| Energy flow networks: deep sets for particle jets PT Komiske, EM Metodiev, J Thaler Journal of High Energy Physics 2019 (1), 1-46, 2019 | 367 | 2019 |
| Deep learning in color: towards automated quark/gluon jet discrimination PT Komiske, EM Metodiev, MD Schwartz Journal of High Energy Physics 2017 (1), 1-23, 2017 | 359 | 2017 |
| Classification without labels: Learning from mixed samples in high energy physics EM Metodiev, B Nachman, J Thaler Journal of High Energy Physics 2017 (10), 1-18, 2017 | 289 | 2017 |
| The machine learning landscape of top taggers G Kasieczka, T Plehn, A Butter, K Cranmer, D Debnath, BM Dillon, ... SciPost Physics 7 (1), 014, 2019 | 273 | 2019 |
| Energy flow polynomials: A complete linear basis for jet substructure PT Komiske, EM Metodiev, J Thaler Journal of High Energy Physics 2018 (4), 1-54, 2018 | 199 | 2018 |
| The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics G Kasieczka, B Nachman, D Shih, O Amram, A Andreassen, ... Reports on progress in physics 84 (12), 124201, 2021 | 187 | 2021 |
| Numerical approach for unstructured quantum key distribution PJ Coles, EM Metodiev, N Lütkenhaus Nature communications 7 (1), 11712, 2016 | 167 | 2016 |
| OmniFold: a method to simultaneously unfold all observables A Andreassen, PT Komiske, EM Metodiev, B Nachman, J Thaler Physical review letters 124 (18), 182001, 2020 | 165 | 2020 |
| Les Houches 2019: physics at TeV colliders: standard model working group report S Amoroso, P Azzurri, J Bendavid, E Bothmann, D Britzger, H Brooks, ... arXiv preprint arXiv:2003.01700, 2020 | 163 | 2020 |
| A storage ring experiment to detect a proton electric dipole moment V Anastassopoulos, S Andrianov, R Baartman, S Baessler, M Bai, ... Review of Scientific Instruments 87 (11), 2016 | 158 | 2016 |
| Metric space of collider events PT Komiske, EM Metodiev, J Thaler Physical review letters 123 (4), 041801, 2019 | 157 | 2019 |
| Pileup mitigation with machine learning (PUMML) PT Komiske, EM Metodiev, B Nachman, MD Schwartz Journal of High Energy Physics 2017 (12), 1-20, 2017 | 126 | 2017 |
| Jet topics: disentangling quarks and gluons at colliders EM Metodiev, J Thaler Physical Review Letters 120 (24), 241602, 2018 | 115 | 2018 |
| Learning to classify from impure samples with high-dimensional data PT Komiske, EM Metodiev, B Nachman, MD Schwartz Physical Review D 98 (1), 011502, 2018 | 106 | 2018 |
| An operational definition of quark and gluon jets PT Komiske, EM Metodiev, J Thaler Journal of High Energy Physics 2018 (11), 1-36, 2018 | 88 | 2018 |
| A theory of quark vs. gluon discrimination AJ Larkoski, EM Metodiev Journal of High Energy Physics 2019 (10), 1-53, 2019 | 77 | 2019 |
| The hidden geometry of particle collisions PT Komiske, EM Metodiev, J Thaler Journal of High Energy Physics 2020 (7), 1-53, 2020 | 69 | 2020 |
| Exploring the space of jets with CMS open data PT Komiske, R Mastandrea, EM Metodiev, P Naik, J Thaler Physical Review D 101 (3), 034009, 2020 | 59 | 2020 |
| Scaffolding simulations with deep learning for high-dimensional deconvolution A Andreassen, PT Komiske, EM Metodiev, B Nachman, A Suresh, ... arXiv preprint arXiv:2105.04448, 2021 | 26 | 2021 |
| Cutting multiparticle correlators down to size PT Komiske, EM Metodiev, J Thaler Physical Review D 101 (3), 036019, 2020 | 24 | 2020 |
Patrick T. Komiske IIIMassachusetts Institute of TechnologyПодтвержден адрес электронной почты в домене mit.edu
