Instrumentation & Measurement Magazine 24-9 - 56

[26] H. Robbins and T. Lai, " Strong consistency of least-squares
estimates in regression models, " J. Multivariate Analysis, vol. 23,
pp. 77-92, 1987.
[27] R. Rifkin and R. Lippert, " Notes on regularized least-squares, "
Tech. Report, Massachusetts Institute of Technology Computer
Science and Artificial Intelligence Laboratory, 2007.
[28] R. Rubinstein, M. Zibulevsky, and M. Elad, " Efficient
implementation of the k-SVD algorithm using batch orthogonal
matching pursuit, " Tech. Report 2008-08, CS Technion, 2008.
[29] Y. Lecun and Y. Bengio, " Convolutional networks for images,
speech, and time-series, " MIT Press, 1995.
[30] E. Fol, R. Tomás, and G. Franchetti, " Supervised learning-based
reconstruction of magnet errors in circular accelerators, " Eur.
Phys. J. Plus, vo. 136, no. 365, 2021.
[31] F. T. Liu, K. M. Ting, and Z.-H. Zhou, " Isolation forest, " in Proc.
8th IEEE Int. Conf. on Data Mining (ICDM'08), IEEE Computer
Society, pp. 413-422, 2008.
[32] E. Fol et al., " Unsupervised machine learning for detection
of faulty beam position monitors, " in Proc. 10th Int. Particle
Accelerator Conf. (IPAC'19), pp. 2668-2671, 2019.
[33] E. Fol, R. Tomás, and G. Franchetti, " Detection of faulty beam
position monitors using unsupervised learning, " Phys. Rev. Accel.
Beams, vol. 102805, 2020.
[34] R. W. Aßmann et al., " Expected performance and beam-based
optimization of the LHC collimation system, " in Proc. 9th
European Particle Accelerator Conf. (EPAC'04), pp. 1825-1827,
2004.
[35] G. Valentino et al., " Semiautomatic beam-based LHC collimator
alignment, " Phys. Rev. ST Accel. Beams, vol. 15, no. 5, 051002, 2012.
[36] P. Arpaia et al., " Machine learning for beam dynamics studies at
the CERN Large Hadron Collider, " Nucl. Instrum. Methods Phys.
Res. A, vol. 985, 164652, 2021.
[37] G. Azzopardi et al., " Automatic spike detection in beam loss
signals for LHC collimator alignment, " Nucl. Instrum. Methods
Phys. Res. A, vol. 934, pp. 10-18, 2019.
[38] S. J. Reeves and Z. Zhe, " Sequential algorithms for observation
selection, " IEEE Trans. Signal Process., vol. 47, no. 1, pp. 123-132,
1999.
[39] G. Azzopardi et al., " Software architecture for automatic LHC
collimator alignment using machine learning, " in Proc. 17th Int.
Conf. on Accelerator and Large Experimental Physics Control Syst.
(ICALEPCS'19), 2019.
[40] G. Azzopardi et al., " Automatic beam loss threshold selection for
LHC collimator alignment, " in Proc. 17th Int. Conf. on Accelerator
and Large Experimental Physics Control Syst. (ICALEPCS'19), 2019.
[41] G. Azzopardi, B. Salvachua, and G. Valentino, " Data-driven crosstalk
modeling of beam losses in LHC collimators, " Phys. Rev.
Accel. Beams, vol. 22, no. 8, 083002, 2019.
[42] G. Azzopardi et al., " Operational results on the fully automatic
LHC collimator alignment, " Phys. Rev. Accel. Beams, vol. 22, no. 9,
093001, 2019.
[43] L. Coyle, " Machine learning applications for hadron colliders:
LHC lifetime optimization, " Master's thesis, Grenoble INP,
France and EPFL, Switzerland, CERN-THESIS-2018-473 (2018).
[44] G. Ke et al., " LightGBM: a highly efficient gradient boosting
decision tree, " in Advances in Neural Information Processing
56
Systems, I. Guyon et al., Eds., pp. 3146-3154, Curran Associates,
Inc., 2017.
[45] J. A. Nelder and R. Mead, " A simplex method for function
minimization, " The Computer J., vol. 7, no. 4, pp. 308-313, 1965.
[46] L. Coyle et al., MD 4510: Working point exploration for use
in lifetime optimization by machine learning, CERN-ACCNOTE-2020-0001
(2020).
[47] L. Carver et al., " Usage of the transverse damper observation box
for high sampling rate transverse position data in the LHC, " Tech.
Report ACC-2017-117, CERN, Switzerland, 2017.
[48] S. Wold, K. Esbensen, and P. Geladi, " Principal component
analysis, " Chemometrics and Intelligent Laboratory Syst., " vol. 2, pp.
37-52, 1987.
[49] D. Müllner, " Modern hierarchical, agglomerative clustering
algorithms, " arXiv:1109.2378, 2011.
[50] G. Rumolo et al., " Electron cloud effects on beam evolution in
a circular accelerator, " Phys. Rev. ST Accel. Beams, vol. 6, no. 8,
081002, 2003.
[51] R. Bruce et al., " Simulations and measurements of beam loss
patterns at the CERN Large Hadron Collider, " Phys. Rev. ST Accel.
Beams, vol. 17, no. 8, 081004, 2014.
[52] A. A. Sokolov and I. M. Ternov, " Synchrotron Radiation, "
(Russian title: Sinkhrotronnoie izluchenie), Akademia Nauk SSSR,
Moskovskoie Obshchestvo Ispytatelei prirody. Sektsia Fiziki.
Sinkhrotron Radiation, Nauka Eds., Moscow, 1966.
[53] C. Zannini, G. Rumolo, and G. Iadarola, " Power loss calculation
in separated and common beam chambers of the LHC, " in Proc.
5th Int. Particle Accelerator Conf. (IPAC'14), pp. 1711-1713, 2014.
[54] O. Gröbner, " Dynamic outgassing, " Tech. Report, CERN, 1999.
[55] B. Salvant et al., " Update on beam induced RF heat- ing in the
LHC, " in Proc. 4th Int. Particle Accelerator Conf. (IPAC'13), pp.
1646-1648, 2013.
[56] J. Jimenez, " LHC: the world's largest vacuum systems being
operated at CERN, " Vacuum, vol. 84, no. 1, pp. 2-7, 2009.
[57] A. Krogh and J. Vedelsby, " Neural network ensembles, cross
validation, and active learning, " Advances in Neural Information
Processing Systems, pp. 231-238, 1995.
[58] J. Neyman, " On the two different aspects of the representative
method: the method of stratified sampling and the method of
purposive selection, " in Breakthroughs in Statistics, pp. 123-150.
Cham, Switzerland: Springer, 1992.
[59] E. Todesco and M. Giovannozzi, " Dynamic aperture estimates
and phase-space distortions in nonlinear betatron motion, " Phys.
Rev. E, vol. 53, pp. 4067-4076, 1996.
[60] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, " A density-based
algorithm for discovering clusters in large spatial databases with
noise, " in Proc. 2nd Int. Conf. on Knowledge Discovery and Data
Mining (KDD'96), pp. 226-231, 1996.
[61] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, " LOF:
identifying density-based local outliers, " in Proc. of the 2000 ACM
SIGMOD Int. Conf. Management of Data, pp. 93-104, 2000.
[62] M. Giovannozzi, E. Maclean, C. E. Montanari, G. Valentino, and
F. F. Van der Veken, " Machine learning applied to the analysis of
nonlinear beam dynamics simulations for the cern large hadron
collider and its luminosity upgrade, " Information, vol. 12, no. 2,
2021.
IEEE Instrumentation & Measurement Magazine
December 2021
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