[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125908-en":3,"doc-seo-125908-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125908,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning","X-ray free-electron lasers provide coherent, high-intensity X-rays used in ultra-fast measurements and dynamic structural imaging, but stochastic self-amplified spontaneous emission and difficult electron injection control produce noisy outputs with limited temporal coherence. Standard diagnostics for two-colour pulses are invasive or costly, making single-shot characterization challenging. The work uses machine learning models, including neural networks and decision trees, to predict central photon energies for attosecond fundamental–second-harmonic pulse pairs from parameters captured in single shots at high repetition rates, using experimental data with feature analysis and training-time optimization, enabling prediction without measuring the other pulse.","arXiv :2311 . 14751v3 [physics .acc-ph] 26 Mar 2024  \nEfficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning  \nKarim K. Alaa El-Din 1,* , Oliver G. Alexander 1 , Leszek J. Frasinski 1 , Florian Mintert 1,3 , Zhaoheng Guo4 , Joseph Duris4 , Zhen Zhang4 , David B. Cesar4 , Paris Franz4 , Taran Driver4 , Peter Walter4 , James P. Cryan4 , Agostino Marinelli4 , Jon P. Marangos 1 , and Rick Mukherjee 1,2**  \n1 Blackett Laboratory, Imperial College London, SW7 2AZ, London, UK  \n2 Center for Optical Quantum Technologies, Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany  \n3 Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden, Germany  \n4 SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA  \n* [karim.alaael-din@physics.ox.ac.uk](karim.alaael-din@physics.ox.ac.uk)  \n** [rick.mukherjee@physnet.uni-hamburg.de](rick.mukherjee@physnet.uni-hamburg.de)  \nABSTRACT  \nX-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes.  \nIntroduction  \nIn recent years, X-ray free-electron lasers (XFELs) [1–3] have emerged as a versatile tool for research with applications ranging from damage-free dynamic imaging of molecules [4] and proteins [5–7], new spectroscopic methods for quantum chemistry [8, 9] and resonant X-ray spectroscopy of nanostructures in condensed matter [10, 11] . The versatility of XFELs is based on their tunability, brightness and very short pulse durations, which make the tracking of ultra-fast dynamics of electrons in matter feasible.  \nXFEL sources generate X-ray pulses by accelerating electron bunches to relativistic speeds in a linear accelerator of radiofrequency (RF) cavities and allowing them to interact with magnetic fields generated by an undulator [1–3], see Fig. 1. An XFEL can emit coherent or partially coherent radiation because of a favourable self-organization of the electrons in a relativistic beam as it passes through an appropriately tuned undulator. Different configurations are chosen that lead to the modulation of the phase space for the electron bunch and lasing. This can be used to generate pulses with different properties. Using an additional pre-modulation of the electron beam energy in a short wiggler section, followed by phase space manipulation to transfer the energy into a very short duration high electron current, leads to so-called enhanced SASE that results in sub-femtosecond pulses of the kind studied here [12] . SASE and enhanced SASE pulse are important techniques in ultrafast science [13], where dynamics can be resolved using pump-pr","cbCaikCUxWbq5u4Z","https://ap.wps.com/l/cbCaikCUxWbq5u4Z","pdf",2762388,7,1,14,"English","en",105,"# Introduction\n## Background on XFELs and two-colour pulses\n## Instabilities and stochastic SASE\n## Motivation for single-shot characterization\n## Machine-learning-based prediction approach","[{\"question\":\"Why are two-colour X-ray pulse measurements at XFELs difficult in practice?\",\"answer\":\"Because self-amplified spontaneous emission is stochastic and pulse properties fluctuate, while standard characterization techniques for two-colour pulses are either invasive or diagnostically expensive.\"},{\"question\":\"What does the proposed machine learning method predict?\",\"answer\":\"It predicts the central photon energies of pairs of attosecond fundamental and second-harmonic pulses using parameters that are easily recorded from single shots.\"},{\"question\":\"How can the method avoid measuring both pulses?\",\"answer\":\"The models can predict the central photon energy for one pulse without measuring the other, allowing use of the spectrometer without extending the detection window.\"}]","Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning | 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are two-colour X-ray pulse measurements at XFELs difficult in practice?","Question",{"text":77,"@type":78},"Because self-amplified spontaneous emission is stochastic and pulse properties fluctuate, while standard characterization techniques for two-colour pulses are either invasive or diagnostically expensive.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What does the proposed machine learning method predict?",{"text":82,"@type":78},"It predicts the central photon energies of pairs of attosecond fundamental and second-harmonic pulses using parameters that are easily recorded from single shots.",{"name":84,"@type":75,"acceptedAnswer":85},"How can the method avoid measuring both pulses?",{"text":86,"@type":78},"The models can predict the central photon energy for one pulse without measuring the other, allowing use of the spectrometer without extending the detection 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