Publications

Raghav Kansal, Carlos Pareja, Javier Duarte (2023). JetNet: A Python package for accessing open datasets and benchmarking machine learning methods in high energy physics. Submitted to JOSS.

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Zichun Hao, Raghav Kansal, Javier Duarte, Nadezda Chernyavskaya (2023). Lorentz group equivariant autoencoders. Eur. Phys. J. C.

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Raghav Kansal, Anni Li, Javier Duarte, Nadezda Chernyavskaya, Maurizio Pierini, Breno Orzari, Thiago Tomei (2023). Evaluating generative models in high energy physics. Phys. Rev. D.

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Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao (2022). FAIR AI Models in High Energy Physics. Submitted to Machine Learning: Science and Technology.

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Farouk Mokhtar, Raghav Kansal, Javier Duarte (2022). Do graph neural networks learn traditional jet substructure?. ML and the Physical Sciences Workshop @ NeurIPS 2022.

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Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte, Dimitrios Gunopulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant (2022). Particle-based fast jet simulation at the LHC with variational autoencoders. Machine Learning: Science and Technology.

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CMS Collaboration (2022). Search for nonresonant pair production of highly energetic Higgs bosons decaying to bottom quarks. Phys. Rev. Lett..

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Artur Apresyan, Daniel Diaz, Javier Duarte, Sanmay Ganguly, Raghav Kansal, Nan Lu, Cristina Mantilla Suarez, Samadrita Mukherjee, Cristían Peña, Brian Sheldon, Si Xie (2022). Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning. Contribution to Snowmass 2022 Summer Study.

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Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao (2022). A FAIR and AI-ready Higgs boson decay dataset. Nature Scientific Data.

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Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos (2021). Particle Cloud Generation with Message Passing Generative Adversarial Networks. NeurIPS 2021.

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Steven Tsan, Raghav Kansal, Anthony Aportela, Daniel Diaz, Javier Duarte, Sukanya Krishna, Farouk Mokhtar, Jean-Roch Vlimant, Maurizio Pierini (2021). Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance. ML and the Physical Sciences Workshop @ NeurIPS 2021.

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Farouk Mokhtar, Raghav Kansal, Daniel Diaz, Javier Duarte, Joosep Pata, Maurizio Pierini, Jean-Roch Vlimant (2021). Explaining machine-learned particle-flow reconstruction. ML and the Physical Sciences Workshop @ NeurIPS 2021.

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Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Javier Duarte, Raghav Kansal, Jean-Roch Vlimant, Dimitrios Gunopulos (2021). Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC. LatinX in AI Research Workshop @ ICML 2021.

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Raghav Kansal, Javier Duarte, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos (2020). Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics. ML and the Physical Sciences Workshop @ NeurIPS 2020.

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