---
author:
- 'Andrew Blance,'
- 'Michael Spannowsky,'
- and Philip Waite
bibliography:
- 'references.bib'
title: 'Adversarially-trained autoencoders for robust unsupervised new physics searches'
---

Introduction {#sec:intro}
============

In recent years machine learning algorithms, and in particular neural networks, have become increasingly popular in analysing large quantities of data. In the context of particle physics two main applications are prevalent: the classification of data according to different hypotheses [@Nachman:2014kla; @Komiske:2016rsd; @Barnard:2016qma; @Dery:2017fap; @Butter:2017cot; @Cohen:2017exh; @Chang:2017kvc; @Pearkes:2017hku; @Louppe:2017ipp; @Kasieczka:2017nvn; @deOliveira:2017pjk; @Luo:2017ncs; @Datta:2017lxt; @Larkoski:2017jix; @Shimmin:2017mfk; @Metodiev:2017vrx; @Roxlo:2018adx; @Brehmer:2018kdj; @Brehmer:2018eca; @Collins:2018epr; @Duarte:2018ite; @Fraser:2018ieu; @Komiske:2018oaa; @Macaluso:2018tck; @Andreassen:2018apy; @deCastro:2018mgh; @DAgnolo:2018cun; @Brehmer:2018hga; @Monk:2018zsb; @Moore:2018lsr; @DeSimone:2018efk; @Bollweg:2019skg; @Cerri:2018anq] and the regression of data to interpolate and extrapolate object-relevant properties [@ATL-PHYS-PUB-2018-013; @Aaboud:2017all; @Khachatryan:2014gga; @Khachatryan:2015hwa; @Khachatryan:2015iwa].

Using multi-variate analysis (MVA) techniques to classify events into signal and background classes based on their radiation profiles should improve the LHC’s experiments’ sensitivity in searches for new physics. Machine learning algorithms are able to analyse multiple observables or inputs simultaneously to find a region in this multi-dimensional parameter space that shows a relative enhancement of signal over background events. To find this region in a supervised-learning approach, pseudo-data for signal and background need to be generated using event generators, e.g. <span style="font-variant:small-caps;">Sherpa</span> 