From 542835a56cae8a08a6a07167b397d1fd2b2906ac Mon Sep 17 00:00:00 2001 From: Siddharth Mishra-Sharma Date: Wed, 4 Sep 2019 13:20:15 -0400 Subject: [PATCH] Small changes to abstract wording --- draft/definitions.tex | 4 ++-- draft/lensing-lfi.tex | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/draft/definitions.tex b/draft/definitions.tex index 16436fc..1005d6c 100644 --- a/draft/definitions.tex +++ b/draft/definitions.tex @@ -4,8 +4,8 @@ \newcommand{\animicon}{{\color{xlinkcolor}\faPlayCircle}\xspace} \newcommand{\nbicon}{{\color{xlinkcolor}\faFileCodeO}\xspace} -\newcommand{\animlink}[1]{\href{https://github.com/smsharma/mining-for-substructure-lens/blob/master/figures/#1.gif}{\animicon}} -\newcommand{\nblink}[1]{\href{https://github.com/smsharma/mining-for-substructure-lens/blob/master/notebooks/#1.ipynb}{\nbicon}} +\newcommand{\animlink}[1]{\href{https://github.com/smsharma/mining-for-substructure-lens/blob/arXiv-v1/figures/#1.gif}{\animicon}} +\newcommand{\nblink}[1]{\href{https://github.com/smsharma/mining-for-substructure-lens/blob/arXiv-v1/notebooks/#1.ipynb}{\nbicon}} \newcommand{\githubmaster}{\href{https://github.com/smsharma/mining-for-substructure-lens}{\faGithub}\xspace} \newcommand{\acronym}[1]{{\small{#1}}\xspace} diff --git a/draft/lensing-lfi.tex b/draft/lensing-lfi.tex index 838ec6a..407b751 100644 --- a/draft/lensing-lfi.tex +++ b/draft/lensing-lfi.tex @@ -38,7 +38,7 @@ \affiliation{Center for Data Science, New York University, 60 Fifth Ave, New York, NY 10011, USA} \begin{abstract}\noindent -The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challenge since the likelihood function for realistic simulations of population-level parameters is intractable. We apply recently-developed simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. By leveraging additional information extracted from the simulator, neural networks are efficiently trained to estimate likelihood ratios associated with population-level parameters characterizing substructure. We show through proof-of-principle application to simulated data that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure. \href{https://github.com/smsharma/StrongLensing-Inference}{\faGithub} +The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challenge since the likelihood function for realistic simulations of population-level parameters is intractable. We apply recently-developed simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. By leveraging additional information extracted from the simulator, neural networks are efficiently trained to estimate likelihood ratios associated with population-level parameters characterizing substructure. Through proof-of-principle application to simulated data, we show that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure. \href{https://github.com/smsharma/StrongLensing-Inference}{\faGithub} \end{abstract} \keywords{