The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an ano...

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Detalles Bibliográficos
Autores principales: Aarrestad, Thea, van Beekveld, Melissa, Bona, Marcella, Boveia, Antonio, Caron, Sascha, Davies, Joe, de Simone, Andrea, Doglioni, Caterina, Duarte, Javier, Farbin, Amir, Gupta, Honey, Hendriks, Luc, Heinrich, Lukas A., Howarth, James, Jawahar, Pratik, Jueid, Adil, Lastow, Jessica, Leinweber, Adam, Mamuzic, Judita, Merényi, Erzsébet, Morandini, Alessandro, Moskvitina, Polina, Nellist, Clara, Ngadiuba, Jennifer, Ostdiek, Bryan, Pierini, Maurizio, Ravina, Baptiste, de Austri, Roberto Ruiz, Sekmen, Sezen, Touranakou, Mary, Vaškeviciute, Marija, Vilalta, Ricardo, Vlimant, Jean-Roch, Verheyen, Rob, White, Martin, Wulff, Eric, Wallin, Erik, Wozniak, Kinga A., Zhang, Zhongyi
Lenguaje:eng
Publicado: 2021
Materias:
stat.ML
Mathematical Physics and Mathematics
physics.data-an
Other Fields of Physics
hep-ex
Particle Physics - Experiment
hep-ph
Particle Physics - Phenomenology
Acceso en línea:https://dx.doi.org/10.21468/SciPostPhys.12.1.043
http://cds.cern.ch/record/2771263

Internet

https://dx.doi.org/10.21468/SciPostPhys.12.1.043
http://cds.cern.ch/record/2771263

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