The Electron-Ion Collider (EIC) Hadron Storage Ring (HSR) will use strong hadron cooling to maintain the beam brightness and high luminosity during long collision experiments. An Energy Recovery Linac is used to deliver the high-current high-brightness electron beam for cooling. For the best cooling effect, the electron beam requires low emittance, small energy spread, and uniform longitudinal distribution. In this work, we simulate and optimize the longitudinal laser-beam distribution shaping at the photo-cathode, modeling space charge forces accurately. Machine parameters such as RF cavity phases are optimized in conjunction with the beam distribution using a genetic optimizer. We demonstrate the improvement to the cooling distribution in key parameters.

The Electron-Ion Collider (EIC) is a cutting-edge accelerator designed to collide highly polarized electrons and ions. For enhanced luminosity, the ion beam is cooled via an electron beam sourced from an energy recovery linac (ERL). The current ERL design accommodates one RF cavity per cryomodule, presenting both beam transport and cost-related challenges. This study investigates the feasibility of reducing the cavity size to accommodate two cavities within a single cryomodule. We analyze two compact cavity design options through frequency scaling, assuming constant loaded quality factor Q and R/Q scaling proportional to the square of the frequency ratio. Our analytical and tracking Beam BreakUp (BBU) model predicts the threshold current for each option. While a smaller cavity footprint is advantageous, maintaining sufficient damping of Higher Order Modes (HOMs) is crucial. We compare the HOM damping effectiveness of the proposed compact design to the existing configuration, which achieves sufficient damping within a slightly larger footprint.

An effective high-power positron converter for electron linear accelerators is not currently available from industry. A commercial source would allow research institutes to have ready access to high-brightness positrons for a wealth of material science, nuclear, particle, and accelerator physics projects. Xelera Research LLC has designed, built, and tested a prototype free-surface liquid-metal (GaInSn) jet converter. Free-surface liquid-metal jets allow for significantly greater electron beam power densities than are possible with solid targets. Higher power densities lead to greater positron production and, importantly, allow continuous wave (CW) operation. A modified version of the GaInSn converter prototype is planned to be constructed and tested at the Thomas Jefferson National Accelerator Facility.

Advances in high-performance computing have enabled detailed physics simulations, including those with nonlinear collective effects such as space charge, to be deployed online in a control room setting to aid operator intuition and be used directly in automatic tuning. Simultaneously, machine learning (ML) has enabled deployment of detailed models online with sub-second execution time, opened up new avenues for adapting simulation models to more closely match real accelerator behavior, and enabled novel ways to combine detailed physics simulations and ML-based tuning. This contribution will provide an overview of how these tools are being developed and successfully applied at SLAC, with an emphasis on experimental demonstrations. This includes improvements in adaptive calibration methods, novel approaches to simulation (e.g. differentiable physics combined with ML), and the use of system models in ML-based tuning (e.g. Bayesian optimization with system model priors, iterative simulation and ML tuning to aid LCLS-II injector commissioning). Discussion of the software infrastructure required to achieve this and deploy these solutions into regular operation will also be discussed.

In order to prevent emittance growth during long stores of the proton beam at the future Electron-Ion Collider (EIC), we need to have some mechanism to provide fast cooling of the dense proton beams. One promising method is coherent electron cooling (CeC), which uses an electron beam to both ``measure'' the positions of protons within the bunch and then apply energy kicks which tend to reduce their longitudinal and transverse actions. In this work, we discuss the underlying physics of this process. We then discuss simulations which constrain the electrons to move only longitudinally in order to perform fast optimizations and long-term tracking of the bunch evolution, and benchmark these results against fully 3D codes. Additionally, we discuss practical challenges, including the necessity of a high-quality electron beam and sub-micron alignment of the electrons and protons.

The Electron-Ion Collider (EIC) is currently under development to be built at Brookhaven National Lab and requires cooling during collisions in order to preserve the quality of the hadron beam despite degradation due to intra-beam scattering and beam-beam effect. An Energy Recovery Linac (ERL) is being designed to deliver the necessary electron beam for Coherent electron Cooling (CeC) of the hadron beam, with an electron bunch charge of 1 nC and an average current of 100 mA; two modes of operation are being developed for 150 and 55 MeV electrons, corresponding to 275 and 100 GeV protons. The injector of this Strong Hadron Cooler ERL (SHC-ERL) is shared with the Precooler ERL, which cools lower energy proton beams via bunched beam cooling, as used in the Low Energy RHIC electron Cooling (LEReC). This paper reviews the current state of the design.

The recent development of advanced black box optimization algorithms has promised order of magnitude improvements in optimization speed when solving accelerator physics problems. These algorithms have been implemented in the python package Xopt, which has been used to solve online and offline accelerator optimization problems at a wide number of facilities, including at SLAC, Argonne, BNL, DESY, ESRF, and others. In this work, we describe updates to the Xopt framework that expand its capabilities and improves optimization performance in solving online optimization problems. We also discuss how Xopt has been incorporated into the Badger graphical user interface that allows easy access to these advanced control algorithms in the accelerator control room. Finally, we describe how to integrate machine learning based surrogate models provided by the LUME-model package into online optimization via Xopt.