暗物质在硅和锗中的电子激发与深度学习

IF 5 2区物理与天体物理 Q1 Physics and Astronomy

Physical Review D Pub Date : 2025-01-14 DOI:10.1103/physrevd.111.l011702

Riccardo Catena, Einar Urdshals

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引用次数: 0

摘要

我们训练了一个深度神经网络（DNN）来输出硅和锗探测器中暗物质（DM）诱导的电子激发率。我们的DNN相对于现有方法（即dark-）提供了大约5个数量级的巨大加速，允许在观察到DM信号的情况下进行广泛的参数扫描。与基于直接计算dm诱导激励率的替代计算框架相比，该网络也更轻，更易于使用。2025年由美国物理学会出版

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Dark matter-induced electron excitations in silicon and germanium with deep learning

We train a deep neural network (DNN) to output rates of dark matter (DM) induced electron excitations in silicon and germanium detectors. Our DNN provides a massive speedup of around 5 orders of magnitude relative to existing methods (i.e., dark-), allowing for extensive parameter scans in the event of an observed DM signal. The network is also lighter and simpler to use than alternative computational frameworks based on a direct calculation of the DM-induced excitation rate.

Published by the American Physical Society

2025

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来源期刊

Physical Review D 物理-天文与天体物理

CiteScore

9.20

自引率

36.00%

发文量

审稿时长

2 months

期刊介绍： Physical Review D (PRD) is a leading journal in elementary particle physics, field theory, gravitation, and cosmology and is one of the top-cited journals in high-energy physics. PRD covers experimental and theoretical results in all aspects of particle physics, field theory, gravitation and cosmology, including: Particle physics experiments, Electroweak interactions, Strong interactions, Lattice field theories, lattice QCD, Beyond the standard model physics, Phenomenological aspects of field theory, general methods, Gravity, cosmology, cosmic rays, Astrophysics and astroparticle physics, General relativity, Formal aspects of field theory, field theory in curved space, String theory, quantum gravity, gauge/gravity duality.