Aug 4 – 8, 2015
America/Detroit timezone

New Developments in Multivariate Machine Learning Methods and their Applications in HEP

Aug 6, 2015, 3:05 PM
Koessler (Michigan League)


Michigan League


Pushpalatha Bhat (Fermi National Accelerator Lab. (US))


Over the past 25 years, Multivariate Analysis (MVA) methods have gained gradual acceptance and are now considered as state of the art methods in high energy physics data analyses. From precision measurements of the top quark mass and other properties at the Tevatron in the mid-90’s to the Higgs discovery in 2012 at the LHC, and in a variety of applications such as object ID and energy corrections, MVA methods have provided huge benefits in the physics results extracted in HEP. I will discuss some new developments in multivariate machine learning methods as well as some older, untapped techniques and their potential applications in the challenging data analysis tasks in LHC Run 2 and beyond.
Oral or Poster Presentation Oral

Primary author

Pushpalatha Bhat (Fermi National Accelerator Lab. (US))

Presentation materials