Removed DOI tag

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} \href{https://doi.org/}{\faTags}
+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}
 \end{abstract}
 
 \keywords{
@@ -486,7 +486,6 @@ \section{Conclusions}
 We are currently at the dawn of a new era in observational cosmology, when ongoing and upcoming surveys---\eg, DES, LSST, \Euclid, and WFIRST---are expected to discover and deliver images of thousands of strong lensing systems. These will harbor the subtle imprint of dark matter substructure, whose characterization could hold the key to unveiling the particle nature of dark matter. In this paper, we have introduced a powerful machine learning-based method that can be used to uncover the properties of small-scale structure within these lenses and in the Universe at large. The techniques presented have the potential to maximize the information that can be extracted from a complex lens sample and zero in on signatures of new physics.
 
 The code used to obtain the results in this paper is available at \url{https://github.com/smsharma/StrongLensing-Inference}\href{https://github.com/smsharma/StrongLensing-Inference}~\githubmaster.
-\newpage
 
 \acknowledgments
 
@@ -506,6 +505,7 @@ \section{Conclusions}
 }
 
 \appendix
+
 \section{Minimum of the loss functional}
 \label{app:variation}