Add to ORKGDiffusion generative models are promising alternatives for fast surrogate models, producing high-fidelity physics simulations. However, the generation time often requires an expensive denoising process with hundreds of function evaluations, restricting the current applicability of these models in a realistic setting. In this work, we report updates on the CaloScore architecture, detailing the changes in the diffusion process, which produces higher quality samples, and the use of progressive distillation, resulting in a diffusion model capable of generating new samples with a single function evaluation. We demonstrate these improvements using the Calorimeter Simulation Challenge 2022 dataset.Comment: 10 pages, 5 figure
Motivated by the high computational costs of classical simulations, machine-learned gen- erative mod...
Modern high energy physics crucially relies on simulation to connect experimental observations to un...
Diffusion models have recently shown great promise for generative modeling, outperforming GANs on pe...
Simulation is crucial for all aspects of collider data analysis, but the available computing budget ...
Score-based generative models are a new class of generative algorithms that have been shown to produ...
Motivated by the high computational costs of classical simulations, machine-learned generative model...
Score based generative models are a new class of generative models that have been shown to accuratel...
Score-based generative models are a new class of generative algorithms that have been shown to produ...
Precision measurements and new physics searches at the Large Hadron Collider require efficient simul...
Motivated by the high computational costs of classical simulations, machine-learned generative model...
Machine learning-based simulations, especially calorimeter simulations, are promising tools for appr...
International audienceDetectors of High Energy Physics experiments, such as the ATLAS dectector [1] ...
The need for large scale and high fidelity simulated samples for the extensive physics program of th...
Generation of simulated detector response to collision products is crucial to data analysis in parti...
A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers ar...
Motivated by the high computational costs of classical simulations, machine-learned gen- erative mod...
Modern high energy physics crucially relies on simulation to connect experimental observations to un...
Diffusion models have recently shown great promise for generative modeling, outperforming GANs on pe...
Simulation is crucial for all aspects of collider data analysis, but the available computing budget ...
Score-based generative models are a new class of generative algorithms that have been shown to produ...
Motivated by the high computational costs of classical simulations, machine-learned generative model...
Score based generative models are a new class of generative models that have been shown to accuratel...
Score-based generative models are a new class of generative algorithms that have been shown to produ...
Precision measurements and new physics searches at the Large Hadron Collider require efficient simul...
Motivated by the high computational costs of classical simulations, machine-learned generative model...
Machine learning-based simulations, especially calorimeter simulations, are promising tools for appr...
International audienceDetectors of High Energy Physics experiments, such as the ATLAS dectector [1] ...
The need for large scale and high fidelity simulated samples for the extensive physics program of th...
Generation of simulated detector response to collision products is crucial to data analysis in parti...
A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers ar...
Motivated by the high computational costs of classical simulations, machine-learned gen- erative mod...
Modern high energy physics crucially relies on simulation to connect experimental observations to un...
Diffusion models have recently shown great promise for generative modeling, outperforming GANs on pe...