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@unpublished{pioli:icalepcs2023-fr2ao03, author = {S. Pioli and E. Chiadroni and A. Cianchi and G. Latini and V. Martinelli and A. Mostacci and R. Pompili and B. Serenellini}, % author = {S. Pioli and E. Chiadroni and A. Cianchi and G. Latini and V. Martinelli and A. Mostacci and others}, % author = {S. Pioli and others}, title = {{Machine Learning Based Virtual Beam Diagnostic Tool for Real-Time Operation at EuPRAXIA}}, % booktitle = {Proc. ICALEPCS'23}, booktitle = {Proc. Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)}, eventdate = {2023-10-09/2023-10-13}, language = {english}, intype = {presented at the}, series = {International Conference on Accelerator and Large Experimental Physics Control Systems}, number = {19}, venue = {Cape Town, South Africa}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {02}, year = {2024}, note = {presented at ICALEPCS'23 in Cape Town, South Africa, unpublished}, abstract = {{The EuPRAXIA@SPARC_LAB facility will equip the Frascati National Laboratories (LNF) of the Italian Institute for Nuclear Physics (INFN) with an infrastructure, in the ESFRI roadmap, capable of an unique combination of a high brightness GeV-range electron beam generated in a state-of-the-art LINAC boosted by a plasma acceleration module designed as top-class quality, user-oriented and at the forefront of new acceleration technologies. In these context we present design of our approach and first results with different Machine Learning (ML) techniques on the SPARC_LAB facility for the Virtual Beam Diagnostic tool under development. The aim of such tool will be the real-time virtualization of beam dynamics images as they would be seen by destructive beam diagnostics flag. This digital-twin based methodology will assist and provide images of the electron beam spot along the EuPRAXIA LINAC without affecting beam uptime to users and improving beam dynamics modeling and optimization of this challenging future facility. }}, }