We introduce jet topics: a framework to identify underlying classes of jets from collider data. Because of a close mathematical relationship between distributions of observables in jets and emergent themes in sets of documents, we can apply recent techniques in "topic modeling" to extract jet topics from data with mini…
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19 results for “Gluon”
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and …
Since the machine learning techniques are improving rapidly, it has been shown that the image recognition techniques in deep neural networks can be used to detect jet substructure. And it turns out that deep neural networks can match or outperform traditional approach of expert features. However, there are disadvantage…
EF21-Muon optimizes deep learning with error feedback, improving efficiency and accuracy.
problem Lack of principled distributed frameworks for non-Euclidean LMO-based optimizers.
method Introduces EF21-Muon, a communication-efficient, non-Euclidean LMO-based optimizer with convergence guarantees.
result First efficient distributed implementation of non-Euclidean LMO-based optimizers, achieving up to 7x communication savings.
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds…
DeepJet improves jet flavor classification and quark-gluon tagging.
problem Jet flavor classification in high-energy physics experiments.
method Proposes a novel deep learning architecture, DeepJet, for improved performance.
result Improves heavy flavor classification performance and extends to quark-gluon tagging.
Gluon optimizes LMO-based methods for large-scale tasks, improving performance and theory-practice gap.
problem LMO-based methods lack theoretical support for practical implementation and smoothness assumptions.
method Introduces Gluon, a new LMO-based method with refined smoothness model.
result Gluon's theoretical stepsizes match fine-tuned values, closing the theory-practice gap.
New model improves QGP simulation efficiency and accuracy.
problem Limited QGP simulation runs due to high computational cost.
method Additive Multi-Index Gaussian process (AdMIn-GP) model.
result Significantly improved surrogate modeling performance.
Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Functionstat.ML
We develop a new loss function for estimating quasiprobabilistic density ratios.
problem Discontinuous or non-surjective relationships between optimal classifiers and target densities.
method Introduce a convex loss function compatible with both probabilistic and quasiprobabilistic densities.
result Achieve state-of-the-art results in estimating di-Higgs production in particle physics.
Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this …
A new transform links rotating calorons to solutions of a differential equation.
problem Existence and characterization of rotating calorons.
method Formulated a Nahm transform to relate rotating calorons to solutions of a delayed-differential equation.
result Existence of an eight-parameter family of rotating calorons with nontrivial holonomy.
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using on…
Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods…
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved di…
Moment Pooling reduces latent space dimensions in machine learning models.
problem High-dimensional latent spaces in machine learning models are hard to interpret.
method Moment Pooling extends Deep Sets networks to arbitrary multivariate moments.
result Latent dimensions as small as 1 can achieve similar performance to higher dimensions.
A method smears likelihood to reveal physical energy scales in jet identification.
problem Interpretability of machine learning in particle physics.
method Smearing or averaging over events within a metric energy distance.
result Discrimination power increases as resolution decreases, showing sensitivity to all energy scales.
Geometric QCD framework establishes stable vacuum for quark confinement.
problem Quark confinement in QCD.
method Geometric construction of stable vacuum using Hodge-dual surfaces.
result Existence and stability of the Hodge-dual surface in 4D ensures quark confinement.
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn directly from sets of features or "point clouds". Adapting and spe…
Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciencesstat.ML
Study evaluates two-sample tests for validating generative models in high dimensions.
problem Validating the performance and efficiency of non-parametric two-sample tests for high-dimensional generative models.
method Proposes and evaluates the sliced Wasserstein distance, mean of Kolmogorov-Smirnov statistics, and novel sliced Kolmogorov-Smirnov statistic.
result One-dimensional-based tests provide comparable sensitivity to other multivariate metrics but with lower computational cost.