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Author Khosa, C.K.; Sanz, V.; Soughton, M. url  doi
openurl 
  Title Using machine learning to disentangle LHC signatures of Dark Matter candidates Type Journal Article
  Year 2021 Publication (down) Scipost Physics Abbreviated Journal SciPost Phys.  
  Volume 10 Issue 6 Pages 151 - 26pp  
  Keywords  
  Abstract We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background (Z+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representations of the data, from a simple event data sample with values of kinematic variables fed into a Logistic Regression algorithm or a Fully Connected Neural Network, to a transformation of the data into images related to probability distributions, fed to Deep and Convolutional Neural Networks. We also study the robustness of our method against including detector effects, dropping kinematic variables, or changing the number of events per image. In the case of signals with more combinatorial possibilities (events with more than one hard jet), the most crucial data features are selected by performing a Principal Component Analysis. We compare the performance of all these methods, and find that using the 2D images of the combined information of multiple events significantly improves the discrimination performance.  
  Address [Khosa, Charanjit Kaur; Sanz, Veronica; Soughton, Michael] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: Charanjit.Kaur@sussex.ac.uk;  
  Corporate Author Thesis  
  Publisher Scipost Foundation Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2542-4653 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000680038800002 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 4927  
Permanent link to this record
 
 
Author Cranmer, K. et al; Sanz, V. url  doi
openurl 
  Title Publishing statistical models: Getting the most out of particle physics experiments Type Journal Article
  Year 2022 Publication (down) Scipost Physics Abbreviated Journal SciPost Phys.  
  Volume 12 Issue 1 Pages 037 - 55pp  
  Keywords  
  Abstract The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases – including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits – we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.  
  Address [Cranmer, Kyle; Held, Alexander] NYU, New York, NY 10003 USA, Email: kyle.cranmer@nyu.edu;  
  Corporate Author Thesis  
  Publisher Scipost Foundation Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2542-4653 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000807448000032 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5255  
Permanent link to this record
 
 
Author Khosa, C.K.; Sanz, V. url  doi
openurl 
  Title Anomaly Awareness Type Journal Article
  Year 2023 Publication (down) Scipost Physics Abbreviated Journal SciPost Phys.  
  Volume 15 Issue 2 Pages 053 - 24pp  
  Keywords  
  Abstract We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.  
  Address [Khosa, Charanjit K.] Univ Manchester, Dept Phys & Astron, Manchester M13 9PL, England  
  Corporate Author Thesis  
  Publisher Scipost Foundation Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2542-4653 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001048488200002 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5610  
Permanent link to this record
 
 
Author Kasieczka, G. et al; Sanz, V. url  doi
openurl 
  Title The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics Type Journal Article
  Year 2021 Publication (down) Reports on Progress in Physics Abbreviated Journal Rep. Prog. Phys.  
  Volume 84 Issue 12 Pages 124201 - 64pp  
  Keywords anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods  
  Abstract A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.  
  Address [Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de;  
  Corporate Author Thesis  
  Publisher IOP Publishing Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0034-4885 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000727698500001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5039  
Permanent link to this record
 
 
Author Huang, F.; Sanz, V.; Shu, J.; Xue, X. url  doi
openurl 
  Title LIGO as a probe of dark sectors Type Journal Article
  Year 2021 Publication (down) Physical Review D Abbreviated Journal Phys. Rev. D  
  Volume 104 Issue 10 Pages 095001 - 9pp  
  Keywords  
  Abstract We show how current LIGO data is able to probe interesting theories beyond the Standard Model, particularly dark sectors where a dark Higgs boson triggers symmetry breaking via a first-order phase transition. We use publicly available LIGO O2 data to illustrate how these sectors, even if disconnected from the Standard Model, can be probed by gravitational wave detectors. We link the LIGO measurements with the model content and mass scale of the dark sector, finding that current O2 data are testing a broad set of scenarios that can be mapped into many different types of dark-sector models where the breaking of SU(N) theories with Nf fermions is triggered by a dark Higgs boson at scales ? similar or equal to 108-109 GeV with reasonable parameters for the scalar potential.  
  Address [Huang, Fei; Shu, Jing; Xue, Xiao] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China, Email: huangf4@uci.edu;  
  Corporate Author Thesis  
  Publisher Amer Physical Soc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2470-0010 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000716446500001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5021  
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