxy–matter correlation function well below the transverse scale at which it is measured. Since small scales are difficult to model, this non-locality has generally required stringent scale cuts or new statistics for cosmological analyses. Using the fact that uncertainty in these contributions corresponds to an uncertainty in the enclosed projected mass around the lens, we provide an analytic marginalization scheme to account for this. Our approach enables the inclusion of measurements on smaller scales without requiring numerical sampling over extra free parameters. We extend the analytic marginalization formalism to retain cosmographic (shear-ratio) information from small-scale measurements that would otherwise be removed due to modelling uncertainties, again without requiring the addition of extra sampling parameters. We test the methodology using simulated likelihood analysis of a Dark Energy Survey Year 5-like galaxy–galaxy lensing and galaxy clustering datavector. We demonstrate that we can remove parameter biases due to the presence of an unmodelled one-halo contamination of the galaxy–galaxy lensing signal, and use the shear-ratio information on small scales to improve cosmological parameter constraints.

Authors:
ORCiD logo [1];  [2];  [3];  [4]
  1. Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA, Department of Physics, The Ohio State University, Columbus, OH 43210, USA
  2. Institute of Physics, Laboratory of Astrophysics, École Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, CH-1290 Versoix, Switzerland
  3. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
  4. Department of Astronomy/Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721-0065, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1580443
Grant/Contract Number:  
desc0007901
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: 491 Journal Issue: 4; Journal ID: ISSN 0035-8711
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

MacCrann, Niall, Blazek, Jonathan, Jain, Bhuvnesh, and Krause, Elisabeth. Controlling and leveraging small-scale information in tomographic galaxy–galaxy lensing. United Kingdom: N. p., 2019. Web. doi:10.1093/mnras/stz2761.
MacCrann, Niall, Blazek, Jonathan, Jain, Bhuvnesh, & Krause, Elisabeth. Controlling and leveraging small-scale information in tomographic galaxy–galaxy lensing. United Kingdom. doi:10.1093/mnras/stz2761.
MacCrann, Niall, Blazek, Jonathan, Jain, Bhuvnesh, and Krause, Elisabeth. Sat . "Controlling and leveraging small-scale information in tomographic galaxy–galaxy lensing". United Kingdom. doi:10.1093/mnras/stz2761.
@article{osti_1580443,
title = {Controlling and leveraging small-scale information in tomographic galaxy–galaxy lensing},
author = {MacCrann, Niall and Blazek, Jonathan and Jain, Bhuvnesh and Krause, Elisabeth},
abstractNote = {ABSTRACT The tangential shear signal receives contributions from physical scales in the galaxy–matter correlation function well below the transverse scale at which it is measured. Since small scales are difficult to model, this non-locality has generally required stringent scale cuts or new statistics for cosmological analyses. Using the fact that uncertainty in these contributions corresponds to an uncertainty in the enclosed projected mass around the lens, we provide an analytic marginalization scheme to account for this. Our approach enables the inclusion of measurements on smaller scales without requiring numerical sampling over extra free parameters. We extend the analytic marginalization formalism to retain cosmographic (shear-ratio) information from small-scale measurements that would otherwise be removed due to modelling uncertainties, again without requiring the addition of extra sampling parameters. We test the methodology using simulated likelihood analysis of a Dark Energy Survey Year 5-like galaxy–galaxy lensing and galaxy clustering datavector. We demonstrate that we can remove parameter biases due to the presence of an unmodelled one-halo contamination of the galaxy–galaxy lensing signal, and use the shear-ratio information on small scales to improve cosmological parameter constraints.},
doi = {10.1093/mnras/stz2761},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 4,
volume = 491,
place = {United Kingdom},
year = {2019},
month = {10}
}

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