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Found 4 papers, 1 papers with code
DeepTreeGANv2: Iterative Pooling of Point Clouds
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors.
Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
We employ the novel Intel low precision optimization tool (iLoT) for quantization and compare the results to the quantized model from TensorFlow Lite.
Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders
We introduce two new loss functions designed to directly optimise the statistical significance of the expected number of signal events when training neural networks to classify events as signal or background in the scenario of a search for new physics at a particle collider.
High Energy Physics - Experiment
