Jet grooming through reinforcement learning

We introduce a novel implementation of a reinforcement learning (RL) algorithm which is designed to find an optimal jet grooming strategy, a critical tool for collider experiments. The RL agent is trained with a reward function constructed to optimize the resulting jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects. Given a suitable reward function, the agent learns how to train a policy which optimally removes soft wide-angle radiation, allowing for a modular grooming technique that can be applied in a wide range of contexts. These results are accessible through the corresponding GroomRL framework.Comment: 11 pages, 10 figures, code available at https://github.com/JetsGame/GroomRL, updated to match published versio

Jet Reconstruction and Graph Neural Networks in Heavy-Ion Collisions

Masteroppgave i fysikkPHYS399MAMN-PHY

Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics

We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program.Comment: 8 page

Using a systematic mapping review to examine equine-assisted activities and therapies for people with mental health through an occupational therapy lens

2017 Spring.Includes bibliographical references.Equine-assisted activities and therapies (EAAT) are one type of complementary and/or alternative treatment for persons with mental illness. Various approaches have been used to improve individual's self-esteem, self-efficacy, and overall health (Bizub, Joy, & Davidson, 2003; Burgon, 2003; Klontz, Bivens, Leinart, & Klontz, 2007). However, literature on the psychosocial benefits of EAAT is fragmented and often lacks rigor (Anestis, Anestis, Zawilinski, Hopkins, & Lilienfeld, 2014; Bachi, 2012). Moreover, occupational therapy is underrepresented in the literature despite its roots in mental health. Therefore, this study uses a systematic mapping review to ascertain theories, interventions, and outcomes within literature on EAAT specific to individuals with mental health concerns. Findings from the study were examined through the perspective of a conceptual framework specific to occupational therapy, the Model of Human Occupation, which consists of three subsystems: volition, habituation, and performance capacity. Specifically, this conceptual framework was used to identify how occupational therapy may address occupational performance deficits with horses and the equine environment. Current theories, interventions, and outcomes within the literature suggest horses and the equine environment may be used to improve aspects of volition, such as self-efficacy and self-esteem, habituation, and performance capacity. Occupational therapy using horses and the equine environment may be particularly well-suited for adolescents who have eating disorder or who have experienced abuse considering the high frequency at which this population is studied. In conclusion, there is great potential for occupational therapy to develop unique interventions that focus on occupational performance deficits using the equine environment

Study of quantum correlations in LHCb simulated heavy flavour jets

openThe LHCb collaboration has already demonstrated that quantum machine learning can be used to classify jets based on the particles flavor in particular, the so called heavy flavor jets. These studies indicates that improvements on jets classification can arise from the study of the correlation among qubits. The thesis evaluates the possibility to measure qubits correlations and study how to exploit these information for a better data classification

2018 Annual Research Symposium Abstract Book

2018 annual volume of abstracts for science research projects conducted by students at Trinity College

Gradient dynamics in reinforcement learning

Despite the success achieved by the analysis of supervised learning algorithms in the framework of statistical mechanics, reinforcement learning has remained largely untouched. Here we move towards closing the gap by analyzing the dynamics of the policy gradient algorithm. For a convex problem, we show that it obeys a drift-diffusion motion with coeffcients tuned by learning rate. Furthermore, we propose a mapping between a non-convex reinforcement learning problem and a disordered system. This mapping enables us to show how the learning rate acts as an effective temperature and thus is capable of smoothing rough landscapes, corroborating what is displayed by the drift-diffusive description and paving the way for physics-inspired algorithmic optimization based on annealing procedures in disordered systems.Comment: 15 pages, 6 figures. Submitted to Physical Review

Jet tagging in the Lund plane with graph networks

The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions. Finally, we consider the computational complexity of this method and its scaling as a function of kinematic Lund plane cuts, showing an order of magnitude improvement in speed over previous graph-based taggers.Comment: 23 pages, 12 figures, code available at https://github.com/fdreyer/lundne