(Machine) Learning to Do More with Less

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail -- both analytically and numerically -- with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC.Comment: 32 pages, 12 figures. Example code is provided at https://github.com/bostdiek/PublicWeaklySupervised . v3: Version published in JHEP, discussion adde

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters that the ML algorithm learns autonomously during the training, without intervention by the user. The choice of hyperparameters is conventionally done manually by the user and often has a significant impact on the performance of the ML algorithm. In this paper, we explore two evolutionary algorithms: particle swarm optimization (PSO) and genetic algorithm (GA), for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.Comment: Corrected typos. Removed a remark on page 2 regarding the similarity of minimization and maximization problem. Removed a remark on page 9 (Summary) regarding thee ANN, since this was not studied in the pape

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Physics methods for image classification with Deep Neural Networks

The studies performed in this thesis see their light in the context of an internship carried out in Porini, a dynamic business versed in digital consulting and software development. The ultimate goal of this research is to develop an algorithm to perform product recognition of common items found in supermarkets or grocery shops. The first part of the analysis will consider a simplified toy model, in order to gain a deeper insight on the data at disposal. In particular, a manual feature extraction will be designed, consisting of an equalisation procedure and a custom-built cropping for the images. A novel classification model will be then defined using average RGB histograms as references for each product class and testing out different metrics to quantify the similarity between two images. This implementation will culminate in the realization of a proof of concept in the form of an application for mobile platforms. In the second part of the study, object detection and recognition will be tackled in a more generalized context. This will require the employment of more advanced, pre-built algorithms, particularly in the form of deep convolutional neural networks. Specifically, a focus will be made on the single-shot approach, where a duly trained detector only observes the image at once, as a whole, before outputting its detection prediction; an exploratory analysis will be performed taking advantage of the YOLO model, a state-of-the-art implementation in the field. The results obtained are very satisfactory: the first part of the study has led to the definition of a new customized algorithm for classification which is robust and well-optimized, while in the second one promising foundations have been laid in the development of advanced object recognition tools for general use cases.ope

Machine learning for event selection in high energy physics

The field of high energy physics aims to discover the underlying structure of matter by searching for and studying exotic particles, such as the top quark and Higgs boson, produced in collisions at modern accelerators. Since such accelerators are extraordinarily expensive, extracting maximal information from the resulting data is essential. However, most accelerator events do not produce particles of interest, so making effective measurements requires event selection, in which events producing particles of interest (signal) are separated from events producing other particles (background). This article studies the use of machine learning to aid event selection. First, we apply supervised learning methods, which have succeeded previously in similar tasks. However, they are suboptimal in this case because they assume that the selector with the highest classification accuracy will yield the best final analysis; this is not true in practice, as such analyses are more sensitive to some backgrounds than others. Second, we present a new approach that uses stochastic optimization techniques to directly search for selectors that maximize either the precision of top quark mass measurements or the sensitivity to the presence of the Higgs boson. Empirical results confirm that stochastically optimized selectors result in substantially better analyses. We also describe a case study in which the best selector is applied to real data from the Fermilab Tevatron accelerator, resulting in the most precise top quark mass measurement of this type to date. Hence, this new approach to event selection has already contributed to our knowledge of the top quark's mass and our understanding of the larger questions upon which it sheds light

Machine learning for event selection in high energy physics

The field of high energy physics aims to discover the underlying structure of matter by searching for and studying exotic particles, such as the top quark and Higgs boson, produced in collisions at modern accelerators. Since such accelerators are extraordinarily expensive, extracting maximal information from the resulting data is essential. However, most accelerator events do not produce particles of interest, so making effective measurements requires event selection, in which events producing particles of interest (signal) are separated from events producing other particles (background). This article studies the use of machine learning to aid event selection. First, we apply supervised learning methods, which have succeeded previously in similar tasks. However, they are suboptimal in this case because they assume that the selector with the highest classification accuracy will yield the best final analysis; this is not true in practice, as such analyses are more sensitive to some backgrounds than others. Second, we present a new approach that uses stochastic optimization techniques to directly search for selectors that maximize either the precision of top quark mass measurements or the sensitivity to the presence of the Higgs boson. Empirical results confirm that stochastically optimized selectors result in substantially better analyses. We also describe a case study in which the best selector is applied to real data from the Fermilab Tevatron accelerator, resulting in the most precise top quark mass measurement of this type to date. Hence, this new approach to event selection has already contributed to our knowledge of the top quark's mass and our understanding of the larger questions upon which it sheds light