arXiv subjects

Yann Coadou

Publications and source records attributed to Yann Coadou.

4 recordsLinked to original sources

arXiv2026

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↗

arXiv2025

Input to European Strategy Update for Particle Physics: Sustainability

Human activity continues to have an enormous negative impact on the ability of the planet to sustain human and other forms of life. Six out of the nine planetary boundaries have been crossed, a seventh is close to threshold. Prominent manifestations of this development are climate change caused by greenhouse gas emissions, as well as loss of biodiversity. In recognition of the urgency of these problems, several international agreements have been ratified to achieve net-zero emissions and to halt and reverse biodiversity loss. Significant reductions in emissions are required by 2030 to meet international climate targets. The field of particle physics has an obligation and an opportunity to contribute to such mitigation efforts and to avoid causing further harm. This document urges the European Strategy Update in Particle Physics to set a clear and bold mandate for embedding environmental sustainability throughout the future scientific programme, and advocates for a series of actions that will enable this.

physics.soc-ph↗

arXiv2022

Boosted decision trees

Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the high-energy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described. Some of their shortcomings are then mitigated with ensemble learning, using boosting algorithms, in particular AdaBoost and gradient boosting. Examples from high-energy physics and software used are also presented.

physics.data-an↗

arXiv2018

Machine Learning in High Energy Physics Community White Paper

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

physics.comp-ph↗