Datasets
Dataset for 'Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks'

Dataset for 'Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning'

Dataset for 'Enhanced Higgs Boson to Tau Tau Search with Deep Learning'

Dataset for 'Jet Substructure Classification in High-Energy Physics with Deep Neural Networks'

Dataset for 'Parameterized Machine Learning for High-Energy Physics'

Dataset for 'Searching for Exotic Particles in High-Energy Physics with Deep Learning'

Dataset for 'Searching for Exotic Particles in High-Energy Physics with Deep Learning'

Dataset for 'Jet Flavor Classification in High-Energy Physics with Deep Neural Networks'

Dataset for 'Efficient Neutrino Oscillation Parameter Inference Using Gaussian Processes'

Dataset for 'Learning to Identify Electrons'

Dataset for 'Sparse Image Generation with Decoupled Generative Models'

Dataset for 'Learning to Isolate Muons'

Dataset for 'SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle Physics'
Publications
Relevant scientific articles published or submitted by our team.
If you use any data or tools from the MLPhysics Portal, please cite the appropriate article.
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Michael J. Fenton, A. Shmakov, T. Ho, S. Hsu, D. Whiteson, P. Baldi
2021.
Learning to Isolate Muons
J. Collado, K. Bauer, E. Witkowski, T. Faucett, D. Whiteson, P. Baldi
2021.
SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle Physics
Y. Lu, J. Collado, D. Whiteson, and P. Baldi
Physical Review D, 2021.
Learning to Identify Electrons
J. Collado, J.N. Howard, T. Faucett, T. Tong, P. Baldi, and D. Whiteson
2021.
Efficient Neutrino Oscillation Parameter Inference Using Gaussian Processes
L. Li, N. Nayak, J. Bian, and P. Baldi
Physical Review D, 2020.
Sparse Image Generation with Decoupled Generative Models
Y. Lu, J. Collado, K.Bauer, D. Whiteson, and P. Baldi
MLPS Workshop at NeurIPS 2019, 2019.
Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Sogaard
Physical Review D, 2017.
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning
P. Sadowski, B. Radics, Ananya, and P. Baldi
Journal of Physics Communications, 2017.
Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
D. Guest, J. Collado, P. Baldi, S. Hsu, G. Urban, and D. Whiteson
Physical Review D, 2016.
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson
Physical Review D, 2016.
Parameterized Machine Learning for High-Energy Physics
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson
The European Physics Journal C, 76(5), 1-7, 2016.
Enhanced Higgs Boson to Tau Tau Search with Deep Learning
P. Baldi, P. Sadowski, and D. Whiteson.
Physical Review Letters, 2015.
Deep Learning, Dark Knowledge, and Dark Matter
P. Sadowski, J. Collado, D. Whiteson, P. Baldi
Journal of Machine Learning Research, 2015.
Searching for Exotic Particles in High-energy Physics with Deep Learning
P. Baldi, P. Sadowski, and D. Whiteson.
Nature Communications, 2014.
Searching for Higgs Boson Decay Modes with Deep Learning
P. Sadowski, D. Whiteson, P. Baldi
Advances in Neural Information Processing Systems, 2014.