# Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics

arXiv: https://arxiv.org/abs/2411.16234 · HTML: https://mlsanity.com/paper/2411.16234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Flow Annealed Importance Sampling Bootstrap](https://mlsanity.com/paper/2208.01893/) (2022)
- [BlackJAX: Composable Bayesian inference in JAX](https://mlsanity.com/paper/2402.10797/) (2024)
- [i-flow: High-dimensional Integration and Sampling with Normalizing Flows](https://mlsanity.com/paper/2001.05486/) (2020)
- [Neural Network-Based Approach to Phase Space Integration](https://mlsanity.com/paper/1810.11509/) (2018)
- [Differentiable Matrix Elements with MadJax](https://mlsanity.com/paper/2203.00057/) (2022)
- [PDFFlow: parton distribution functions on GPU](https://mlsanity.com/paper/2009.06635/) (2020)
- [Exhaustive Neural Importance Sampling applied to Monte Carlo event generation](https://mlsanity.com/paper/2005.12719/) (2020)
- [Accessible Color Sequences for Data Visualization](https://mlsanity.com/paper/2107.02270/) (2021)

## Abstract (arXiv)

High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.