Riccardo Crupi, G. Dilillo, G. Della Casa, Fabrizio Fiore, Andrea Vacchi
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引用次数: 0
摘要
利用空间X/伽马射线光子探测器探测伽马射线暴(GRB)取决于可靠的背景计数率估计。本研究的重点是评估一种基于神经网络的数据驱动背景估算器,该估算器旨在适应各种 X 射线/伽马射线空间望远镜。为了评估所提出的估计器的有效性和局限性,进行了三次试验。首先,我们采用了量子回归法来获得具有置信区间预测的估计值。其次,我们评估了神经网络的性能,强调四个月的数据集足以进行训练。我们测试了其在不同时间背景下的适应性,确定了其局限性,并建议针对每个特定时期进行再训练。第三,利用可解释人工智能(XAI)技术,我们深入研究了神经网络的输出,确定了在太阳最大期间训练的网络与在太阳最小期间训练的网络之间的区别。这需要对不同太阳条件下的神经网络行为进行全面分析。
Enhancing Gamma-Ray Burst Detection: Evaluation of Neural Network Background Estimator and Explainable AI Insights
The detection of Gamma-Ray Bursts (GRBs) using spaceborne X/gamma-ray photon detectors depends on a reliable background count rate estimate. This study focuses on evaluating a data-driven background estimator based on a neural network designed to adapt to various X/gamma-ray space telescopes. Three trials were conducted to assess the effectiveness and limitations of the proposed estimator. Firstly, quantile regression was employed to obtain an estimation with a confidence range prediction. Secondly, we assessed the performance of the neural network, emphasizing that a dataset of four months is sufficient for training. We tested its adaptability across various temporal contexts, identified its limitations and recommended re-training for each specific period. Thirdly, utilizing Explainable Artificial Intelligence (XAI) techniques, we delved into the neural network output, determining distinctions between a network trained during solar maxima and one trained during solar minima. This entails conducting a thorough analysis of the neural network behavior under varying solar conditions.
GalaxiesPhysics and Astronomy-Astronomy and Astrophysics
CiteScore
4.90
自引率
12.00%
发文量
100
审稿时长
11 weeks
期刊介绍:
Es una revista internacional de acceso abierto revisada por pares que proporciona un foro avanzado para estudios relacionados con astronomía, astrofísica y cosmología. Areas temáticas Astronomía Astrofísica Cosmología Astronomía observacional: radio, infrarrojo, óptico, rayos X, neutrino, etc. Ciencia planetaria Equipos y tecnologías de astronomía. Ingeniería Aeroespacial Análisis de datos astronómicos. Astroquímica y Astrobiología. Arqueoastronomía Historia de la astronomía y cosmología. Problemas filosóficos en cosmología.