Searching for fundamental theories of Nature with unsupervised machine learning

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。这些理论的提出是为了解决像1这样的谜团。)暗宇宙是由什么组成的，2)为什么宇宙中的物质比反物质多得多，以及3)。光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。数据来源多种多样，包括大型强子对撞机的复杂测量、地下暗物质探测实验和关于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框架中，以允许我们测试假设，这通常是通过统计分析来完成的，例如贝叶斯分析，它提供了一种衡量假设能否很好地解释当前观察的指标。遗憾的是，这种方法到目前为止还没有取得任何成果，而且正在将粒子物理领域推向僵局。在这个项目中，我们将采取一种不同的、新颖的方法来探索新的物理。我们将假设，我们无法发现新物理是由于到目前为止指导分析的强烈的理论偏见。取而代之的是，我们将开发无监督的搜索技术，挖掘新现象的数据，尽可能避免理论上的偏见。该项目有很强的理论成分，因为候选人将学习包括暗物质、希格斯粒子和暴胀在内的新物理理论的数学/物理基础。考生还将学习目前的无监督学习技术和高能物理的数据解释。为这个项目所采取的策略有可能开辟高能物理研究的新途径。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，将需要对不同纯度水平的大量数据(原始测量数据、伪可观测数据、重新解释的数据)进行建模，并找到由于关注较小的信息组而没有被检测到的模式。因此，我们相信，在这一背景下对无监督学习的研究将具有超越学术追求的深远应用。随着世界变得越来越以数据为导向，我们对新算法的依赖也越来越多，以理解我们拥有的信息。举几个例子，我们可以很容易地预期无监督学习的发展集成到面部识别软件中，并帮助发现新的药物，这分别在安全和医疗领域提供了促进。

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.