Mariel Pettee

Mariel Pettee's picture
Chamberlain Fellow
She/her/hers
Berkeley Lab
Research Areas: 
Experimental Particle Physics
Biographical Sketch: 

Honors & Awards

2015 Leigh Page Prize
Seed Grant Award from the Womens Faculty Forum

Education: 
Ph.D. 2021, Yale University
Thesis Advisor: 
Sarah Demers
Dissertation Title: 
Interdisciplinary Machine Learning Methods for Particle Physics
Dissertation Abstract: 

Following the discovery of a Higgs boson-like particle in the summer of 2012 at the Large Hadron Collider (LHC) at CERN, the high-energy particle physics community has prioritized its thorough study. As part of a comprehensive plan to investigate the many combinations of production and decay of the Standard Model Higgs boson, this thesis describes a continued search for this particle produced in association with a leptonically-decaying vector boson (i.e. a W or Z boson) and decaying into a pair of tau leptons.
In Run 1 at the LHC, ATLAS researchers were able to set an upper constraint on the signal strength of this process at μ = σ/σ_SM < 5.6 with 95% confidence using 20.3 fb^-1 of collision data collected at a center-of-mass energy of √s = 8 TeV. My thesis work, which builds upon and extends the Run 1 analysis structure, takes advantage of an increased center-of-mass energy in Run 2 of the LHC of √s = 13 TeV as well as 139 fb^-1 of data, approximately seven times the amount used for the Run 1 analysis. While the higher center-of-mass energy in Run 2 yields a higher expected cross-section for this process, the analysis faces the additional challenges of two newly-considered final states, a higher number of simultaneous interactions per event, and a novel neural network-based background estimation technique. I also describe advanced machine learning techniques I have developed to support tau identification in the ATLAS High-Level Trigger as well as predicting and analyzing the dynamics of many-body systems such as 3D motion capture data of choreography.

Degree Date: 
May, 2021