Evidential Deep Learning for uncertainty quantification in jet tagging deep neural network model

Evidential Deep Learning (EDL) is an uncertainty-aware deep learning method used in order to provide confidence about the input data. The learning based on the input data is treated as an evidence acquisition process and more evidence is interpreted as the increased predictive confidence. In this way, the model with EDL is able to quantify the epistemic uncertainty (uncertainty) of the input data and detect the anomaly in the data. In this study, we explore the integration of Evidential Deep Learning with the Particle Flow Identification Network (PFIN), a deep neural network model tailored for jet tagging in high-energy physics. We adapted EDL principles to enhance the PFIN model, enabling it not only to make predictions but also to estimate the confidence level of those predictions and detect possible anomaly. This adaptation involved developing an evidence layer within the DNN architecture, allowing the model to dynamically assess and quantify uncertainty by interpreting the input data's reliability and relevance. Our results show that the EDL-enhanced PFIN model significantly outperforms conventional models in uncertainty quantification without sacrificing predictive accuracy. This improvement is particularly notable in the detection of anomalous data, where the model's ability to quantify uncertainty provides a robust mechanism for identifying out-of-distribution data. Such capabilities are critical in high-energy physics experiments, where precise and reliable data interpretation can lead to groundbreaking discoveries.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Wang, Xiwei

Contributors dc:contributor

• Kindratenko, Volodymyr
• Neubauer, Mark

• High Energy Physics
• Evidential Deep Learning
• Jet Tagging

Statement dc:rights

• Copyright 2024 Xiwei Wang

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124591

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124591