Search Results for author:
Found 13 papers, 7 papers with code
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.
SuperCalo: Calorimeter shower super-resolution
In this work, we introduce SuperCalo, a flow-based super-resolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly upsampled from coarse-grained showers.
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.
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.
