Calorimeter Channels Research Articles

Single particle detection system for strong-field QED experiments

Measuring signatures of strong-field quantum electrodynamics (SF-QED) processes in an intense laser field is an experimental challenge: it requires detectors to be highly sensitive to single electrons and positrons in the presence of the typically very strong x-ray and γ-photon background levels. In this paper, we describe a particle detector capable of diagnosing single leptons from SF-QED interactions and discuss the background level simulations for the upcoming Experiment-320 at FACET-II (SLAC National Accelerator Laboratory). The single particle detection system described here combines pixelated scintillation LYSO screens and a Cherenkov calorimeter. We detail the performance of the system using simulations and a calibration of the Cherenkov detector at the ELBE accelerator. Single 3 GeV leptons are expected to produce approximately 537 detectable photons in a single calorimeter channel. This signal is compared to Monte-Carlo simulations of the experiment. A signal-to-noise ratio of 18 in a single Cherenkov calorimeter detector is expected and a spectral resolution of 2% is achieved using the pixelated LYSO screens.

Artificial Neural Networks on FPGAs for Real-Time Energy Reconstruction of the ATLAS LAr Calorimeters

The ATLAS experiment at the Large Hadron Collider (LHC) is operated at CERN and measures proton–proton collisions at multi-TeV energies with a repetition frequency of 40 MHz. Within the phase-II upgrade of the LHC, the readout electronics of the liquid-argon (LAr) calorimeters of ATLAS are being prepared for high luminosity operation expecting a pileup of up to 200 simultaneous proton–proton interactions. Moreover, the calorimeter signals of up to 25 subsequent collisions are overlapping, which increases the difficulty of energy reconstruction by the calorimeter detector. Real-time processing of digitized pulses sampled at 40 MHz is performed using field-programmable gate arrays (FPGAs). To cope with the signal pileup, new machine learning approaches are explored: convolutional and recurrent neural networks outperform the optimal signal filter currently used, both in assignment of the reconstructed energy to the correct proton bunch crossing and in energy resolution. The improvements concern in particular energies derived from overlapping pulses. Since the implementation of the neural networks targets an FPGA, the number of parameters and the mathematical operations need to be well controlled. The trained neural network structures are converted into FPGA firmware using automated implementations in hardware description language and high-level synthesis tools. Very good agreement between neural network implementations in FPGA and software based calculations is observed. The prototype implementations on an Intel Stratix-10 FPGA reach maximum operation frequencies of 344–640 MHz. Applying time-division multiplexing allows the processing of 390–576 calorimeter channels by one FPGA for the most resource-efficient networks. Moreover, the latency achieved is about 200 ns. These performance parameters show that a neural-network based energy reconstruction can be considered for the processing of the ATLAS LAr calorimeter signals during the high-luminosity phase of the LHC.

Design and Performance of Data Acquisition and Control System for the Muon g-2 Laser Calibration

The Muon g-2 Experiment at Fermilab (E989) will measure the muon magnetic anomaly with unprecedented precision (0.14 ppm), which yields a factor of 4 improvement with respect to the previous measurements at Brookhaven National Laboratory (BNL) (E821). To achieve this goal, the relative response of each calorimeter channel must be calibrated and monitored at a level better than $10^{-3}$ in the time window of the muon fill. The calibration system uses a laser source and photodetectors. The data acquisition (DAQ) of the system is designed around two field-programmable gate array (FPGA)-based boards and a custom crate bus. The front-end board manages the photodetector operation and signal processing and performs a first-level data concentration task. Up to 12 FPGA boards can be housed in a 6U crate. A readout master controls the boards, implements event-building functionalities, manages the monitoring interface, and facilitates calibration and debugging tasks. A gigabit-ethernet interface is used to transfer data to the on-line farm for storage and further processing. Presently, the system is working at Fermi National Accelerator Laboratory (FNAL). In this article, we present the DAQ system design, run control user interface, and system evaluation.

The monitoring electronics of the laser calibration system in the Muon g-2 experiment

Abstract The new Muon g-2 experiment at Fermilab (E989) will measure the muon anomaly a μ = (g μ -2)/2 to an uncertainty of 16 x 10−11 (0.14 ppm). The experiment is running with a positive muon beam. The decay positrons are detected by 24 electromagnetic calorimeters placed on the inner radius of the magnetic storage ring. As the gain fluctuation of each calorimeter channel must be corrected to a few parts in 104, a state-of-art laser calibration system has been realized which provides short laser pulses to the calorimeters. The monitoring of these light signals is done by specific photo-detectors read by a specialized Monitoring Electronics, which is organized in devoted crates and performs the full data acquisition of the calibration signals starting from pre-amplification, then digitization and finally transfer of the information. Here we describe few key elements of the whole system, namely the single readout channel of the Monitoring Board.

Performance of the ATLAS Tile Hadronic Calorimeter at LHC in Run 1 and planned upgrades

The Tile Calorimeter (TileCal) is the central section of the ATLAS hadronic calorimeter at the Large Hadron Collider, a key detector for the measurements of hadrons, jets, tau leptons and missing transverse energy. Scintillation light produced in the tiles is transmitted by wavelength shifting fibres to photomultiplier tubes (PMTs). The resulting electronic signals from approximately 10000 PMTs are digitized before being transferred to off-detector data-acquisition systems. The data quality procedures used during the LHC data-taking and the evolution of the detector status are explained in the presentation. The energy and the time reconstruction performance of the digitized signals is presented and the noise behaviour and its improvement during the detector consolidation in maintenance periods are shown. A set of calibration systems allow monitoring and equalization of the calorimeter channels responses via signal sources that act at every stage of the signal path, from scintillation light to digitized signal. These partially overlapping systems are described in detail, their individual performance is discussed as well as the comparative results from measurements of the evolution of the calorimeter response with time during the full LHC data-taking period. The TileCal upgrade aims at replacing the majority of the on- and off-detector electronics so that all calorimeter signals will be directly digitized and sent to the off-detector electronics in the counting room. To achieve the required reliability, redundancy has been introduced at different levels. For the off-detector electronics a special pre-processor board is being developed, which will take care of the initial trigger processing, while the main data are temporarily stored in the pipeline and de-randomiser memories.

The NA62 Liquid Krypton calorimeter readout module

The NA62 experiment [1] at CERN SPS (Super Proton Synchrotron) accelerator will be focused on precision tests of the Standard Model via studies of ultra-rare decays of charged kaons. The high resolution Liquid Krypton (LKr) calorimeter of the former NA48 experiment [2], together with other detectors, will provide a photon-veto with hermetic coverage from zero out to large angles from the decay region. The old backend electronics [3] does not satisfy the NA62 specifications and the study of a new readout system began in 2008.This paper presents the Calorimeter REAdout Module (CREAM), an upgrade project for the backend part of the LKr data acquisition chain [3]. The CREAMs will provide 40 MHz sampling of 13248 calorimeter channels, data buffering during the SPS spill, zero suppression, and programmable trigger sums for the experiment trigger processor.

Identification and filtering of uncharacteristic noise in the CMShadron calorimeter

Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the data. The methods have been tested on cosmic ray muon data, calorimeter noise data, and single beam data collected with CMS in 2008. The noise rejection algorithms can be applied to LHC collision data at the trigger level or in the offline analysis. The application of the algorithms at the trigger level is shown to remove 90% of noise events with fake missing transverse energy above 100 GeV, which is sufficient for the CMS physics trigger operation.

DSP Online Algorithms for The ATLAS TileCal Read-Out Drivers

TileCal is the hadronic tile calorimeter of the ATLAS experiment at LHC/CERN. The central element of the back-end system of the TileCal detector is the read-out driver (ROD).The main components of the TileCal ROD are the Digital Signal Processors (DSPs) placed on the processing unit (PU) daughterboards. This paper presents a detailed description of the code developed for the DSPs. The code is divided into two different parts: the first part contains the core functionalities and the second part the reconstruction algorithms. The core acts as an operating system and controls configuration, data reception and transmission and synchronization between front-end data and the timing, trigger and control (TTC) information. The reconstruction algorithms implemented on the DSP are the optimal filtering (OF), muon tagging (MTag) and missing calculation. The OF algorithm reconstructs the deposited energy and the phase of the signal for every calorimeter channel within a front-end module. This reconstructed energy is used by the MTag algorithm to tag low transverse momentum muons that may escape the ATLAS muon spectrometer Level 1 trigger whereas the Missing algorithm computes the total transverse energy and the projection on and axis for the entire module that will be used by the Level 2 trigger system.

Design and test of prototype boards for the L1 calorimeter trigger upgrade at the D0 experiment

This paper presents the development and test of some of the electronic boards that have been designed for the upgrade of the L1 calorimeter trigger at the D0 experiment. The analog to digital converter and filter board (ADF) digitizes 32 calorimeter channels, applies digital filtering, and sends data to the next processing stage via 2 Gb/s links. The board is designed for sustained operation at 7.57 MHz with an end-to-end latency of less than ~1 mus. In order to test the high-speed output links of the ADF board, we made a tester that comprises a custom made Channel Link receiver card. To connect a prototype ADF board to the D0 detector without disturbing the existing trigger system, we designed a splitter board that duplicates the analog signals from eight calorimeter channels. The serial command link distribution card (SCLD) is designed to accurately distribute synchronization signals to the 80 ADF boards of the final system. We made single channel versions of the ADF and SCLD boards. These cards run in a simple desktop PC environment and are helpful for tests and collaborative work. We describe the hardware, firmware and software aspects of the various developments and show the operation of the different boards. We present performance measurements made on the prototype ADF board, and explain how we defined the production model of this board

Algorithms and architecture for the L1 calorimeter trigger at D0 run IIb

This paper presents the algorithms and the architecture proposed for the upgrade of the level 1 calorimeter trigger for the D0 experiment. We describe the digital-signal-processing algorithm applied to individual trigger tower signals and the physics algorithms that process the complete array of trigger towers. We investigate the performance of these algorithms and justify our choices. We present the hardware architecture of the system designed to analyze the signals of the 2560 calorimeter trigger tower samples and construct trigger primitives in /spl sim/3 /spl mu/s at a rate of 7.57 MHz. We give a detailed description of the two prototype boards that are being built: the analog-to-digital converter and filter board (ADF) that performs the analog conversion and digital processing of 32 calorimeter channels, and the trigger algorithm board (TAB) designed to run the physics selection algorithms on one eighth of the 480 Gbit/s of data produced by the complete set of ADF boards.

Monitoring of a light distribution system with HAMAMATSU PN photodiodes in radioactive environment

Silicon PN photodiodes will be used in the light monitoring system of the electromagnetic calorimeter of CMS in order to calibrate the light injected onto each calorimeter channel. Since the calorimeter will undergo heavy radiation, we studied the behaviour of PN photodiodes under fast neutron irradiation performed at Saclay.

Performance of the DELPHI small angle tile calorimeter

The DELPHI STIC detector is a lead-scintillator sampling calorimeter with wavelength shifting optical fibers used for light collection. The main goal of the calorimeter at LEP100 is to measure the luminosity with an accuracy better than 0.1%. The detector has been in operation since the 1994 LEP run. Presented here is the performance measured during the 1994-1995 LEP runs, with the emphasis on the achieved energy and space resolution, the long-term stability and the efficiency of the detector. The new bunch-trains mode of LEP requires a rather sophisticated trigger and timing scheme which is also presented. To control the trigger efficiency and stability of the calorimeter channels, a LED-based monitoring system has been developed.

A fast cluster finding system for future HEP experiments

The recent rapid improvements in performance of commercially available digital signal processors (DSP) and data-driven array processors make their efficient integration into the front-end trigger and data acquisition electronics of future detectors for high energy physics (HEP) attactive. These devices provide an alternative to VLSI circuits such as ASICs (application specific integrated circuit) for this special purpose application. A parallel-processing system based on data-driven array processors, digital signal processors (DSP) and Transputers for trigger decision, data acquisition and compaction is described. The system is modular and suitable for all calorimeter sizes including the ones proposed for the LHC (large hadron collider) and SSC (superconducting supercollider) experiments. The aim is to give full programmability to the trigger decisions and data compaction, over the entire calorimeter at the single-channel granularity level, with no boundary limitation. The fast cluster finding system is based on two cooperating levels, the higher one utilizing FDPP (fast digital parallel processing modules), and the lower one, the new data-controlled array processor for video signal processing, DataWave. The proposed architecture assigns one DataWave processing element (PE) to each calorimeter channel and one FDPP module for every 256 DataWave PE. These numbers have been chosen for optimum performance but may be changed. Fast inter-processor communication within the DataWave array overcomes the problem of overlapping areas, and effectively provides a continuous array of PEs. Local maxima, along with their total energy, are found in a very short time by the data-driven array processors. These are easily selected and transfered, with the data of their region of interest, to a higher-level DSP array-processor system even if part of the data falls outside the region originally attributed to a particular FDPP.