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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 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 (up) 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 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 (up) 2542-4653 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001048488200002 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5610  
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