489 citations · 949 across the 29 of their papers we have counts for
5 papers · 1 filter
Learning Broken Symmetries with Approximate Invariance
Seth Nabat, Aishik Ghosh, Edmund Witkowski +2
Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the…
Fast multi-geometry calorimeter simulation with conditional self-attention variational autoencoders
Dylan Smith, Aishik Ghosh, Junze Liu +2
The simulation of detector response is a vital aspect of data analysis in particle physics, but current Monte Carlo methods are computationally expensive. Machine learning methods,…
FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, Wahid Bhimji, Paolo Calafiura +26
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
Alexander Shmakov, Kevin Greif, Michael James Fenton +3
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, stati…
Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra
Len Brandes, Chirag Modi, Aishik Ghosh +6
Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation…