Particle accelerators are extremely complex machines that are challenging to simulate,
design, and control. Over the past decade, artificial intelligence (AI) and machine learning …

We present a general adaptive latent space tuning approach for improving the robustness of
machine learning tools with respect to time variation and distribution shift. We demonstrate …

High-power particle accelerators are complex machines with thousands of pieces of
equipment that are frequently running at the cutting edge of technology. In order to improve …

The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most
prevalent factors limiting the high-gradient performance of normal conducting rf cavities in …

Searching for new physics (NP) is one of the areas of high-energy physics that requires the
most processing of large amounts of data. At the same time, quantum computing has huge …

We present a python-based program for phenomenological investigations in particle physics
using machine learning algorithms, called MLAnalysis. The program is able to convert LHE …

Anomaly detection algorithms have been proved to be useful in the search of new physics
beyond the Standard Model. However, a prerequisite for using an anomaly detection …

In this paper we look at the properties of the Spallation Neutron Source (SNS) Differential
Beam Current Monitor (DCM) data and various methods of data transformation to improve …

The search of the new physics (NP) beyond the Standard Model is one of the most important
topics in current high energy physics. With the increasing luminosities at the colliders, the …

Optimizing the injection process in particle accelerators is crucial for enhancing beam
quality and operational efficiency. This paper presents a framework for utilizing …