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Author Barenboim, G.; Hirn, J.; Sanz, V. url  doi
openurl 
  Title Symmetry meets AI Type Journal Article
  Year 2021 Publication Scipost Physics Abbreviated Journal SciPost Phys.  
  Volume 11 Issue 1 Pages (up) 014 - 11pp  
  Keywords  
  Abstract We explore whether Neural Networks (NNs) can discover the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a decoy task based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh.  
  Address [Barenboim, Gabriela; Hirn, Johannes; Sanz, Veronica] Univ Valencia, CSIC, Dept Fis Teor, E-46100 Burjassot, Spain  
  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:000680039500002 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 4920  
Permanent link to this record
 

 
Author LHC BSM Reinterpretation Forum (Abdallah, W. et al); Mitsou, V.A.; Sanz, V. url  doi
openurl 
  Title Reinterpretation of LHC results for new physics: status and recommendations after run 2 Type Journal Article
  Year 2020 Publication Scipost Physics Abbreviated Journal SciPost Phys.  
  Volume 9 Issue 2 Pages (up) 022 - 45pp  
  Keywords  
  Abstract We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.  
  Address [Abdallah, Waleed; Dutta, Juhi] Harish Chandra Res Inst HBNI, Allahabad 211019, Uttar Pradesh, India, Email: Andy.Buckley@glasgow.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:000573102600007 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 4547  
Permanent link to this record
 

 
Author Gomez Ambrosio, R.; ter Hoeve, J.; Madigan, M.; Rojo, J.; Sanz, V. url  doi
openurl 
  Title Unbinned multivariate observables for global SMEFT analyses from machine learning Type Journal Article
  Year 2023 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 03 Issue 3 Pages (up) 033 - 66pp  
  Keywords SMEFT; Higgs Properties  
  Abstract Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits.  
  Address [Ambrosio, Raquel Gomez] Univ Milano Bicocca, Dipartimento Fis G Occhialini, Piazza Sci 3, I-20126 Milan, Italy, Email: raquel.gomezambrosio@unito.it;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000946004000003 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5501  
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 Scipost Physics Abbreviated Journal SciPost Phys.  
  Volume 12 Issue 1 Pages (up) 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 Escudero, M.; Rius, N.; Sanz, V. url  doi
openurl 
  Title Sterile neutrino portal to Dark Matter I: the U(1)(B-L) case Type Journal Article
  Year 2017 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 02 Issue 2 Pages (up) 045 - 27pp  
  Keywords Beyond Standard Model; Neutrino Physics  
  Abstract In this paper we explore the possibility that the sterile neutrino and Dark Matter sectors in the Universe have a common origin. We study the consequences of this assumption in the simple case of coupling the dark sector to the Standard Model via a global U(1)(B-L), broken down spontaneously by a dark scalar. This dark scalar provides masses to the dark fermions and communicates with the Higgs via a Higgs portal coupling. We find an interesting interplay between Dark Matter annihilation to dark scalars – the CP-even that mixes with the Higgs and the CP-odd which becomes a Goldstone boson, the Majoron and heavy neutrinos, as well as collider probes via the coupling to the Higgs. Moreover, Dark Matter annihilation into sterile neutrinos and its subsequent decay to gauge bosons and quarks, charged leptons or neutrinos lead to indirect detection signatures which are close to current bounds on the gamma ray flux from the galactic center and dwarf galaxies.  
  Address [Escudero, Miguel; Rius, Nuria] Univ Valencia, Dept Fis Teor, CSIC, C Catedrat Jose Beltran 2, E-46980 Paterna, Spain, Email: miguel.escudero@ific.uv.es;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000394747600008 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 3018  
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