| Paper | Title | Page |
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| MOPOPT047 | Experimental Demonstration of Machine Learning Application in LHC Optics Commissioning | 359 |
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| Recently, we conducted successful studies on the suitability of machine learning (ML) methods for optics measurements and corrections, incorporating novel ML-based methods for local optics corrections and reconstruction of optics functions. After performing extensive verifications on simulations and past measurement data, the newly developed techniques became operational in the LHC commissioning 2022. We present the experimental results obtained with the ML-based methods and discuss future improvements. Besides, we also report on improving the Beam Position Monitor (BPM) diagnostics with the help of the anomaly detection technique capable to identify malfunctioning BPMs along with their possible fault causes. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT047 | |
| About • | Received ※ 07 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 06 July 2022 | |
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| WEPOST008 | Optics Correction Strategy for Run 3 of the LHC | 1687 |
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| After almost 4 years of shutdown the LHC is again operational in 2022. Experience from the previous Long Shutdown (LS) has shown that the local errors around the triplet magnets changed significantly and it is likely we will again see different errors in 2022. In the LHC there is an interplay between the linear and the nonlinear correction which can make the corrections difficult and time-consuming to find. In this article, we describe the measurements and corrections performed during the commissioning in 2022 in order to control both the linear and the nonlinear optics to high precision. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOST008 | |
| About • | Received ※ 08 June 2022 — Revised ※ 25 June 2022 — Accepted ※ 04 July 2022 — Issue date ※ 10 July 2022 | |
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| WEPOPT007 | First Interaction Region Local Coupling Corrections in the LHC Run 3 | 1838 |
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Funding: This research is supported by the LIV. DAT Center for Doctoral Training, STFC and the European Organization for Nuclear Research The successful operation of large scale particle accelerators depends on the precise correction of unavoidable magnetic field or magnet alignment errors present in the machine. During the LHC Run 2, local linear coupling in the interaction regions (IR) was shown to have a significant impact on the beam size, making its proper handling a necessity for Run 3 and the High Luminosity LHC (HL-LHC). A new approach to accurately minimise the local IR linear coupling based on correlated external variables such as the |C-| had been proposed, which relies on the application of a rigid waist shift in order to create an asymmetry in the IR optics. In this contribution, preliminary corrections from the 2021 beam test and the early 2022 commissioning are presented, as well as first results of the new method’s experimental configuration tests in the LHC Run 3 commissioning. |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOPT007 | |
| About • | Received ※ 03 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 19 June 2022 | |
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| WEPOPT008 | Supervised Machine Learning for Local Coupling Sources Detection in the LHC | 1842 |
| SUSPMF001 | use link to see paper's listing under its alternate paper code | |
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Funding: This research is supported by the LIV. DAT Center for Doctoral Training, STFC and the European Organization for Nuclear Research Local interaction region (IR) linear coupling in the LHC has been shown to have a negative impact on beam size and luminosity, making its accurate correction for Run 3 and beyond a necessity. In view of determining corrections, supervised machine learning has been applied to the detection of linear coupling sources, showing promising results in simulations. An evaluation of different applied models is given, followed by the presentation of further possible application concepts for linear coupling corrections using machine learning. |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOPT008 | |
| About • | Received ※ 03 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 29 June 2022 | |
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