NEURAL NETWORK DISCRIMINATION OF HEAVY FLAVOR JETS: A SURVEY

A short survey of the use of neural networks and statistical discriminants in high energy physics for recognition of heavy flavor jets is presented. After illustrating the various neural and statistical classifiers currently used, some assessment of their comparative performance for top and bottom jets is made

A Comparison of the Use of Binary Decision Trees and Neural Networks in Top Quark Detection

The use of neural networks for signal vs.~background discrimination in high-energy physics experiment has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top quark identification produced a neural network that, for a given top quark mass, yielded a higher signal to background ratio in Monte Carlo simulation than a corresponding set of conventional cuts. In this article we discuss another pattern-recognition algorithm, the binary decision tree. We have applied a binary decision tree to top quark identification at the Tevatron and found it to be comparable in performance to the neural network. Furthermore, reservations about the "black box" nature of neural network discriminators do not apply to binary decision trees; a binary decision tree may be reduced to a set of kinematic cuts subject to conventional error analysis.Comment: 14pp. Plain TeX + mtexsis.tex (latter available through 'get mtexsis.tex'.) Two postscript files avail. by emai

Machine Learning tools for global PDF fits

The use of machine learning algorithms in theoretical and experimental high-energy physics has experienced an impressive progress in recent years, with applications from trigger selection to jet substructure classification and detector simulation among many others. In this contribution, we review the machine learning tools used in the NNPDF family of global QCD analyses. These include multi-layer feed-forward neural networks for the model-independent parametrisation of parton distributions and fragmentation functions, genetic and covariance matrix adaptation algorithms for training and optimisation, and closure testing for the systematic validation of the fitting methodology.Comment: 12 pages, 9 figures, to appear in the proceedings of the XXIIIth Quark Confinement and the Hadron Spectrum conference, 1-6 August 2018, University of Maynooth, Irelan

A spintronic Huxley-Hodgkin-analogue neuron implemented with a single magnetic tunnel junction

Spiking neural networks aim to emulate the brain's properties to achieve similar parallelism and high-processing power. A caveat of these neural networks is the high computational cost to emulate, while current proposals for analogue implementations are energy inefficient and not scalable. We propose a device based on a single magnetic tunnel junction to perform neuron firing for spiking neural networks without the need of any resetting procedure. We leverage two physics, magnetism and thermal effects, to obtain a bio-realistic spiking behavior analogous to the Huxley-Hodgkin model of the neuron. The device is also able to emulate the simpler Leaky-Integrate and Fire model. Numerical simulations using experimental-based parameters demonstrate firing frequency in the MHz to GHz range under constant input at room temperature. The compactness, scalability, low cost, CMOS-compatibility, and power efficiency of magnetic tunnel junctions advocate for their broad use in hardware implementations of spiking neural networks.Comment: 23 pages, 6 figures, 2 table

EV-Planner: Energy-Efficient Robot Navigation via Event-Based Physics-Guided Neuromorphic Planner

Vision-based object tracking is an essential precursor to performing autonomous aerial navigation in order to avoid obstacles. Biologically inspired neuromorphic event cameras are emerging as a powerful alternative to frame-based cameras, due to their ability to asynchronously detect varying intensities (even in poor lighting conditions), high dynamic range, and robustness to motion blur. Spiking neural networks (SNNs) have gained traction for processing events asynchronously in an energy-efficient manner. On the other hand, physics-based artificial intelligence (AI) has gained prominence recently, as they enable embedding system knowledge via physical modeling inside traditional analog neural networks (ANNs). In this letter, we present an event-based physics-guided neuromorphic planner (EV-Planner) to perform obstacle avoidance using neuromorphic event cameras and physics-based AI. We consider the task of autonomous drone navigation where the mission is to detect moving gates and fly through them while avoiding a collision. We use event cameras to perform object detection using a shallow spiking neural network in an unsupervised fashion. Utilizing the physical equations of the brushless DC motors present in the drone rotors, we train a lightweight energy-aware physics-guided neural network with depth inputs. This predicts the optimal flight time responsible for generating near-minimum energy paths. We spawn the drone in the Gazebo simulator and implement a sensor-fused vision-to-planning neuro-symbolic framework using Robot Operating System (ROS). Simulation results for safe collision-free flight trajectories are presented with performance analysis and potential future research direction

GPCALMA: a Grid Approach to Mammographic Screening

The next generation of High Energy Physics experiments requires a GRID approach to a distributed computing system and the associated data management: the key concept is the "Virtual Organisation" (VO), a group of geographycally distributed users with a common goal and the will to share their resources. A similar approach is being applied to a group of Hospitals which joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), which will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. HEP techniques come into play in writing the application code, which makes use of neural networks for the image analysis and shows performances similar to radiologists in the diagnosis. GRID technologies will allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated to screening programs.Comment: 4 pages, 3 figures; to appear in the Proceedings of Frontier Detectors For Frontier Physics, 9th Pisa Meeting on Advanced Detectors, 25-31 May 2003, La Biodola, Isola d'Elba, Ital

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis

Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference

Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption. Two popular techniques for reducing computation in neural networks are pruning, removing insignificant synapses, and quantization, reducing the precision of the calculations. In this work, we explore the interplay between pruning and quantization during the training of neural networks for ultra low latency applications targeting high energy physics use cases. Techniques developed for this study have potential applications across many other domains. We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task. Further, quantization-aware pruning typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization. Surprisingly, while networks with different training configurations can have similar performance for the benchmark application, the information content in the network can vary significantly, affecting its generalizability.Comment: 22 pages, 7 Figures, 1 Tabl

Support Vector Machines and Generalisation in HEP

5 pages, 6 figures. Contribution to the proceedings of the 17th International workshop on Advanced Computing and Analysis Techniques in physics research - ACAT 2016, 18 - 22 January 2016, Valpara\'iso, ChileInternational audienceWe review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework