Speaker
Description
The betatron tune in the Large Hadron Collider (LHC) is measured using a Base-Band
Tune (BBQ) system. The processing of these BBQ signals is often perturbed by 50 Hz noise harmonics
present in the beam. This causes the tune measurement algorithm, currently based on peak detection,
to provide incorrect tune estimates during the acceleration cycle with values that oscillate between
neighbouring harmonics. The LHC tune feedback (QFB) cannot be used to its full extent in these
conditions as it relies on stable and reliable tune estimates. In this work, we propose new tune
estimation algorithms, designed to mitigate this problem through different techniques. As ground-
truth of the real tune measurement does not exist, we developed a surrogate model, which allowed
us to perform a comparative analysis of a simple weighted moving average, Gaussian Processes and
different deep learning techniques. The simulated dataset used to train the deep models was also
improved using a variant of Generative Adversarial Networks (GANs) called SimGAN. In addition,
we demonstrate how these methods perform with respect to the present tune estimation algorithm.
