Machine learning pour la recherches de nouvelle physique/ Marchine learning for searches for New Physics

Personnes impliquées / People involved

J. Donini (responsable / leader)

E. Mayer, Development of innovative statistical learning methods for the search of new physics signatures in the ATLAS detector at the LHC , since October 2023, directed by J. Donini, S. Calvet

(arXiv:2305.15179): GAN-AE : An anomaly detection algorithm for New Physics search in LHC data
I. Dinu: Ph.D. thesis: NSW trigger processor performance studies and search for new physics phenomena using machine learning methods in the ATLAS experiment at the LHC (co-tutelle avec Bucarest IFIN-HH), directed by C. Alexa (IFIN-HH), J. Donini
(arXiv:2211.07446): Fitting the BumpHunter test statistic distribution and global p-value estimation
L. Vaslin: Ph.D. thesis: Search for New Physics with the ATLAS experiment using unsupervised and semi-supervised Machine Learning techniques
(financement I-Site CAP2025), directed by J. Donini, V. Barra (LIMOS)
SciPost Phys. Codebases 15 (2023) (arXiv:2208.14760): pyBumpHunter: A model independent bump hunting tool in Python for High Energy Physics analyses
RIP 7 (2021) 100063 (arXiv:2105.07530): Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
Reports on Progress in Physics (arxiv:2101.08320): The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
F. Jimenez Morales: Ph.D. thesis: Model independent searches for New Physics using Machine Learning at the ATLAS experiment
(financement Marie Curie), directed by J. Donini
AMVA4NewPhysics-D4.2: Report on a Statistical Learning method for Model-Independent searches for New Physics

Last update on 2024-01-11