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One never walks alone the effect of the perturber population on subhalo measurements in strong gravitational lenses

A Coogan, NA Montel, K Karchev, MW Grootes… - arXiv preprint arXiv …, 2022 - arxiv.org
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… Here we show that a simulation-based inference method called truncated marginal neural ratio estimation (TMNRE) makes it possible to relax these requirements by training neural …

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BibTeX

@article{2209.09918v2,
Author        = {Adam Coogan and Noemi Anau Montel and Konstantin Karchev and Meiert W. Grootes and Francesco Nattino and Christoph Weniger},
Title         = {One never walks alone: the effect of the perturber population on subhalo
  measurements in strong gravitational lenses},
Eprint        = {2209.09918v2},
DOI           = {10.1093/mnras/stad2925},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Analyses of extended arcs in strong gravitational lensing images to date have
constrained the properties of dark matter by measuring the parameters of one or
two individual subhalos. However, since such analyses are reliant on
likelihood-based methods like Markov-chain Monte Carlo or nested sampling, they
require various compromises to the realism of lensing models for the sake of
computational tractability, such as ignoring the numerous other subhalos and
line-of-sight halos in the system, assuming a particular form for the source
model and requiring the noise to have a known likelihood function. Here we show
that a simulation-based inference method calledTruncated Marginal Neural Ratio
Estimation (TMNRE) makes it possible to relax these requirements by training
neural networks to directly compute marginal posteriors for subhalo parameters
from lensing images. By performing a set of inference tasks on mock data, we
verify the accuracy of TMNRE and show it can compute posteriors for subhalo
parameters marginalized over populations of hundreds of substructures, as well
as lens and source uncertainties. We also find the \gls*{mlp} Mixer network
works far better for such tasks than the convolutional architectures explored
in other lensing analyses. Furthermore, we show that since \gls*{tmnre} learns
a posterior function it enables direct statistical checks that would be
extremely expensive with likelihood-based methods. Our results show that TMNRE
is well-suited for analyzing complex lensing data, and that the full subhalo
and line-of-sight halo population must be included when measuring the
properties of individual dark matter substructures with this technique.},
Year          = {2022},
Month         = {Sep},
Note          = {Monthly Notices of the Royal Astronomical Society, Volume 527,
  Issue 1, January 2024, Pages 66-78},
Url           = {http://arxiv.org/abs/2209.09918v2},
File          = {2209.09918v2.pdf}
}

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