.png){kind=icon}
返回
Anomaly-aware summary statistic from data batches
.png){kind=icon}
OA
AI
DOI：10.1007/JHEP12(2024)093.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Signal-agnostic data exploration based on machine learning could unveil very subtle statistical deviations of collider data from the expected Standard Model of particle physics. The beneficial impact of a large training sample on machine learning solutions motivates the exploration of increasingly large and inclusive samples of acquired data with resource efficient computational methods. In this work we consider the New Physics Learning Machine (NPLM), a multivariate goodness-of-fit test built on the Neyman-Pearson maximum-likelihood-ratio construction, and we address the problem of testing large size samples under computational and storage resource constraints. We propose to perform parallel NPLM routines over batches of the data, and to combine them by locally aggregating over the data-to-reference density ratios learnt by each batch. The resulting data hypothesis defining the likelihood-ratio test is thus shared over the batches, and complies with the assumption that the expected rate of new physical processes is time invariant. We show that this method outperforms the simple sum of the independent tests run over the batches, and can recover, or even surpass, the sensitivity of the single test run over the full data. Beside the significant advantage for the offline application of NPLM to large size samples, the proposed approach offers new prospects toward the use of NPLM to construct anomaly-aware summary statistics in quasi-online data streaming scenarios.
Keyword:
Minimum Bias
Hadron-Hadron Scattering
期刊
IF:
5.5
论文数:
4.0W
被引数:
13.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
A Simple Analytical Expression for the Gradient Induced Potential on Active Implants During MRI
PREAI
分享收藏A kinematic and kinetic analysis of walking after total knee arthroplasty with and without patellar resurfacing全膝关节置换术后有无髌骨表面置换的步态运动学和动力学分析
PREAI
分享收藏Superstitious seasonality in precious metals markets? Evidence from GARCH models with time-varying skewness and kurtosis贵金属市场的迷信季节性？具有时变偏度和峰度的GARCH模型的证据
PREAI
分享收藏Fast kernel methods for data quality monitoring as a goodness-of-fit test用于数据质量监控的快速内核方法作为拟合优度测试OAAI
OAAI分享收藏
查看更多内容