# Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators

arXiv: https://arxiv.org/abs/2312.10040 · HTML: https://mlsanity.com/paper/2312.10040/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Anomaly Detection With Conditional Variational Autoencoders](https://mlsanity.com/paper/2010.05531/) (2020)
- [Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory](https://mlsanity.com/paper/2006.06562/) (2020)
- [Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL](https://mlsanity.com/paper/2012.09737/) (2020)
- [A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators](https://mlsanity.com/paper/2102.00786/) (2021)
- [Machine learning applied to single-shot x-ray diagnostics in an XFEL](https://mlsanity.com/paper/1610.03378/) (2016)
- [Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster](https://mlsanity.com/paper/2105.12847/) (2021)
- [Uncertainty Aware Deep Learning for Particle Accelerators](https://mlsanity.com/paper/2309.14502/) (2023)
- [Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives](https://mlsanity.com/paper/2209.04587/) (2022)
- [Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions](https://mlsanity.com/paper/2307.02367/) (2023)

## Built on by (most cited first, 4 of 4)

- [Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal](https://mlsanity.com/paper/2408.07847/) (2024)
- [Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems](https://mlsanity.com/paper/2602.22618/) (2026)
- [Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source](https://mlsanity.com/paper/2605.31259/) (2026)
- [A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility](https://mlsanity.com/paper/2607.24663/) (2026)

## Abstract (arXiv)

Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, particle accelerators can fault and abort operations for numerous reasons. These faults impact the availability of particle accelerators during scheduled run-time and hamper the efficiency and the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle accelerators. Semi-supervised Machine Learning (ML) based anomaly detection approaches such as autoencoders and variational autoencoders are often used for such tasks. However, supervised ML techniques such as Siamese Neural Network (SNN) models can outperform unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data's variability due to system configuration changes. To address this challenge, we employ Conditional Siamese Neural Network (CSNN) models and Conditional Variational Auto Encoder (CVAE) models to predict errant beam pulses at the Spallation Neutron Source (SNS) under different system configuration conditions and compare their performance. We demonstrate that CSNN outperforms CVAE in our application.