2023-11-30
10:48 |
Performance of new jet techniques based on machine learning for $H \to b \bar{b}$ and $H \to c \bar{c}$ searches
/LHCb Collaboration
n this document, the performance of two different machine learning techniques for jet physics at LHCb are presented: a new regression method for improving the jet energy correction, and a new jet identification method based on a Deep Neural Network for distinguish $b$, $c$ and light jets. These new algorithms will be used to perform the inclusive search for $H\to b\bar{b}$ and $H \to c \bar{c}$ decays [...]
LHCB-FIGURE-2023-029.-
Geneva : CERN, 2023 - 11.
Fulltext: LHCb-FIGURE-2023-029(1) - ZIP; main - PDF;
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2023-10-10
17:01 |
Downstream track reconstructio in HLT1
/LHCb Collaboration
Long-lived particles (LLPs) are present in the SM and in many new physics scenarios beyond it but they are very challenging to reconstruct at LHC due to their very displaced vertices. A new algorithm, called "Downstream", has been developed at LHCb which is able to reconstruct and select LLPs in real time at the first level of the trigger (HLT1). [...]
LHCB-FIGURE-2023-028.-
Geneva : CERN, 2023 - 10.
Fulltext: LHCb-FIGURE-2023-028 - ZIP; main - PDF;
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2023-10-10
16:58 |
Performance of RETINA reconstruction of T-track primitives
/LHCb Collaboration
A real-time tracking device (RETINA) is proposed based on distributed system of FPGAs, dedicated to the reconstruction of particles trajectories in the forward Scintillating Fibre tracker detector, with the aim to preserve the full physics potential of the experiment in Run 4, and in view of Run 5 and 6 (Upgrade II) at higher instantaneous luminosity. This system would enhance the DAQ system of the experiment, and would run in real time during physics data taking, reconstructing track primitives on-the-fly at the LHC collision rate, before the complete event is built. [...]
LHCB-FIGURE-2023-027.-
Geneva : CERN, 2023 - 8.
Fulltext: main - PDF; LHCb-FIGURE-2023-027 - ZIP;
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2023-10-10
16:53 |
HLT2 reconstruction performance for T-track selections
/LHCb Collaboration
This document describes the performance of secondary vertex fitting and MVA-based track filtering for selections with T tracks in the second level of the trigger (HLT2) in LHCb, and shows how these perform for Beyond the Standard Model long-lived particle searches. The reconstruction of particle decays using only tracks in the SciFi detector have specific considerations compared to long and downstream tracks. [...]
LHCB-FIGURE-2023-026.-
Geneva : CERN, 2023 - 6.
Fulltext: main - PDF; LHCb-FIGURE-2023-026 - ZIP;
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2023-10-10
16:47 |
Track reconstruction by the RETINA Testbed demonstrator
/LHCb Collaboration
he RETINA demonstrator is a prototype device for a 30 MHz real-time track reconstruction in the LHCb VELO detector, implemented in PCIe-hosted FPGA cards interconnected by fast optical links. The system is capable of processing live LHCb data opportunistically during physics data taking, thanks to a dedicated testbed facility fed by the experiment monitoring system. [...]
LHCB-FIGURE-2023-025.-
Geneva : CERN, 2023 - 5.
Fulltext: main - PDF; LHCb-FIGURE-2023-025 - ZIP;
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2023-10-10
16:41 |
Performance of a GNN-Based Pipeline for Track Finding in the Velo, using simulated p-p collisions samples in the Upgrade.
/LHCb collaboration
Over the next decade, increases in instantaneous luminosity and detector granularity will increase the amount of data that has to be analyzed by high-energy physics experiments, whether in real time or offline, by an order of magnitude. The reconstruction of charged particles, which has always been a crucial element of offline data processing pipelines, must increasingly be deployed from the very first stages of the real time processing to enable experiments to achieve their physics goals. [...]
LHCB-FIGURE-2023-024.-
Geneva : CERN, 10 - 36.
Fulltext: main - PDF; LHCb-FIGURE-2023-024 - ZIP;
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2023-09-20
12:17 |
Hadron PID performance with 2023 data
/LHCb Collaboration
Particle identification (PID) plays an important role in many LHCb analyses. This information is obtained by combining information from the sub-detectors, with in particular two Ring Imaging Cherenkov (RICH) detectors [...]
LHCB-FIGURE-2023-023.-
Geneva : CERN, 2023 - 4.
Fulltext: LHCb-FIGURE-2023-023 - ZIP; main - PDF;
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2023-09-08
12:13 |
Ecal versus SciFi correlation in 2022 ion data
/LHCb Collaboration
The occupancy plot is produced using data recorded on November 19th and 20th 2022 at the LHC. The data corresponds to two fills of Pb-Pb collisions at 5 TeV, during which Argon gas was injected into the opened VELO vessel thanks to the SMOG system..
LHCB-FIGURE-2023-022.-
Geneva : CERN, 2023 - 3.
Fulltext: LHCb-FIGURE-2023-022 - ZIP; LHCb-FIGURE-2023-022_1 - PDF;
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2023-09-08
10:12 |
First measurement of the hit detection efficiency of the LHCb SciFi Tracker
/LHCb Collaboration
We present a preliminary measurement of the unbiased SciFi hit detection efficiency using early LHCb Run 3 data. The hit detection efficiency is determined by excluding the layer under study from the track reconstruction and matching tracks to SciFi hits where the distance of the clostest approach between the track and the hit is required to be less than 1.0 mm. [...]
LHCB-FIGURE-2023-021.-
Geneva : CERN, 2023 - 4.
Fulltext: LHCb-FIGURE-2023-021_1 - ZIP; LHCb-FIGURE-2023-021 - PDF;
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2023-09-01
11:16 |
Rich2 versus Ecal correlation in 2022 ion data
/LHCb Collaboration
The occupancy plots present in this document are produced using data recorded on November 19th and 20th 2022 at the LHC by the LHCb collaboration. The data corresponds to two fills of Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV, during which Argon gas was injected into the opened VELO vessel thanks to the SMOG system..
LHCB-FIGURE-2023-020.-
Geneva : CERN, 2023 - 3.
Fulltext: LHCb-FIGURE-2023-020 - PDF; LHCb-FIGURE-ZIP-2023-020 - ZIP;
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