Eliminating Cosmic Ray Backgrounds for SBND’s sub-GeV Dark Matter Search

Eliminating Cosmic Ray Backgrounds for SBND’s sub-GeV Dark Matter Search Pub Date : 1900-01-01 DOI:10.2172/1879671

Joseph P. Surdutovich

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Abstract

In late Fall 2023 the SBND (Short-Baseline Near Detector) experiment on the Booster Neutrino Beam (BNB) at Fermilab will begin taking data. Fixed-target neutrino experiments can be used to probe the sub-GeV Dark Matter (DM) spectrum.1 Due to SBND’s proximity to the beam target, it will have access to very high statistics, including DM particles produced in the beam. DM scattering events are simulated using the BdNMC event generator.2 The DM events simulated in SBND are subsequently reconstructed using the Pandora pattern recognition suite of algorithms. In this work we focus on DM-e neutral current (NC) elastic scattering with no other hadronic behavior at the vertex. We next implement two models in order to differentiate between the cosmic and DM slices. We build both a traditional BDT, which was used in the original CRUMBS analysis and a more sophisticated nonlinear Neural Network (NN). Both were trained separately on distinctly optimized parameters to perform binary classification between the two types of models. We train both models on 20 epochs and use a 0.35 learning rate for the BDT, compared to a learning rate of 0.001 for the NN.

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消除宇宙射线背景为SBND的亚gev暗物质搜索

2023年秋末，费米实验室的增强型中微子束(BNB)上的SBND(短基线近探测器)实验将开始采集数据。固定目标中微子实验可用于探测亚gev暗物质(DM)光谱由于SBND靠近波束目标，它将获得非常高的统计数据，包括波束中产生的DM颗粒。利用BdNMC事件发生器模拟DM散射事件随后，利用Pandora模式识别算法对在SBND中模拟的DM事件进行重构。在本工作中，我们重点研究了在顶点没有其他强子行为的DM-e中性电流(NC)弹性散射。接下来我们将实现两个模型，以区分宇宙切片和DM切片。我们建立了一个传统的BDT(用于最初的碎屑分析)和一个更复杂的非线性神经网络(NN)。两者分别在明显优化的参数上进行训练，在两种模型之间进行二值分类。我们在20个epoch上训练两个模型，BDT的学习率为0.35，而NN的学习率为0.001。

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