Choosing the right thesis topic can be a struggle. Approximately eighteen months ago I had to pick my bachelor thesis assignment and my intention was to work on something new and fresh that could help at least a small area of people, and not just to repeat already existing processes. While browsing the offered topics, one stood out for me as a physics fan. The focus of the assignment was the analysis of data from CERN with the application of machine learning algorithms, that could lead to a discovery of a new particle. Needless to say, I applied immediately. Even though physics is not the main focus of my studies, the work on the thesis was very satisfying in numerous aspects and definitely enriched my understanding of physics and scientific processes overall.
选择正确的论文主题可能会很困难。 大约18个月前，我不得不选择学士学位论文，我的目的是研究一些新事物，这些新事物至少可以帮助一小部分人，而不仅仅是重复已经存在的过程。 浏览提供的主题时，一个人对我来说是物理迷。 任务的重点是使用机器学习算法对CERN的数据进行分析，这可能会导致发现新的粒子。 不用说，我立即申请。 尽管物理不是我研究的重点，但论文的工作却在许多方面都非常令人满意，并且无疑丰富了我对物理学和科学过程的整体理解。
Looking at the technological achievements in recent history, one definitely stands out. It is the construction of Large Hadron Collider at CERN, in which scientists are able to accelerate particles almost up to the speed of light and cause them to collide. When two particles collide, they release immense amounts of energy and scatter into quarks and gluons from which they are made of. These processes can lead to a creation of a new particle that only exists for a very brief period of time before it decays into other particles. Therefore the problem physicists face is how to observe and study such particles, when their lifetime is so short.
纵观近代史上的技术成就，无疑是很突出的。 它是欧洲核子研究组织(CERN)的大型强子对撞机的建造，科学家可以在其中加速粒子直至达到光速并使其碰撞。 当两个粒子碰撞时，它们释放出大量能量，并散布到由它们构成的夸克和胶子中。 这些过程可以导致产生一个新的粒子，该粒子仅在很短的时间内就存在，然后才分解为其他粒子。 因此，物理学家们面临的问题是，当它们的寿命很短时，如何观察和研究这些颗粒。
One of the approaches is to use the detectors at LHC to observe photons scattered during the interactions in the accelerator. These photons have varying invariant mass and by analysing its distribution the physicists observed that it follows a smooth falling line, which is called background. If the conditions for creating a new particle are met during the collisions, the photons into which the newly created particle decays inherit its mass, and therefore the invariant mass distribution shows a peak above the background at that certain mass. If the peak is significant enough, it could signify a production of a new particle. (For example, this approach was applied in search for the Higgs boson and the process can be seen on the following figure.)
一种方法是使用LHC处的检测器观察在加速器相互作用期间散射的光子。 这些光子具有不变的质量，通过分析其分布，物理学家观察到它遵循一条平滑的下降线，这称为背景。 如果在碰撞过程中满足了创建新粒子的条件，则新创建的粒子会衰减到其中的光子会继承其质量，因此不变质量分布会在该质量下在背景上方显示一个峰值。 如果峰值足够显着，则可能表示产生了新粒子。 (例如，此方法被用于搜索希格斯玻色子，其过程如下图所示。)
One of the particles that is currently being searched for is the axion-like particle  that could be mediated by the emitted photons in proton ultra-peripheral collisions . In these collision the beam particles do not collide head-on, but get into an interaction range (very close proximity to each other) in which the clouds of photons that surround the protons travelling almost at the speed of light interact and create a new particle that later decays into another set of photons. This interaction is called light-by-light scattering. The beam protons lose a fraction of their energy which can be observed by the detectors on both sides relative to the interaction point as they are almost intact.
当前正在寻找的粒子之一是质子超外围碰撞中可能由发射的光子介导的类似轴突的粒子。 在这些碰撞中，束粒子不会正面碰撞，而是进入相互作用范围(彼此非常接近)，在该相互作用范围内，围绕质子的光子云几乎以光速行进，相互作用并生成一个新粒子后来衰减成另一组光子。 这种相互作用称为逐光散射。 光束质子损失了一部分能量，相对于相互作用点，两侧的探测器可以观察到它们的能量，因为它们几乎完好无损。
The purpose of the thesis was to simulate how the peak above the background would look like if the axion-like particle production was observed, and to analyse the measured data from CERN and model the background using machine learning methods as precisely as possible, as the current models do not achieve the desired precision.
The work started with performing simulation runs with the SuperChic-3 Monte Carlo event generator . Apart from the production of axion-like particle, the productions of photons, muons and electrons were simulated as well, and their observational probabilities, energies and invariant mass distributions were compared. This analysis showed how much the axion-like particle production differs from the already observed events. The most important observation from the analysis was that with lower coupling (the strength of the ultra-peripheral interaction between the emitted photons), the peak above the background becomes more sharper, and therefore more easily detectable. In other words, to observe a significant peak above the background, the number of events in which the axion-like particle is produced can be lower with lower coupling.
该工作开始于使用SuperChic-3 Monte Carlo事件生成器进行仿真运行。 除了产生轴状粒子外，还模拟了光子，μ子和电子的产生，并比较了它们的观测概率，能量和不变质量分布。 该分析表明，类似轴突的颗粒产生与已经观察到的事件有多少不同。 分析中最重要的观察结果是，在较低的耦合(发射的光子之间的超外围相互作用的强度)下，背景上方的峰变得更加尖锐，因此更易于检测。 换句话说，为了观察到本底以上的显着峰，可以以较低的耦合来减少产生轴突状颗粒的事件的数量。
Once the events were simulated, the real experimental data from CERN was imported and validated. By applying multiple restrictions on the observed events, the dataset was modified and three subsets corresponding to different observational strategies were extracted. The strategies are dependent on the beam proton energy loss that is observable by the detectors. The first strategy, called no AFP matching, does not consider any limitations on the event selection. Strategy called A or C considers events in which at least one of the detectors observed a proton with relative energy loss between 2% to 10%, and A and C matching requires both protons from the event to be in that specific range. Understandably, these restrictions significantly reduce the number of analysed events and increase the modelling difficulty.
模拟事件后，将导入并验证来自CERN的真实实验数据。 通过对观察到的事件施加多个限制，可以修改数据集并提取与不同观察策略相对应的三个子集。 这些策略取决于检测器可观察到的束质子能量损失。 第一种策略称为无AFP匹配，它不考虑事件选择的任何限制。 称为A或C的策略考虑事件，其中至少一个检测器观察到质子的相对能量损失在2％至10％之间，并且A和C匹配要求事件中的两个质子都在该特定范围内。 可以理解，这些限制大大减少了分析事件的数量并增加了建模难度。
Based on these subsets, three backgrounds were modelled using the widely-used curve fitting method with the formula physicist already used in previous analyses of photon backgrounds, which was optimised by the the Levenberg–Marquardt algorithm [4,5]. By measuring the precision of the fit it was clear that the curve does not achieve the required precision, and therefore additional regression analysis was made with the Gaussian Process . The Gaussian Process does not use a fixed parametric function to describe the data, but instead it lets the data to pick the functional form itself, and therefore it is a powerful tool in terms of machine learning. The process is formally defined as an infinite collection of random functions, where any finite subset of which creates a joint Gaussian distribution. This approach is gaining in popularity in particle physics and was already applied in numerous data explorations. The results of the Gaussian Process were then compared to the standard fit and it showed a significant improvement.
基于这些子集，使用广泛使用的曲线拟合方法对三个背景进行了建模，其中公式物理学家已在以前的光子背景分析中使用，并通过Levenberg-Marquardt算法进行了优化[4,5]。 通过测量拟合的精度，很明显该曲线没有达到所需的精度，因此使用高斯过程进行了额外的回归分析。 高斯过程不使用固定的参数函数来描述数据，而是让数据自行选择功能形式，因此它是机器学习方面的强大工具。 该过程被正式定义为随机函数的无限集合，其中随机函数的任何有限子集都会产生联合高斯分布。 这种方法在粒子物理学中越来越流行，并且已经在许多数据探索中得到应用。 然后将高斯过程的结果与标准拟合进行比较，结果显示出显着改善。
The backgrounds were then used together with the observational probabilities calculated from the simulator runs to determine how many events are needed to be observed anywhere on top of the background to observe a significant peak. For those events, the required coupling was calculated (separately for each matching strategy). As mentioned above, with lower coupling, the peak becomes sharper (i.e. it is both higher and not as wide for the same amount of events), and therefore the number of events needed to be observed is lower, because the deviation from background is achieved more easily. This means that when searching for the hypothetical axion-like particle, with lower coupling it does not need to be produced as often in order to be potentially discovered.
然后将背景与从模拟器运行中计算出的观察概率一起使用，以确定需要在背景顶部的任何位置观察多少事件才能观察到明显的峰。 对于这些事件，计算了所需的耦合(针对每个匹配策略分别计算)。 如上所述，在较低耦合的情况下，峰变得更尖锐(即，对于相同数量的事件，它既较高又不那么宽)，因此需要观察的事件数较少，因为实现了与背景的偏离更容易。 这意味着，在搜索假设的轴突状颗粒时，如果偶合程度较低，则不需要频繁地生产该颗粒以便被潜在地发现。
The analysis showed that the best approach to detect the particle is to combine to detection strategies based on the axion-like particle mass. For lower masses up to 800 GeV, A and C matching should be applied. In range between 800 GeV and 1600 GeV, A or C seems as the most promising, and for even higher masses, no AFP matching appears to be the best option.
分析表明，检测颗粒的最佳方法是结合基于轴突样颗粒质量的检测策略。 对于质量不超过800 GeV的较小质量，应采用A和C匹配。 在800 GeV和1600 GeV之间的范围内，A或C似乎是最有前途的，对于更高的质量，没有AFP匹配似乎是最佳选择。
The past year and a half was very challenging since diving into a completely different area of research required a lot of learning and understanding. However, thanks to the kind and patient approach of the members from the international ATLAS Collaboration, and mainly my supervisor doc. Dr. André Sopczak, working on the thesis was very fulfilling. I am glad that I had the opportunity to contribute to the ongoing research in particle physics and also that my thesis was classified by CERN as relevant to their research.
过去的一年半是非常艰巨的，因为要进入一个完全不同的研究领域需要大量的学习和理解。 但是，由于国际ATLAS协作组织成员的热情和耐心的对待，主要是我的主管文件。 AndréSopczak博士从事论文的工作非常令人满意。 我很高兴有机会为正在进行的粒子物理学研究做出贡献，而且我的论文被CERN归类为与他们的研究相关。