1,903 research outputs found

### Distributed Training and Optimization Of Neural Networks

Deep learning models are yielding increasingly better performances thanks to
multiple factors. To be successful, model may have large number of parameters
or complex architectures and be trained on large dataset. This leads to large
requirements on computing resource and turn around time, even more so when
hyper-parameter optimization is done (e.g search over model architectures).
While this is a challenge that goes beyond particle physics, we review the
various ways to do the necessary computations in parallel, and put it in the
context of high energy physics.Comment: 20 pages, 4 figures, 2 tables, Submitted for review. To appear in
"Artificial Intelligence for Particle Physics", World Scientific Publishin

### Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Using variational autoencoders trained on known physics processes, we develop
a one-sided threshold test to isolate previously unseen processes as outlier
events. Since the autoencoder training does not depend on any specific new
physics signature, the proposed procedure doesn't make specific assumptions on
the nature of new physics. An event selection based on this algorithm would be
complementary to classic LHC searches, typically based on model-dependent
hypothesis testing. Such an algorithm would deliver a list of anomalous events,
that the experimental collaborations could further scrutinize and even release
as a catalog, similarly to what is typically done in other scientific domains.
Event topologies repeating in this dataset could inspire new-physics model
building and new experimental searches. Running in the trigger system of the
LHC experiments, such an application could identify anomalous events that would
be otherwise lost, extending the scientific reach of the LHC.Comment: 29 pages, 12 figures, 5 table

### Graph Neural Networks in Particle Physics

Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphsâ€”sets of elements and their pairwise relationsâ€”and are a central method within the broader field of geometric deep learning. They are very expressive and have demonstrated superior performance to other classical deep learning approaches in a variety of domains. The data in particle physics are often represented by sets and graphs and as such, graph neural networks offer key advantages. Here we review various applications of graph neural networks in particle physics, including different graph constructions, model architectures and learning objectives, as well as key open problems in particle physics for which graph neural networks are promising

### Graph Neural Networks in Particle Physics

Particle physics is a branch of science aiming at discovering the fundamental
laws of matter and forces. Graph neural networks are trainable functions which
operate on graphs -- sets of elements and their pairwise relations -- and are a
central method within the broader field of geometric deep learning. They are
very expressive and have demonstrated superior performance to other classical
deep learning approaches in a variety of domains. The data in particle physics
are often represented by sets and graphs and as such, graph neural networks
offer key advantages. Here we review various applications of graph neural
networks in particle physics, including different graph constructions, model
architectures and learning objectives, as well as key open problems in particle
physics for which graph neural networks are promising.Comment: 29 pages, 11 figures, submitted to Machine Learning: Science and
Technology, Focus on Machine Learning for Fundamental Physics collectio

### Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks

### Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the
problem of detecting new physics processes in proton-proton collisions at the
Large Hadron Collider. Anomaly detection based on ALAD matches performances
reached by Variational Autoencoders, with a substantial improvement in some
cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show
how a data-driven anomaly detection and characterization would work in real
life, re-discovering the top quark by identifying the main features of the
t-tbar experimental signature at the LHC.Comment: 16 pages, 9 figure

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