arXiv subjects

Edoardo Franzoso

Publications and source records attributed to Edoardo Franzoso.

3 recordsLinked to original sources

arXiv2026

Development and characterization of hybrid MCP-PMT with embedded Timepix4 ASIC used as pixelated anode

We present a novel single-photon detector based on a vacuum tube incorporating a photocathode, a microchannel plate (MCP), and a Timepix4 CMOS ASIC functioning as a pixelated anode. Designed to handle photon rates up to 1 billion per second across a 7 cm$^2$ active area, the detector achieves outstanding spatial and temporal resolutions of 5-10 $μ$m and below 50 ps r.m.s., respectively. The Timepix4 ASIC comprises approximately 230,000 pixels, each integrating analog and digital front-end electronics. This enables data-driven acquisition and supports data transmission rates up to 160 Gb/s. External FPGA-based electronics manage both configuration and readout. In order to test the timing performance of the Timepix4 ASIC we performed preliminary characterization of an assembly bonded to a 100 $μ$m thick n-on-p silicon sensor using a pulsed infrared laser, which demonstrated a per-pixel timing resolution of 110 ps, with cluster-based averaging methods improving to below 50 ps. Six prototype detectors incorporating different MCP stack configurations and end-spoiling depths were produced by Hamamatsu Photonics. We report on their characterization, including dark count rates, gain, and spatial and timing resolutions, assessed both in laboratory conditions and during a test-beam campaign at CERN's SPS facility.

physics.ins-det↗

arXiv2025

Luminosity measurement with the LHCb RICH detectors in Run 3

The LHCb Ring-Imaging Cherenkov detectors are built to provide charged hadron identification over a large range of momentum. The upgraded detectors are also capable of providing an independent measurement of the luminosity for the LHCb experiment during LHC Run 3. The modelling of the opto-electronics chain, the application of the powering strategy during operations, the calibration procedures and the proof of principle of a novel technique for luminosity determination are presented. In addition, the preliminary precision achieved during the 2023 data-taking year for real-time and offline luminosity measurements is reported.

hep-ex↗

arXiv2022

A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme

Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables. Predicting the probability density function (pdf) of such classifiers in the multivariate space covering the relevant experimental features is usually challenging. The detailed simulation of the detector response from first principles cannot provide the reliability needed for the most precise physics measurements. Data-driven modelling is usually preferred, though sometimes limited by the available data size and different coverage of the feature space by the control channels. In this paper, we discuss a novel approach to the modelling of particle identification classifiers using machine-learning techniques. The marginal pdf of the classifiers is described with a Gaussian Mixture Model, whose parameters are predicted by Multi Layer Perceptrons trained on calibration data. As a proof of principle, the method is applied to the data acquired by the LHCb experiment in its fixed-target configuration. The model is trained on a data sample of proton-neon collisions and applied to smaller data samples of proton-helium and proton-argon collisions collected at different centre-of-mass energies. The method is shown to perform better than a detailed simulation-based approach, to be fast and suitable to be applied to a large variety of use cases.

hep-ex↗