Artificial intelligence and machine learning for simulation-based inference in B0 -> k*0 l+ l-

Simple item page
dc.contributor.advisorBrowder, Tom
dc.contributor.authorLee, Ethan
dc.contributor.departmentPhysics
dc.date.accessioned2026-02-23T21:18:18Z
dc.date.available2026-02-23T21:18:18Z
dc.date.issued2025
dc.description.degreeM.S.
dc.identifier.urihttps://hdl.handle.net/10125/113034
dc.subjectParticle physics
dc.subjectArtificial intelligence
dc.subjectPhysics
dc.subjectArtificial Intelligence
dc.subjectB -> K* l l
dc.subjectBelle II
dc.subjectMachine Learning
dc.subjectSimulation-Based Inference
dc.subjectWilson Coefficients
dc.titleArtificial intelligence and machine learning for simulation-based inference in B0 -> k*0 l+ l-
dc.typeThesis
dcterms.abstractWe investigate neural simulation-based inference approaches to fitting the deviation of Wilson Coefficient 9 (C_9) from its Standard Model value (C_9_SM) given B0 -> K*0 mu+ mu- events simulated in the context of the Belle II experiment. We denote this deviation as delta_C_9. We compare three neural network-based approaches to this multi-dimensional fitting problem. The first approach converts the dataset into a three-dimensional grid and fits for delta_C_9 using computer vision techniques. The second approach uses the deep sets architecture to predict delta_C_9 from a dataset while enforcing the permutation invariance of events. The third approach trains a classification model to predict a binned probability distribution over delta_C_9 given a single event. Predictions are then aggregated using the independence of events to obtain the binned delta_C_9 probability distribution given the entire dataset. We train and evaluate models on simulated datasets with and without detector effects. We also train and evaluate models on a dataset that includes simulated background events from the M_bc sideband.
dcterms.extent47 pages
dcterms.languageen
dcterms.publisherUniversity of Hawai'i at Manoa
dcterms.rightsAll UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.typeText
local.identifier.alturihttps://www.proquest.com/LegacyDocView/DISSNUM/32395679

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Lee_hawii_0085O_12823.pdf
Size:
6.33 MB
Format:
Adobe Portable Document Format
Download

Collections

M.S. - Physics