Statistical Issues on the Neutrino Mass Hierarchy with

IF 1.5 4区 物理与天体物理 Q3 PHYSICS, PARTICLES & FIELDS Advances in High Energy Physics Pub Date : 2024-05-14 DOI:10.1155/2024/9339959
F. Sawy, L. Stanco
{"title":"Statistical Issues on the Neutrino Mass Hierarchy with","authors":"F. Sawy, L. Stanco","doi":"10.1155/2024/9339959","DOIUrl":null,"url":null,"abstract":"The neutrino mass hierarchy determination (<svg height=\"6.20643pt\" style=\"vertical-align:-0.2585797pt\" version=\"1.1\" viewbox=\"-0.0498162 -5.94785 6.02377 6.20643\" width=\"6.02377pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g></svg> MHD) is one of the main goals of the major current and future neutrino experiments. The statistical analysis usually proceeds from a standard method, a single-dimensional estimator <svg height=\"15.2296pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 59.3592 15.2296\" width=\"59.3592pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,4.498,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,10.738,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,23.234,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,33.771,0)\"><use xlink:href=\"#g113-133\"></use></g><g transform=\"matrix(.013,0,0,-0.013,42.098,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,49.666,-5.741)\"><use xlink:href=\"#g50-51\"></use></g><g transform=\"matrix(.013,0,0,-0.013,54.612,0)\"></path></g></svg> that shows some drawbacks and concerns, together with a debatable strategy. The drawbacks and considerations of the standard method will be explained through the following three main issues. The first issue corresponds to the limited power of the standard method. The <svg height=\"15.2296pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 21.0047 15.2296\" width=\"21.0047pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-133\"></use></g><g transform=\"matrix(.013,0,0,-0.013,8.327,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,15.895,-5.741)\"><use xlink:href=\"#g50-51\"></use></g></svg> estimator provides us with different results when different simulation procedures were used. Regarding the second issue, when <svg height=\"16.9233pt\" style=\"vertical-align:-5.3319pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 43.1456 16.9233\" width=\"43.1456pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,7.568,-5.741)\"><use xlink:href=\"#g50-51\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,6.981,3.784)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,14.57,3.784)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,17.073,3.784)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,22.198,3.784)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,25.456,3.784)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,32.218,3.784)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,39.24,3.784)\"></path></g></svg> and <svg height=\"16.9233pt\" style=\"vertical-align:-5.3319pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 39.4737 16.9233\" width=\"39.4737pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,7.568,-5.741)\"><use xlink:href=\"#g50-51\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,6.981,3.784)\"><use xlink:href=\"#g58-107\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,14.57,3.784)\"><use xlink:href=\"#g58-103\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,17.073,3.784)\"><use xlink:href=\"#g58-108\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,22.198,3.784)\"><use xlink:href=\"#g50-41\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,25.456,3.784)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,28.559,3.784)\"><use xlink:href=\"#g190-73\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,35.568,3.784)\"><use xlink:href=\"#g50-42\"></use></g></svg> are drawn in a 2D map, their strong positive correlation manifests <svg height=\"15.2296pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 12.6404 15.2296\" width=\"12.6404pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,7.568,-5.741)\"><use xlink:href=\"#g50-51\"></use></g></svg> as a bidimensional variable, instead of a single-dimensional estimator. The overlapping between the <svg height=\"15.2296pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 12.6404 15.2296\" width=\"12.6404pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,7.568,-5.741)\"><use xlink:href=\"#g50-51\"></use></g></svg> distributions of the two hypotheses leads to an experiment sensitivity reduction. The third issue corresponds to the robustness of the standard method. When the JUNO sensitivity is obtained using different procedures, either with <svg height=\"15.2296pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 21.0047 15.2296\" width=\"21.0047pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-133\"></use></g><g transform=\"matrix(.013,0,0,-0.013,8.327,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,15.895,-5.741)\"><use xlink:href=\"#g50-51\"></use></g></svg> as one-dimensional or <svg height=\"15.2296pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 12.6404 15.2296\" width=\"12.6404pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,7.568,-5.741)\"><use xlink:href=\"#g50-51\"></use></g></svg> as two-dimensional estimator, the experimental sensitivity varies with the different values of the atmospheric mass, the input parameter. We computed the oscillation of <svg height=\"16.9259pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -13.2877 27.8683 16.9259\" width=\"27.8683pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g><rect height=\"0.65243\" width=\"20.905\" x=\"3.419\" y=\"-12.5855\"></rect><g transform=\"matrix(.013,0,0,-0.013,3.419,0)\"><use xlink:href=\"#g113-133\"></use></g><g transform=\"matrix(.013,0,0,-0.013,11.746,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,19.314,-4.176)\"><use xlink:href=\"#g50-51\"></use></g><g transform=\"matrix(.013,0,0,-0.013,24.324,0)\"><use xlink:href=\"#g113-9\"></use></g></svg> with the input parameter values, <span><svg height=\"16.9559pt\" style=\"vertical-align:-5.3645pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 50.5505 16.9559\" width=\"50.5505pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-9\"></use></g><g transform=\"matrix(.013,0,0,-0.013,3.419,0)\"><use xlink:href=\"#g113-133\"></use></g><g transform=\"matrix(.013,0,0,-0.013,11.746,0)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,22.003,-5.741)\"><use xlink:href=\"#g50-51\"></use></g><g transform=\"matrix(.013,0,0,-0.013,26.95,0)\"><use xlink:href=\"#g113-9\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,30.369,3.132)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,32.807,3.132)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,37.612,3.132)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,42.279,3.132)\"></path></g><g transform=\"matrix(.0091,0,0,-0.0091,47.002,3.132)\"></path></g></svg>.</span> The MH significance using the standard method, <span><svg height=\"15.2296pt\" style=\"vertical-align:-3.6382pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 21.0047 15.2296\" width=\"21.0047pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-133\"></use></g><g transform=\"matrix(.013,0,0,-0.013,8.327,0)\"><use xlink:href=\"#g113-244\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,15.895,-5.741)\"><use xlink:href=\"#g50-51\"></use></g></svg>,</span> strongly depends on the values of the parameter <span><svg height=\"16.9559pt\" style=\"vertical-align:-5.3645pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 50.5505 16.9559\" width=\"50.5505pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-9\"></use></g><g transform=\"matrix(.013,0,0,-0.013,3.419,0)\"><use xlink:href=\"#g113-133\"></use></g><g transform=\"matrix(.013,0,0,-0.013,11.746,0)\"><use xlink:href=\"#g113-110\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,22.003,-5.741)\"><use xlink:href=\"#g50-51\"></use></g><g transform=\"matrix(.013,0,0,-0.013,26.95,0)\"><use xlink:href=\"#g113-9\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,30.369,3.132)\"><use xlink:href=\"#g190-106\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,32.807,3.132)\"><use xlink:href=\"#g190-111\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,37.612,3.132)\"><use xlink:href=\"#g190-113\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,42.279,3.132)\"><use xlink:href=\"#g190-118\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,47.002,3.132)\"><use xlink:href=\"#g190-117\"></use></g></svg>.</span> Consequently, the experiment sensitivity depends on the precision of the atmospheric mass. This evaluation of the standard method confirms the drawbacks.","PeriodicalId":7498,"journal":{"name":"Advances in High Energy Physics","volume":"36 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in High Energy Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1155/2024/9339959","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, PARTICLES & FIELDS","Score":null,"Total":0}
引用次数: 0

Abstract

The neutrino mass hierarchy determination ( MHD) is one of the main goals of the major current and future neutrino experiments. The statistical analysis usually proceeds from a standard method, a single-dimensional estimator that shows some drawbacks and concerns, together with a debatable strategy. The drawbacks and considerations of the standard method will be explained through the following three main issues. The first issue corresponds to the limited power of the standard method. The estimator provides us with different results when different simulation procedures were used. Regarding the second issue, when and are drawn in a 2D map, their strong positive correlation manifests as a bidimensional variable, instead of a single-dimensional estimator. The overlapping between the distributions of the two hypotheses leads to an experiment sensitivity reduction. The third issue corresponds to the robustness of the standard method. When the JUNO sensitivity is obtained using different procedures, either with as one-dimensional or as two-dimensional estimator, the experimental sensitivity varies with the different values of the atmospheric mass, the input parameter. We computed the oscillation of with the input parameter values, . The MH significance using the standard method, , strongly depends on the values of the parameter . Consequently, the experiment sensitivity depends on the precision of the atmospheric mass. This evaluation of the standard method confirms the drawbacks.
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中微子质量层次的统计问题与
中微子质量层次测定(MHD)是当前和未来主要中微子实验的主要目标之一。统计分析通常采用一种标准方法,即一种单维度估算方法,这种方法存在一些缺陷和问题,其策略也值得商榷。标准方法的缺点和问题将通过以下三个主要问题来解释。第一个问题是标准方法的能力有限。在使用不同的模拟程序时,估算器会提供不同的结果。关于第二个问题,当和在二维地图中绘制时,它们之间的强正相关性表现为二维变量,而不是单维估计值。两个假设的分布重叠导致实验灵敏度降低。第三个问题与标准方法的稳健性有关。当使用不同的程序(一维或二维估计器)获得 JUNO 的灵敏度时,实验灵敏度会随着大气质量(输入参数)的不同值而变化。我们计算了大气质量随输入参数值(......)的摆动。使用标准方法计算的 MH 值(Ⅳ)与参数值(Ⅴ)密切相关。因此,实验灵敏度取决于大气质量的精度。对标准方法的评估证实了其缺点。
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来源期刊
Advances in High Energy Physics
Advances in High Energy Physics PHYSICS, PARTICLES & FIELDS-
CiteScore
3.40
自引率
5.90%
发文量
55
审稿时长
6-12 weeks
期刊介绍: Advances in High Energy Physics publishes the results of theoretical and experimental research on the nature of, and interaction between, energy and matter. Considering both original research and focussed review articles, the journal welcomes submissions from small research groups and large consortia alike.
期刊最新文献
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