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Found 8 papers, 1 papers with code
The LHCb ultra-fast simulation option, Lamarr: design and validation
In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector.
Generative models uncertainty estimation
In recent years fully-parametric fast simulation methods based on generative models have been proposed for a variety of high-energy physics detectors.
Towards Reliable Neural Generative Modeling of Detectors
The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced.
Machine Learning on sWeighted Data
Data analysis in high energy physics has to deal with data samples produced from different sources.
Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced.
Machine Learning on data with sPlot background subtraction
In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all.
Cherenkov Detectors Fast Simulation Using Neural Networks
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details.
Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuvers
The number of space objects will grow several times in a few years due to the planned launches of constellations of thousands microsatellites.
