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. The
detailed calculation of complete models makes the Monte Carlo generators slow
and impractical. We present Exhaustive Neural Importance Sampling (ENIS), a
method based on normalizing flows to find a suitable proposal density for
rejection sampling automatically and efficiently, and discuss how this
technique solves common issues of the rejection algorithm.

介绍了一种名为 “Exhaustive Neural Importance Sampling” 的神经归一化流方法，该方法可自动高效地找到适当的拒绝采样建议密度，并解决了拒绝算法的常见问题，从而实现了用于中微子振荡实验所需的精准中微子 - 核相互作用模型的生成。

应用穷尽神经重要性抽样于蒙特卡罗事件生成

Exhaustive Neural Importance Sampling applied to Monte Carlo event  generation

We present a study for the generation of events from a physical process with
deep generative models. The simulation of physical processes requires not only
the production of physical events, but also to ensure these events occur with
the correct frequencies. We investigate the feasibility of learning the event
generation and the frequency of occurrence with Generative Adversarial Networks
(GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo
generators. We study three processes: a simple two-body decay, the processes
$e^+e^-\to Z \to l^+l^-$ and $p p \to t\bar{t} $ including the decay of the top
quarks and a simulation of the detector response. We find that the tested GAN
architectures and the standard VAE are not able to learn the distributions
precisely. By buffering density information of encoded Monte Carlo events given
the encoder of a VAE we are able to construct a prior for the sampling of new
events from the decoder that yields distributions that are in very good
agreement with real Monte Carlo events and are generated several orders of
magnitude faster. Applications of this work include generic density estimation
and sampling, targeted event generation via a principal component analysis of
encoded ground truth data, anomaly detection and more efficient importance
sampling, e.g. for the phase space integration of matrix elements in quantum
field theories.

使用生成对抗网络和变分自编码器生成物理过程事件的研究表明，根据编码的蒙特卡罗事件缓冲区密度信息，可以构造新的先验以从解码器中采样新事件，提高采样效率且能生成与真实蒙特卡罗事件非常接近的分布，应用范围包括密度估计和采样、目标事件生成、异常检测，以及矩阵元素相空间积分的重要性采样等。

使用深度生成模型和密度信息缓冲进行物理事件生成和统计采样

Event Generation and Statistical Sampling for Physics with Deep  Generative Models and a Density Information Buffer

A file repository for calculations of cross sections and kinematic
distributions using Monte Carlo generators for high-energy collisions is
discussed. The repository is used to facilitate effective preservation and
archiving of data from theoretical calculations, as well as for comparisons
with experimental data. The HepSim data library is publicly accessible and
includes a number of Monte Carlo event samples with Standard Model predictions
for current and future experiments. The HepSim project includes a software
package to automate the process of downloading and viewing online Monte Carlo
event samples. A data streaming over a network for end-user analysis is
discussed.

讨论了使用蒙特卡洛发生器进行高能碰撞截面和动力学分布计算的文件库，以促进理论计算数据的有效保存和归档，包括 Monte Carlo 事件样本的公共可访问的 HepSim 数据库并自带软件包以及讨论了用于最终用户分析的网络数据流传输。