arXiv subjects

A. Cusimano

Publications and source records attributed to A. Cusimano.

5 recordsLinked to original sources

arXiv2023

The ASTRI Cherenkov Camera: from the prototype to the industrial version for the Mini-Array

The observation of energetic astronomical sources emitting very high-energy gamma-rays in the TeV spectral range (as e.g. supernova remnants or blazars) is mainly based on detecting the Cherenkov light induced by relativistic particles in the showers produced by the photon interaction with the Earth atmosphere. The ASTRI Mini-Array is an INAF-led project aimed observing such celestial objects in the 1 - 100 TeV energy range. It consists of an array of nine innovative imaging atmospheric Cherenkov telescopes that are an evolution of the dual-mirror aplanatic ASTRI-Horn telescope operating at the INAF "M.C. Fracastoro" observing station (Serra La Nave, Mount Etna, Italy). The ASTRI Mini-Array is currently under construction at the Observatorio del Teide (Tenerife, Spain). In this paper, we present the compact (diameter 660mm, height 520mm, weight 73kg) ASTRI-Horn prototype Cherenkov Camera based on a modular multipixel Silicon Photon Multiplier (SiPM) detector, has been acquiring data since 2016 and allowing us to obtain both scientific data and essential lessons. In this contribution, we report the main features of the camera and its evolution toward the new Cherenkov camera, which will be installed on each ASTRI Mini-Array telescope to cover an unprecedented field of view of 10.5°.

astro-ph.IM↗

arXiv2015

Simulation and performance of an artificial retina for 40 MHz track reconstruction

We present the results of a detailed simulation of the artificial retina pattern-recognition algorithm, designed to reconstruct events with hundreds of charged-particle tracks in pixel and silicon detectors at LHCb with LHC crossing frequency of $40\,\rm MHz$. Performances of the artificial retina algorithm are assessed using the official Monte Carlo samples of the LHCb experiment. We found performances for the retina pattern-recognition algorithm comparable with the full LHCb reconstruction algorithm.

physics.ins-det↗

arXiv2014

The artificial retina processor for track reconstruction at the LHC crossing rate

We present results of an R&D study for a specialized processor capable of precisely reconstructing, in pixel detectors, hundreds of charged-particle tracks from high-energy collisions at 40 MHz rate. We apply a highly parallel pattern-recognition algorithm, inspired by studies of the processing of visual images by the brain as it happens in nature, and describe in detail an efficient hardware implementation in high-speed, high-bandwidth FPGA devices. This is the first detailed demonstration of reconstruction of offline-quality tracks at 40 MHz and makes the device suitable for processing Large Hadron Collider events at the full crossing frequency.

physics.ins-det↗

arXiv2014

The artificial retina for track reconstruction at the LHC crossing rate

We present the results of an R&D study for a specialized processor capable of precisely reconstructing events with hundreds of charged-particle tracks in pixel and silicon strip detectors at $40\,\rm MHz$, thus suitable for processing LHC events at the full crossing frequency. For this purpose we design and test a massively parallel pattern-recognition algorithm, inspired to the current understanding of the mechanisms adopted by the primary visual cortex of mammals in the early stages of visual-information processing. The detailed geometry and charged-particle's activity of a large tracking detector are simulated and used to assess the performance of the artificial retina algorithm. We find that high-quality tracking in large detectors is possible with sub-microsecond latencies when the algorithm is implemented in modern, high-speed, high-bandwidth FPGA devices.

physics.ins-det↗

arXiv2014

A Specialized Processor for Track Reconstruction at the LHC Crossing Rate

We present the results of an R&D study of a specialized processor capable of precisely reconstructing events with hundreds of charged-particle tracks in pixel detectors at 40 MHz, thus suitable for processing LHC events at the full crossing frequency. For this purpose we design and test a massively parallel pattern-recognition algorithm, inspired by studies of the processing of visual images by the brain as it happens in nature. We find that high-quality tracking in large detectors is possible with sub-$μ$s latencies when this algorithm is implemented in modern, high-speed, high-bandwidth FPGA devices. This opens a possibility of making track reconstruction happen transparently as part of the detector readout.

physics.ins-det↗