| Colloquium: Machine learning in nuclear physics A Boehnlein, M Diefenthaler, N Sato, M Schram, V Ziegler, C Fanelli, ... Reviews of Modern Physics 94 (3), 031003, 2022 | 114 | 2022 |
| Intruder Configurations in the Isobars: and V Tripathi, SL Tabor, PF Mantica, Y Utsuno, P Bender, J Cook, ... Physical review letters 101 (14), 142504, 2008 | 73 | 2008 |
| Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) Y Alanazi, N Sato, T Liu, W Melnitchouk, P Ambrozewicz, F Hauenstein, ... arXiv preprint arXiv:2001.11103, 2020 | 59 | 2020 |
| Evidence for the microscopic formation of mixed-symmetry states from magnetic moment measurements V Werner, N Benczer-Koller, G Kumbartzki, JD Holt, P Boutachkov, ... Physical Review C 78 (3), 031301, 2008 | 45 | 2008 |
| Approaching the “island of inversion”: PC Bender, CR Hoffman, M Wiedeking, JM Allmond, LA Bernstein, ... Physical Review C 80 (1), 014302, 2009 | 44 | 2009 |
| Commissioning of the active-target time projection chamber J Bradt, D Bazin, F Abu-Nimeh, T Ahn, Y Ayyad, SB Novo, L Carpenter, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2017 | 43 | 2017 |
| Competition between normal and intruder states inside the “island of inversion” V Tripathi, SL Tabor, PF Mantica, Y Utsuno, P Bender, J Cook, ... Physical Review C 76 (2), 021301, 2007 | 42 | 2007 |
| AI for nuclear physics P Bedaque, A Boehnlein, M Cromaz, M Diefenthaler, L Elouadrhiri, ... The European Physical Journal A 57, 1-27, 2021 | 39 | 2021 |
| Excited intruder states in V Tripathi, SL Tabor, P Bender, CR Hoffman, S Lee, K Pepper, M Perry, ... Physical Review C 77 (3), 034310, 2008 | 39 | 2008 |
| Machine learning methods for track classification in the AT-TPC MP Kuchera, R Ramanujan, JZ Taylor, RR Strauss, D Bazin, J Bradt, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2019 | 35 | 2019 |
| Precision studies of QCD in the low energy domain of the EIC VD Burkert, L Elouadrhiri, A Afanasev, J Arrington, M Contalbrigo, ... Progress in Particle and Nuclear Physics 131, 104032, 2023 | 32 | 2023 |
| LISE++ software updates and future plans MP Kuchera, OB Tarasov, D Bazin, B Sherril, KV Tarasova Journal of Physics: Conference Series 664 (7), 072029, 2015 | 21 | 2015 |
| Complementary studies of and the systematics of intruder states TA Hinners, V Tripathi, SL Tabor, A Volya, PC Bender, CR Hoffman, S Lee, ... Physical Review C 77 (3), 034305, 2008 | 19 | 2008 |
| Bayesian neural networks for fast SUSY predictions BS Kronheim, MP Kuchera, HB Prosper, A Karbo Physics Letters B 813, 136041, 2021 | 15 | 2021 |
| Study of spectroscopic factors at N= 29 using isobaric analogue resonances in inverse kinematics J Bradt, Y Ayyad, D Bazin, W Mittig, T Ahn, SB Novo, BA Brown, ... Physics Letters B 778, 155-160, 2018 | 15 | 2018 |
| Higher-spin structures in and JM VonMoss, SL Tabor, V Tripathi, A Volya, B Abromeit, PC Bender, ... Physical Review C 92 (3), 034301, 2015 | 14 | 2015 |
| Plans for performance and model improvements in the LISE++ software MP Kuchera, OB Tarasov, D Bazin, BM Sherrill, KV Tarasova Nuclear Instruments and Methods in Physics Research Section B: Beam …, 2016 | 13 | 2016 |
| cfat-gan: Conditional simulation of electron–proton scattering events with variate beam energies by a feature augmented and transformed generative adversarial network L Velasco, E McClellan, N Sato, P Ambrozewicz, T Liu, W Melnitchouk, ... Deep Learning Applications, Volume 3, 245-261, 2022 | 11 | 2022 |
| TensorBNN: Bayesian inference for neural networks using TensorFlow BS Kronheim, MP Kuchera, HB Prosper Computer Physics Communications 270, 108168, 2022 | 11 | 2022 |
| Machine learning-based event generator for electron-proton scattering Y Alanazi, P Ambrozewicz, M Battaglieri, ANH Blin, MP Kuchera, Y Li, ... Physical Review D 106 (9), 096002, 2022 | 10 | 2022 |
