[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126005-en":3,"doc-seo-126005-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126005,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning based longitudinal virtual diagnostics at SwissFEL","Machine learning methods are used to predict electron bunch length and reconstruct the beam longitudinal profile for the SwissFEL free-electron laser. The work addresses limitations of conventional, fully destructive TDS measurements and systematic discrepancies in synchrotron radiation monitor results. A non-invasive SRM-based approach was previously proposed; here the authors implement and optimize an ML model to bridge from SRM inputs to TDS-equivalent absolute characterization. Predictions cover bunch lengths from roughly 10 fs to 2 ps and profile reconstruction at the first compression stage.","This is the author’s peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.  \nPLEASE CITE THIS ART ICLE AS DOI: 10.1063/5. 0179712  \nMachine learning based longitudinal virtual diagnostics at SwissFEL  \nS. Bettoni,a) G. L. Orlandi,b) F. Salomone,c) R. Boiger, R. Ischebeck, and R. Xued) Paul Scherrer Institut, 5232 Villigen, Switzerland  \nA. Mostacci  \nSapienza University of Rome, 00161 Rome, Italy  \n(Dated: 7 December 2023)  \nThe bunch length in linac driven Free Electron Laser (FEL) is a major parameter to be characterized to optimize the final accelerator performances. In linear machines this observable is typically determined from the beam imaged on a screen located downstream of a Transverse Deflecting Structure (TDS) used to impinge a time dependent kick along the longitudinal coordinate of the beam. This measurement is typically performed during the machine setup and only sporadically to check the beam duration, but it cannot be continuously repeated, because time consuming and invasive. A non-invasive method to determine the electron bunch length was already presented in the past. This method is based on the analysis of the synchrotron radiation light spot emitted by the bunch passing through a magnetic chicane, provided that the energy chirp impinged on the bunch by the upstream radiofrequency structures is known. In order to overcome a systematic discrepancy affecting the synchrotron radiation monitor based results compared to the absolute TDS based ones, we implemented and optimized a Machine Learning approach to predict the bunch length downstream of the two SwissFEL compression stages-from about 10 fs up to about 2 ps-as well as the beam longitudinal profile at the first one.  \nI. INTRODUCTION  \nIn the latest decades several Free Electron Laser (FEL) facilities 1–4 are available all around the world to investigate material properties on the atomic scale5–7 , for instance, to explore chemical8,9 and biological 10,11 mechanisms. One of the main advantages ofthe FELs over storage ring synchrotrons is the possibility to perform time resolved measurements exploiting the very short pulse length achievable in FELs (sub-fs and even shorter pulse modes) . For these measurements the knowledge of FEL pulse length hitting the samples is crucial not only for the setup of the lasing but also for the correct interpretation of the results as well. The length of the electron bunch represents an upper limit to the duration of the photon pulse. A preliminary calibration of the photon length versus the electron bunch at the machine set-up for a photon beam time session can be used as a reference during the entire users’ experiment session.  \nSeveral techniques are used in a FEL facility to characterize the bunch length such as streak cameras, electrooptic sampling 12 , and active 13–15 or passive streaking 16,17 devices. The most used technique is based on the Transverse Deflecting Structures (TDSs).The RF field resonating in a TDS introduces a time-dependent transverse kick (streak) along the electron bunch longitudinal coordinate. The TDS streaked beam can be imaged by a view-screen. Provided that the calibration of the centroid of the streaked beam versus TDS phase has been  \na) Electronic mail: [simona.bettoni@psi.ch](simona.bettoni@psi.ch)  \nb) Electronic mail: [gianluca.orlandi@psi.ch](gianluca.orlandi@psi.ch)  \nc) Also with Sapienza University of Rome, 00161 Rome, Italy  \nd) Presently at II. Institute of Physics C, RWTH-Aachen University, 52074 Aachen, Germany  \nperformed, from the knowledge of the TDS frequency an absolute measurement of the the time duration of the electron pulse as well as the longitudinal profile can be determined. Such a measurement is absolute, very reliable, but fully destructive for the beam, and it is time consuming since it may require the adjustment of the strength of some quadrupoles to optimize the measurement r","cbCainsHOFwQpWgh","https://ap.wps.com/l/cbCainsHOFwQpWgh","pdf",322425,1,11,"English","en",105,"# Introduction\n## Motivation and limitations of standard bunch-length measurements\n## Non-invasive SRM method and need for systematic correction\n## ML approach for virtual diagnostics at SwissFEL","[{\"question\":\"Why is electron bunch length characterization important at SwissFEL?\",\"answer\":\"It is a major parameter for optimizing accelerator performance and for accurately interpreting time-resolved FEL measurements because the bunch length sets an upper limit to the photon pulse duration.\"},{\"question\":\"What limitations affect the standard TDS-based bunch-length measurement?\",\"answer\":\"TDS measurements are time-consuming and can be invasive; they may require additional machine adjustments for resolution and can create losses or secondary particle showers at certain locations.\"},{\"question\":\"How does the proposed method improve over an SRM-only approach?\",\"answer\":\"The study implements and optimizes a machine learning model to predict absolute bunch length and reconstruct the longitudinal profile, overcoming systematic discrepancies between SRM-monitor based results and TDS-based measurements.\"}]","Machine learning based longitudinal virtual diagnostics at SwissFEL | PDF",1785902511,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-longitudinal-virtual-diagnostics-at-swissfel","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-longitudinal-virtual-diagnostics-at-swissfel/126005/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is electron bunch length characterization important at SwissFEL?","Question",{"text":76,"@type":77},"It is a major parameter for optimizing accelerator performance and for accurately interpreting time-resolved FEL measurements because the bunch length sets an upper limit to the photon pulse duration.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations affect the standard TDS-based bunch-length measurement?",{"text":81,"@type":77},"TDS measurements are time-consuming and can be invasive; they may require additional machine adjustments for resolution and can create losses or secondary particle showers at certain locations.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed method improve over an SRM-only approach?",{"text":85,"@type":77},"The study implements and optimizes a machine learning model to predict absolute bunch length and reconstruct the longitudinal profile, overcoming systematic discrepancies between SRM-monitor based results and TDS-based measurements.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]