Introduction and news

• Markus Stoye (Imperial College (GB))
• Steven Schramm (Universite de Geneve (CH))
• Paul Seyfert (CERN)
• Rudiger Haake (Yale University (US))
• Lorenzo Moneta (CERN)

Room: 40/S2-A01 - Salle Anderson

ML community white paper path forward

• Sergei Gleyzer (University of Florida (US))

Room: 40/S2-A01 - Salle Anderson

Guiding New Physics Searches with Unsupervised Learning

• Andrea De Simone (SISSA)

Room: 40/S2-A01 - Salle Anderson I will describe an approach to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics. I will propose a new statistical test, built upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points. The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space. As a by-product, the technique is also a useful tool to identify regions of interest for further study. As a proof-of-concept, I will show the power of the method when applied to synthetic Gaussian data, and to a simulated dark matter signal at the LHC.

Learning New Physics from a machine

• Andrea Wulzer (CERN)

Room: 40/S2-A01 - Salle Anderson We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The model-independent nature of our approach, and its ability to deal with rare signals such as those expected at the LHC, is quantitatively assessed in toy examples.

Machine learning as an instrument for data unfolding

• Alexander Glazov (Deutsches Elektronen-Synchrotron (DE))

Room: 40/S2-A01 - Salle Anderson