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

arXiv: https://arxiv.org/abs/2507.23297 · HTML: https://mlsanity.com/paper/2507.23297/

## 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 (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows](https://mlsanity.com/paper/1805.07226/) (2018)
- [Benchmarking Simulation-Based Inference](https://mlsanity.com/paper/2101.04653/) (2021)
- [An Exponential Learning Rate Schedule for Deep Learning](https://mlsanity.com/paper/1910.07454/) (2019)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Real-time gravitational-wave science with neural posterior estimation](https://mlsanity.com/paper/2106.12594/) (2021)
- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [Likelihood-free inference with emulator networks](https://mlsanity.com/paper/1805.09294/) (2018)
- [Mining gold from implicit models to improve likelihood-free inference](https://mlsanity.com/paper/1805.12244/) (2018)
- [Conditional Density Estimation with Bayesian Normalising Flows](https://mlsanity.com/paper/1802.04908/) (2018)
- [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://mlsanity.com/paper/2405.14806/) (2024)
- [MadMiner: Machine learning-based inference for particle physics](https://mlsanity.com/paper/1907.10621/) (2019)
- [Constraining Effective Field Theories with Machine Learning](https://mlsanity.com/paper/1805.00013/) (2018)
- [Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning](https://mlsanity.com/paper/1909.02005/) (2019)
- [Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders](https://mlsanity.com/paper/2503.00131/) (2025)
- [Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors](https://mlsanity.com/paper/2309.06782/) (2023)
- [Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows](https://mlsanity.com/paper/2002.09436/) (2020)
- [Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach](https://mlsanity.com/paper/2206.09040/) (2022)

## Built on by (most cited first, 2 of 2)

- [Gradient estimators for parameter inference in discrete stochastic kinetic models](https://mlsanity.com/paper/2604.02121/) (2026)
- [Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers](https://mlsanity.com/paper/2603.22342/) (2026)

## 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.