New method identifies quark and gluon jets from collider data.
problem Determine quark and gluon jet distributions from collider data.
method Apply topic modeling to jet distributions, using parton shower and theoretical predictions.
result Determined separate quark and gluon jet distributions and spectra.
Recursive neural networks improve quark/gluon tagging performance.
problem Improving quark/gluon tagging accuracy using machine learning.
method Recursive neural networks (RecNNs) that embed jet clustering history recursively.
result RecNNs outperform traditional boosted decision tree (BDT) by a few percent in gluon rejection rate.
Deep learning improves jet discrimination in particle physics.
problem Automating quark/gluon jet discrimination in collider physics.
method Convolutional neural networks trained on color-enhanced jet images.
result Deep networks outperform traditional jet variables in discrimination.
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.
GluonTS simplifies deep learning for time series tasks.
problem Developing and experimenting with time series models.
method Library for deep learning-based time series modeling.
result Simplified development and experimentation for common tasks.
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.
Paper tackles classification without labels using statistical mixtures in collider physics.
problem Training models on imperfect simulations in high energy physics.
method Classification without labels (CWoLa) paradigm, distinguishing statistical mixtures of classes.
result Optimal classifier in CWoLa is also optimal in fully-supervised case.
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.
New method improves machine learning in physics.
problem Improving machine learning performance in physics with limited data.
method Weakly supervised classification using class proportions as input.
result Weakly supervised classification matches fully supervised algorithms in quark vs gluon tagging.
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.
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.
Machine learning improves jet charge classification.
problem Classifying jets according to their electric charge.
method Convolutional, recurrent, and recursive neural networks, including distance within the jet and clustering history.
result Significant improvement in jet charge extraction over traditional methods.
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.
Improved jet tagging reduces systematic uncertainties and enhances signal purity.
problem Boosted resonance decay signals from jets are difficult to distinguish from background.
method Adversarial neural networks to decorrelate jet substructure tagger.
result Adversarial trained tagger outperforms conventional methods in discovery significance.
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.
Deep Sets improve jet discrimination in particle physics.
problem Representing and learning from collider events with variable-length particle sets.
method Energy Flow Networks and Particle Flow Networks, based on Deep Sets framework.
result Improved or similar performance in discriminating quark jets from gluon jets compared to existing methods.
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.