Searching for fundamental theories of Nature with unsupervised machine learning

通过无监督机器学习寻找自然的基础理论

• 批准号: 2131196
• 金额: --
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2131196
• 关键词: Searching; fundamental; theories; Nature; unsupervised

In High Energy Particle Physics we contrast data with new theories of Nature. Thosetheories are proposed to solve mysteries such as 1.) what is the Dark Universe made of, 2.)why there is so much more matter than antimatter in the Universe, and 3.) how can a lightHiggs particle exist.To answer these questions, we propose mathematical models and compare withobservations. Sources of data are quite varied and include complex measurements fromthe Large Hadron Collider, underground Dark Matter detection experiments and satelliteinformation on the Cosmic Microwave Background. We need to incorporate all this data ina framework which allows us to test hypotheses, and this is usually done via a statisticalanalysis, e.g. Bayesian, which provides a measure of how well a hypothesis can explaincurrent observations. Alas, this approach has so far been unfruitful and is driving the fieldof Particle Physics to an impasse.In this project, we will take a different and novel approach to search for new physics. Wewill assume that our inability to discover new physics stems from strong theoretical biaseswhich have so far guided analyses. We will instead develop unsupervised searchingtechniques, mining on data for new phenomena, avoiding as much theoretical prejudicesas possible. The project has a strong theoretical component, as the candidate will learn themathematical/physical basis of new physics theories including Dark Matter, the Higgsparticle and Inflation. The candidate will also learn about current unsupervised-learningtechniques and the interpretation of data in High-Energy Physics.The strategy adopted for this project holds the potential to open a new avenue of researchin High Energy Physics. We are convinced that this departure from conventional statisticalanalyses mentioned above is the most effective way to discover new physics from the hugeamount of data produced in the Large Hadron Collider and other experiments of similarscale.Reaching the scientific goals outlined here would require modelling huge amounts of dataat different levels of purity (raw measurements, pseudo-observables, re-interpreted data),and finding patterns which had not been detected due to a focus on smaller sets ofinformation. Hence, we believe that research into unsupervised learning in this context willhave far reaching applications beyond academic pursuits. As the world becomesincreasingly data-orientated, so does our reliance on novel algorithms to make sense of theinformation we have in our possession. To give some examples, we can easily expect thedevelopment of unsupervised learning integrated into facial recognition software and assistin the discovery of new drugs, which provides a boost in the security and medical sectorrespectively.

在高能粒子物理学中，我们将数据与新的自然理论进行对比。提出这些理论是为了解决一些谜题，比如：1)黑暗宇宙是由什么构成的；2)为什么宇宙中的物质比反物质多得多；3)轻的希格斯粒子是如何存在的。为了回答这些问题，我们提出了数学模型，并与观测结果进行了比较。数据的来源非常多样，包括大型强子对撞机的复杂测量，地下暗物质探测实验和宇宙微波背景的卫星信息。我们需要将所有这些数据整合到一个允许我们检验假设的框架中，这通常是通过统计分析来完成的，例如贝叶斯，它提供了一个假设如何很好地解释当前观察结果的度量。遗憾的是，这种方法到目前为止还没有取得成果，并将粒子物理学领域推向了一个僵局。在这个项目中，我们将采取一种不同的、新颖的方法来寻找新的物理学。我们假定，我们无法发现新的物理现象，是因为迄今为止指导分析的强烈的理论偏见。相反，我们将开发无监督搜索技术，从数据中挖掘新现象，尽可能避免理论偏见。该项目有很强的理论成分，因为候选人将学习新的物理理论的数学/物理基础，包括暗物质，希格斯粒子和通货膨胀。候选人还将学习当前的无监督学习技术和高能物理数据的解释。这个项目所采用的策略有可能为高能物理学开辟一条新的研究途径。我们相信，这种与上述传统统计分析的背离，是从大型强子对撞机和其他类似规模的实验中产生的大量数据中发现新物理的最有效方法。要实现这里概述的科学目标，需要对大量不同纯度的数据（原始测量数据、伪观测数据、重新解释的数据）进行建模，并发现由于关注较小的信息集而未被检测到的模式。因此，我们相信在这种情况下对无监督学习的研究将具有远远超出学术追求的应用。随着世界变得越来越以数据为导向，我们也越来越依赖于新的算法来理解我们所拥有的信息。举几个例子，我们可以很容易地预期，无监督学习的发展将集成到面部识别软件中，并协助新药的发现，这将分别促进安全和医疗领域的发展。