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Articles 1 - 3 of 3
Full-Text Articles in Physics
Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides
Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides
Computer Science Faculty Publications
In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …
Artificial Intelligence For The Electron Ion Collider (Ai4eic), C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, M. Battaglieri, J. Bernauer, M. Bondì, N. Branson, T. Britton, A. Butter, I. Chahrour, P. Chatagnon, E. Cisbani, E. W. Cline, S. Dash, C. Dean, W. Deconinck, A. Deshpande, M. Diefenthaler, R. Ent, C. Fanelli, M. Finger, M. Finger Jr., E. Fol, S. Furletov, Y. Gao, J. Giroux, N. C. Gunawardhana Waduge, O. Hassan, P. L. Hegde, R. J. Hernandez-Pinto, A. Hiller Blin, T. Horn, J. Huang, A. Jalotra, D. Jayakodige, B. Joo, M. Junaid, N. Kalantarians, P. Karande, B. Kriesten, R. Kunnawalkam Elayavalli, Y. Li, M. Lin, F. Liu, S. Liuti, G. Matousek, M. Mceneaney, D. Mcspadden, T. Menzo, T. Miceli, V. Mikuni, R. Montgomery, B. Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, G. N. Perdue, W. Phelps, M. L. Purschke, H. Rajendran, K. Rajput, Y. Ren, D. F. Renteria-Estrada, D. Richford, B. J. Roy, D. Roy, A. Saini, N. Sato, T. Satogata, G. Sborlini, M. Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, A. -N. Tawfik, F. Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, A. Vossen, E. Walter, D. Whiteson, M. Williams, S. Wu, N. Zachariou, P. Zurita
Artificial Intelligence For The Electron Ion Collider (Ai4eic), C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, M. Battaglieri, J. Bernauer, M. Bondì, N. Branson, T. Britton, A. Butter, I. Chahrour, P. Chatagnon, E. Cisbani, E. W. Cline, S. Dash, C. Dean, W. Deconinck, A. Deshpande, M. Diefenthaler, R. Ent, C. Fanelli, M. Finger, M. Finger Jr., E. Fol, S. Furletov, Y. Gao, J. Giroux, N. C. Gunawardhana Waduge, O. Hassan, P. L. Hegde, R. J. Hernandez-Pinto, A. Hiller Blin, T. Horn, J. Huang, A. Jalotra, D. Jayakodige, B. Joo, M. Junaid, N. Kalantarians, P. Karande, B. Kriesten, R. Kunnawalkam Elayavalli, Y. Li, M. Lin, F. Liu, S. Liuti, G. Matousek, M. Mceneaney, D. Mcspadden, T. Menzo, T. Miceli, V. Mikuni, R. Montgomery, B. Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, G. N. Perdue, W. Phelps, M. L. Purschke, H. Rajendran, K. Rajput, Y. Ren, D. F. Renteria-Estrada, D. Richford, B. J. Roy, D. Roy, A. Saini, N. Sato, T. Satogata, G. Sborlini, M. Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, A. -N. Tawfik, F. Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, A. Vossen, E. Walter, D. Whiteson, M. Williams, S. Wu, N. Zachariou, P. Zurita
Computer Science Faculty Publications
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. …
Gpu Accelerated Long-Term Simulations Of Beam-Beam Effects In Colliders, B. Terzić, V. Morozov, Y. Roblin, F. Lin, H. Zhang, M. Aturban, D. Ranjan, M. Zubair
Gpu Accelerated Long-Term Simulations Of Beam-Beam Effects In Colliders, B. Terzić, V. Morozov, Y. Roblin, F. Lin, H. Zhang, M. Aturban, D. Ranjan, M. Zubair
Computer Science Faculty Publications
We present an update on the development of the new code for long-term simulation of beam-beam effects in particle colliders. The underlying physical model relies on a matrix-based arbitrary-order particle tracking (including a symplectic option) for beam transport and the generalized Bassetti-Erskine approximation for beam-beam interaction. The computations are accelerated through a parallel implementation on a hybrid GPU/CPU platform. With the new code, previously computationally prohibitive long-term simulations become tractable. The new code will be used to model the proposed Medium-energy Electron-Ion Collider (MEIC) at Jefferson Lab.