10 citations · 32 across the 19 of their papers we have counts for
8 papers · 1 filter
Branching fraction measurements of and anomalous decays
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1187
Branching fractions of and anomalous decays are measured using collision data at a center-of-mass energy of collected by the LHCb exper…
Associated + production as a probe of multiparton interactions in the forward region
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1187
This letter reports a study of associated boson and prompt production in proton-proton collisions at TeV with the LHCb detector in the forward rapidity regi…
First evidence of the decay
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1182
The first evidence for the decay is reported using proton-proton collision data recorded by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV, c…
Measurement of inclusive production of charmonium states in -hadron decays via their decay into
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1176
The inclusive production of the , and charmonium states in -hadron decays is studied with LHCb Run~2 data, corresponding to an integrated luminosity o…
Measurement of CP asymmetries in and decays
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1190
Measurements of the combined CP asymmetries in and decays are ma…
-jet fragmentation with decays in TeV collisions at LHCb
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1166
The collinear and transverse-momentum-dependent jet fragmentation function and the radial profile for mesons in jets are measured. The mesons are reconstructed…
First measurement of time-dependent violation in the flavor-changing neutral-current decay
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1165
A flavor-tagged time-dependent analysis of decays is performed across the full dimuon mass range excluding the and resonance re…
Machine learning techniques for jet reconstruction at LHCb and application to the search for and in TeV collisions
LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb +1194
Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm…