Benjamin Nachman

According to our database, Benjamin Nachman authored at least 40 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning.

Carlos Cardona-Giraldo, Cristiano Fanelli, James Giroux, Cole Granger, Benjamin Nachman, Gerald Sabin

CoRR, March, 2026

EveNet: A Foundation Model for Particle Collision Data Analysis.

Ting-Hsiang Hsu, Bai-Hong Zhou, Qibin Liu, Yue Xu, Shu Li, George Wei-Shu Hou, Benjamin Nachman, Shih-Chieh Hsu, Vinicius Mikuni, Yuan-Tang Chou, Yulei Zhang

CoRR, January, 2026

2025

SEAL - A Symmetry EncourAging Loss for High Energy Physics.

Pradyun Hebbar, Thandikire Madula, Vinicius Mikuni, Benjamin Nachman, Nadav Outmezguine, Inbar Savoray

CoRR, November, 2025

Generator Based Inference (GBI).

Chi Lung Cheng, Ranit Das, Runze Li, Radha Mastandrea, Vinicius Mikuni, Benjamin Nachman, David Shih, Gup Singh

CoRR, June, 2025

Fast Calorimeter Simulation Challenge 2022 - Submissions Dataset 1 Pions.

Michele Faucci Giannelli, Gregor Kasieczka, Claudius Krause, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Florian Ernst, Anthony L. Caterini, Federico A. G. Corchia, Jesse C. Cresswell, Etienne Dreyer, Luigi Favaro, Matteo Franchini, Eilam Gross, Shih-Chieh Hsu, Taewoo Kim, Dmitrii Kobylianskii, Marco Letizia, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Ian Pang, Kevin Pedro, Tilman Plehn, Humberto Reyes-González, Lorenzo Rinaldi, Brendan Leigh Ross, Chase Shimmin, Eli Shlizerman, Shu Li, Nathalie Soybelman, Rui Zhang

Dataset, June, 2025

Fast Calorimeter Simulation Challenge 2022 - Submissions Dataset 1 Photons.

Michele Faucci Giannelli, Gregor Kasieczka, Claudius Krause, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Anthony L. Caterini, Federico A. G. Corchia, Jesse C. Cresswell, Etienne Dreyer, Florian Ernst, Luigi Favaro, Matteo Franchini, Eilam Gross, Shih-Chieh Hsu, Taewoo Kim, Dmitrii Kobylianskii, Marco Letizia, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Vinicius Mikuni, Ian Pang, Kevin Pedro, Tilman Plehn, Humberto Reyes-González, Lorenzo Rinaldi, Brendan Leigh Ross, Chase Shimmin, Eli Shlizerman, Shu Li, Nathalie Soybelman, Rui Zhang

Dataset, June, 2025

FAIR Universe - HiggsML Uncertainty Challenge Public Dataset.

Wahid Bhimji, Paolo Calafiura, Ragansu Chakkappai, Po-Wen Chang, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Steven Farrell, Aishik Ghosh, Isabelle Guyon, Christopher J. Harris, Shih-Chieh Hsu, Elham E Khoda, Benjamin Nachman, Peter Nugent, David Rousseau, Ben Thorne, Ihsan Ullah, Yulei Zhang

Dataset, April, 2025

Discriminative versus generative approaches to simulation-based inference.

Benjamin Sluijter, Sascha Diefenbacher, Wahid Bhimji, Benjamin Nachman

Mach. Learn. Sci. Technol., 2025

2024

Artificial Intelligence for the Electron Ion Collider (AI4EIC).

C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, T. Britton, Anja Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, E. Fol, S. Furletov, Y. Gao, James Giroux, N. C. Gunawardhana Waduge, Omar Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, A. Jalotra, D. Jayakodige, B. Joo, M. Junaid, N. Kalantarians, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, Y. Li, Meifeng Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, Salvador A. Ochoa-Oregon, J. Oleniacz, J. D. Osborn, Churamani Paudel, Connor Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, H. Rajendran, Kishansingh Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, A. Saini, Nobuo Sato, T. Satogata, German Sborlini, Malachi Schram, David Shih, J. Singh, R. Singh, Andrzej Siódmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, Abdel Nasser Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, Richard Tyson, S. Volkova, Anselm Vossen, Eric Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita

Comput. Softw. Big Sci., December, 2024

Generative Unfolding with Distribution Mapping.

Anja Butter, Sascha Diefenbacher, Nathan Huetsch, Vinicius Mikuni, Benjamin Nachman, Sofia Palacios Schweitzer, Tilman Plehn

CoRR, 2024

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation.

Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-González, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang

CoRR, 2024

FAIR Universe HiggsML Uncertainty Challenge Competition.

Wahid Bhimji, Paolo Calafiura, Ragansu Chakkappai, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Steven Farrell, Aishik Ghosh, Isabelle Guyon, Chris Harris, Shih-Chieh Hsu, Elham E Khoda, Rémy Lyscar, Alexandre Michon, Benjamin Nachman, Peter Nugent, Mathis Reymond, David Rousseau, Benjamin Sluijter, Benjamin Thorne, Ihsan Ullah, Yulei Zhang

CoRR, 2024

The Landscape of Unfolding with Machine Learning.

Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael James Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn

CoRR, 2024

2023

Designing Observables for Measurements with Deep Learning.

Owen Long, Benjamin Nachman

CoRR, 2023

The Optimal use of Segmentation for Sampling Calorimeters.

Fernando Torales Acosta, Bishnu Karki, Piyush Karande, Aaron Angerami, Miguel Arratia, Kenneth Barish, Ryan Milton, Sebastián Morán, Benjamin Nachman, Anshuman Sinha

CoRR, 2023

Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation.

Tobias Golling, Samuel Klein, Radha Mastandrea, Benjamin Nachman, John Andrew Raine

CoRR, 2023

Improving Generative Model-based Unfolding with Schrödinger Bridges.

Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni, Benjamin Nachman, Weili Nie

CoRR, 2023

Artificial Intelligence for the Electron Ion Collider (AI4EIC).

C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, Thomas Britton, Anja Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, M. Finger Jr., E. Fol, S. Furletov, Yuan Gao, James Giroux, N. C. Gunawardhana Waduge, R. Harish, Omar Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, D. Jayakodige, B. Joo, M. Junaid, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, M. Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, Salvador A. Ochoa-Oregon, J. Oleniacz, J. D. Osborn, Churamani Paudel, Connor Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, Kishansingh Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, Nobuo Sato, T. Satogata, German Sborlini, Malachi Schram, David Shih, J. Singh, R. Singh, Andrzej Siódmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, Abdel Nasser Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, Richard Tyson, S. Volkova, Anselm Vossen, Eric Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita

CoRR, 2023

Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation.

Fernando Torales Acosta, Vinicius Mikuni, Benjamin Nachman, Miguel Arratia, Bishnu Karki, Ryan Milton, Piyush Karande, Aaron Angerami

CoRR, 2023

High-dimensional and Permutation Invariant Anomaly Detection.

Vinicius Mikuni, Benjamin Nachman

CoRR, 2023

Weakly-Supervised Anomaly Detection in the Milky Way.

Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih, Matthew R. Buckley, Jack H. Collins

CoRR, 2023

2022

Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer.

Wonho Jang, Koji Terashi, Masahiko Saito, Christian W. Bauer, Benjamin Nachman, Yutaro Iiyama, Ryunosuke Okubo, Ryu Sawada

Quantum, September, 2022

When, Where, and How to Open Data: a Personal Perspective.

Benjamin Nachman

Comput. Softw. Big Sci., 2022

Resonant Anomaly Detection with Multiple Reference Datasets.

Mayee F. Chen, Benjamin Nachman, Frederic Sala

CoRR, 2022

Machine-Learning Compression for Particle Physics Discoveries.

Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson

CoRR, 2022

Score-based Generative Models for Calorimeter Shower Simulation.

Vinicius Mikuni, Benjamin Nachman

CoRR, 2022

Exploring the Universality of Hadronic Jet Classification.

Kingman Cheung, Yi-Lun Chung, Shih-Chieh Hsu, Benjamin Nachman

CoRR, 2022

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation.

Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner

CoRR, 2022

2021

Learning from learning machines: a new generation of AI technology to meet the needs of science.

Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko M. Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel B. Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus H. Zwart, Neeraj Kumar, Amy Justice, Claire J. Tomlin, Daniel A. Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Kenneth Kreutz-Delgado, Michael W. Mahoney, James B. Brown

CoRR, 2021

Online-compatible Unsupervised Non-resonant Anomaly Detection.

Vinicius Mikuni, Benjamin Nachman, David Shih

CoRR, 2021

A Cautionary Tale of Decorrelating Theory Uncertainties.

Aishik Ghosh, Benjamin Nachman

CoRR, 2021

New Methods and Datasets for Group Anomaly Detection From Fundamental Physics.

Gregor Kasieczka, Benjamin Nachman, David Shih

CoRR, 2021

Latent Space Refinement for Deep Generative Models.

Ramon Winterhalder, Marco Bellagente, Benjamin Nachman

CoRR, 2021

Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution.

Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Adi Suresh, Jesse Thaler

CoRR, 2021

A Living Review of Machine Learning for Particle Physics.

Matthew Feickert, Benjamin Nachman

CoRR, 2021

2019

AI Safety for High Energy Physics.

Benjamin Nachman, Chase Shimmin

CoRR, 2019

2017

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis.

Luke de Oliveira, Michela Paganini, Benjamin Nachman

Comput. Softw. Big Sci., November, 2017

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks.

Michela Paganini, Luke de Oliveira, Benjamin Nachman

CoRR, 2017

Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters.

Luke de Oliveira, Michela Paganini, Benjamin Nachman

CoRR, 2017

2015

Superposition coding is almost always optimal for the Poisson broadcast channel.

Hyeji Kim, Benjamin Nachman, Abbas El Gamal

Proceedings of the IEEE International Symposium on Information Theory, 2015