Needles in the Quantum Haystack: CMS Anomaly Detection with Normalizing Flows
Name
Yunus-myunus-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
18.68 MB
Format
Adobe PDF
Checksum (MD5)
116be2759e1acd2aaee8d94d790471af
Author(s)
Yunus, Mikaeel
Advisor(s)
Harris, Phillip C.
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Recent experimental searches for particles beyond the Standard Model (BSM) have yielded little in the realm of new physics discoveries. A number of research efforts have adopted new anomaly detection strategies which utilize density estimation algorithms based on unsupervised and semi-supervised machine learning. However, these efforts rely exclusively on QCD background priors, and thus drastically limit their own anomaly detection capabilities.
In this thesis, we integrate an unsupervised density estimation algorithm, neural spline normalizing flows, into an anomaly detection strategy called Quasi-Anomalous Knowledge (QUAK), which allows us to take advantage of signal priors in addition to QCD background priors. The introduction of a signal prior allows us to learn the features of a particular type of BSM dijet event, giving us insight into the underlying variable distributions of hidden signals in CMS data. Through several studies on both Monte Carlo samples and 13 TeV data from CMS, we demonstrate that QUAK with normalizing flows (QUAK-NF) can be a powerful tool for conducting searches for BSM physics.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
