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2024-09-02
08:43
Precision determination of the track-position resolution of beam telescopes / Antonello, Massimiliano (Hamburg U.) ; Eikelmann, Lukas (Hamburg U.) ; Garutti, Erika (Hamburg U.) ; Klanner, Robert (Hamburg U.) ; Schwandt, Joern (Hamburg U.) ; Steinbrueck, Georg (Hamburg U.) ; Vauth, Annika (Hamburg U.)
Beam tests using tracking telescopes are a standard method for determining the spatial resolution of detectors. This requires the precise knowledge of the position resolution of beam tracks reconstructed at the Device Under Test (DUT). [...]
CMS-NOTE-2024-007; CERN-CMS-NOTE-2024-007.- Geneva : CERN, 2024 - 18 p. Fulltext: PDF;

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2024-07-22
09:34
Low transverse-momentum hadronic tau lepton reconstruction performance in the Run 3 Scouting dataset / The CMS Collaboration /CMS Collaboration
A technique for the reconstruction of low transverse momentum ($p_\text{T}$) hadronically decaying tau leptons in the CMS Run~3 Scouting datastream is presented. The addition of hadronic taus to the set of physics objects in Scouting opens possibilities for searches in new regions of phase space. [...]
CMS-NOTE-2024-006; CERN-CMS-NOTE-2024-006.- Geneva : CERN, 2024 - 11 p. Fulltext: PDF;

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2024-06-18
10:22
Unfolding the jet axis decorrelation in pp and PbPb collisions at 5.02 TeV with CMS / The CMS Collaboration /CMS Collaboration
In this study, the process of unfolding is studied for an isolated photon-tagged jet axis decorrelation measurement in pp and PbPb collisions at 5.02 TeV. The jet axis decorrelation, $\Delta j$, is defined as the difference between the energy-weight and winner-take-all jet axes. [...]
CMS-NOTE-2024-004; CERN-CMS-NOTE-2024-004.- Geneva : CERN, 2024 - 21 p. Fulltext: PDF;

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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;

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2024-04-08
14:06
Beam Test Performance Studies of CMS Phase-2 Outer Tracker Module Prototypes / The Tracker Group of the CMS Collaboration /CMS Collaboration
A new tracking detector will be installed as part of the Phase-2 upgrade of the CMS detector for the high-luminosity LHC era. This tracking detector includes the Inner Tracker, equipped with silicon pixel sensor modules, and the Outer Tracker, consisting of modules with two parallel stacked silicon sensors. [...]
CMS-NOTE-2024-002; CERN-CMS-NOTE-2024-002.- Geneva : CERN, 2021 - 41 p. Fulltext: PDF;

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2024-02-05
10:49
Simultaneous determination of several differential observables with statistical correlations / The CMS Collaboration /CMS Collaboration
In the context of the determination of standard model parameters from CMS data, we explain and illustrate the simultaneous unfolding of several observables, such as jet cross sections, in a single fit based on the same data set, and the use of Monte Carlo integration to apply operations on unfolded observables, such as forming cross section ratios..
CMS-NOTE-2024-001; CERN-CMS-NOTE-2024-001.- Geneva : CERN, 2024 - 13 p.

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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;

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2023-12-18
14:21
Towards Real-Time Machine Learning Based Signal/Background Selection in the CMS Detector Using Quantized Neural Networks and Input Data Reduction / Burazin Misura, Arijana (Split Tech. U.) ; Music, Josip (Split Tech. U.) ; Prvan, Marina (Split Tech. U.) ; Lelas, Damir (Split Tech. U.)
To boost its discovery potential, the Large Hadron Collider (LHC) is being prepared for an extensive upgrade. The new phase, High Luminosity LHC (HL-LHC), will operate at luminosity (number proportional to the rate of collisions) increased by a factor of five. Such an increase in luminosity consequently will result in enormous amounts of generated data, the vast majority of which is uninteresting data or pile up (PU). [...]
CMS-NOTE-2023-014; CERN-CMS-NOTE-2023-014.- Geneva : CERN, 2023 - 25 p. Fulltext: PDF;

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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;

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2023-11-06
17:25
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier / Groenroos, Sonja (Helsinki U.) ; Pierini, Maurizio (CERN) ; Chernyavskaya, Nadezda (CERN)
More than a thousand 8'' silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning- based algorithm that pre-selects potentially anomalous images of the sensor surface in real time has been developed to automate the visual inspection. [...]
CMS-NOTE-2023-012; CERN-CMS-NOTE-2023-012.- Geneva : CERN, 2023 - 17 p. Fulltext: PDF;

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