Kasieczka, Gregor, Plehn, Tilman, Butter, Anja, Cranmer, Kyle, Debnath, Dipsikha, Dillon, Barry M., Fairbairn, Malcolm, Faroughy, Darius A., Fedorko, Wojtek, Gay, Christophe, Gouskos, Loukas, Kamenik, Jernej Fesel, Komiske, Patrick, Leiss, Simon, Lister, Alison, Macaluso, Sebastian, Metodiev, Eric, Moore, Liam, Nachman, Benjamin, Nordström, Karl, Pearkes, Jannicke, Qu, Huilin, Rath, Yannik, Rieger, Marcel, Shih, David, Thompson, Jennifer, and Varma, Sreedevi. The Machine Learning landscape of top taggers. Retrieved from https://par.nsf.gov/biblio/10167451. SciPost Physics 7.1 Web. doi:10.21468/SciPostPhys.7.1.014.

Kasieczka, Gregor, Plehn, Tilman, Butter, Anja, Cranmer, Kyle, Debnath, Dipsikha, Dillon, Barry M., Fairbairn, Malcolm, Faroughy, Darius A., Fedorko, Wojtek, Gay, Christophe, Gouskos, Loukas, Kamenik, Jernej Fesel, Komiske, Patrick, Leiss, Simon, Lister, Alison, Macaluso, Sebastian, Metodiev, Eric, Moore, Liam, Nachman, Benjamin, Nordström, Karl, Pearkes, Jannicke, Qu, Huilin, Rath, Yannik, Rieger, Marcel, Shih, David, Thompson, Jennifer, & Varma, Sreedevi.. The Machine Learning landscape of top taggers. SciPost Physics, 7 (1). Retrieved from https://par.nsf.gov/biblio/10167451. https://doi.org/10.21468/SciPostPhys.7.1.014

Kasieczka, Gregor, Plehn, Tilman, Butter, Anja, Cranmer, Kyle, Debnath, Dipsikha, Dillon, Barry M., Fairbairn, Malcolm, Faroughy, Darius A., Fedorko, Wojtek, Gay, Christophe, Gouskos, Loukas, Kamenik, Jernej Fesel, Komiske, Patrick, Leiss, Simon, Lister, Alison, Macaluso, Sebastian, Metodiev, Eric, Moore, Liam, Nachman, Benjamin, Nordström, Karl, Pearkes, Jannicke, Qu, Huilin, Rath, Yannik, Rieger, Marcel, Shih, David, Thompson, Jennifer, and Varma, Sreedevi.. "The Machine Learning landscape of top taggers". SciPost Physics 7 (1). Country unknown/Code not available. https://doi.org/10.21468/SciPostPhys.7.1.014. https://par.nsf.gov/biblio/10167451.

@article{osti_10167451,
place = {Country unknown/Code not available}, title = {The Machine Learning landscape of top taggers}, url = {https://par.nsf.gov/biblio/10167451}, DOI = {10.21468/SciPostPhys.7.1.014}, abstractNote = {Based on the established task of identifying boosted, hadronicallydecaying top quarks, we compare a wide range of modern machine learningapproaches. Unlike most established methods they rely on low-levelinput, for instance calorimeter output. While their networkarchitectures are vastly different, their performance is comparativelysimilar. In general, we find that these new approaches are extremelypowerful and great fun.}, journal = {SciPost Physics}, volume = {7}, number = {1}, author = {Kasieczka, Gregor and Plehn, Tilman and Butter, Anja and Cranmer, Kyle and Debnath, Dipsikha and Dillon, Barry M. and Fairbairn, Malcolm and Faroughy, Darius A. and Fedorko, Wojtek and Gay, Christophe and Gouskos, Loukas and Kamenik, Jernej Fesel and Komiske, Patrick and Leiss, Simon and Lister, Alison and Macaluso, Sebastian and Metodiev, Eric and Moore, Liam and Nachman, Benjamin and Nordström, Karl and Pearkes, Jannicke and Qu, Huilin and Rath, Yannik and Rieger, Marcel and Shih, David and Thompson, Jennifer and Varma, Sreedevi}, }