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Luca Grandi

Publications and source records attributed to Luca Grandi.

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The Darkside-20k Data Acquisition System

DarkSide-20k is a WIMP search experiment using liquid argon as a target, designed to perform a background-free search for dark matter with unprecedented sensitivity, and is currently under construction at INFN Laboratori Nazionali del Gran Sasso, Italy. The detector comprises a dual-phase Time Projection Chamber complemented with external veto systems and is equipped with a total of 2720 SiPM-based readout channels. This work presents the DAQ system designed for DarkSide-20k. The system is capable of continuous, triggerless digitisation of the waveforms with high single-photoelectron detection efficiency and online processing, ensuring data reduction for long-term storage. The DarkSide-20k DAQ system employs commercial CAEN VX2745 digitisers with custom FPGA firmware implementation. Timing and synchronisation across all 48 digitisers are provided by custom Global and Crate Data Manager boards distributing a phase-aligned clock derived from a disciplined rubidium standard. Waveform segments are processed in real time by Front End Processor machines. Data are organised into collections containing whole detector information and distributed across a farm of Time Slice Processors for event reconstruction, classification, and further reduction before storage and offline analysis. A full "Quadrant" of the system, corresponding to one quarter of the final DAQ, has been assembled and validated at TRIUMF laboratory in Canada. The Quadrant has been stress-tested with simultaneous pulses and demonstrated sustained digitizer readout exceeding expected physics rates and stable long-term performance.

physics.ins-det

Report of the Topical Group on Particle Dark Matter for Snowmass 2021

This report summarizes the findings of the CF1 Topical Subgroup to Snowmass 2021, which was focused on particle dark matter. One of the most important scientific goals of the next decade is to reveal the nature of dark matter (DM). To accomplish this goal, we must delve deep, to cover high priority targets including weakly-interacting massive particles (WIMPs), and search wide, to explore as much motivated DM parameter space as possible. A diverse, continuous portfolio of experiments at large, medium, and small scales that includes both direct and indirect detection techniques maximizes the probability of discovering particle DM. Detailed calibrations and modeling of signal and background processes are required to make a convincing discovery. In the event that a candidate particle is found through different means, for example at a particle collider, the program described in this report is also essential to show that it is consistent with the actual cosmological DM. The US has a leading role in both direct and indirect detection dark matter experiments -- to maintain this leading role, it is imperative to continue funding major experiments and support a robust R\&D program.

hep-ph

The XENON1T Data Distribution and Processing Scheme

The XENON experiment is looking for non-baryonic particle dark matter in the universe. The setup is a dual phase time projection chamber (TPC) filled with 3200 kg of ultra-pure liquid xenon. The setup is operated at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. We present a full overview of the computing scheme for data distribution and job management in XENON1T. The software package Rucio, which is developed by the ATLAS collaboration, facilitates data handling on Open Science Grid (OSG) and European Grid Infrastructure (EGI) storage systems. A tape copy at the Center for High Performance Computing (PDC) is managed by the Tivoli Storage Manager (TSM). Data reduction and Monte Carlo production are handled by CI Connect which is integrated into the OSG network. The job submission system connects resources at the EGI, OSG, SDSC's Comet, and the campus HPC resources for distributed computing. The previous success in the XENON1T computing scheme is also the starting point for its successor experiment XENONnT, which starts to take data in autumn 2019.

cs.DC