I am an coauthor of 184 papers of which 174 are published in peer reviewed journals as a member of the ATLAS Collaborations. The full list can be found at:
https://inspirehep.net/authors/1605504. Below highlights only the publications which I have made key contributions. In experimental high energy physics it is conventional to list all authors alphabetically regardless of specific contribution.
2023
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Integrating Particle Flavor into Deep Learning Models for Hadronization
Jay Chan, Xiangyang Ju, Adam Kania, Benjamin Nachman, and
2 more authors
Dec 2023
Hadronization models used in event generators are physics-inspired functions with many tunable parameters. Since we do not understand hadronization from first principles, there have been multiple proposals to improve the accuracy of hadronization models by utilizing more flexible parameterizations based on neural networks. These recent proposals have focused on the kinematic properties of hadrons, but a full model must also include particle flavor. In this paper, we show how to build a deep learning-based hadronization model that includes both kinematic (continuous) and flavor (discrete) degrees of freedom. Our approach is based on Generative Adversarial Networks and we show the performance within the context of the cluster hadronization model within the Herwig event generator.
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Physics Performance of the ATLAS GNN4ITk Track
Reconstruction Chain
Jared Dynes Burleson, Sylvain Caillou, Paolo Calafiura, Jay Chan, and
9 more authors
Dec 2023
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Unbinned Profiled Unfolding
Jay Chan, and Benjamin Nachman
In 1st Workshop on the Synergy of Scientific and Machine Learning Modeling @ ICML2023, Dec 2023
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Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb^-1 of \sqrts = 13 TeV pp collision data
Georges Aad, and others
Jun 2023
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Fitting a deep generative hadronization model
Jay Chan, Xiangyang Ju, Adam Kania, Benjamin Nachman, and
2 more authors
JHEP, May 2023
Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are more flexible and may be able to improve the overall precision. Proof of principle studies have shown how to use neural networks to emulate specific hadronization when trained using the inputs and outputs of classical methods. However, these approaches will not work with data, where we do not have a matching between observed hadrons and partons. In this paper, we develop a protocol for fitting a deep generative hadronization model in a realistic setting, where we only have access to a set of hadrons in data. Our approach uses a variation of a Generative Adversarial Network with a permutation invariant discriminator. We find that this setup is able to match the hadronization model in Herwig with multiple sets of parameters. This work represents a significant step forward in a longer term program to develop, train, and integrate machine learning-based hadronization models into parton shower Monte Carlo programs.
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Investigation of Higgs Boson Decaying to Di-muon, Dark Matter Produced in Association with a Higgs Boson Decaying to b-quarks and Unbinned Profiled Unfolding
Jay Chan
May 2023
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Unbinned profiled unfolding
Jay Chan, and Benjamin Nachman
Phys. Rev. D, May 2023
Unfolding is an important procedure in particle physics experiments which corrects for detector effects and provides differential cross section measurements that can be used for a number of downstream tasks, such as extracting fundamental physics parameters. Traditionally, unfolding is done by discretizing the target phase space into a finite number of bins and is limited in the number of unfolded variables. Recently, there have been a number of proposals to perform unbinned unfolding with machine learning. However, none of these methods (like most unfolding methods) allow for simultaneously constraining (profiling) nuisance parameters. We propose a new machine learning-based unfolding method that results in an unbinned differential cross section and can profile nuisance parameters. The machine learning loss function is the full likelihood function, based on binned inputs at detector-level. We first demonstrate the method with simple Gaussian examples and then show the impact on a simulated Higgs boson cross section measurement.
2022
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A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery
The ATLAS Collaboration (Coauthor)
Nature, May 2022
2021
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Combined measurements of Higgs boson production and decay using up to 139 fb^-1 of proton-proton collision data at \sqrts= 13 TeV collected with the ATLAS experiment
The ATLAS Collaboration (Coauthor)
Nov 2021
All figures including auxiliary figures are available at
https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2021-053
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Search for dark matter produced in association with a Standard Model Higgs boson decaying into b-quarks using the full Run 2 dataset from the ATLAS detector
The ATLAS Collaboration (Coauthor)
JHEP, Nov 2021
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Combination and summary of ATLAS dark matter searches
using 139 fb-1 of √s = 13 TeV p p collision data and
interpreted in a two-Higgs-doublet model with a
pseudoscalar mediator
The ATLAS Collaboration (Coauthor)
Jul 2021
All figures including auxiliary figures are available at
https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2021-036
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A search for the dimuon decay of the Standard Model Higgs boson with the ATLAS detector
The ATLAS Collaboration (Coauthor)
Phys. Lett. B, Jul 2021
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Search for Dark Matter produced in association with a
Standard Model Higgs boson decaying to b-quarks using the
full Run 2 collision data with the ATLAS detector
The ATLAS Collaboration (Coauthor)
Mar 2021
All figures including auxiliary figures are available at
https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2021-006
2019
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A search for the dimuon decay of the Standard Model Higgs
boson in pp collisions at \sqrts = 13 TeV with the
ATLAS Detector
The ATLAS Collaboration (Coauthor)
Jul 2019
All figures including auxiliary figures are available at
https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2019-028
2017
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Vector Boson Fusion versus Gluon Fusion
Chen-Hsun Chan, Kingman Cheung, Yi-Lun Chung, and Pai-Hsien Hsu
Phys. Rev. D, Jul 2017