数据分析、统计与概率

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[1] arXiv:2509.04675 (交叉列表自 physics.ins-det) [中文pdf, pdf, 其他]

标题： 检测和校正TES微测热计中的增益跃迁 显示英文标题

标题： Detecting and Correcting Gain Jumps in TES Microcalorimeters

Thomas A. Baker, Daniel T. Becker, Joseph W. Fowler, Mark W. Keller, Daniel S. Swetz, Joel N. Ullom

期刊参考： IEEE应用超导学报，第35卷，第5期，第1-5页，2025年8月，文章号2100405

主题： 仪器与探测器 (physics.ins-det) ; 数据分析、统计与概率 (physics.data-an)

基于过渡边缘传感器（TES）的微热量计阵列正在被积极部署到世界各地的实验室中。 一个TES微热量计阵列会产生大量的数据，而这些设备的用户经验水平各不相同，因此提供能够无需用户监督即可运行的数据采集和分析的稳健软件非常重要。 这种软件应能够处理可能对光谱质量产生不利影响的常见现象。 增益跳跃是其中一种现象，其特征是设备增益的突然变化。 如果不加以处理，增益跳跃会导致光谱质量下降，并引入虚假峰。 我们尚未发现任何之前发表的方法可以在数据采集过程中重置增益跳跃，也没有现有的算法可以校正因增益跳跃而退化的数据。 我们已经开发了用于检测和校正伽马射线TES微热量计中增益跳跃的自动化方法。 我们提出了一种在实时数据采集过程中重置增益跳跃的程序，该程序涉及使用偏置电流将TES短暂驱动到其正常状态。 我们还描述了一种算法，用于定位增益跳跃并在现有微热量计数据中识别独特的增益状态。 最后，我们提供了一种在增益跳跃被识别后进行校正的可能方法。 显示英文摘要

Arrays of microcalorimeters based on transition-edge sensors (TESs) are being actively deployed to laboratories all over the world. A TES microcalorimeter array produces very large quantities of data and users of these devices have varying levels of experience, so it is important to provide robust software for data acquisition and analysis that can function with minimal user supervision. This software should be capable of addressing common phenomena that can adversely affect spectrum quality. Gain jumping is one such phenomenon that is characterized by abrupt changes in the gain of a device. Left unaddressed, gain jumps can degrade spectra by introducing false peaks. We are not aware of any previously published methods for resetting gain jumps during data acquisition or existing algorithms for correcting data that is degraded by gain jumps. We have developed automated methods for detecting and correcting gain jumps in gamma-ray TES microcalorimeters. We present a procedure for resetting gain jumps during a live data acquisition that involves briefly driving the TES into its normal state using the bias current. We also describe an algorithm for locating gain jumps and identifying unique gain states within existing microcalorimeter data. Finally, we provide a possible approach for correcting gain jumps after they have been identified.

[2] arXiv:2509.04718 (交叉列表自 stat.ME) [中文pdf, pdf, html, 其他]

标题： 当对回归均值进行校正比不进行校正更糟糕 显示英文标题

标题： When correcting for regression to the mean is worse than no correction at all

José F. Fontanari, Mauro Santos

主题： 方法论 (stat.ME) ; 数据分析、统计与概率 (physics.data-an) ; 定量方法 (q-bio.QM)

回归到均值（RTM）效应在生物变化研究的统计推断中普遍存在，这使得推断变得复杂。 我们证明了常见的RTM校正方法存在缺陷：由Berry等人提出的、由Kelly和Price在《美国自然学家》中推广的方法在假设检验中不可靠，会导致假阳性结果和假阴性结果，而理论上无偏的Blomqvist方法在样本量有限时效率较差。 我们的研究结果表明，处理RTM最稳健的方法不是校正数据，而是使用粗略斜率并结合对实验可重复性的评估。 最终，我们认为，在没有明确理解实验可重复性的情况下，任何关于差异处理效果的结论在统计上都是没有根据的。 显示英文摘要

The ubiquitous regression to the mean (RTM) effect complicates statistical inference in biological studies of change. We demonstrate that common RTM correction methods are flawed: the Berry et al. method popularized by Kelly & Price in The American Naturalist is unreliable for hypothesis testing, leading to both false positives and negatives, while the theoretically unbiased Blomqvist method has poor efficiency in limited sample sizes. Our findings show that the most robust approach to handling RTM is not to correct the data but to use the crude slope in conjunction with an assessment of the experiment's repeatability. Ultimately, we argue that any conclusion about a differential treatment effect is statistically unfounded without a clear understanding of the experiment's repeatability.

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[3] arXiv:2506.14578 (替换) [中文pdf, pdf, html, 其他]

标题： 大型强子对撞机实验ALICE、ATLAS、CMS和LHCb中的机器学习实时分析综述 显示英文标题

标题： Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb

Laura Boggia, Carlos Cocha, Fotis Giasemis, Joachim Hansen, Patin Inkaew, Kaare Endrup Iversen, Pratik Jawahar, Henrique Pineiro Monteagudo, Micol Olocco, Sten Astrand, Martino Borsato, Leon Bozianu, Steven Schramm, the SMARTHEP Network

评论： 20页，3图

主题： 高能物理 - 实验 (hep-ex) ; 数据分析、统计与概率 (physics.data-an)

高能物理（HEP）领域近年来在使用机器学习（ML）技术方面出现了显著增长。 应用的普及彻底改变了对撞机实验中的数据处理流程，包括大型强子对撞机（LHC）。 在本白皮书中，我们讨论了ML在LHC实时分析（RTA）中日益重要的作用，特别是在大型LHC实验触发系统所带来的独特挑战背景下。 我们描述了在大型LHC实验中使用的少量ML应用，以展示其应用案例的广泛性。 接着，我们强调了HEP社区与工业界之间的合作和互动的重要性，突出了两者之间的共同点和协同效应。 在工业背景下的实时分析的几个跨学科示例中展示了相互的好处。 由SMARTHEP网络编写的这份白皮书并未提供LHC上ML的全面综述，而是提供了特定实时用例的高层次概述。 显示英文摘要

The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionised many aspects of the data processing pipeline at collider experiments including the Large Hadron Collider (LHC). In this whitepaper, we discuss the increasingly crucial role that ML plays in real-time analysis (RTA) at the LHC, namely in the context of the unique challenges posed by the trigger systems of the large LHC experiments. We describe a small selection of the ML applications in use at the large LHC experiments to demonstrate the breadth of use-cases. We continue by emphasising the importance of collaboration and engagement between the HEP community and industry, highlighting commonalities and synergies between the two. The mutual benefits are showcased in several interdisciplinary examples of RTA from industrial contexts. This whitepaper, compiled by the SMARTHEP network, does not provide an exhaustive review of ML at the LHC but rather offers a high-level overview of specific real-time use cases.