Benjamin Nachman

According to our database, Benjamin Nachman authored at least 32 papers between 2016 and 2024.

Collaborative distances:

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

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, O. 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, 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, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, H. Rajendran, K. 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, R. 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, O. 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, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, K. 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, R. 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

2016

Superposition Coding Is Almost Always Optimal for the Poisson Broadcast Channel.

Hyeji Kim, Benjamin Nachman, Abbas El Gamal

IEEE Trans. Inf. Theory, 2016