This talk is intended to provide a conceptual overview of how ML is used in particle physics, in order to provide a basic level of understanding for all attendees such that people can follow the subsequent talks. Detailed tutorials on the application of ML techniques will instead take place on Friday.
This is an introduction to Deep Learning, for Particle Physicists. These techniques can provide excellent performance for separating different categories of events, pattern recognition, etc. In order to achieve this it is highly desirable for users to understand the basic concepts underlying their operation. The Statistical Mechanics and Information Theory aspects of this will be addressed in...
The number of high-dimensional datasets recording multiple aspects of interrelated phenomena is increasing in many areas, from medicine to finance. This drives the need for mathematical frameworks that can simultaneously identify the similar and dissimilar among multiple matrices and tensors, and thus create a single coherent model from the multiple datasets. The generalized singular value...
Empirically it has been observed numerous times that trained neural networks often have high degrees of parameter-redundancy. It remains an open theoretical question why this parameter redundancy cannot be reduced before training by using smaller neural networks. On the other hand, the recent scientific literature reports a plethora of practical methods to "compress" neural networks during or...
Machine learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics of a theoretical model are not fully understood. Uncertainties in the training data add towards the complexity of performing machine learning tasks such as...
A more dedicated study on the information flow in DNNs will help us understand their behaviour and the deep connection between DNN models and the corresponding tasks. Taking into account our well-established physics analysis framework (observable-based), we present a novel way to interpret DNNs results for HEP, which not only gives a clear physics picture but also inspires interfaces with the...
Invariance of learned representations of neural networks against certain sensitive attributes of the input data is a desirable trait in many modern-day applications of machine learning, such as precision measurements in experimental high-energy physics and enforcing algorithmic fairness in the social and financial domain. We present a method for enforcing this invariance through regularization...
Neural networks are so powerful universal approximator of complicated patterns in large-scale data, leading the explosive developments of AI in terms of deep learning. However, in many cases, usual neural networks are trained to possess poor level of abstraction, so that the model's predictability and generalizability can be quite unstable, depending on the quality and amount of the data used...
Physicists want to use modern open source machine learning tools developed by industry for machine learning projects and analyses in high energy physics. The software environment that a physicist prototypes, tests, and runs these projects in is ideally the same regardless of compute site (be it their laptop or on the GRID). However, historically it has been difficult to find compute sites that...
Machine learning methods are being increasingly and successfully applied to many different physics problems. However, currently uncertainties in machine learning methods are not modelled well, if at all. In this talk I will discuss how using Bayesian neural networks can give us a handle on uncertainties in machine learning. I will use tagging tops vs. QCD as an example of how these networks...
The HL-LHC will see ATLAS and CMS see proton bunch collisions reaching track multiplicity up to 10.000 charged tracks per event. Algorithms need to be developed to harness the increased combinatorial complexity. To engage the Computer Science community to contribute new ideas, we have organized a Tracking Machine Learning challenge (TrackML). Participants were provided events with 100k 3D...
Surrogate generative models demonstrate extraordinary progress in current years. Although most applications are dedicated to image generation and similar commercial
goals, this approach is also very promising for natural sciences, especially for tasks like fast event simulation in HEP experiments. However, application of such generative models to scientific research implies specific...
In this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural networks that is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MadGraph5 + Pythia8, and Delphes3 fast detector simulation. A number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be...
High Energy Physics simulation typically involves Monte Carlo method. Today >50% of WLCG resources are used for simulation that will increase further as detector granularity and luminosity increase. Machine learning has been very successful in the field of image recognition and generation. We have explored image generation techniques for speeding up HEP detector simulation. Calorimeter...
The extensive physics program of the ATLAS experiment at the Large Hadron Collider (LHC) relies on large scale and high fidelity simulation of the detector response to particle interactions. Current full simulation techniques using Geant4 provide accurate modeling of the underlying physics processes, but are inherently resource intensive. In light of the high-luminosity upgrade of the LHC and...
LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production...
We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter values that give images that best match the true images. The best match parameter values that produce the most...
We present a study for the generation of events from a physical process with generative deep learning. To simulate physical processes it is not only important to produce physical events, but also to produce the events with the right frequency of occurrence (density). We investigate the feasibility to learn the event generation and the frequency of occurrence with Generative Adversarial...
[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...
In the transition to Run 3 in 2021, LHCb will undergo a major luminosity upgrade, going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event, and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. We will present a novel prototype algorithm for vertexing in the LHCb...
Jet flavour identification is a fundamental component for the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. Moreover, the classification of boosted jets has acquired an increasing importance in the physics program of CMS. In this presentation we will present the performance on both simulated and...
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point cloud, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry....
We introduce a novel implementation of a reinforcement learning algorithm which is adapted to the problem of jet grooming, a crucial component of jet physics at hadron colliders. We show that the grooming policies trained using a Deep Q-Network model outperform state-of-the-art tools used at the LHC such as Recursive Soft Drop, allowing for improved resolution of the mass of boosted objects....
In 2017, the ATLAS experiment implemented an ensemble of neural networks (NeuralRinger algorithm) dedicated to reduce the latency of the first, fast, online software (HLT) selection stage for electrons with transverse energy above 15 GeV. In order to minimize detector response and shower development fluctuations, and being inspired in the ensemble of likelihood models currently operating in...
The Level-0 Muon Trigger system of the ATLAS experiment will undergo a full upgrade for HL-LHC to stand the challenging performances requested with the increasing instantaneous luminosity. The upgraded trigger system foresees to send RPC raw hit data to the off-detector trigger processors, where the trigger algorithms run on new generation of Field-Programmable Gate Arrays (FPGAs). The FPGA...
Many standard model extensions predict long-lived massive particles that can be detected by looking for displaced decay vertices in the inner detector volume. Current approaches to seek for these events in high-energy particle collisions rely on the presence of additional energetic signatures to make an online selection during data-taking. Enabling trigger-level reconstruction of displaced...
