{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:59:39Z","timestamp":1761562779409,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006132","name":"Office of Science","doi-asserted-by":"publisher","award":["DE-SC0019979"],"award-info":[{"award-number":["DE-SC0019979"]}],"id":[{"id":"10.13039\/100006132","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets, and losses in rings. However, dedicated efforts to understand how to apply ML for anomaly detection in linear accelerators have been limited. In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.<\/jats:p>","DOI":"10.3390\/info12060238","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T23:33:33Z","timestamp":1622504013000},"page":"238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Autoencoder Based Analysis of RF Parameters in the Fermilab Low Energy Linac"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1518-0652","authenticated-orcid":false,"given":"Jonathan P.","family":"Edelen","sequence":"first","affiliation":[{"name":"RadiaSoft LLC, Boulder, CO 80301, USA"}]},{"given":"Christopher C.","family":"Hall","sequence":"additional","affiliation":[{"name":"RadiaSoft LLC, Boulder, CO 80301, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1109\/TNS.2016.2543203","article-title":"Neural Networks for Modeling and Control of Particle Accelerators","volume":"63","author":"Edelen","year":"2016","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_2","unstructured":"Edelen, A., Mayes, C., Bowring, D., Ratner, D., Adelmann, A., Ischebeck, R., Snuverink, J., Agapov, I., Kammering, R., and Edelen, J. (2018). Opportunities in Machine Learning for Particle Accelerators. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112802","DOI":"10.1103\/PhysRevAccelBeams.21.112802","article-title":"Machine learning-based longitudinal phase space prediction of particle accelerators","volume":"21","author":"Emma","year":"2018","journal-title":"Phys. Rev. Accel. Beams"},{"key":"ref_4","unstructured":"Edelen, A.L., Biedron, S.G., Milton, S.V., and Edelen, J.P. (2016, January 9\u201314). First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks. Proceedings of the 2016 North American Particle Accelerator Conference, Chicago, IL, USA."},{"key":"ref_5","unstructured":"Edelen, A.L., Edelen, J.P., Biedron, S.G., Milton, S.V., and van der Slot, P.J.M. (2017, January 4\u20139). Using Neural Network Control Policies For Rapid Switching Between Beam Parameters in a Free Electron Laser. Proceedings of the 2017 Deep Learning for Physical Sciences workshop at the Neural Information Processing Systems Conference, Long Beach, CA, USA."},{"key":"ref_6","unstructured":"Edelen, J.P., Cook, N.M., Brown, K.A., and Dyer, P.M. (2021, May 31). Optimal Control for Rapid Switching of Beam Energies for the ATR Line at BNL. Available online: http:\/\/accelconf.web.cern.ch\/icalepcs2019\/papers\/tucpl07.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"044801","DOI":"10.1103\/PhysRevLett.121.044801","article-title":"Demonstration of Model-Independent Control of the Longitudinal Phase Space of Electron Beams in the Linac-Coherent Light Source with Femtosecond Resolution","volume":"121","author":"Scheinker","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"044601","DOI":"10.1103\/PhysRevAccelBeams.23.044601","article-title":"Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems","volume":"23","author":"Edelen","year":"2020","journal-title":"Phys. Rev. Accel. Beams"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nawaz, A.S., Pfeiffer, S., Lichtenberg, G., and Schlarb, H. (2016, January 7\u20139). Self-organzied critical control for the European XFEL using black box parameter identification for the quench detection system. Proceedings of the 2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol), Barcelona, Spain.","DOI":"10.1109\/SYSTOL.2016.7739750"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1016\/j.ifacol.2018.09.554","article-title":"Anomaly Detection for the European XFEL using a Nonlinear Parity Space Method","volume":"51","author":"Nawaz","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_11","unstructured":"Nawaz, A., Lichtenberg, G., Pfeiffer, S., and Rostalski, P. (May, January 29). Anomaly Detection for Cavity Signals\u2014Results from the European XFEL. Proceedings of the 9th International Particle Accelerator Conference, Vancouver, BC, Canada."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.nima.2017.06.020","article-title":"Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets","volume":"867","author":"Wielgosz","year":"2017","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fol, E., Coello de Portugal, J.M., and Tomas, R. (2019, January 19\u201324). Unsupervised machine learning for detection of faulty beam position monitors. Proceedings of the 10th International Particle Accelerator Conference (IPAC2019), Melbourne, Australia.","DOI":"10.1103\/PhysRevAccelBeams.23.102805"},{"key":"ref_14","unstructured":"Dewitte, T., Meert, W., Van Wolputte, E., and Van Trappen, P. (2019, January 5\u201311). Anomaly Detection for CERN Beam Transfer Installations Using Machine Learning. Proceedings of the 17th International Conference on Accelerator and Large Experimental Control Systems (ICALEPCS 2019), New York, NY, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.future.2020.08.010","article-title":"A framework for anomaly detection and classification in Multiple IoT scenarios","volume":"114","author":"Cauteruccio","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.inffus.2018.11.010","article-title":"Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance","volume":"52","author":"Cauteruccio","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_17","unstructured":"Soma, T., Takagi, M., Ishii, K., and Yoshioka, M. (2017, January 1\u20133). Predictive Detection and Diagnosis of Accelerator System Using System Invariant Analysis Technology (SIAT). Proceedings of the 14th annual meeting of Particle Accelerator Society of Japan, Sapporo, Japan."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/1742-6596\/874\/1\/012002","article-title":"Anomaly Detection for Beam Loss Maps in the Large Hadron Collider","volume":"874","author":"Valentino","year":"2017","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/aic.690370209","article-title":"Nonlinear principal component analysis using autoassociative neural networks","volume":"37","author":"Kramer","year":"1991","journal-title":"AIChE J."},{"key":"ref_20","unstructured":"Webber, R.C. (2000, January 8\u201311). Tutorial on Beam Current Monitoring. Proceedings of the 9th Workshop on Beam Instrumentation (BIW 2000), Cambridge, MA, USA. Technical Report, FERMILAB-Conf-00-119."},{"key":"ref_21","unstructured":"Butler, T.A., Allen, L.J., Branlard, J., Chase, B., Paul, W., Joireman, E.C., Kucera, M., Tupikov, V., and Varghese, P. (October, January 29). New LLRF System for Fermilab 201.25 MHz linac. Proceedings of the LINAC08, Victoria, BC, Canada."},{"key":"ref_22","unstructured":"Chollet, F. (2021, May 27). Keras Homepage. Available online: https:\/\/keras.io."},{"key":"ref_23","unstructured":"Doolittle, L. (February, January 29). Low-level RF control system design and architecture. Proceedings of the Asian Particle Accelerator Conference, Indore, India."},{"key":"ref_24","unstructured":"Edelen, J.P., Chase, B.E., Cullerton, E., Einstein-Curtis, J., Holzbauer, J., Klepec, D., Pischalnikov, Y., Schappert, W., Varghese, P., and Joshi, G. (2018). Low Level RF Control for the PIP-II Accelerator. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/6\/238\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:09:40Z","timestamp":1760162980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/6\/238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,31]]},"references-count":24,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["info12060238"],"URL":"https:\/\/doi.org\/10.3390\/info12060238","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2021,5,31]]}}}