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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 (down) 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 Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. url  doi
openurl 
  Title A deep Generative Artificial Intelligence system to predict species coexistence patterns Type Journal Article
  Year 2022 Publication Methods in Ecology and Evolution Abbreviated Journal Methods Ecol. Evol.  
  Volume (down) 13 Issue Pages 1052-1061  
  Keywords artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders  
  Abstract Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.  
  Address [Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es  
  Corporate Author Thesis  
  Publisher Wiley Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2041-210x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000765239700001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5155  
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 (down) 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 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 (down) 11 Issue 1 Pages 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 Bonilla, J.; Brivio, I.; Gavela, M.B.; Sanz, V. url  doi
openurl 
  Title One-loop corrections to ALP couplings Type Journal Article
  Year 2021 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume (down) 11 Issue 11 Pages 168 - 57pp  
  Keywords Beyond Standard Model; Effective Field Theories; Renormalization Group  
  Abstract The plethora of increasingly precise experiments which hunt for axion-like particles (ALPs), as well as their widely different energy reach, call for the theoretical understanding of ALP couplings at loop-level. We derive the one-loop contributions to ALP-SM effective couplings, including finite corrections. The complete leading-order – dimension five – effective linear Lagrangian is considered. The ALP is left off-shell, which is of particular impact on LHC and accelerator searches of ALP couplings to gamma gamma, ZZ, WW, Z gamma gluons and fermions. All results are obtained in the covariant Rg gauge. A few phenomenological consequences are also explored as illustration, with flavour diagonal channels in the case of fermions: in particular, we explore constraints on the coupling of the ALP to top quarks, that can be extracted from LHC data, from astrophysical sources and from Dark Matter direct detection experiments such as PandaX, LUX and XENONIT. Furthermore, we clarify the relation between alternative ALP bases, the role of gauge anomalous couplings and their interface with chirality-conserving and chirality-flip fermion interactions, and we briefly discuss renormalization group aspects.  
  Address [Bonilla, J.; Gavela, M. B.] Univ Autonoma Madrid, Dept Fis Teor, E-28049 Madrid, Spain, Email: jesus.bonilla@ua.m.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:000721914800006 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5029  
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