Search Results for author:
Found 26 papers, 13 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).
Enhancing generalization in high energy physics using white-box adversarial attacks
This study aims to enhance the generalization properties of supervised models by reducing the sharpness of local minima.
Variational inference for pile-up removal at hadron colliders with diffusion models
In this paper, we present a novel method for pile-up removal of pp interactions using variational inference with diffusion models, called Vipr.
Is Tokenization Needed for Masked Particle Modelling?
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics.
PIPPIN: Generating variable length full events from partons
This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques.
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data.
Improving new physics searches with diffusion models for event observables and jet constituents
We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC.
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.
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
We propose a protocol called flows for flows for training normalizing flows to morph one dataset into another even if the underlying probability density of neither dataset is known explicitly.
PC-Droid: Faster diffusion and improved quality for particle cloud generation
Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds.
Decorrelation using Optimal Transport
Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences.
$ν^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos.
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation
Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC.
Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting
We present an alternative to reweighting techniques for modifying distributions to account for a desired change in an underlying conditional distribution, as is often needed to correct for mis-modelling in a simulated sample.
Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of particle physics decays and the flexibility of message passing graph neural networks.
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi.
Decorrelation with conditional normalizing flows
The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events.
Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation
Normalizing flows are constructed from a base distribution with a known density and a diffeomorphism with a tractable Jacobian.
Flowification: Everything is a Normalizing Flow
On the other hand, neural networks only perform a forward pass on the input, there is neither a notion of an inverse of a neural network nor is there one of its likelihood contribution.
Turbo-Sim: a generalised generative model with a physical latent space
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model.
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
The InfoSCC-GAN architecture is based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an attribute classifier and an EigenGAN generator.
Funnels: Exact maximum likelihood with dimensionality reduction
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model.
The Tracking Machine Learning challenge : Throughput phase
Both were measured on the Codalab platform where the participants had to upload their software.
Hashing and metric learning for charged particle tracking
We propose a novel approach to charged particle tracking at high intensity particle colliders based on Approximate Nearest Neighbors search.
Variational Autoencoders for Anomalous Jet Tagging
To build a performant mass-decorrelated anomalous jet tagger, we propose the Outlier Exposed VAE (OE-VAE), for which some outlier samples are introduced in the training process to guide the learned information.
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
