Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error

In cosmological analyses it is common to combine different types of measurement from the same survey. In this paper we use simulated DES Y3 and LSST Y1 data to explore differences in sensitivity to intrinsic alignments (IA) between cosmic shear and galaxy-galaxy lensing. We generate mock shear, gala...

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Main Authors: Simon Samuroff, Andresa Campos, Anna Porredon, Jonathan Blazek
Format: Article
Language:English
Published: Maynooth Academic Publishing 2024-05-01
Series:The Open Journal of Astrophysics
Online Access:https://doi.org/10.33232/001c.117964
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author Simon Samuroff
Andresa Campos
Anna Porredon
Jonathan Blazek
author_facet Simon Samuroff
Andresa Campos
Anna Porredon
Jonathan Blazek
author_sort Simon Samuroff
collection DOAJ
description In cosmological analyses it is common to combine different types of measurement from the same survey. In this paper we use simulated DES Y3 and LSST Y1 data to explore differences in sensitivity to intrinsic alignments (IA) between cosmic shear and galaxy-galaxy lensing. We generate mock shear, galaxy-galaxy lensing and galaxy clustering data, contaminated with a range of IA scenarios. Using a simple 2-parameter IA model (NLA) in a DES Y3 like analysis, we show that the galaxy-galaxy lensing + galaxy clustering combination ($2\times2$pt) is significantly more robust to IA mismodelling than cosmic shear. IA scenarios that produce up to $5\sigma$ biases for shear are seen to be unbiased at the level of $\sim1\sigma$ for $2\times2$pt. We demonstrate that this robustness can be largely attributed to the redshift separation in galaxy-galaxy lensing, which provides a cleaner separation of lensing and IA contributions. We identify secondary factors which may also contribute, including the possibility of cancellation of higher-order IA terms in $2\times2$pt and differences in sensitivity to physical scales. Unfortunately this does not typically correspond to equally effective self-calibration in a $3\times2$pt analysis of the same data, which can show significant biases driven by the cosmic shear part of the data vector. If we increase the precision of our mock analyses to a level roughly equivalent to LSST Y1, we find a similar pattern, with considerably more bias in a cosmic shear analysis than a $2\times2$pt one, and significant bias in a joint analysis of the two. Our findings suggest that IA model error can manifest itself as internal tension between $\xi_\pm$ and $\gamma_t + w$ data vectors. We thus propose that such tension (or the lack thereof) can be employed as a test of model sufficiency or insufficiency when choosing a fiducial IA model, alongside other data-driven methods.
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spelling doaj-art-13cc7db7435c41fb8d7054ad350d7ee92025-08-20T01:47:36ZengMaynooth Academic PublishingThe Open Journal of Astrophysics2565-61202024-05-01710.33232/001c.117964Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling errorSimon SamuroffAndresa CamposAnna PorredonJonathan BlazekIn cosmological analyses it is common to combine different types of measurement from the same survey. In this paper we use simulated DES Y3 and LSST Y1 data to explore differences in sensitivity to intrinsic alignments (IA) between cosmic shear and galaxy-galaxy lensing. We generate mock shear, galaxy-galaxy lensing and galaxy clustering data, contaminated with a range of IA scenarios. Using a simple 2-parameter IA model (NLA) in a DES Y3 like analysis, we show that the galaxy-galaxy lensing + galaxy clustering combination ($2\times2$pt) is significantly more robust to IA mismodelling than cosmic shear. IA scenarios that produce up to $5\sigma$ biases for shear are seen to be unbiased at the level of $\sim1\sigma$ for $2\times2$pt. We demonstrate that this robustness can be largely attributed to the redshift separation in galaxy-galaxy lensing, which provides a cleaner separation of lensing and IA contributions. We identify secondary factors which may also contribute, including the possibility of cancellation of higher-order IA terms in $2\times2$pt and differences in sensitivity to physical scales. Unfortunately this does not typically correspond to equally effective self-calibration in a $3\times2$pt analysis of the same data, which can show significant biases driven by the cosmic shear part of the data vector. If we increase the precision of our mock analyses to a level roughly equivalent to LSST Y1, we find a similar pattern, with considerably more bias in a cosmic shear analysis than a $2\times2$pt one, and significant bias in a joint analysis of the two. Our findings suggest that IA model error can manifest itself as internal tension between $\xi_\pm$ and $\gamma_t + w$ data vectors. We thus propose that such tension (or the lack thereof) can be employed as a test of model sufficiency or insufficiency when choosing a fiducial IA model, alongside other data-driven methods.https://doi.org/10.33232/001c.117964
spellingShingle Simon Samuroff
Andresa Campos
Anna Porredon
Jonathan Blazek
Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error
The Open Journal of Astrophysics
title Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error
title_full Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error
title_fullStr Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error
title_full_unstemmed Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error
title_short Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error
title_sort joint constraints from cosmic shear galaxy galaxy lensing and galaxy clustering internal tension as an indicator of intrinsic alignment modelling error
url https://doi.org/10.33232/001c.117964
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