Projects funded by the NCN
Developing a new direction for collider BSM searches with Machine Learning
2021/41/N/ST2/00972
Keywords:
BSM Particle Physics mono-jet model independence Dark Matter Supersymmetry LHC Machine Learning Artificial Neural Networks Autoencoder
Descriptors:
- ST2_002:
- ST9_013:
- ST6_011:
Panel:
ST2 - Fundamental constituents of matter: particle, nuclear, plasma, atomic, molecular, gas and optical physics
Host institution :
Uniwersytet Warszawski, Wydział Fizyki
woj. mazowieckie
Principal investigator (from the host institution):
Number of co-investigators in the project: 2
Call: PRELUDIUM 20 - announced on 2021-03-15
Amount awarded: 76 610 PLN
Project start date (Y-m-d): 2022-02-03
Project end date (Y-m-d): 2025-02-02
Project duration:: 36 months (the same as in the proposal)
Project status: Project completed
Project description
Download the project description in a pdf file
Note - project descriptions were prepared by the authors of the applications themselves and placed in the system in an unchanged form.
Information in the final report
- Publication in academic press/journals (2)
- Monojet signatures from gluino and squark decaysAuthors:Iñaki Lara, Trygve Buanes, Rafał Masełek, Mihoko M. Nojiri, Krzysztof Rolbiecki, Kazuki SakuraiAcademic press:Journal of High Energy Physics (rok: 2022, tom: 2022, strony: 150), Wydawca: SpringerOpenStatus:PublishedDOI:10.1007/JHEP10(2022)150 - link to the publication
- Monojet signatures from gluino and squark decaysAuthors:Iñaki Lara, Trygve Buanes, Rafał Masełek, Mihoko M. Nojiri, Krzysztof Rolbiecki, Kazuki SakuraiAcademic press:Journal of High Energy Physics (rok: 2022, tom: 2022, strony: 150), Wydawca: SpringerOpenStatus:PublishedDOI:10.1007/JHEP10(2022)150 - link to the publication