arXiv subjects

Pierre-Alexandre Petitjean

Publications and source records attributed to Pierre-Alexandre Petitjean.

8 recordsLinked to original sources

arXiv2025

JUNO 20-inch PMT and electronics system characterization using large pulses of PMT dark counts at the Pan-Asia testing platform

The main goal of the JUNO experiment is to determine the neutrino mass ordering with a 20kt liquid-scintillator detector. The 20-inch PMT and its 1F3 (one for three) electronics are crucial to realize the excellent energy resolution of at least 3% at 1MeV. The knowledge on the PMT and 1F3 electronics response is critical for detector performance understanding. A study of the JUNO 20-inch PMT and 1F3 electronics system characterization is presented using large pulses of PMT dark count at the Pan-Asia testing platform in China. Thanks to its broad amplitude range and high rate, the large pulse signals are also used to investigate the PMT after pulse response.

physics.ins-det↗

arXiv2023

Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics.

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arXiv2023

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%.

physics.ins-det↗

arXiv2022

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.

physics.ins-det↗

arXiv2022

The JUNO experiment and its electronics readout system

The main goal of the Jiangmen Underground Neutrino Observatory (JUNO) under construction in China is to determine the neutrino mass hierarchy and to measure oscillation parameters to the sub-percent level. The detector consists of 20 ktons of liquid scintillator instrumented by 17612 20-inch photo-multiplier tubes, and 25600 3-inch small PMTs, with photo-cathode coverage of 77%. The electronics system is separated into two main parts. The front-end system, sitting under water, performs analog signal processing. The back-end electronics system, sitting outside water, consists of the DAQ and the trigger. The current production status of the experiment as well as the design of the electronics system will be reported in the presentation.

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arXiv2021

Design of an Internet of Things - based multi-channel temperature monitoring system

In the scope of the Jiangmen Underground Neutrino Observatory (JUNO) project, 6 back-end card (BEC) mezzanines connected to one BEC base board are in charge of compensating the attenuated incoming data from 48 front-end channels over 48 100-meter-long ethernet cables. Each of the mezzanines has 16 equalizers that may be subject to overheating. It is important therefore to monitor their temperature in real time. However, collecting data from a relatively large (1080) number of mezzanines is not a trivial task. In this work we propose a solution based on Wi-Fi mesh. Both the technical details and the first test results performed are be reported.

physics.ins-det↗

arXiv2020

Study of using machine learning for level 1 trigger decision in JUNO experiment

A study on the use of a machine learning algorithm for the level 1 trigger decision in the JUNO experiment ispresented. JUNO is a medium baseline neutrino experiment in construction in China, with the main goal of determining the neutrino mass hierarchy. A large liquid scintillator (LS)volume will detect the electron antineutrinos issued from nuclear reactors. The LS detector is instrumented by around 20000 large photomultiplier tubes. The hit information from each PMT will be collected into a center trigger unit for the level 1 trigger decision. The current trigger algorithm used to select a neutrino signal event is based on a fast vertex reconstruction. We propose to study an alternative level 1 (L1) trigger in order to achieve a similar performance as the vertex fitting trigger but with less logic resources by using firmware implemented machine learning model at the L1 trigger level. We treat the trigger decision as a classification problem and train a Multi-Layer Perceptron (MLP)model to distinguish the signal events with an energy higher than a certain threshold from noise events. We use JUNO software to generate datasets which include 100K physics events with noise and 100K pure noise events coming from PMT dark noise.For events with energy higher than 100 keV, the L1 trigger based on the converged MLP model can achieve an efficiency higher than 99%. After the training performed on simulations,we successfully implemented the trained model into a Kintex 7FPGA. We present the technical details of the neural network development and training, as well as its implementation in the hardware with the FPGA programming. Finally the performance of the L1 trigger MLP implementation is discussed.

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arXiv2020

Automatic test system of the back-end card for the JUNO experiment

The Jiangmen Underground Neutrino Observatory (JUNO) is a medium-baseline neutrino experiment under construction in China, with the goal to determine the neutrino mass hierarchy. The JUNO electronics readout system consists of an underwater front-end electronics system and an outside-water back-end electronics system. These two parts are connected by 100-meter Ethernet cables and power cables. The back-end card (BEC) is the part of the JUNO electronics readout system used to link the underwater boxes to the trigger system is connected to transmit the system clock and triggered signals. Each BEC is connected to 48 underwater boxes, and in total around 150 BECs are needed. It is essential to verify the physical layer links before applying real connection with the underwater system. Therefore, our goal is to build an automatic test system to check the physical link performance. The test system is based on a custom designed FPGA board, in order to make the design general, only JTAG is used as the interface to the PC. The system can generate and check different data pattern at different speeds for 96 channels simultaneously. The test results of 1024 continuously clock cycles are automatically uploaded to PC periodically. We describe the setup of the automatic test system of the BEC and present the latest test results.

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