IPAC2019 - Table of Session: TUZZPLM (Contributed Orals: Beam Instrumentation, Controls, Feedback and Op.)

Paper	Title	Page
TUZZPLM1	Operational Results of LHC Collimator Alignment Using Machine Learning	1208
SUSPFO053	use link to see paper's listing under its alternate paper code
	G. Azzopardi, A. Muscat, G. Valentino University of Malta, Information and Communication Technology, Msida, Malta S. Redaelli, B. Salvachua CERN, Geneva, Switzerland
	A complex collimation system is installed in the Large Hadron Collider to protect sensitive equipment from unavoidable beam losses. The collimators are positioned close to the beam in the form of a hierarchy, which is guaranteed by precisely aligning each collimator with a precision of a few tens of micrometers. During past years, collimator alignments were performed semi-automatically, such that collimation experts had to be present to oversee and control the alignment. In 2018, machine learning was introduced to develop a new fully-automatic alignment tool, which was used for collimator alignments throughout the year. This paper discusses how machine learning was used to automate the alignment, whilst focusing on the operational results obtained when testing the new software in the LHC. Automatically aligning the collimators decreased the alignment time at injection by a factor of three whilst maintaining the accuracy of the results. G.Valentino et al., "Semi-automatic beam-based LHC collimator alignment", PRSTAB, no.5, 2012.
	Slides TUZZPLM1 [6.060 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM1
About •	paper received ※ 10 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUZZPLM2	Status of Automated Optimization Procedures at the European XFEL Accelerator	1212
	S. Tomin EuXFEL, Schenefeld, Germany L. Fröhlich, M. Scholz DESY, Hamburg, Germany
	The European XFEL is in the operational stage since fall 2017. Since then, tuning of the FEL performance (e.g. of the photon pulse energy) has become increasingly important. Due to a large number of parameters to which FEL facilities are highly sensitive and their complex correlations, controlling and optimizing them in a speedy manner is becoming a very important and challenging task. Several automated optimization procedures were developed to optimize the FEL beam quality. In this work, we present the status and the results of these activities, as well as the optimization statistics.
	Slides TUZZPLM2 [5.882 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM2
About •	paper received ※ 13 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUZZPLM3	The EPICS Software Framework Moves from Controls to Physics	1216
	G.R. White, M.V. Shankar SLAC, Menlo Park, California, USA T.M. Cobb DLS, Oxfordshire, United Kingdom L.R. Dalesio, M.A. Davidsaver Osprey DCS LLC, Ocean City, USA S.M. Hartman, K.-U. Kasemir, M.R. Pearson, K. Vodopivec ORNL, Oak Ridge, Tennessee, USA D.G. Hickin EuXFEL, Schenefeld, Germany A.N. Johnson, M.L. Rivers, G. Shen, S. Veseli ANL, Argonne, Illinois, USA H. Junkes FHI, Berlin, Germany M.G. Konrad, G. Shen FRIB, East Lansing, Michigan, USA T. Korhonen ESS, Lund, Sweden M.R. Kraimer Self Employment, Private address, USA R. Lange ITER Organization, St. Paul lez Durance, France M. Sekoranja Cosylab, Ljubljana, Slovenia K. Shroff BNL, Upton, Long Island, New York, USA D. Zimoch PSI, Villigen PSI, Switzerland
	The Experimental Physics and Industrial Control System (EPICS), is an open-source software framework for high-performance distributed control, and is at the heart of many of the world’s large accelerators and telescopes. Recently, EPICS has undergone a major revision, with the aim of better computing supporting for the next generation of machines and analytical tools. Many new data types, such as matrices, tables, images, and statistical descriptions, plus users’ own data types, now supplement the simple scalar and waveform types of the former EPICS. New computational architectures for scientific computing have been added for high-performance data processing services and pipelining. Python and Java bindings have enabled powerful new user interfaces. The result has been that controls are now being integrated with modelling and simulation, machine learning, enterprise databases, and experiment DAQs. We introduce this new EPICS (version 7) from the perspective of accelerator physics and review early adoption cases in accelerators around the world.
	Slides TUZZPLM3 [4.271 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM3
About •	paper received ※ 14 May 2019 paper accepted ※ 23 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

TUZZPLM1

Operational Results of LHC Collimator Alignment Using Machine Learning

1208

SUSPFO053

use link to see paper's listing under its alternate paper code

G. Azzopardi, A. Muscat, G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta
S. Redaelli, B. Salvachua
CERN, Geneva, Switzerland

A complex collimation system is installed in the Large Hadron Collider to protect sensitive equipment from unavoidable beam losses. The collimators are positioned close to the beam in the form of a hierarchy, which is guaranteed by precisely aligning each collimator with a precision of a few tens of micrometers. During past years, collimator alignments were performed semi-automatically*, such that collimation experts had to be present to oversee and control the alignment. In 2018, machine learning was introduced to develop a new fully-automatic alignment tool, which was used for collimator alignments throughout the year. This paper discusses how machine learning was used to automate the alignment, whilst focusing on the operational results obtained when testing the new software in the LHC. Automatically aligning the collimators decreased the alignment time at injection by a factor of three whilst maintaining the accuracy of the results.
*G.Valentino et al., "Semi-automatic beam-based LHC collimator alignment", PRSTAB, no.5, 2012.

Slides TUZZPLM1 [6.060 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM1

About •

paper received ※ 10 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019

Export •

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

TUZZPLM2

Status of Automated Optimization Procedures at the European XFEL Accelerator

1212

S. Tomin
EuXFEL, Schenefeld, Germany
L. Fröhlich, M. Scholz
DESY, Hamburg, Germany

The European XFEL is in the operational stage since fall 2017. Since then, tuning of the FEL performance (e.g. of the photon pulse energy) has become increasingly important. Due to a large number of parameters to which FEL facilities are highly sensitive and their complex correlations, controlling and optimizing them in a speedy manner is becoming a very important and challenging task. Several automated optimization procedures were developed to optimize the FEL beam quality. In this work, we present the status and the results of these activities, as well as the optimization statistics.

Slides TUZZPLM2 [5.882 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM2

About •

paper received ※ 13 May 2019 paper accepted ※ 21 May 2019 issue date ※ 21 June 2019

Export •

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

TUZZPLM3

The EPICS Software Framework Moves from Controls to Physics

1216

G.R. White, M.V. Shankar
SLAC, Menlo Park, California, USA
T.M. Cobb
DLS, Oxfordshire, United Kingdom
L.R. Dalesio, M.A. Davidsaver
Osprey DCS LLC, Ocean City, USA
S.M. Hartman, K.-U. Kasemir, M.R. Pearson, K. Vodopivec
ORNL, Oak Ridge, Tennessee, USA
D.G. Hickin
EuXFEL, Schenefeld, Germany
A.N. Johnson, M.L. Rivers, G. Shen, S. Veseli
ANL, Argonne, Illinois, USA
H. Junkes
FHI, Berlin, Germany
M.G. Konrad, G. Shen
FRIB, East Lansing, Michigan, USA
T. Korhonen
ESS, Lund, Sweden
M.R. Kraimer
Self Employment, Private address, USA
R. Lange
ITER Organization, St. Paul lez Durance, France
M. Sekoranja
Cosylab, Ljubljana, Slovenia
K. Shroff
BNL, Upton, Long Island, New York, USA
D. Zimoch
PSI, Villigen PSI, Switzerland

The Experimental Physics and Industrial Control System (EPICS), is an open-source software framework for high-performance distributed control, and is at the heart of many of the world’s large accelerators and telescopes. Recently, EPICS has undergone a major revision, with the aim of better computing supporting for the next generation of machines and analytical tools. Many new data types, such as matrices, tables, images, and statistical descriptions, plus users’ own data types, now supplement the simple scalar and waveform types of the former EPICS. New computational architectures for scientific computing have been added for high-performance data processing services and pipelining. Python and Java bindings have enabled powerful new user interfaces. The result has been that controls are now being integrated with modelling and simulation, machine learning, enterprise databases, and experiment DAQs. We introduce this new EPICS (version 7) from the perspective of accelerator physics and review early adoption cases in accelerators around the world.

Slides TUZZPLM3 [4.271 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-TUZZPLM3

About •

paper received ※ 14 May 2019 paper accepted ※ 23 May 2019 issue date ※ 21 June 2019

Export •

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