IPAC2021 - List of Authors (Fol, E.)

Paper	Title	Page
MOPAB184	Unsupervised Learning Techniques for Tune Cleaning Measurement	624
	H. Garcia Morales Oxford University, Physics Department, Oxford, Oxon, United Kingdom E. Fol, R. Tomás García CERN, Geneva, Switzerland
	Precise measurements of tune and its stability are crucial for various optics analyses in the LHC, e.g. for the determination of the beta star using K-modulation. LHC BBQ system provides tune measurements online and stores the tune data. We apply unsupervised machine learning techniques on BBQ tune data in order to provide an automatic outlier detection method for better measurements of tune shifts and unexpected tune jitters.
	Poster MOPAB184 [0.354 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB184
About •	paper received ※ 14 May 2021 paper accepted ※ 09 June 2021 issue date ※ 11 August 2021
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WEPAB026	Optics Measurements and Correction Plans for the HL-LHC	2656
	T.H.B. Persson, X. Buffat, F.S. Carlier, R. De Maria, J. Dilly, E. Fol, D. Gamba, H. Garcia Morales, A. García-Tabarés Valdivieso, M. Giovannozzi, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, P.K. Skowroński, F. Soubelet, R. Tomás García, F.F. Van der Veken, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt CERN, Meyrin, Switzerland J.M. Coello de Portugal PSI, Villigen PSI, Switzerland
	The High Luminosity LHC (HL-LHC) will require stringent optics correction to operate safely and deliver the design luminosity to the experiments. In order to achieve this, several new methods for optics correction have been developed. In this article, we outline some of these methods and we describe the envisioned strategy of how to use them in order to reach the challenging requirements of the HL-LHC physics program.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB026
About •	paper received ※ 17 May 2021 paper accepted ※ 27 July 2021 issue date ※ 30 August 2021
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WEPAB027	Optics Correction Strategy for Run 3 of the LHC	2660
	T.H.B. Persson, R. De Maria, J. Dilly, E. Fol, H. Garcia Morales, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, F. Soubelet, R. Tomás García, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt CERN, Meyrin, Switzerland J.F. Cardona UNAL, Bogota D.C, Colombia
	The Run 3 of the LHC will continue to provide new challenges for optics corrections. In order to succeed and go beyond what was achieved previously, several new methods to measure and correct the optics have been developed. In this article we describe these methods and outline the plans for the optics commissioning in 2022.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB027
About •	paper received ※ 17 May 2021 paper accepted ※ 12 July 2021 issue date ※ 11 August 2021
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THPAB055	Reconstruction of Linear Optics Observables Using Supervised Learning	3875
	E. Fol, H. Garcia, R. Tomás García CERN, Geneva, Switzerland
	In the LHC, most of the optical functions can be obtained from turn-by-turn beam centroid data. However, the measurement of such observables as β^* and the dispersion function require special dedicated techniques and additional operational time. In this work, we propose an alternative approach to estimate these observables using supervised machine learning, in case the dedicated measurements are not available but turn-by-turn data are. The performance of developed estimators is demonstrated on LHC simulations. Comparison to traditional techniques for the computation of beta-function will be also provided.
	Poster THPAB055 [0.713 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB055
About •	paper received ※ 19 May 2021 paper accepted ※ 19 July 2021 issue date ※ 15 August 2021
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THPAB068	Denoising of Optics Measurements Using Autoencoder Neural Networks	3915
	E. Fol, R. Tomás García CERN, Geneva, Switzerland
	Noise artefacts can appear in optics measurements data due to instrumentation imperfections or uncertainties in the applied analysis methods. A special type of semi-supervised neural networks, autoencoders, are widely applied to denoising tasks in image and signal processing as well as to generative modeling. Recently, an autoencoder-based approach for denoising and reconstruction of missing data has been developed to improve the quality of phase measurements obtained from harmonic analysis of LHC turn-by-turn data. We present the results achieved on simulations demonstrating the potential of the new method and discuss the effect of the noise in light of optics corrections computed from the cleaned data.
	Poster THPAB068 [0.881 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB068
About •	paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 02 September 2021
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THPAB168	Optics Measurement by Excitation of Betatron Oscillations in the CERN PSB	4078
	E.H. Maclean, F. Antoniou, F. Asvesta, H. Bartosik, C. Bracco, J. Dilly, E. Fol, H. Garcia Morales, M. Hofer, J. Keintzel, M. Le Garrec, T.E. Levens, L. Malina, T.H.B. Persson, T. Prebibaj, E. Renner, P.K. Skowroński, F. Soubelet, R. Tomás García, A. Wegscheider, L. van Riesen-Haupt CERN, Meyrin, Switzerland
	Optics measurement from analysis of turn-by-turn BPM data of betatron oscillations excited with a kicker magnet has been employed very successfully in many machines but faces particular challenges in the CERN PSB where BPM to BPM phase advances are sub-optimal for optics reconstruction. Experience using turn-by-turn oscillation data for linear optics measurements during PSB commissioning in2021 is presented, with implications for the prospect of such techniques in the PSB more generally.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB168
About •	paper received ※ 19 May 2021 paper accepted ※ 14 July 2021 issue date ※ 27 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Unsupervised Learning Techniques for Tune Cleaning Measurement

624

H. Garcia Morales
Oxford University, Physics Department, Oxford, Oxon, United Kingdom
E. Fol, R. Tomás García
CERN, Geneva, Switzerland

Precise measurements of tune and its stability are crucial for various optics analyses in the LHC, e.g. for the determination of the beta star using K-modulation. LHC BBQ system provides tune measurements online and stores the tune data. We apply unsupervised machine learning techniques on BBQ tune data in order to provide an automatic outlier detection method for better measurements of tune shifts and unexpected tune jitters.

Poster MOPAB184 [0.354 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB184

About •

paper received ※ 14 May 2021 paper accepted ※ 09 June 2021 issue date ※ 11 August 2021

Export •

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

WEPAB026

Optics Measurements and Correction Plans for the HL-LHC

2656

T.H.B. Persson, X. Buffat, F.S. Carlier, R. De Maria, J. Dilly, E. Fol, D. Gamba, H. Garcia Morales, A. García-Tabarés Valdivieso, M. Giovannozzi, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, P.K. Skowroński, F. Soubelet, R. Tomás García, F.F. Van der Veken, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt
CERN, Meyrin, Switzerland
J.M. Coello de Portugal
PSI, Villigen PSI, Switzerland

The High Luminosity LHC (HL-LHC) will require stringent optics correction to operate safely and deliver the design luminosity to the experiments. In order to achieve this, several new methods for optics correction have been developed. In this article, we outline some of these methods and we describe the envisioned strategy of how to use them in order to reach the challenging requirements of the HL-LHC physics program.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB026

About •

paper received ※ 17 May 2021 paper accepted ※ 27 July 2021 issue date ※ 30 August 2021

Export •

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

WEPAB027

Optics Correction Strategy for Run 3 of the LHC

2660

T.H.B. Persson, R. De Maria, J. Dilly, E. Fol, H. Garcia Morales, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, F. Soubelet, R. Tomás García, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt
CERN, Meyrin, Switzerland
J.F. Cardona
UNAL, Bogota D.C, Colombia

The Run 3 of the LHC will continue to provide new challenges for optics corrections. In order to succeed and go beyond what was achieved previously, several new methods to measure and correct the optics have been developed. In this article we describe these methods and outline the plans for the optics commissioning in 2022.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB027

About •

paper received ※ 17 May 2021 paper accepted ※ 12 July 2021 issue date ※ 11 August 2021

Export •

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

THPAB055

Reconstruction of Linear Optics Observables Using Supervised Learning

3875

E. Fol, H. Garcia, R. Tomás García
CERN, Geneva, Switzerland

In the LHC, most of the optical functions can be obtained from turn-by-turn beam centroid data. However, the measurement of such observables as β^* and the dispersion function require special dedicated techniques and additional operational time. In this work, we propose an alternative approach to estimate these observables using supervised machine learning, in case the dedicated measurements are not available but turn-by-turn data are. The performance of developed estimators is demonstrated on LHC simulations. Comparison to traditional techniques for the computation of beta-function will be also provided.

Poster THPAB055 [0.713 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB055

About •

paper received ※ 19 May 2021 paper accepted ※ 19 July 2021 issue date ※ 15 August 2021

Export •

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

THPAB068

Denoising of Optics Measurements Using Autoencoder Neural Networks

3915

E. Fol, R. Tomás García
CERN, Geneva, Switzerland

Noise artefacts can appear in optics measurements data due to instrumentation imperfections or uncertainties in the applied analysis methods. A special type of semi-supervised neural networks, autoencoders, are widely applied to denoising tasks in image and signal processing as well as to generative modeling. Recently, an autoencoder-based approach for denoising and reconstruction of missing data has been developed to improve the quality of phase measurements obtained from harmonic analysis of LHC turn-by-turn data. We present the results achieved on simulations demonstrating the potential of the new method and discuss the effect of the noise in light of optics corrections computed from the cleaned data.

Poster THPAB068 [0.881 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB068

About •

paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 02 September 2021

Export •

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

THPAB168

Optics Measurement by Excitation of Betatron Oscillations in the CERN PSB

4078

E.H. Maclean, F. Antoniou, F. Asvesta, H. Bartosik, C. Bracco, J. Dilly, E. Fol, H. Garcia Morales, M. Hofer, J. Keintzel, M. Le Garrec, T.E. Levens, L. Malina, T.H.B. Persson, T. Prebibaj, E. Renner, P.K. Skowroński, F. Soubelet, R. Tomás García, A. Wegscheider, L. van Riesen-Haupt
CERN, Meyrin, Switzerland

Optics measurement from analysis of turn-by-turn BPM data of betatron oscillations excited with a kicker magnet has been employed very successfully in many machines but faces particular challenges in the CERN PSB where BPM to BPM phase advances are sub-optimal for optics reconstruction. Experience using turn-by-turn oscillation data for linear optics measurements during PSB commissioning in2021 is presented, with implications for the prospect of such techniques in the PSB more generally.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB168

About •

paper received ※ 19 May 2021 paper accepted ※ 14 July 2021 issue date ※ 27 August 2021

Export •

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