[en] One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.
Disciplines :
Physics Computer science
Author, co-author :
Brehmer, Johann
Cranmer, Kyle
Espejo, Irina
Kling, Felix
Louppe, Gilles ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
Pavez, Juan
Language :
English
Title :
Effective LHC measurements with matrix elements and machine learning
Publication date :
June 2019
Event name :
19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019)