For the 2024 100 GeV proton run at RHIC, the new sPHENIX detector will require a maximum amount of collisions within ±10 cm of its central Interaction Point (IP), and preferably few or no collisions outside this range. To maximize the collisions within the vertex, a large crossing angle of up to 2 mrad will be used, operating the Large Piwinski Angle (LPA) scheme. To compensate for the reduction in luminosity from the large Piwinski angle, a β=50 cm lattice has been designed and supported with dynamic aperture simulations. To further compensate the luminosity reduction, injector studies have been performed to support up to a 45% increase in the injected intensity relative to the previous 100 GeV run in 2015.1

The Relativistic Heavy Ion Collider (RHIC) Run 23 program consisted of collisions of 100 GeV gold beams at two collision points for the first time since 2016; the sPHENIX collaboration used the beam to commission their new detector systems while STAR took physics data. Completion of sPHENIX construction pushed the start of the run to May, forcing the collider complex to operate over the summer months and incurring lower than normal availability due to heat and power dip related problems. Issues with dynamic pressure rise during acceleration through transition resulted in a slower ramp up of intensity compared to prior years. Finally, a failure of a warm-to-cold current lead interface in the valve box for the Main Magnet power supply forced the run to end. This paper will discuss the progress made by each experiment and the failure mode, repair and mitigation efforts in preparation for Run 24.

The RHIC heavy ion program relies on a series of RF bunch merge gymnastics to combine individual source pulses into bunches of suitable intensity. Intensity and emittance preservation during these gymnastics require careful setup of the voltages and phases of RF cavities operating at several different harmonic numbers. The optimum setting tends to drift over time, degrading performance and requiring operator attention to correct. We describe a reinforcement learning approach to learning and maintaining an optimum configuration, accounting for the relevant RF parameters and external perturbations (e.g., a changing main dipole field) using a physics-based simulator at Brookhaven Alternating Gradient Synchrotron (AGS).