Conveners
ML Workshop: I session
- Steven Schramm (Universite de Geneve (CH))
We present CURTAINs, a fully data driven paradigm that improves on the weakly supervised searches. CURTAINs is designed to be sensitive to small density perturbations in n-dimensional feature space caused by the presence of signals. CURTAINs can be shown to be very robust in the absence of any signals, and yet be highly sensitive to signals even at very low signal to background...
A particularly interesting application of autoencoders (AE) for High Energy Physics is their use as anomaly detection (AD) algorithms to perform a signal-agnostic search for new physics. This is achieved by training AEs on standard model physics and tagging potential new physics events as anomalies. The use of an AE as an AD algorithm relies on the assumption that the network better...
In place of traditional cut and count methods, machine learning techniques offer powerful ways to optimise our searches for new physics. At the FCC-ee, we will probe the highest intensities and energies ever seen at a lepton collider, opening the possibly for discovery of massive right-handed neutrino states. In this work, existing searches for HNLs at the FCC-ee are optimised using a BDT and...
The ongoing Run-3 at the LHC is providing proton-proton collision data at the record energy of 13.6 TeV and, as of May 2024, a total integrated luminosity of almost 90 fb^-1 has been recorded by the ATLAS detector. This contribution presents preliminary results on the search for the pair production of stop squarks, the scalar supersymmetric partner of the top quark, based on the Run-2 and...
