On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods. Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PyTorch called LUMIN is presented, which incorporates all of the techniques studied.Comment: Preprint V4: Fixing typographical error and correcting two plots. Mach. Learn.: Sci. Technol (2020

Deep Learning Methods Applied to Higgs Physics at the LHC

The impact that machine learning (ML) has had on research in high-energy physics (HEP) is undeniable; the use of ML-based classifiers in many analyses is now the norm, and they have a long history of being used within reconstruction algorithms (e.g. $b$-tagging at LEP, 1995). The combined effect of advances in both hardware and algorithms, and the vast datasets now available (both collected and simulated), has facilitated the paradigm shift towards deep learning (DL). The true effect of this is only beginning to be felt in the field of HEP, but it is something that can be expected to continue into the future; overturning several "foundational" principles, and challenging more traditional thinking. Over the course of my PhD I have developed and applied a range of advanced ML and DL algorithms to the study of the fundamental interactions of matter. This thesis documents over four and a half years of work; beginning from preliminary feasibility studies, through algorithm optimisation studies and dedicated software development, and concluding with a full study leveraging the refined methods. From a pragmatic perspective, no matter how beautiful a new method is, it is only applicable if either it provides better performance than alternative methods, or it savesthe applier time and effort. Preferably, the new method should meet both of these criteria and throughout this thesis I test DL methods against them. As a context for studyingthese new methods, I pick the sub-field of Higgs physics. With the discovery of the Higgs boson in 2012, this area has now moved into the regime of precision measurements and experimental confirmation of theoretically predicted channels of production and decay; any deviation from the theoretical model (the Standard Model (SM)) could be a sign of new physics, and open up further avenues for progressing humanity's understanding of Nature. The importance of these studies, therefore, goes far beyond demonstrating the applicability of new methods. One key focus of my research is the search for the simultaneous production of two Higgs bosons. One property of the Higgs boson that has yet to be measured is the strength with which they couple to themselves (the Higgs interacts with particles that have mass, and therefore is expected to interact with itself). Beyond academic interest, the value of this parameter can allow us to more precisely estimate the stability of the Universe and determine whether we are at risk of suddenly being snuffed from existence (as we know it) by spontaneous vacuum decay. The production of such di-Higgs processes, however, is extremely rare, meaning that whilst we are unlikely to discover it at the LHC (barring the effects of new physics), it is a perfect testing ground for DL methods; even moderate improvements in sensitivity, through whatever means, can correspond to months or years of less data-taking required to achieve discovery. At the time of writing, the Large Hadron Collider (LHC) has finished its Run-II datataking and three years worth of $\sqrt{s}$ = 13 TeV proton-proton collision-data (137.19 fb$^{-1}$ of integrated luminosity) stands ready to be scrutinised in minute detail for any hints of new phenomena, such as super-SM rates of production of di-Higgs. The concluding chapter of this thesis documents my contributions to the examination of this extremely large dataset

3rd IML Machine Learning Workshop

[LUMIN][1] aims to become a deep-learning and data-analysis ecosystem for High-Energy Physics, and perhaps other scientific domains in the future. Similar to Keras and fastai it is a wrapper framework for a graph computation library (PyTorch), but includes many useful functions to handle domain-specific requirements and problems. It also intends to provide easy access to to state-of-the-art methods, but still be flexible enough for users to inherit from base classes and override methods to meet their own demands. In this talk I will be introducing the library, discussing some of its distinguishing characteristics, and going through an example workflow. There will also be a general invitation for people to test out the library and provide feedback, suggestions, or contributions. [1]: https://github.com/GilesStrong/lumi

Beyond the Standard Model in Vector Boson Scattering Signatures

The high-energy scattering of massive electroweak bosons, known as vector boson scattering (VBS), is a sensitive probe of new physics. VBS signatures will be thoroughly and systematically investigated at the LHC with the large data samples available and those that will be collected in the near future. Searches for deviations from Standard Model (SM) expectations in VBS facilitate tests of the Electroweak Symmetry Breaking (EWSB) mechanism. Current state-of-the-art tools and theory developments, together with the latest experimental results, and the studies foreseen for the near future are summarized. A review of the existing Beyond the SM (BSM) models that could be tested with such studies as well as data analysis strategies to understand the interplay between models and the effective field theory paradigm for interpreting experimental results are discussed. This document is a summary of the EU COST network 'VBScan' workshop on the sensitivity of VBS processes for BSM frameworks that took place December 4-5, 2019 at the LIP facilities in Lisbon, Portugal. In this manuscript we outline the scope of the workshop, summarize the different contributions from theory and experiment, and discuss the relevant findings

Toward Machine Learning Optimization of Experimental Design

International audienceThe effective design of instruments that rely on the interaction of radiation with matter for their operation is a complex task. A full optimization of the many parameters involved may still be sought by leveraging recent progress in computer science. Key to such a goal is the definition of a utility function that models the true goals of the instrument. Such a function must account for the interplay between physical processes that are intrinsically stochastic in nature and the vast space of possible choices for the physical characteristics of the instrument. The construction of a differentiable model of all the ingredients of the information-extraction procedures, including data collection, detector response, pattern recognition, and all existing constraints, then allows the automatic exploration of the vast space of design choices and the search for their best combination.In this document we succinctly describe the research program of the MODE Collaboration (an acronym for Machine-learning Optimized Design of Experiments), which aims at developing tools based on deep learning techniques to achieve end-to-end optimization of the design of instruments via a fully differentiable pipeline capable of exploring the Pareto-optimal frontier of the utility function. The goal of MODE is to demonstrate those techniques on small-scale applications such as muon tomography or hadron therapy, to then gradually adapt them to the more ambitious task of exploring innovative solutions to the design of detectors for future particle collider experiments

Beyond the Standard Model in Vector Boson Scattering Signatures

The high-energy scattering of massive electroweak bosons, known as vector boson scattering (VBS), is a sensitive probe of new physics. VBS signatures will be thoroughly and systematically investigated at the LHC with the large data samples available and those that will be collected in the near future. Searches for deviations from Standard Model (SM) expectations in VBS facilitate tests of the Electroweak Symmetry Breaking (EWSB) mechanism. Current state-of-the-art tools and theory developments, together with the latest experimental results, and the studies foreseen for the near future are summarized. A review of the existing Beyond the SM (BSM) models that could be tested with such studies as well as data analysis strategies to understand the interplay between models and the effective field theory paradigm for interpreting experimental results are discussed. This document is a summary of the EU COST network “VBScan” workshop on the sensitivity of VBS processes for BSM frameworks that took place December 4-5, 2019 at the LIP facilities in Lisbon, Portugal. In this manuscript we outline the scope of the workshop, summarize the different contributions from theory and experiment, and discuss the relevant findings