Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the
forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor
antineutrino interactions. In this context, inverse beta decay represents the
golden channel for antineutrino detection, providing a pair of correlated
events, thus a strong experimental signature to distinguish the signal from a
variety of backgrounds. However, given the low cross-section of antineutrino
interactions, the development of a powerful event selection algorithm becomes
imperative to achieve effective discrimination between signal and backgrounds.
In this study, we introduce a machine learning (ML) model to achieve this goal:
a fully connected neural network as a powerful signal-background discriminator
for a large liquid scintillator detector. We demonstrate, using the JUNO
detector as an example, that, despite the already high efficiency of a
cut-based approach, the presented ML model can further improve the overall
event selection efficiency. Moreover, it allows for the retention of signal
events at the detector edges that would otherwise be rejected because of the
overwhelming amount of background events in that region. We also present the
first interpretable analysis of the ML approach for event selection in reactor
neutrino experiments. This method provides insights into the decision-making
process of the model and offers valuable information for improving and updating
traditional event selection approaches.

通过机器学习模型，我们展示了在液体闪烁体探测器中，如何用全连接神经网络作为强大的事例选择工具，以区分信号和背景，并能在探测器边缘仍保留信号事件，提供了反应堆中微子实验中事件选择的解释性分析，从而可用于改进和更新传统的事件选择方法。

大型液体闪烁体探测器中电子反中微子选择的可解释机器学习方法

Interpretable machine learning approach for electron antineutrino  selection in a large liquid scintillator detector

Constructing a timeline requires identifying the chronological order of
events in an article. In prior timeline construction datasets, temporal orders
are typically annotated by either event-to-time anchoring or event-to-event
pairwise ordering, both of which suffer from missing temporal information. To
mitigate the issue, we develop a new evaluation dataset, TimeSET, consisting of
single-document timelines with document-level order annotation. TimeSET
features saliency-based event selection and partial ordering, which enable a
practical annotation workload. Aiming to build better automatic timeline
construction systems, we propose a novel evaluation framework to compare
multiple task formulations with TimeSET by prompting open LLMs, i.e., Llama 2
and Flan-T5. Considering that identifying temporal orders of events is a core
subtask in timeline construction, we further benchmark open LLMs on existing
event temporal ordering datasets to gain a robust understanding of their
capabilities. Our experiments show that (1) NLI formulation with Flan-T5
demonstrates a strong performance among others, while (2) timeline construction
and event temporal ordering are still challenging tasks for few-shot LLMs. Our
code and data are available at this https URL

构建时间线需要识别文章中事件的时间顺序。为了解决缺失时间信息的问题，我们开发了一个新的评估数据集 TimeSET，其中包含具有文档级顺序注释的单文档时间线。通过使用基于显著性的事件选择和部分排序，TimeSET 能够实现实用的注释工作负载。为了构建更好的自动时间线构建系统，我们提出了一个新的评估框架，通过提示开放式 LLMs（例如 Llama 2 和 Flan-T5）使用 TimeSET 来比较多个任务表述。考虑到识别事件的时间顺序是时间线构建中的核心子任务，我们还在现有的事件时间顺序数据集上对开放式 LLMs 进行了基准测试，以获得他们能力的稳健理解。我们的实验结果表明，（1）采用 Flan-T5 的 NLI 表述在其他表述中呈现出较强的性能，而（2）时间线构建和事件时间顺序仍然是少样本 LLMs 面临的挑战性任务。我们的代码和数据可以在此链接找到。

使用 LLMs 进行时间线构建的配方比较

Formulation Comparison for Timeline Construction using LLMs

Experimental particle physics demands a sophisticated trigger and acquisition
system capable to efficiently retain the collisions of interest for further
investigation. Heterogeneous computing with the employment of FPGA cards may
emerge as a trending technology for the triggering strategy of the upcoming
high-luminosity program of the Large Hadron Collider at CERN. In this context,
we present two machine-learning algorithms for selecting events where neutral
long-lived particles decay within the detector volume studying their accuracy
and inference time when accelerated on commercially available Xilinx FPGA
accelerator cards. The inference time is also confronted with a CPU- and
GPU-based hardware setup. The proposed new algorithms are proven efficient for
the considered benchmark physics scenario and their accuracy is found to not
degrade when accelerated on the FPGA cards. The results indicate that all
tested architectures fit within the latency requirements of a second-level
trigger farm and that exploiting accelerator technologies for real-time
processing of particle-physics collisions is a promising research field that
deserves additional investigations, in particular with machine-learning models
with a large number of trainable parameters.

本研究对于 FPGA 卡驱动的机器学习算法的使用进行探讨，并证明在高亮度程序触发策略和事件选择中，这种算法具有较高的准确性和较低的推理时间。