2024-04-08 14:06 |
詳細記錄 - 相似記錄
|
2024-02-05 10:49 |
詳細記錄 - 相似記錄
|
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;
|
詳細記錄 - 相似記錄
|
2023-12-18 14:21 |
詳細記錄 - 相似記錄
|
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;
|
詳細記錄 - 相似記錄
|
2023-11-06 17:25 |
詳細記錄 - 相似記錄
|
2023-10-16 14:14 |
詳細記錄 - 相似記錄
|
2023-09-11 12:26 |
詳細記錄 - 相似記錄
|
2023-09-04 16:16 |
詳細記錄 - 相似記錄
|
2023-08-29 09:40 |
詳細記錄 - 相似記錄
|
|
|