Fast and Flexible Analysis of Direct Dark Matter Search Data with Machine Learning
LUX Collaboration, D. S. Akerib, S. Alsum, H. M. Araújo, X. Bai, J. Balajthy, J. Bang, A. Baxter, E. P. Bernard, A. Bernstein, T. P. Biesiadzinski, E. M. Boulto… 展开作者
LUX Collaboration, D. S. Akerib, S. Alsum, H. M. Araújo, X. Bai, J. Balajthy, J. Bang, A. Baxter, E. P. Bernard, A. Bernstein, T. P. Biesiadzinski, E. M. Boulton, B. Boxer, P. Brás, S. Burdin, D. Byram, N. Carrara, M. C. Carmona-Benitez, C. Chan, J. E. Cutter, L. de Viveiros, E. Druszkiewicz, J. Ernst, A. Fan, S. Fiorucci, R. J. Gaitskell, C. Ghag, M. G. D. Gilchriese, C. Gwilliam, C. R. Hall, S. J. Haselschwardt, S. A. Hertel, D. P. Hogan, M. Horn, D. Q. Huang, C. M. Ignarra, R. G. Jacobsen, O. Jahangir, W. Ji, K. Kamdin, K. Kazkaz, D. Khaitan, E. V. Korolkova, S. Kravitz, V. A. Kudryavtsev, E. Leason, B. G. Lenardo, K. T. Lesko, J. Liao, J. Lin, A. Lindote, M. I. Lopes, A. Manalaysay, R. L. Mannino, N. Marangou, D. N. McKinsey, D. -M. Mei, J. A. Morad, A. St. J. Murphy, A. Naylor, C. Nehrkorn, H. N. Nelson, F. Neves, A. Nilima, K. C. Oliver-Mallory, K. J. Palladino, C. Rhyne, Q. Riffard, G. R. C. Rischbieter, P. Rossiter, S. Shaw, T. A. Shutt, C. Silva, M. Solmaz, V. N. Solovov, P. Sorensen, T. J. Sumner, N. Swanson, M. Szydagis, D. J. Taylor, R. Taylor, W. C. Taylor, B. P. Tennyson, P. A. Terman, D. R. Tiedt, W. H. To, L. Tvrznikova, U. Utku, A. Vacheret, A. Vaitkus, V. Velan, R. C. Webb, J. T. White, T. J. Whitis, M. S. Witherell, F. L. H. Wolfs, D. Woodward, X. Xian, J. Xu, C. Zhang
We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture non-linear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs such as light and charge signal strengths. This technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable.
Testing cosmological structure formation in a Unified Dark Matter-Energy model with fast transition
Instituto de Astrofísica e Ciências do Espaço, Faculdade de Ciências, Universidade de Lisboa（里斯本大学理学院空间科学与天体物理研究所）；Departamento de Física, Faculdade de Ciências, Universidade de Lisboa（里斯本大学理学院物理系）；Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼查大学）
Fast and Flexible Neutrino Decoupling Part I: The Standard Model
CERN（欧洲核子研究中心）；SUBATECH UMR 6457 (IMT Atlantique, Nantes Université, IN2P3/CNRS)（SUBATECH联合研究实验室（南特高等矿业学院，南特大学，法国国家科学研究中心））；University of Bern（伯尔尼大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
