Software Trigger Research Articles

First results on the performance of the upgraded LHCb RICH detectors

With the start of LHC Run 3 the upgraded LHCb experiment, designed to run at an instantaneous luminosity five times larger with respect to previous running periods, is aiming to collect L=50fb−1 of pp collisions data by the end of Run 4. In addition, thanks to a trigger-less readout and a full software trigger, the selection efficiency for fully hadronic decay channels of heavy hadrons increases up to a factor two. The LHCb RICH detectors have been renewed to manage the increased detector occupancy, ensuring outstanding charged hadron discrimination in the more demanding Run 3 environment and at the maximum LHC interaction rate. This process is detailed, outlining the automated calibration procedures of the RICH system to achieve optimal signal efficiency and background rejection during the initial stages of the Run 3 data-taking. The early performance of the newly installed detectors is presented, including the determination of performance parameters such as the Cherenkov angle resolution. It is also reported that the particle identification capabilities are already approaching the design level and outperforming those of Run 1 and 2.

The LHCb VELO Upgrade II: design and development of the readout electronics

The LHCb collaboration proposes a Phase-II Upgrade of the detector, to be installed during the LHC Long Shutdown 4 (2032–2034). Operating in the HL-LHC environment poses significant challenges to the design of the upgraded detector, and in particular to its tracking system. The primary and secondary vertices reconstruction will become more difficult due to the increase, by a factor of 7.5, of the average number of interactions per bunch crossing. The performance of the VErtex LOcator (VELO), which is the tracking detector surrounding the interaction region, is essential to the success of this Phase-II Upgrade. Data rates are especially critical for the LHCb full software trigger, and with the expected higher particle flux, the VELO Upgrade-II detector will have to tolerate a dramatically increased data rate: assuming the same hybrid pixel design and detector geometry, the front-end electronics (ASICs) of the VELO Upgrade-II will have to cope with rates as high as 8 Ghits/s, with the hottest pixels reaching up to 500 khits/s. With this input rate, the data output from the VELO will exceed 30 Tbit/s, with potentially a further increase if more information is added to the read-out. This paper outlines the challenges being addressed and the solutions under investigation for reading out the VELO sub-detector.

Event and data persistency models for the LHCb Real Time Analysis System

Starting in 2022, the upgraded LHCb detector is collecting data with a pure software trigger. In its first stage, reducing the rate from 30MHz to about 1MHz, GPUs are used to reconstruct and trigger on B and D meson topologies and high-pT objects in the event. In its second stage, a CPU farm is used to reconstruct the full event and perform candidate selections, which are persisted for offline use with an output rate of about 10GB/s. Fast data processing, flexible and custom-designed data structures tailored for SIMD architectures and efficient storage of the intermediate data at various steps of the processing pipeline onto persistent media, e.g. tapes is essential to guarantee the full physics program of LHCb. We present the event model and data persistency developments for the trigger of LHCb in Run 3. Particular emphasis is given to the novel software-design aspects with respect to the Run 1+2 data taking, the performance improvements which can be achieved and the experience of restructuring a major part of the reconstruction software in a large HEP experiment.

OPTICAM triple-channel astronomical image acquisition control software and external triggering synchronization system

We present the development of software to control the multichannel image acquisition process and the design of an external timing module (ETM) for the OPTICAM system (Castro et al., 2019). The ETM is used to generate high-speed synchronized pulses for OPTICAM’s three different sCMOS cameras and is based on an ESP32-S2 micro-controller. The graphical user interface (GUI) of this instrument is also described, which allows users to enter the configuration parameters of the observations and execute actions such as starting and stopping image acquisition sequences or initializing cameras. 3 mini PCs are in charge of reading each one of the cameras, respectively. The software and hardware tools presented here have been successfully implemented at the Observatorio Astronómico Nacional (OAN-SPM), Mexico. By analyzing the recorded image sequences, it is possible to perform high-precision multi-channel photometric measurements of astrophysical objects that exhibit high temporal variability, revealing information about periodicities, inter-band delays, and quasi-periodic oscillations.

Hardware and Software Implementation of the Embedded Controlling System for the TuMag Camera

The main objective of this paper is to present the design and implementation of an embedded controlling system for the Tunable Magnetograph (TuMag) instrument camera sensor and test-bench software application for camera laboratory characterization. The TuMag camera is based on a new scientific sensor GPIXEL SENSE400 device family equipped with an FPGA. The camera sensor has an active area of 2048 × 2048 pixels and reaches a frame rate of up to 48 frames per second. The embedded controlling system was implemented on an Artix 7 FPGA, which oversees controlling the image sensor through a configuration interface. It controls the read-out of the sensor data and the communication of commands with the host device. The camera has a standard CoaXPress communication interface of up to 3125 Gbps. The FPGA embedded control system allows bit, word, and serial channel calibration and the modification of several parameters, such as the region of interest (RoI) size, exposition time, hardware or software trigger, single or continuous acquisition modes, sensor gain, and adjustment of the black level offset and exposition time. In addition, the firmware has a temperature controller, voltage level monitoring, and an enable–disable power sensor. In addition, the test-bench software application is a library developed in Python 3.7. It is a wrapper for the standard GeniCan commercial frame grabber interpreter. This wrapper permits the improvement of the interaction and interface between the user and the hardware in the camera calibration. The test-bench software application permits the reduction of costs and the transport risk of the TuMag camera between different laboratories. All TuMag embedded control systems and test-bench software application functionalities were successfully tested according to scientific requirements.

Streaming DAQ Software Prototype at the J-PARC Hadron Experimental Facility

In recent years, experiments in particle physics and nuclear physics have required faster data collection systems and advanced trigger systems as the beam intensity has been increased. The current data acquisition (DAQ) system at the Japan Proton Accelerator Research Complex (J-PARC) Hadron Experimental Facility (HEF) uses fixed- and low-latency triggers with dedicated hardware to reduce data and event-building software that merges data into a single endpoint. This conventional DAQ system is expected to be inadequate for future experiments. It has to be replaced with a new simplified but powerful system called streaming DAQ, which collects the detector signals and filters them with software running on many computers. Therefore, we developed a prototype of streaming DAQ software for the J-PARC HEF using FairMQ and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Redis</i> as middleware. The key features include simplicity and a low learning cost to develop and be operated by a small number of people. The software can be used in a fully streaming readout system and in the triggered DAQ and the combined DAQ of the hardware and software triggers.

A FPGA-Based Architecture for Real-Time Cluster Finding in the LHCb Silicon Pixel Detector

This article describes a custom VHDL firmware implementation of a two-dimensional cluster-finder architecture for reconstructing hit positions in the new vertex pixel detector (VELO) that is part of the LHCb Upgrade. This firmware has been deployed to the existing FPGA cards that perform the readout of the VELO, as a further enhancement of the DAQ system, and will run in real time during physics data taking, reconstructing VELO hits coordinates on-the-fly at the LHC collision rate. This pre-processing allows the first level of the software trigger to accept a 11% higher rate of events, as the ready-made hits coordinates accelerate the track reconstruction and consumes significantly less electrical power. It additionally allows the raw pixel data to be dropped at the readout level, thus saving approximately 14% of the DAQ bandwidth. Detailed simulation studies have shown that the use of this real-time cluster finding does not introduce any appreciable degradation in the tracking performance in comparison to a full-fledged software implementation. This work is part of a wider effort aimed at boosting the real-time processing capability of HEP experiments by delegating intensive tasks to dedicated computing accelerators deployed at the earliest stages of the data acquisition chain.

Deep machine learning for the PANDA software trigger

Deep machine learning methods have been studied for the software trigger of the future PANDA experiment at FAIR, using Monte Carlo simulated data from the GEANT-based detector simulation framework PandaRoot. Ten physics channels that cover the main physics topics, including electromagnetic, exotic, charmonium, open charm, and baryonic reaction channels, have been investigated at four different anti-proton beam momenta. Different classification concepts and network architectures have been studied. Finally a residual convolutional neural network with four residual blocks in a binary classification scheme has been chosen due to its extendability, performance and stability. The presented study represents a feasibility study of a completely software-based trigger system. Compared to a conventional selection method, the deep machine learning approach achieved a significant efficiency gain of up to 200%, while keeping the background reduction factor at the required level of 1/1000. Furthermore, it is shown that the use of additional input variables can improve the data quality for subsequent analysis. This study shows that the PANDA software trigger can benefit greatly from the deep machine learning methods.

Commissioning of the new muon-to-central-trigger-processor interface at ATLAS

The ATLAS trigger system includes a Level-1 (L1) trigger based on custom electronics and firmware, and a high-level software trigger running on off-the-shelf hardware. The L1 trigger system uses information from the forward detectors, the calorimeters and the muon trigger detectors. Once information from all muon trigger sectors has been received, trigger candidate multiplicities are calculated by the Muon-to-Central-Trigger-Processor Interface (MUCTPI). Muon multiplicity information is sent to the Central-Trigger-Processor (CTP) and trigger objects are sent to the L1 Topological Trigger Processor (L1Topo). The CTP combines the information received from the MUCTPI with the trigger information from the forward detectors, the calorimeters and the L1Topo, and takes the L1 trigger decision. As part of the ATLAS L1 trigger system upgrade for Run-3 of the Large Hadron Collider (LHC) a new MUCTPI has been designed and commissioned. We discuss the commissioning and operation of the new MUCTPI used in ATLAS from the beginning of Run-3. In particular, we describe the integration tests which have been carried out for the commissioning and operation of the new MUCTPI.

The NA62 level 0 calorimetric trigger fast readout implementation, commissioning and data taking performances

The NA62 experiment at the CERN SPS aims to measure the branching ratio of the very rare kaon decay . The Calorimetric Level 0 Trigger identifies clusters in the electromagnetic and hadronic calorimeters. Along with the trigger data sent to the L0 Trigger Processor, readout data is collected to be sent to the L1 software trigger. In this work we present the novel implementation of the readout data collection and forwarding system in the multiple layers of the calorimetric trigger structure. We will also present the commissioning of the system and the performance evaluation on current data taking.

Status of the data acquisition, trigger, and slow control systems of the Mu2e experiment at Fermilab

The Mu2e experiment at the Fermilab will search for a coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10,000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq framework as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates several framework components — an artdaq-based DAQ, an art-based event processing, and an EPICS-based detector control system (DCS), and provides a uniform multi-user interface to its components through a web browser. Data streams from the Mu2e tracker and calorimeter are handled by the artdaq-based DAQ and processed by a one-level software trigger implemented within the art framework. Events accepted by the trigger have their data combined, post-trigger, with the separately read out data from the Mu2e Cosmic Ray Veto system. Foundation of the Mu2e DCS, EPICS – an Experimental Physics and Industrial Control System – is an open-source platform for monitoring, controlling, alarming, and archiving. A prototype of the TDAQ and the DCS systems has been built and tested over the last three years at Fermilab’s Feynman Computing Center, and now the production system installation is underway. This work presents their status and focus on the installation plans and procedures for racks, workstations, network switches, gateway computers, DAQ hardware, slow controls implementation, and testing.

Concept of KamLAND2 DAQ system

The KamLAND-Zen experiment is searching for neutrinoless double-beta decay of 136Xe. We are preparing for the upgrade of the KamLAND detector, KamLAND2. It is designed to improve the discrimination power of two-neutrino double-beta decay and cosmic-ray muon spallation backgrounds. Data acquisition of all neutrino events from nearby supernova is also an important issue of the KamLAND2 experiment and the data rate is a key factor to design a new DAQ system. High speed sampling will be realized with RFSoC on the front-end electronics. Data is read out from FEE to DAQ computers via 10 GbE, and FairMQ is one of the candidates for KamLAND2 DAQ software. In the KamLAND DAQ, the whole trigger system is implemented in hardware, while the KamLAND2 DAQ adopts hardware trigger for photon detection and software trigger to extract physics events. A simulation study of the software trigger is proceeding in order to detect low energy events using timing and charge information.

FPGA-based real-time data processing for accelerating reconstruction at LHCb

In run-3, beginning in 2022, the LHCb software trigger will start reconstructing events at the LHC average crossing rate of 30 MHz. Within the upgraded DAQ system, LHCb established a testbed for new heterogeneous computing solutions for real-time event reconstruction, in view of future runs at even higher luminosities. One such solution is a highly-parallelized custom tracking processor (“Artificial Retina”), implemented in state of the art FPGA devices connected by fast serial links. We describe the status of a realistic prototype for the reconstruction of pixel tracking detectors that will run on real data during run-3.

Measurements of energy spectra of relativistic electrons and gamma-rays from avalanches developed in the thunderous atmosphere with Aragats Solar Neutron Telescope

Aragats solar neutron telescope (ASNT) is a unique instrument allowing to measure the energy spectra of electrons accelerated and multiplied in the strong electric fields of the atmosphere. We describe the instrument setup, its operation condition, software, and hardware triggers. We present energy spectra of a very large thunderstorm ground enhancement (TGE) event observed on 6 October 2021. The detector response function, algorithm to recover energy spectra from the energy release histograms also are presented. The spectra recovery procedure is verified by simulation of the response function of the SEVAN detector, operating nearby ASNT. SEVAN is a stacked 3 layered detector, interlayered by lead filters registering both charged and neutral species of cosmic rays. The simulated and measured count rates of all 3 layers of the SEVAN detector show good agreement within 20%.

A Generalized Approach to Longitudinal Momentum Determination in Cylindrical Straw Tube Detectors

The upcoming PANDA experiment at FAIR will be among a new generation of particle physics experiments to employ a novel event filtering system realised purely in software, i.e. a software trigger. To educate its triggering decisions, online reconstruction algorithms need to offer outstanding performance in terms of efficiency and track quality. We present a method to reconstruct longitudinal track parameters in PANDA’s Straw Tube Tracker, which is general enough to be easily added to other track finding algorithms that focus on transversal reconstruction. For the pattern recognition part of this method, three approaches are employed and compared: a combinatorial path finding approach, a Hough transformation, and a recursive annealing fit. In a systematic comparison, the recursive annealing fit was found to outperform the other approaches in every category of quality parameters and reaches a reconstruction efficacy of 95% and higher. Due to its computational simplicity, the recursive annealing fit was also found to have faster execution times compared to the other algorithms.

White Rabbit Time Synchronization for Radiation Detector Readout Electronics

As radiation detector arrays in nuclear physics applications become larger and physically more separated, the time synchronization and trigger distribution between many channels of detector readout electronics become more challenging. Clocks and triggers are traditionally distributed through dedicated cabling, but newer methods such as the IEEE 1588 Precision Time Protocol and White Rabbit allow clock synchronization through the exchange of timing messages over Ethernet. Consequently, we report here the use of White Rabbit in a new detector readout module, the Pixie-Net XL. The White Rabbit core, data capture from multiple digitizing channels, and subsequent pulse processing for pulse height and constant fraction timing are implemented in a Kintex 7 FPGA. The detector data records include White Rabbit time stamps and are transmitted to storage through the White Rabbit core's gigabit Ethernet data path or a slower diagnostic/control link using an embedded Zynq processor. The performance is characterized by time-of-flight style measurements and by time correlation of high energy background events from cosmic showers in detectors separated by longer distances. Software for the Zynq processor can implement "software triggering", for example to limit recording of data to events where a minimum number of channels from multiple modules detect radiation at the same time.

Perspectives of lowering CUORE thresholds with Optimum Trigger

CUORE is a cryogenic experiment that focuses on the search of neutrinoless double beta decay in 130Te and it is located at the Gran Sasso National Laboratories. Its detector consists of 988 TeO2 crystals operating at a base temperature of ∼10 mK. It is the first ton-scale bolometric experiment ever realized for this purpose. Thanks to its large target mass and ultra-low background, the CUORE detector is also suitable for the search of other rare phenomena. In particular the low energy part of the spectra is interesting for the detection of WIMP-nuclei scattering reactions. One of the most important requirements to perform these studies is represented by the achievement of a stable energy threshold lower than 10 keV. Here, the CUORE capability to accomplish this purpose using a low energy software trigger will be presented and described.

Supernova neutrino detection in NOvA

The NOvA long-baseline neutrino experiment uses a pair of large, segmented, liquid-scintillator calorimeters to study neutrino oscillations, using GeV-scale neutrinos from the Fermilab NuMI beam. These detectors are also sensitive to the flux of neutrinos which are emitted during a core-collapse supernova through inverse beta decay interactions on carbon at energies of \U0001d4aa(10 MeV). This signature provides a means to study the dominant mode of energy release for a core-collapse supernova occurring in our galaxy. We describe the data-driven software trigger system developed and employed by the NOvA experiment to identify and record neutrino data from nearby galactic supernovae. This technique has been used by NOvA to self-trigger on potential core-collapse supernovae in our galaxy, with an estimated sensitivity reaching out to 10 kpc distance while achieving a detection efficiency of 23% to 49% for supernovae from progenitor stars with masses of 9.6 M⊙ to 27 M⊙, respectively.

A new scheduling algorithm for the LHCb upgrade trigger application

During Run3 of the LHC the LHCb detector will process a 30 MHz event rate with a full detector readout followed by a software trigger. To deal with the increased computational requirements, the software framework is reviewed and optimized on a large scale. One challenge is the efficient scheduling of O(103)-O(104) algorithms in the High Level Trigger (HLT) application. This document describes the design of a new algorithm scheduler which allows for static-order intra-event scheduling with minimum complexity while still providing the required flexibility.

A hybrid deep learning approach to vertexing

During the LHC Run 3, the instantaneous luminosity received by LHCb will be increased going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event. To face this challenge, the LHCb detector will be upgraded and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. We will present a novel prototype algorithm for vertexing in the LHCb upgrade conditions. We use a custom kernel to transform the sparse 3D space of hits and tracks into a dense 1D dataset, and then apply Deep Learning techniques to find PV locations. By training networks on our kernels using several Convolutional Neural Network layers, we have achieved better than 90% efficiency with no more than 0.2 False Positives (FPs) per event. Beyond its physics performance, this algorithm also provides a rich collection of possibilities for visualization and study of 1D convolutional networks. We will discuss the design, performance, and future potential areas of improvement and study, such as possible ways to recover the full 3D vertex information.