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Seek and Ye Shall Find : = Machine Learning and Searches for New Physics.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Seek and Ye Shall Find :/
Reminder of title:	Machine Learning and Searches for New Physics.
Author:	Bradshaw, Layne N.
Description:	1 online resource (164 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Physics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379957544

Seek and Ye Shall Find : = Machine Learning and Searches for New Physics.
Bradshaw, Layne N.

Seek and Ye Shall Find :Machine Learning and Searches for New Physics. - 1 online resource (164 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--University of Oregon, 2023.

Includes bibliographical references

The discovery of the Higgs boson confirmed that the Standard Model is the correct description of nature below some high energy scale. However, we know the Standard Model is incomplete and have yet to find significant deviations from it. Without well-motivated directions to guide new physics searches, we need to reconsider where and how we search. We explore this in 3 parts here.We start by identifying 3- and 4-point on-shell amplitudes involving top quarks that are most susceptible to new physics. Using the Hilbert series as a cross-check, we are able to create an independent set of amplitudes for four-fermion and two-fermion, two-boson interactions. After translating these amplitudes to the lowest-dimension SMEFT-like operator, we use pertubative unitarity to place an upper bound on the coupling, under the assumption that the new physics appears around the TeV scale. With this, we find a number of top quark decay modes that could be probed at the HL-LHC.Next, we compare the efficacy of a number of methods to decorrelate the output of a machine learned classifier from the invariant jet mass. This decorrelation preserves the background dominated sidebands in the invariant mass distribution as tighter cuts are made on the network's output. This increases the potential discovery significance of the new physics. We compare 4 techniques which broadly fall into one of 2 categories-data augmentation or training augmentation. We find that the simpler and computationally cheaper data augmentation techniques perform comparably to the training augmentation techniques across a variety of qualitatively different signals.Finally, we turn to machine learning based anomaly detection, with the aim of explaining the physics learned by an image-based autoencoder. Adapting techniques from the literature, we make use of two strategies to mimic the autoencoder. Despite fundamental differences, we find that both techniques, when compared to the autoencoder, order background events similarly and perform comparably as anomaly detectors across a wide swath of signals. The mimicker networks independently use the same high-level observables, giving us confidence that these features are indeed those learned by the autoencoder.This dissertation includes previously published co-authored material.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379957544Subjects--Topical Terms:

564049
Physics.
Subjects--Index Terms:

High energy physicsIndex Terms--Genre/Form:

554714
Electronic books.

Seek and Ye Shall Find : = Machine Learning and Searches for New Physics.
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520 $a The discovery of the Higgs boson confirmed that the Standard Model is the correct description of nature below some high energy scale. However, we know the Standard Model is incomplete and have yet to find significant deviations from it. Without well-motivated directions to guide new physics searches, we need to reconsider where and how we search. We explore this in 3 parts here.We start by identifying 3- and 4-point on-shell amplitudes involving top quarks that are most susceptible to new physics. Using the Hilbert series as a cross-check, we are able to create an independent set of amplitudes for four-fermion and two-fermion, two-boson interactions. After translating these amplitudes to the lowest-dimension SMEFT-like operator, we use pertubative unitarity to place an upper bound on the coupling, under the assumption that the new physics appears around the TeV scale. With this, we find a number of top quark decay modes that could be probed at the HL-LHC.Next, we compare the efficacy of a number of methods to decorrelate the output of a machine learned classifier from the invariant jet mass. This decorrelation preserves the background dominated sidebands in the invariant mass distribution as tighter cuts are made on the network's output. This increases the potential discovery significance of the new physics. We compare 4 techniques which broadly fall into one of 2 categories-data augmentation or training augmentation. We find that the simpler and computationally cheaper data augmentation techniques perform comparably to the training augmentation techniques across a variety of qualitatively different signals.Finally, we turn to machine learning based anomaly detection, with the aim of explaining the physics learned by an image-based autoencoder. Adapting techniques from the literature, we make use of two strategies to mimic the autoencoder. Despite fundamental differences, we find that both techniques, when compared to the autoencoder, order background events similarly and perform comparably as anomaly detectors across a wide swath of signals. The mimicker networks independently use the same high-level observables, giving us confidence that these features are indeed those learned by the autoencoder.This dissertation includes previously published co-authored material.
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