【AIGC One Sentence Reading】:This study explores the optimization and control of synchrotron emission in interactions between ultraintense lasers and solids, utilizing machine learning techniques. The focus is on enhancing the efficiency and precision of these interactions, particularly concerning radiation reactions and gamma ray emissions, through advanced algorithms like Bayesian optimization.
【AIGC Short Abstract】:This CORRIGENDUM presents advancements in optimizing and controlling synchrotron emission during ultraintense laser-solid interactions, leveraging machine learning techniques. By harnessing the power of data-driven methods, the study refines the processes involved in these high-intensity interactions, aiming to enhance the efficiency and control of gamma ray production. The approach demonstrates the potential of modern algorithms in fine-tuning complex physical processes.
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Due to an isolated error in the 3D simulation parameters, the laser energy and intensity (calculated using the energy) values were incorrectly stated as 10.9 J and 3×1022 W cm−2, respectively, in Sections 3.3, 7 and 8. The correct values are 39.8 J and 1.1×1023 W cm−2. Similarly, the values stated for the higher energy case, 109 J and 3×1023 W cm−2 in Section 7, should be 398 J and 1.1×1024 W cm−2, respectively.
The conversion efficiencies (which are calculated using the laser energy) shown in Figures 9(d)–9(f) are corrected by multiplying by a constant factor of 0.273. With corrected energies, the synchrotron conversion efficiencies in Section 3.3 now become 4.32%, 4.50% and 1.67% for xf = 0, zR and −zR, respectively, corresponding to changes of +4% and −61% for the positive and negative defocus, respectively.
This error does not affect the conclusions of the article.
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[1] J. Goodman, M. King, E. J. Dolier, R. Wilson, R. J. Gray, P. McKenna. Optimization and control of synchrotron emission in ultraintense laser–solid interactions using machine learning. High Power Laser Science and Engineering, 11(2023).
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J. Goodman, M. King, E. J. Dolier, R. Wilson, R. J. Gray, P. McKenna. Optimization and control of synchrotron emission in ultraintense laser–solid interactions using machine learning – CORRIGENDUM[J]. High Power Laser Science and Engineering, 2024, 12(1): 01000e12