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Found 31 papers, 18 papers with code
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs).
OmniJet-$α_C$: Learning point cloud calorimeter simulations using generative transformers
Using the tokenizer and generative part of the OmniJet-${\alpha}$ model, we represent the hits in the detector as sequences of integers.
Learning Broken Symmetries with Approximate Invariance
Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited.
Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks.
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge.
Calibrating Bayesian Generative Machine Learning for Bayesiamplification
Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks.
Universal New Physics Latent Space
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories.
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier.
OmniJet-$α$: The first cross-task foundation model for particle physics
This is the first successful transfer between two different and actively studied classes of tasks and constitutes a major step in the building of foundation models for particle physics.
Ultrafast jet classification on FPGAs for the HL-LHC
Three machine learning models are used to perform jet origin classification.
AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization
Uncertainty estimation is a key issue when considering the application of deep neural network methods in science and engineering.
Residual ANODE
We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability.
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
We introduce the first generative model trained on the JetClass dataset.
Statistical guarantees for stochastic Metropolis-Hastings
A Metropolis-Hastings step is widely used for gradient-based Markov chain Monte Carlo methods in uncertainty quantification.
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation.
CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up.
CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations.
Feature Selection with Distance Correlation
Choosing which properties of the data to use as input to multivariate decision algorithms -- a. k. a.
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
Machine Learning in the Search for New Fundamental Physics
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications.
Shared Data and Algorithms for Deep Learning in Fundamental Physics
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.
How to GAN Higher Jet Resolution
QCD-jets at the LHC are described by simple physics principles.
Super-Resolution
High Energy Physics - Phenomenology
DCTRGAN: Improving the Precision of Generative Models with Reweighting
We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.
GANplifying Event Samples
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
Accurate simulation of physical processes is crucial for the success of modern particle physics.
Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
DisCo Fever: Robust Networks Through Distance Correlation
While deep learning has proven to be extremely successful at supervised classification tasks at the LHC and beyond, for practical applications, raw classification accuracy is often not the only consideration.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
Deep-learning Top Taggers or The End of QCD?
Machine learning based on convolutional neural networks can be used to study jet images from the LHC.
High Energy Physics - Phenomenology
Resonance Searches with an Updated Top Tagger
The performance of top taggers, for example in resonance searches, can be significantly enhanced through an increased set of variables, with a special focus on final-state radiation.
High Energy Physics - Phenomenology
