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Model independent searches for New Physics using Machine Learning at the ATLAS experiment

Abstract : We address the problem of model-independent searches for New Physics (NP), at the Large Hadron Collider (LHC) using the ATLAS detector. Particular attention is paid to the development and testing of novel Machine Learning techniques for that purpose. The present work presents three main results. Firstly, we put in place a system for automatic generic signature monitoring within TADA, a software tool from ATLAS. We explored over 30 signatures in the data taking period of 2017 and no particular discrepancy was observed with respect to the Standard Model processes simulations. Secondly, we propose a collective anomaly detection method for model-independent searches for NP at the LHC. We propose the parametric approach that uses a semi-supervised learning algorithm. This approach uses penalized likelihood and is able to simultaneously perform appropriate variable selection and detect possible collective anomalous behavior in data with respect to a given background sample. Thirdly, we present preliminary studies on modeling background and detecting generic signals in invariant mass spectra using Gaussian processes (GPs) with no mean prior information. Two methods were tested in two datasets: a two-step procedure in a dataset taken from Standard Model simulations used for ATLAS General Search, in the channel containing two jets in the final state, and a three-step procedure from a simulated dataset for signal (Z′) and background (Standard Model) in the search for resonances in the top pair invariant mass spectrum case. Our study is a first step towards a method that takes advantage of GPs as a modeling tool that can be applied to several signatures in a more model independent setup.
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Submitted on : Tuesday, December 10, 2019 - 2:43:15 PM
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2019CLFAC030_JIMENEZ.pdf
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  • HAL Id : tel-02402488, version 1

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Fabricio Jimenez. Model independent searches for New Physics using Machine Learning at the ATLAS experiment. Accelerator Physics [physics.acc-ph]. Université Clermont Auvergne, 2019. English. ⟨NNT : 2019CLFAC030⟩. ⟨tel-02402488⟩

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