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Taoli Cheng

Alumni

Publications

Versatile Energy-Based Probabilistic Models for High Energy Physics
Taoli Cheng
Aaron Courville
As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy functio… (voir plus)n. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicative aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification.
2022-12-31
arXiv.org (prépublication)
doi.org
openreview.net
Invariant representation driven neural classifier for anti-QCD jet tagging
Taoli Cheng
Aaron Courville
We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD si… (voir plus)gnal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a \emph{well-calibrated} and \emph{powerful enough feature extractor}, a well-trained \emph{mass-decorrelated} supervised Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing \emph{data-augmented} mass-invariance (and thus decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate of 51 and a significance improvement factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.
2022-10-23
Journal of High Energy Physics (publié)
doi.org
arxiv.org