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Found 18 papers, 10 papers with code
Quantum Diffusion Model for Quark and Gluon Jet Generation
Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train.
Lie-Equivariant Quantum Graph Neural Networks
Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds.
Quantum Attention for Vision Transformers in High Energy Physics
We present a novel hybrid quantum-classical vision transformer architecture incorporating quantum orthogonal neural networks (QONNs) to enhance performance and computational efficiency in high-energy physics applications.
A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface
This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface.
SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation Functions
We show that our model can perform better than or comparable to B-Spline KAN models and an alternative KAN implementation based on periodic cosine and sine functions representing a Fourier Series.
Quantum Vision Transformers for Quark-Gluon Classification
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons.
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks.
$\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN).
Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors
To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals.
Locating Hidden Exoplanets in ALMA Data Using Machine Learning
Exoplanets in protoplanetary disks cause localized deviations from Keplerian velocity in channel maps of molecular line emission.
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction.
End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon.
Graph Generative Models for Fast Detector Simulations in High Energy Physics
Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community.
Deep Learning the Morphology of Dark Matter Substructure
With thousands of strong lensing images anticipated with the coming launch of LSST, we expect that supervised and unsupervised deep learning models will play a crucial role in determining the nature of dark matter.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology
End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data
We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data.
End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC
This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
