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[n]	H. Jiang et al., “Studies of the Low Energy Proton Beam Halo Experiment”, in Proc. NAPAC'13, Pasadena, CA, USA, Sep.-Oct. 2013, paper TUODB1, pp. 436-438. 

[n]	N. Kuklev, “Fast linac optics measurement with machine learning methods”, presented at the LINAC'24, Chicago, IL, USA, Aug. 2024, paper THPB003, unpublished. 

For LaTeX

%\cite{Jiang:NAPAC13-TUODB1} \bibitem{Jiang:NAPAC13-TUODB1} H. Jiang \emph{et al.}, \textquotedblleft{Studies of the Low Energy Proton Beam Halo Experiment}\textquotedblright, in \emph{Proc. NAPAC’13}, Pasadena, CA, USA, Sep.-Oct. 2013, paper TUODB1, pp. 436--438. %\cite{Kuklev:LINAC24-THPB003} \bibitem{Kuklev:LINAC24-THPB003} N. Kuklev, \textquotedblleft{Fast linac optics measurement with machine learning methods}\textquotedblright, presented at the LINAC’24, Chicago, IL, USA, Aug. 2024, paper THPB003, unpublished.

For BibTeX

@inproceedings{jiang:napac13-tuodb1, author = {H. Jiang and others}, title = {{Studies of the Low Energy Proton Beam Halo Experiment}}, booktitle = {Proc. NAPAC'13}, pages = {436--438}, paper = {TUODB1}, venue = {Pasadena, CA, USA, Sep.-Oct. 2013}, publisher = {JACoW Publishing, Geneva, Switzerland}, url = {https://jacow.org/PAC2013/papers/TUODB1.pdf}, language = {english} } @unpublished{kuklev:linac24-thpb003, author = {N. Kuklev}, title = {{Fast linac optics measurement with machine learning methods}}, booktitle = {Proc. LINAC'24}, pages = {}, paper = {THPB003}, venue = {Chicago, IL, USA}, intype = {presented at the}, series = {Linear Accelerator Conference}, number = {32}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {8}, year = {2024}, issn = {2226-0366}, isbn = {978-3-95-450219-6}, note = {presented at LINAC'24, Chicago, IL, USA, Aug. 2024, paper THPB003, unpublished}, language = {english} }

References

• H. Jiang et al., “Studies of the Low Energy Proton Beam Halo Experiment”, in Proc. North American Particle Accelerator Conf. (NAPAC'13), Pasadena, CA, USA, Sep.-Oct. 2013, paper TUODB1, pp. 436-438.
• N. Kuklev, “Fast linac optics measurement with machine learning methods”, presented at the 32nd Linear Accelerator Conf. (LINAC'24), Chicago, IL, USA, Aug. 2024, paper THPB003, unpublished.