2024-09-02 08:43 |
Подробен запис - Подобни записи
|
2024-07-22 09:34 |
Подробен запис - Подобни записи
|
2024-06-18 10:22 |
Подробен запис - Подобни записи
|
2024-05-21 14:49 |
Simulation studies for a search for dark photons decaying to lepton jets
/ The CMS Collaboration
/CMS Collaboration
A Run 2 full simulation sample for dark photon production with decay to a lepton jet is used to study the shape of the reconstructed dark photon mass distribution over the range 0.1 GeV to 4.0 GeV for generated mass. The normalized mass distributions of dimuon pairs from simulated standard model Drell-Yan events are compared between opposite sign and like sign muon pairs. [...]
CMS-NOTE-2024-003; CERN-CMS-NOTE-2024-003.-
Geneva : CERN, 2024 - 6 p.
Fulltext: PDF;
|
Подробен запис - Подобни записи
|
2024-04-08 14:06 |
Подробен запис - Подобни записи
|
2024-02-05 10:49 |
Подробен запис - Подобни записи
|
2023-12-18 14:21 |
Simulation of on- and off-shell $\rm t\bar{t}$ production with the Monte Carlo generator b\_bbar\_4l at CMS
/ The CMS Collaboration
/CMS Collaboration
This note presents performance studies of the b\_bbar\_4l package of the POWHEG BOX RES Monte Carlo generator used to model top quark production for the CMS experiment at the LHC. The b\_bbar\_4l package includes next-to-leading order treatment of the interference between top quark pair production, the associated production of a single top quark and a W boson, as well as non-resonant production of two charged leptons, two neutrinos, and two b quarks. [...]
CMS-NOTE-2023-015; CERN-CMS-NOTE-2023-015.-
Geneva : CERN, 2023 - 12 p.
Fulltext: PDF;
|
Подробен запис - Подобни записи
|
2023-12-18 14:21 |
Подробен запис - Подобни записи
|
2023-11-20 14:28 |
Machine learning techniques for model-independent searches in dijet final states
/ Harris, Philip (MIT) ; Mccormack, William Patrick (MIT) ; Park, Sang Eon (MIT) ; Quadfasel, Tobias (Hamburg U.) ; Sommerhalder, Manuel (Hamburg U.) ; Moureaux, Louis Jean (Hamburg U.) ; Kasieczka, Gregor (Hamburg U.) ; Amram, Oz (Fermilab) ; Maksimovic, Petar (Johns Hopkins U.) ; Maier, Benedikt (KIT, Karlsruhe, EKP) et al.
We present the performance of Machine Learning--based anomaly detection techniques for extracting potential new physics phenomena in a model-agnostic way with the CMS Experiment at the Large Hadron Collider. We introduce five distinct outlier detection or density estimation techniques, namely CWoLa, Tag N' Train, CATHODE, QUAK, and QR-VAE, tailored for the identification of anomalous jets originating from the decay of unknown heavy particles. [...]
CMS-NOTE-2023-013; CERN-CMS-NOTE-2023-013.-
Geneva : CERN, 2023 - 11 p.
Fulltext: PDF;
|
Подробен запис - Подобни записи
|
2023-11-06 17:25 |
Подробен запис - Подобни записи
|
|
|