Feasibility of hardware acceleration in the LHC orbit feedback controller

Orbit correction in accelerators typically make use of a linear model of the machine, called the Response Matrix(RM), that relates local beam deflections to position changes.The RM is used to obtain a Pseudo-Inverse (PI), which is used in a feedback configuration, where positional errors from the reference orbit as measured by Beam Position Monitors (BPMs) are used to calculate the required change in the current flowing through the Closed Orbit Dipoles (CODs).The calculation of the PIs from the RMs is a crucial part in the LHC’s Orbit Feedback Controller (OFC), however in the present implementation of the OFC this calculation is omitted as it takes too much time to calculate and thus is unsuitable in a real-time system. As a temporary solution the LHC operators pre-calculate the new PIs outside the OFC, and then manually upload them to the OFC in advance. In this paper we aim to find a solution to this computational bottleneck through hardware acceleration in order to act automatically and as quickly as possible to COD and/or BPM failures by re-calculating the PIs within the OFC. These results will eventually be used in the renovation of the OFCfor the LHC’s Run 3.peer-reviewe

Application of reinforcement learning in the LHC tune feedback

The Beam-Based Feedback System (BBFS) was primarily responsible for correcting the beam energy, orbit and tune in the CERN Large Hadron Collider (LHC). A major code renovation of the BBFS was planned and carried out during the LHC Long Shutdown 2 (LS2). This work consists of an explorative study to solve a beam-based control problem, the tune feedback (QFB), utilising state-of-the-art Reinforcement Learning (RL). A simulation environment was created to mimic the operation of the QFB. A series of RL agents were trained, and the best-performing agents were then subjected to a set of well-designed tests. The original feedback controller used in the QFB was reimplemented to compare the performance of the classical approach to the performance of selected RL agents in the test scenarios. Results from the simulated environment show that the RL agent performance can exceed the controller-based paradigm.peer-reviewe

A machine learning approach for the tune estimation in the LHC

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.peer-reviewe

Renovation of the Beam-Based Feedback Controller in the LHC

This work presents an extensive overview of the design choices and implementation of the Beam-Based Feedback System (BBFS) used in operation until the LHC Run 2. The main limitations of the BBFS are listed and a new design called BFCLHC, which uses the CERN Front-End Software Architecture (FESA), framework is proposed. The main implementation details and new features which improve upon the usability of the new design are then emphasised. Finally, a hardware agnostic testing framework developed by the LHC operations section is introduced

An Alternative Processing Algorithm for the Tune Measurement System in the LHC

The betatron tune in the Large Hadron Collider (LHC) is measured using a Base-Band-Tune (BBQ) system. Processing of these BBQ signals is often perturbed by 50Hz 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 clearly oscillate between neighbouring harmonics. The LHC tune feedback cannot be used to its full extent in these conditions as it relies on stable and reliable tune estimates. In this work we present two alternative tune measurement algorithms, designed to mitigate this problem by ignoring small frequency bands around the 50Hz harmonics and estimating the tune from spectra with gaps. One is based on Gaussian Processes and the other is based on a weighted moving average. We compare the tune estimates of the new and present algorithms and put forward a proposal that can be implemented during the renovation of the BBQ system for the next physics run of the LHC

Renovation of the beam-based feedback controller in the LHC

This work presents an extensive overview of the design choices and implementation of the Beam-Based Feedback System (BBFS) used in operation until the LHC Run 2. The main limitations of the BBFS are listed and a new design called BFCLHC, which uses the CERN Front-End Software Architecture (FESA), framework is proposed. The main implementation details and new features which improve upon the usability of the new design are then emphasised. Finally, a hardware agnostic testing framework developed by the LHC operations section is introduced.peer-reviewe