Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
Abstract
ABSTRACT Strong gravitational lensing has emerged as a promising approach for probing dark matter (DM) models on sub-galactic scales. Recent work has proposed the subhalo effective density slope as a more reliable observable than the commonly used subhalo mass function. The subhalo effective density slope is a measurement independent of assumptions about the underlying density profile and can be inferred for individual subhaloes through traditional sampling methods. To go beyond individual subhalo measurements, we leverage recent advances in machine learning and introduce a neural likelihood-ratio estimator to infer an effective density slope for populations of subhaloes. We demonstrate that our method is capable of harnessing the statistical power of multiple subhaloes (within and across multiple images) to distinguish between characteristics of different subhalo populations. The computational efficiency warranted by the neural likelihood-ratio estimator over traditional sampling enables statistical studies of DM perturbers and is particularly useful as we expect an influx of strong lensing systems from upcoming surveys.
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1896341
- Grant/Contract Number:
- SC0012567
- 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: 517 Journal Issue: 3; Journal ID: ISSN 0035-8711
- Publisher:
- Oxford University Press
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Zhang, Gemma, Mishra-Sharma, Siddharth, and Dvorkin, Cora. Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation. United Kingdom: N. p., 2022.
Web. doi:10.1093/mnras/stac3014.
Zhang, Gemma, Mishra-Sharma, Siddharth, & Dvorkin, Cora. Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation. United Kingdom. https://doi.org/10.1093/mnras/stac3014
Zhang, Gemma, Mishra-Sharma, Siddharth, and Dvorkin, Cora. Thu .
"Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation". United Kingdom. https://doi.org/10.1093/mnras/stac3014.
@article{osti_1896341,
title = {Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation},
author = {Zhang, Gemma and Mishra-Sharma, Siddharth and Dvorkin, Cora},
abstractNote = {ABSTRACT Strong gravitational lensing has emerged as a promising approach for probing dark matter (DM) models on sub-galactic scales. Recent work has proposed the subhalo effective density slope as a more reliable observable than the commonly used subhalo mass function. The subhalo effective density slope is a measurement independent of assumptions about the underlying density profile and can be inferred for individual subhaloes through traditional sampling methods. To go beyond individual subhalo measurements, we leverage recent advances in machine learning and introduce a neural likelihood-ratio estimator to infer an effective density slope for populations of subhaloes. We demonstrate that our method is capable of harnessing the statistical power of multiple subhaloes (within and across multiple images) to distinguish between characteristics of different subhalo populations. The computational efficiency warranted by the neural likelihood-ratio estimator over traditional sampling enables statistical studies of DM perturbers and is particularly useful as we expect an influx of strong lensing systems from upcoming surveys.},
doi = {10.1093/mnras/stac3014},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 3,
volume = 517,
place = {United Kingdom},
year = {Thu Oct 20 00:00:00 EDT 2022},
month = {Thu Oct 20 00:00:00 EDT 2022}
}
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