mlsanityData to 14 Oct 2026

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning

arXiv 2507.23297 ↗First posted 31 Jul 2025physics.data-an cs.LG hep-excited by 2

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Abstract (arXiv)

Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators that model likelihoods of calibration data: conditional normalizing flows and a transformer-based regressor. We adopt JUNO - a large neutrino experiment - as a case study. The energy response of JUNO depends on several parameters, all of which should be tuned, given their non-linear behavior and strong correlations in the calibration data. To this end, we integrate the modeled likelihoods with Bayesian nested sampling for parameter inference, achieving uncertainties limited only by statistics with near-zero systematic biases. The normalizing flows model enables unbinned likelihood analysis, while the transformer provides an efficient binned alternative. By providing both options, our framework offers flexibility to choose the most appropriate method for specific needs. Finally, our approach establishes a template for similar applications across experimental neutrino and broader particle physics.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 101 references that are in the map. First posted on arXiv 31 Jul 2025.

Builds on

32 of 101 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Gradient estimators for parameter inference in discrete stochastic kinetic modelscited by 1
  2. 2026Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformerscited by 0