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[n]	S. Kuroguchi et al., “Harnessing machine learning for the optimal design of ILC e-driven positron source”, in Proc. IPAC'24, Nashville, TN, USA, May 2024, pp. 886-888. doi:10.18429/JACoW-IPAC2024-MOPS69

[n]	F. Poirier, C. Lassalle, D. Mateus, and J. Rioult, “First anomalies exploration from data mining and machine learning at the ARRONAX cyclotron C70XP”, in Proc. IPAC'23, Venice, Italy, May 2023, pp. 2273-2276. doi:10.18429/JACoW-IPAC2023-TUPM036

For LaTeX

%\cite{Kuroguchi:IPAC24-MOPS69} \bibitem{Kuroguchi:IPAC24-MOPS69} S. Kuroguchi \emph{et al.}, \textquotedblleft{Harnessing machine learning for the optimal design of ILC e-driven positron source}\textquotedblright, in \emph{Proc. IPAC’24}, Nashville, TN, USA, May 2024, pp. 886--888. \doi{10.18429/JACoW-IPAC2024-MOPS69} %\cite{Poirier:IPAC23-TUPM036} \bibitem{Poirier:IPAC23-TUPM036} F. Poirier, C. Lassalle, D. Mateus, and J. Rioult, \textquotedblleft{First anomalies exploration from data mining and machine learning at the ARRONAX cyclotron C70XP}\textquotedblright, in \emph{Proc. IPAC’23}, Venice, Italy, May 2023, pp. 2273--2276. \doi{10.18429/JACoW-IPAC2023-TUPM036}

For BibTeX

@inproceedings{kuroguchi:ipac24-mops69, author = {S. Kuroguchi and others}, title = {{Harnessing machine learning for the optimal design of ILC e-driven positron source}}, booktitle = {Proc. IPAC'24}, pages = {886--888}, paper = {MOPS69}, venue = {Nashville, TN, USA}, series = {International Particle Accelerator Conference}, number = {15}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {7}, year = {2024}, issn = {2673-5490}, isbn = {978-3-95-450247-9}, doi = {10.18429/JACoW-IPAC2024-MOPS69}, language = {english} } @inproceedings{poirier:ipac23-tupm036, author = {F. Poirier and C. Lassalle and D. Mateus and J. Rioult}, title = {{First anomalies exploration from data mining and machine learning at the ARRONAX cyclotron C70XP}}, booktitle = {Proc. IPAC'23}, pages = {2273--2276}, paper = {TUPM036}, venue = {Venice, Italy}, series = {International Particle Accelerator Conference}, number = {14}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {9}, year = {2023}, issn = {2673-5490}, isbn = {978-3-95-450231-8}, doi = {10.18429/JACoW-IPAC2023-TUPM036}, language = {english} }

References

• S. Kuroguchi et al., “Harnessing machine learning for the optimal design of ILC e-driven positron source”, in Proc. 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper MOPS69, pp. 886-888.
• F. Poirier, C. Lassalle, D. Mateus, and J. Rioult, “First anomalies exploration from data mining and machine learning at the ARRONAX cyclotron C70XP”, in Proc. 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper TUPM036, pp. 2273-2276.