Accurate and efficient particle tracking through Siberian Snakes is crucial to building comprehensive accelerator simulation model. At the Alternating Gradient Synchrotron (AGS) and Relativistic Heavy Ion Collider (RHIC), Siberian Snakes are traditionally modeled in MAD-X by Taylor map matrices generated at specific current and energy configurations. This method falls short during ramping due to the nonphysical jumps between matrices. Another common method is to use grid field maps for the Snakes, but field map files are usually very large and thus cumbersome to use. In this work, we apply a new method called the Generalized Gradient (GG) map formalism to model complex fields in Siberian Snakes. GG formalism provides an analytic function in x and y for which automatic differentiation, i.e. Differential Algebra or Truncated Power Series Algebra can find accurate high order maps. We present simulation results of the Siberian Snakes in both the AGS and RHIC using the Bmad toolkit for accelerator simulation, demonstrating that GG formalism provides accurate particle tracking results.

Physics models, particularly for online operations, such as for our MAD-X or Bmad models, depend on a good understanding of the magnet characteristics. While we often measure the magnets or some subset of the magnets, those measurements are only meant to verify that the magnets meet specifications before being installed. We often have magnets that are not precisely understood. As a result, we end up adjusting the coefficients in our models to match beam-based measurements with little or no theoretical basis. In this work, we present a new method for deriving these coefficients using orbit response matrix (ORM) methods. This new approach utilizes a neural network (NN) surrogate model to establish the mapping between ORM measurements and quadrupole kicks. The NN model is trained to identify quadrupole kick as a source of error by observing the difference between measured ORM and model ORM with no quadrupole kick. With actual kick values from the NN model and power supply current values from the control system, we can calculate the magnet transfer function coefficients using a polynomial fit. We will present results from preliminary beam studies in the AGS Booster.

The ionization profile monitors (IPMs) are used to measure the transverse profiles of the beams accelerated at the Brookhaven National Laboratory (BNL) AGS. These devices use multi-channel plates (MCP) to collect electrons generated by ionization of the residual gas to get an image of the beam projection onto the two transverse planes. The gains of each of the 64 channels in the MCP can vary from channel to channel due to both initial fabrication variations and over time as the channel exposed to more signal degrade and become less sensitive. There are also systematic errors associated with varying delays in the digitization paths for different groups of channels. We describe a reinforcement learning approach to accounting for and calibrating these errors using historical data from the Brookhaven AGS IPMs.

The Alternating Gradient Synchrotron (AGS) is a particle accelerator at Brookhaven National Laboratory (BNL) that accelerates protons and heavy ions using the strong focusing principle. In this work, we perform simulation studies on the AGS ring of a machine error detection method by comparing simulated and measured orbit response matrices (ORMs). We also present preliminary results of building two machine learning (ML) surrogate models of the AGS system. The first ML model is a surrogate model for the ORM, which describes mapping between orbit distortions and corrector settings. Building a self-adaptive model of ORM eliminates the need to re-measure ORM using the traditional time-consuming procedure. The second ML model is an error identification model, which maps the correlation between measurement errors (differences between measurement and model) and sources of such errors. The most relevant error sources for the error model are determined by performing sensitivity studies of the ORM.