An Unsupervised Machine Learning Method for Electron–Proton Discrimination of the DAMPE Experiment

An Unsupervised Machine Learning Method for Electron–Proton Discrimination of the DAMPE Experiment
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DAMPE 实验电子质子辨别的无监督机器学习方法

DOI：
10.3390/universe8110570
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发表时间：
2022-10
期刊：
Universe
影响因子：
2.9
通讯作者：
Qiang Yuan
Qiang Yuan
中科院分区：
物理与天体物理3区
文献类型：
--
作者：
Zhihui Xu;Xiang Li;Mingyang Cui;Chuan Yue;Wei Jiang;Wenhao Li;Qiang Yuan

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银河系宇宙射线主要由高能原子核组成，电子（和正电子）不到1%。电子和正电子分量的精确测量需要一种非常有效的方法来拒绝核背景，主要是质子。在这项工作中，我们开发了一种无监督的机器学习方法来识别暗物质粒子探测器（DAMPE）实验中宇宙射线质子中的电子和正电子。与DAMPE实验中使用的有监督学习方法相比，这种无监督方法除了背景估计过程之外完全依赖于真实的数据。因此，它可以有效地减少模拟的不确定性。对于80-128 GeV、350-700 GeV和2-5 TeV三个能量范围的电子和正电子，发现电子样品中的剩余背景分数约为（0.45 ± 0.02）%，（0.52 ± 0.04）%和（10.55 ± 1.80）%，背景抑制能力分别约为（6.21 ± 0.03）× 104、（9.03 ± 0.05）× 104和（3.06 ± 0.32）× 104。该方法在所有能量范围内都比传统的形态参数化方法具有更高的背景抑制能力，并且与监督机器学习方法相比，达到了相当的背景抑制性能。
Galactic cosmic rays are mostly made up of energetic nuclei, with less than 1% of electrons (and positrons). Precise measurement of the electron and positron component requires a very efficient method to reject the nuclei background, mainly protons. In this work, we develop an unsupervised machine learning method to identify electrons and positrons from cosmic ray protons for the Dark Matter Particle Explorer (DAMPE) experiment. Compared with the supervised learning method used in the DAMPE experiment, this unsupervised method relies solely on real data except for the background estimation process. As a result, it could effectively reduce the uncertainties from simulations. For three energy ranges of electrons and positrons, 80–128 GeV, 350–700 GeV, and 2–5 TeV, the residual background fractions in the electron sample are found to be about (0.45 ± 0.02)%, (0.52 ± 0.04)%, and (10.55 ± 1.80)%, and the background rejection power is about (6.21 ± 0.03) × 104, (9.03 ± 0.05) × 104, and (3.06 ± 0.32) × 104, respectively. This method gives a higher background rejection power in all energy ranges than the traditional morphological parameterization method and reaches comparable background rejection performance compared with supervised machine learning methods.
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DOI： 10.1016/j.scib.2021.12.015
发表时间： 2021-12
期刊： Science bulletin
影响因子： 18.9
作者：
Francesco Alemanno;Qimeng An;P. Azzarello;F. Barbato;P. Bernardini;Xiaomei Bi;M. Cai;E. Casilli
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发表时间： 2019
期刊： Astroparticle Physics
影响因子： 3.5
作者：
Ambrosi G.;An Q.;Asf;iyarov R.;Azzarello P.;Bernardini P.;Cai M. S.;Caragiulo M.;Chang J.;Chen D. Y.;Chen H. F.;Chen J. L.;Chen W.;Cui M. Y.;Cui T. S.;Dai H. T.;D'Amone A.;De Benedittis A.;De Mitri I.;Ding M.;Di Santo M.;Dong J. N.;Dong T. K.;Dong Y. F.
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DOI： 10.22323/1.236.0273
发表时间： 2016-08
期刊： Physical review letters
影响因子： 8.6
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Automatic Choice of Dimensionality for PCA
DOI： --
发表时间： 2000
期刊： --
影响因子： --
作者：
T. Minka
通讯作者： T. Minka
The Energy Spectrum of Cosmic-Ray Electrons from 10 to 100 GeV Observed with a Highly Granulated Imaging Calorimeter
DOI： 10.1086/322274
发表时间： 2001-10
期刊： The Astrophysical Journal
影响因子： --
作者：
S. Torii;T. Tamura;N. Tateyama;K. Yoshida;J. Nishimura;T. Yamagami;H. Murakami;T. Kobayashi;Y. Komori;K. Kasahara;T. Yuda
通讯作者： S. Torii;T. Tamura;N. Tateyama;K. Yoshida;J. Nishimura;T. Yamagami;H. Murakami;T. Kobayashi;Y. Komori;K. Kasahara;T. Yuda