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Found 4 papers, 0 papers with code
Data-driven detector signal characterization with constrained bottleneck autoencoders
A common technique in high energy physics is to characterize the response of a detector by means of models tunned to data which build parametric maps from the physical parameters of the system to the expected signal of the detector.
Exhaustive Neural Importance Sampling applied to Monte Carlo event generation
The generation of accurate neutrino-nucleus cross-section models needed for neutrino oscillation experiments require simultaneously the description of many degrees of freedom and precise calculations to model nuclear responses.
Efficient sampling generation from explicit densities via Normalizing Flows
For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challenging through the inverse transform.
Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows
In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression.
