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5 226 Details
- +https://edips.lisn.upsaclay.fr/tiki-download_item_attachment.php?attId=1766
3 180 Abstract
-Today, machine learning methods are routinely used in high-energy particle physics to accelerate the discovery of new phenomena. Typically, standard classification algorithms are used for signal/background separation both in the online selection (trigger) step, and in the offline analysis. The first goal of this thesis to answer some of the theoretical questions raised by these unorthodox machine learning applications, and to design new algorithms that improve the analyses. In the second theme we propose to go beyond the standard setup of ``manual'' feature extraction followed by classification and to investigate the applicability of recently developed techniques on unsupervised representation learning. Both themes are motivated by concrete particle and astroparticlephysics experiments, the future InternationalLinear Collider (ILC), and the Pierre Auger Experiment (Auger). Data provided by these experiments will be a natural testbed for methodolgies developed in the thesis. +Today, machine learning methods are routinely used in high-energy particle physics to accelerate the discovery of new phenomena. Typically, standard classification algorithms are used for signal/background separation both in the online selection (trigger) step, and in the offline analysis. The first goal of this thesis to answer some of the theoretical questions raised by these unorthodox machine learning applications, and to design new algorithms that improve the analyses. In the second theme we propose to go beyond the standard setup of ``manual'' feature extraction followed by classification and to investigate the applicability of recently developed techniques on unsupervised representation learning. Both themes are motivated by concrete particle and astroparticle physics experiments, the future International Linear Collider (ILC), and the Pierre Auger Experiment (Auger). Data provided by these experiments will be a natural testbed for methodolgies developed in the thesis.
1 186 Expected funding
-IDI +Institutional funding

Ecole Doctorale Informatique Paris-Sud

Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps
ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr