Zobrazeno 1 - 10
of 1 491
pro vyhledávání: '"Whiteson, Daniel"'
The simulation of detector response is a vital aspect of data analysis in particle physics, but current Monte Carlo methods are computationally expensive. Machine learning methods, which learn a mapping from incident particle to detector response, ar
Externí odkaz:
http://arxiv.org/abs/2411.05996
Autor:
Sha, Qiyu, Murnane, Daniel, Fieg, Max, Tong, Shelley, Zakharyan, Mark, Fang, Yaquan, Whiteson, Daniel
Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in ML-based tra
Externí odkaz:
http://arxiv.org/abs/2410.00269
Autor:
Huetsch, Nathan, Villadamigo, Javier Mariño, Shmakov, Alexander, Diefenbacher, Sascha, Mikuni, Vinicius, Heimel, Theo, Fenton, Michael, Greif, Kevin, Nachman, Benjamin, Whiteson, Daniel, Butter, Anja, Plehn, Tilman
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches ar
Externí odkaz:
http://arxiv.org/abs/2404.18807
Autor:
Shmakov, Alexander, Greif, Kevin, Fenton, Michael James, Ghosh, Aishik, Baldi, Pierre, Whiteson, Daniel
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently,
Externí odkaz:
http://arxiv.org/abs/2404.14332
Autor:
Brandes, Len, Modi, Chirag, Ghosh, Aishik, Farrell, Delaney, Lindblom, Lee, Heinrich, Lukas, Steiner, Andrew W., Weber, Fridolin, Whiteson, Daniel
Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such
Externí odkaz:
http://arxiv.org/abs/2403.00287
Autor:
Holder, Ryan, Reddick, John, Cremonesi, Matteo, Berry, Doug, Cheng, Kun, Low, Matthew, Tait, Tim M. P., Whiteson, Daniel
Particle collisions at the energy frontier can probe the nature of invisible dark matter via production in association with recoiling visible objects. We propose a new potential production mode, in which dark matter is produced by the decay of a heav
Externí odkaz:
http://arxiv.org/abs/2311.13578
Autor:
Romero, Alexis, Whiteson, Daniel
Embedding symmetries in the architectures of deep neural networks can improve classification and network convergence in the context of jet substructure. These results hint at the existence of symmetries in jet energy depositions, such as rotational s
Externí odkaz:
http://arxiv.org/abs/2311.06686
Autor:
Witkowski, Edmund, Whiteson, Daniel
Recognizing symmetries in data allows for significant boosts in neural network training. In many cases, however, the underlying symmetry is present only in an idealized dataset, and is broken in the training data, due to effects such as arbitrary and
Externí odkaz:
http://arxiv.org/abs/2311.05952
Searches for new physics in the top quark sector are of great theoretical interest, yet some powerful avenues for discovery remain unexplored. We characterize the expected statistical power of the LHC dataset to constrain the single production of hea
Externí odkaz:
http://arxiv.org/abs/2311.00121
Autor:
Fenton, Michael James, Shmakov, Alexander, Okawa, Hideki, Li, Yuji, Hsiao, Ko-Yang, Hsu, Shih-Chieh, Whiteson, Daniel, Baldi, Pierre
Publikováno v:
Commun Phys 7, 139 (2024)
Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. Anapproach based on a generalized attention mechanism, s
Externí odkaz:
http://arxiv.org/abs/2309.01886