IPAC2018 - List of Authors (Valentino, G.)

Paper	Title	Page
WEPAF080	Beam Size Measurements Based on Movable Quadrupolar Pick-ups	2028
	A. Sounas, M. Gąsior, T. Lefèvre, A. Mereghetti, J. Olexa, S. Redaelli, G. Valentino CERN, Geneva, Switzerland
	Measurements with quadrupolar pick-ups (PU) have attracted particular interest as non-intercepting diagnostics for determining the transverse beam size. They are based on processing the signals of an electromagnetic PU for the extraction of the second-order moment, which contains information about the beam size. Despite the simplicity of the concept, quadrupololar measurements have always been highly challenging in reality. This comes from the fact that the quadrupolar moment constitutes only a very small part of the total PU signal dominated by the intensity and the position signals. Therefore, the beam size information can easily be lost due to small imperfections in the signal processing chain, such as asymmetries in the electronics and cables. In this paper, we present a new method for quadrupolar measurements using movable PUs. Through position and aperture scans, our technique minimizes the parasitic beam position signal and takes into account imperfections of the PU, cables and electronics, thus enabling an efficient auto-calibration of the measurement system. Preliminary studies, using collimators with embedded electrostatic PUs in the LHC at CERN, have shown very promising results.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF080
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WEPAF078	Machine Learning Applied at the LHC for Beam Loss Pattern Classification	2020
	G. Valentino University of Malta, Information and Communication Technology, Msida, Malta B. Salvachua CERN, Geneva, Switzerland
	Beam losses at the LHC are constantly monitored because they can heavily impact the performance of the machine. One of the highest risks is to quench the LHC superconducting magnets in the presence of losses leading to a long machine downtime in order to recover cryogenic conditions. Smaller losses are more likely to occur and have an impact on the machine performance, reducing the luminosity production or reducing the lifetime of accelerator systems due to radiation effects, such as magnets. Understanding the characteristics of the beam loss, such as the beam and the plane, is crucial in order to correct them. Regularly during the year, dedicated loss map measurements are performed in order to validate the beam halo cleaning of the collimation system. These loss maps have the particular advantage that they are performed in well controlled conditions and can therefore be used by a machine learning algorithm to classify the type of losses during the LHC machine cycle. This study shows the result of the beam loss classification and its retrospective application to beam loss data from the 2017 run.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF078
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Beam Size Measurements Based on Movable Quadrupolar Pick-ups

2028

A. Sounas, M. Gąsior, T. Lefèvre, A. Mereghetti, J. Olexa, S. Redaelli, G. Valentino
CERN, Geneva, Switzerland

Measurements with quadrupolar pick-ups (PU) have attracted particular interest as non-intercepting diagnostics for determining the transverse beam size. They are based on processing the signals of an electromagnetic PU for the extraction of the second-order moment, which contains information about the beam size. Despite the simplicity of the concept, quadrupololar measurements have always been highly challenging in reality. This comes from the fact that the quadrupolar moment constitutes only a very small part of the total PU signal dominated by the intensity and the position signals. Therefore, the beam size information can easily be lost due to small imperfections in the signal processing chain, such as asymmetries in the electronics and cables. In this paper, we present a new method for quadrupolar measurements using movable PUs. Through position and aperture scans, our technique minimizes the parasitic beam position signal and takes into account imperfections of the PU, cables and electronics, thus enabling an efficient auto-calibration of the measurement system. Preliminary studies, using collimators with embedded electrostatic PUs in the LHC at CERN, have shown very promising results.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF080

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAF078

Machine Learning Applied at the LHC for Beam Loss Pattern Classification

2020

G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta
B. Salvachua
CERN, Geneva, Switzerland

Beam losses at the LHC are constantly monitored because they can heavily impact the performance of the machine. One of the highest risks is to quench the LHC superconducting magnets in the presence of losses leading to a long machine downtime in order to recover cryogenic conditions. Smaller losses are more likely to occur and have an impact on the machine performance, reducing the luminosity production or reducing the lifetime of accelerator systems due to radiation effects, such as magnets. Understanding the characteristics of the beam loss, such as the beam and the plane, is crucial in order to correct them. Regularly during the year, dedicated loss map measurements are performed in order to validate the beam halo cleaning of the collimation system. These loss maps have the particular advantage that they are performed in well controlled conditions and can therefore be used by a machine learning algorithm to classify the type of losses during the LHC machine cycle. This study shows the result of the beam loss classification and its retrospective application to beam loss data from the 2017 run.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF078

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)