Precisely calibrating RF superconducting radio-frequency linear accelerators is crucial for accurately assessing cavity bandwidth and detuning, which provides valuable insights into cavity performance, facilitates optimal accelerator operation, and enables effective fault detection and diagnosis. In practice, however, calibration of RF signals can present several challenges, with calibration drift being a significant issue, especially in settings prone to humidity and temperature fluctuations. In this paper, we delve into the effect of environmental factors on the calibration drift of superconducting RF cavities. Specifically, we examine long-term calibration drifts and explore how environmental variables such as humidity, temperature, and environmental noise affect this phenomenon. The results show that environmental factors, particularly relative humidity, significantly influence calibration drifts. Moreover, we observe and analyze the lag in their influence. By analyzing these correlations, appropriate compensation algorithms can be designed to mitigate and eliminate these effects, thus optimizing calibration accuracy and stability.

Many accelerator physics problems such as beamline design, beam dynamics model calibration or interpreting experimental measurements rely on solving an optimization problem that use a simulation of beam dynamics. However, it is difficult to solve high dimensional optimization problems using current beam dynamics simulations because calculating gradients of simulated objectives with respect to input parameters is computationally expensive in high dimensions. To address this problem, backwards differentiable beam dynamics simulations have been developed that enable computationally inexpensive calculations of objective gradients that are independent of the number of input parameters. In this work, we highlight current and future applications of differentiable beam dynamics simulations in accelerator physics, such as improving accelerator design, model calibration, and machine learning. We also describe current collaborative efforts between SLAC, DESY, KIT, and LBNL to implement fast, backwards differentiable beam dynamics simulations in Python. These tools will enable unprecedented improvements in optimization efficiency and speed when using beam dynamics simulations, leading to enhanced control and detailed understanding of physical accelerator systems.