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arXiv 1807.09273hep-phastro-ph.CO
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寻找暗物质的统计挑战

Statistical challenges in the search for dark matter

Sara Algeri, Melissa van Beekveld, Nassim Bozorgnia, Alyson Brooks, J. Alberto Casas, Jessi Cisewski-Kehe, Francis-Yan Cyr-Racine, Thomas D. P. Edwards, Fabio I… 展开作者

Sara Algeri, Melissa van Beekveld, Nassim Bozorgnia, Alyson Brooks, J. Alberto Casas, Jessi Cisewski-Kehe, Francis-Yan Cyr-Racine, Thomas D. P. Edwards, Fabio Iocco, Bradley J. Kavanagh, Judita Mamužić, Siddharth Mishra-Sharma, Wolfgang Rau, Roberto Ruiz de Austri, Benjamin R. Safdi, Pat Scott, Tracy R. Slatyer, Yue-Lin Sming Tsai, Aaron C. Vincent, Christoph Weniger, Jennifer Rittenhouse West, Robert L. Wolpert

摘要:

对暗物质的粒子性质的研究已经引起了许多实验、理论和统计上的挑战。在这里,我们报告了一些统计挑战和解决这些挑战的新技术,正如2018年2月26日至3月3日在阿尔伯塔省班夫的班夫数学创新和发现国际研究站(BIRS)举行的DMStat研讨会上所讨论的那样。

英文摘要:

The search for the particle nature of dark matter has given rise to a number of experimental, theoretical and statistical challenges. Here, we report on a number of these statistical challenges and new techniques to address them, as discussed in the DMStat workshop held Feb 26 - Mar 3 2018 at the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, Alberta.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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