{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T07:57:06Z","timestamp":1764403026680,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T00:00:00Z","timestamp":1608854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2020-808."],"award-info":[{"award-number":["075-15-2020-808."]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments. We see that binning can crucially affect the performance of SVM and GBT, and, to a less extent, FCNN and CNN. This can be interpreted as the latter methods being able to effectively learn the optimal binning, discarding unnecessary information. Nevertheless, given limited training sets, the results indicate that the efficiency can be increased by optimizing the binning scale along with other hyperparameters. We present specific measurements of accuracy that can be useful for planning of experiments in the specified research area.<\/jats:p>","DOI":"10.3390\/e23010021","type":"journal-article","created":{"date-parts":[[2020,12,27]],"date-time":"2020-12-27T20:04:58Z","timestamp":1609099498000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2962-9366","authenticated-orcid":false,"given":"Yury","family":"Rodimkov","sequence":"first","affiliation":[{"name":"Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evgeny","family":"Efimenko","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics of the Russian Academy of Sciences, 603950 Nizhni Novgorod, Russia"},{"name":"Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1075-1329","authenticated-orcid":false,"given":"Valentin","family":"Volokitin","sequence":"additional","affiliation":[{"name":"Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia"},{"name":"Mathematical Center, Lobachevsky University, 603950 Nizhni Novgorod, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Panova","sequence":"additional","affiliation":[{"name":"Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey","family":"Polovinkin","sequence":"additional","affiliation":[{"name":"Adv Learning Systems, TDAA, Intel, Chandler, AZ 85226, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6905-2050","authenticated-orcid":false,"given":"Iosif","family":"Meyerov","sequence":"additional","affiliation":[{"name":"Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia"},{"name":"Mathematical Center, Lobachevsky University, 603950 Nizhni Novgorod, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2056-081X","authenticated-orcid":false,"given":"Arkady","family":"Gonoskov","sequence":"additional","affiliation":[{"name":"Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia"},{"name":"Institute of Applied Physics of the Russian Academy of Sciences, 603950 Nizhni Novgorod, Russia"},{"name":"Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2019.03.001","article-title":"A high-bias, low-variance introduction to machine learning for physicists","volume":"810","author":"Mehta","year":"2019","journal-title":"Phys. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., and Zdeborov\u00e1, L. (2019). Machine learning and the physical sciences. Rev. Mod. Phys., 91.","DOI":"10.1103\/RevModPhys.91.045002"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7043","DOI":"10.1038\/s41598-019-43465-3","article-title":"Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics","volume":"9","author":"Gonoskov","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1214\/aos\/1176346785","article-title":"Bayesianly justifiable and relevant frequency calculations for the applies statistician","volume":"12","author":"Rubin","year":"1984","journal-title":"Ann. Stat."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1093\/genetics\/162.4.2025","article-title":"Approximate Bayesian computation in population genetics","volume":"162","author":"Beaumont","year":"2002","journal-title":"Genetics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15324","DOI":"10.1073\/pnas.0306899100","article-title":"Markov chain Monte Carlo without likelihoods","volume":"100","author":"Marjoram","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sisson, S.A., Fan, Y., and Beaumont, M.A. (2019). Handbook of Approximate Bayesian Computation, CRC Press.","DOI":"10.1201\/9781315117195"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2874","DOI":"10.1093\/mnras\/sty819","article-title":"Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology","volume":"477","author":"Alsing","year":"2018","journal-title":"MNRAS"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Charnock, T., Lavaux, G., and Wandelt, B.D. (2018). Automatic physical inference with information maximizing neural networks. Phys. Rev. D, 97.","DOI":"10.1103\/PhysRevD.97.083004"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1103\/RevModPhys.84.1177","article-title":"Extremely high-intensity laser interactions with fundamental quantum systems","volume":"84","author":"Hatsagortsyan","year":"2012","journal-title":"Rev. Mod. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cole, J.M., Behm, K.T., Gerstmayr, E., Blackburn, T.G., Wood, J.C., Baird, C.D., Duff, M.J., Harvey, C., Ilderton, A., and Joglekar, A.S. (2018). Experimental evidence of radiation reaction in the collision of a high-intensity laser pulse with a laser-wakefield accelerated electron beam. Phys. Rev. X, 8.","DOI":"10.1103\/PhysRevX.8.011020"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Poder, K., Tamburini, M., Sarri, G., di Piazza, A., Kuschel, S., Baird, C.D., Behm, K., Bohlen, S., Cole, J.M., and Corvan, D.J. (2018). Experimental signatures of the quantum nature of radiation reaction in the field of an ultraintense laser. Phys. Rev. X, 8.","DOI":"10.1103\/PhysRevX.8.031004"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Harvey, C.N., Gonoskov, A., Ilderton, A., and Marklund, M. (2017). Quantum quenching of radiation losses in short laser pulses. Phys. Rev. Lett., 118.","DOI":"10.1103\/PhysRevLett.118.105004"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"909","DOI":"10.3938\/jkps.75.909","article-title":"Machine learning analysis for the soliton formation in resonant nonlinear three-wave interactions","volume":"75","author":"Kim","year":"2019","journal-title":"J. Korean Phys. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gonoskov, A., Bastrakov, S., Efimenko, E., Ilderton, A., Marklund, M., Meyerov, I., Muraviev, A., Sergeev, A., Surmin, I., and Wallin, E. (2015). Extended particle-in-cell schemes for physics in ultrastrong laser fields: Review and developments. Phys. Rev. E, 92.","DOI":"10.1103\/PhysRevE.92.023305"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Arran, C., Cole, J.M., Gerstmayr, E., Blackburn, T.G., Mangles, S.P.D., and Ridgers, C.P. (2019). Optimal parameters for radiation reaction experiments. Plasma Phys. Control. Fusion, 61.","DOI":"10.1088\/1361-6587\/ab20f6"},{"key":"ref_17","unstructured":"(2020, December 05). Hi-Chi Project. Available online: https:\/\/github.com\/hi-chi\/pyHiChi."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Taflove, A., and Hagness, S.C. (2005). Computational Electrodynamics: The Finite-Difference Time-Domain Method, Artech house. [3rd ed.].","DOI":"10.1002\/0471654507.eme123"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1002\/(SICI)1098-2760(19970620)15:3<158::AID-MOP11>3.0.CO;2-3","article-title":"The PSTD algorithm: A time-domain method requiring only two cells per wavelength","volume":"15","author":"Liu","year":"1997","journal-title":"Microw. Opt. Technol. Lett."},{"key":"ref_20","unstructured":"Haber, I., Lee, R., Klein, H., and Boris, J. (1973, January 16\u201318). Advances in electromagnetic simulation techniques. Proceedings of the Sixth Conference on Numerical Simulation of Plasmas, Berkeley, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jcp.2013.03.010","article-title":"A domain decomposition method for pseudo-spectral electromagnetic simulations of plasmas","volume":"243","author":"Vay","year":"2013","journal-title":"J. Comput. Phys."},{"key":"ref_22","unstructured":"Leh\u00e9, R., and Vay, J.L. (2018, January 20\u201324). Review of spectral maxwell solvers for electromagnetic particle-in-cell: Algorithms and advantages. Proceedings of the 13th International Computational Accelerator Physics Conference, Key West, FL, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Muraviev, A., Bashinov, A., Efimenko, E., Volokitin, V., Meyerov, I., and Gonoskov, A. (2020). Strategies for particle resampling in PIC simulations. arXiv.","DOI":"10.1016\/j.cpc.2021.107826"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.cpc.2016.02.004","article-title":"Particle-in-Cell laser-plasma simulation on Xeon Phi coprocessors","volume":"202","author":"Surmin","year":"2016","journal-title":"Comput. Phys. Commun."},{"key":"ref_25","first-page":"319","article-title":"Co-design of a particle-in-cell plasma simulation code for Intel Xeon Phi: A first look at Knights Landing","volume":"Volume 10049","author":"Surmin","year":"2016","journal-title":"Lecture Notes in Computer Science, Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing, Granada, Spain, 14\u201316 December 2016"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hager, G., and Wellein, G. (2010). Introduction to High Performance Computing for Scientists and Engineers, CRC Press.","DOI":"10.1201\/EBK1439811924"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, New York, NY, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_28","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_30","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Wadsworth, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/BF02134016","article-title":"Approximation by superpositions of a sigmoidal function","volume":"5","author":"Cybenko","year":"1992","journal-title":"Math. Control. Syst."},{"key":"ref_32","first-page":"6231","article-title":"The expressive power of neural networks: A view from the width","volume":"30","author":"Lu","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_34","unstructured":"(2020, December 05). XGBoost Documentation. Available online: https:\/\/xgboost.readthedocs.io\/."},{"key":"ref_35","unstructured":"(2020, December 05). Scikit-Learn Documentation. Available online: https:\/\/scikit-learn.org\/."},{"key":"ref_36","unstructured":"XGBoost Documentation (2020, December 21). Python API. Available online: https:\/\/xgboost.readthedocs.io\/en\/latest\/python\/python_api.html."},{"key":"ref_37","unstructured":"Scikit-Learn Documentation (2020, December 21). Python API (SVR). Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.svm.SVR.html."},{"key":"ref_38","unstructured":"(2020, December 05). Keras Documentation. Available online: https:\/\/keras.io\/."},{"key":"ref_39","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_40","unstructured":"Scikit-Learn Documentation (2020, December 05). Python API (PCA). Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.PCA.html."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gorban, A., K\u00e9gl, B., Wunsch, D., and Zinovyev, A. (2008). Principal manifolds for data visualization and dimension reduction. Lect. Notes Comput. Sci. Eng., 58.","DOI":"10.1007\/978-3-540-73750-6"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/1\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:46:17Z","timestamp":1760179577000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/1\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,25]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["e23010021"],"URL":"https:\/\/doi.org\/10.3390\/e23010021","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2020,12,25]]}}}