Online Trigger System Research Articles

Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

Stack-CNN algorithm: A new approach for the detection of space objects

We present a new trigger algorithm combining a stacking procedure and a Convolutional Neural Network that could be applied to any space object moving linearly or with a known trajectory in the field of view of a telescope. This includes the detection of high velocity fragmentation debris in orbit. A possible implementation is as trigger system for an orbiting Space Debris remediation system. The algorithm was initially developed as offline system for the Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory (Mini-EUSO), on the International Space Station. We evaluated the performance of the algorithm on simulated data and compared it with those obtained by means of a more conventional trigger algorithm. Results indicate that this method would allow to recognise signals with ∼1% Signal over Background Ratio (SBR) on poissonian random fluctuations with a negligible fake trigger rate. Such promising results lead us to not only consider this technique as an online trigger system, but also as an offline method for searching moving signals and their characteristics (speed and direction). More generally, any kind of telescope (on the ground or in space) such as those used for space debris, meteors monitoring or cosmic ray science, could benefit from this automatized technique. The content of this paper is part of the recent Italian patent proposal submitted by the authors (patent application number: 102021000009845).

Trigger slow control system of the Belle II experiment

The Belle II experiment at the SuperKEKB e+e− collider in KEK, Japan, started physics data-taking with a complete detector from early 2019. An online trigger system is indispensable for the Belle II experiment to reduce the beam background events associated with high electron and positron beam currents without sacrificing the target physics-oriented events. During the Belle II operation upon beam collision, the trigger system must be consistently controlled and its status must be carefully monitored in the process of data acquisition against unexpected situations. For this purpose, we have developed a slow control system for the newly developed Belle II trigger system based on the custom-made Belle II DAQ/slow control package. Around seventy thousand configuration parameters are saved in the Belle II central database server for every run when a run starts and stops. These parameters play an essential role in offline validation of the quality of runs. Around three thousand real-time variables are stored in the Belle II main archiving server, and the trend of some of these variables are regularly used for online and offline monitoring purposes. Various operator interface tools have been prepared and used. When the configuration parameters are not correctly applied, or some of the processes are unexpectedly terminated, the slow control system detects it, stops the data-taking process, and generates an alarm. In this article, we report how we constructed the Belle II trigger slow control system, and how we successfully managed to operate during its initial stage.

An FPGA-Based Trigger System With Online Track Recognition in COMET Phase-I

An FPGA-based online trigger system has been developed for the COMET Phase-I experiment. This experiment searches for muon-to-electron conversion, which has never been observed yet. A drift chamber and trigger counters detect a mono-energetic electron from the conversion process in a 1-T solenoidal magnetic field. A highly intense muon source is applied to reach unprecedented experimental sensitivity. It also generates undesirable background particles, and a trigger rate due to these particles is expected to be much higher than an acceptable trigger rate in the data acquisition system. By using hit information from the drift chamber too, the online trigger system efficiently suppresses a background trigger rate while keeping signal-event acceptance large. A characteristic of this system is the utilization of the machine learning technique in the form of look-up tables on hardware. An initial simulation study indicates that the signal-event acceptance of the online trigger is 96% while the background trigger rate is reduced from over $90\,\mathrm{kHz}$ to $13\,\mathrm{kHz}$. For this scenario, we have produced trigger-related electronics that construct a distributed trigger architecture. The total latency of the trigger system was estimated to be $3.2\,\mathrm{\mu s}$, and the first operation test was carried out by using a part of the drift-chamber readout region.

Iterative Retina for High Track Multiplicity in a Barrel-Shaped Tracker and High Magnetic Field

Real-time track tracking in high energy physics experiments at colliders running at high luminosity is very challenging for trigger systems. To perform pattern-recognition and track fitting in online trigger system, the artificial Retina algorithm has been introduced in the field. Retina can be implemented in the state of the art FPGA devices. Our developments use Retina in an iterative way to identify track for barrel-shape tracker embedded in a high magnetic field and with high track multiplicity. As a benchmark we simulate LHC t-tbar events, with a pile-up of 200 and a GEANT-4 based simulation of a 6-layers barrel tracker detector made of silicon modules. With this sample the performance of the hardware design (resource usage, latency) is evaluated. Both efficiency and purity of the Retina fitting are over 90%. Moreover we have also added a Kalman filter after the Retina fit to improve the resolution on the track parameters. Our simulation results show that the Kalman filter can work well together with the Retina algorithm to find track through t-tbar event and provides high resolutions of the reconstructed parameters.

Radiation Tolerance of Online Trigger System for COMET Phase-I

The COMET experiment aims to search for the neutrinoless muon to electron transition process with new sensitivity levels. The online trigger system is an integral part of achieving the sensitivity levels required and will be subject to an expected neutron fluence of up to $10^{12}$ $n \cdot \mathrm{cm}^{-2}\;$ within regions inside the detector solenoid. Consequently a significant number of soft errors in the logic of the onboard field programmable gate arrays (FPGA) can occur, requiring error correction for single event upsets and firmware reprogramming schemes for unrecoverable soft errors. We studied the radiation tolerance of the COMET Phase-I front-end trigger system, called COTTRI, subject to neutron fluence on order $10^{12}$ $n \cdot \mathrm{cm}^{-2}\;$ with multiple error correcting codes and automatic firmware reconfiguration. The regions measured were the configuration RAM, block RAM and also in a multi-gigabit transfer link using copper cables that will be used for communication between different trigger boards during Phase-I. The resulting cross sections observed suggest the most significant impact to the experiment will come from unrecoverable soft errors in configuration RAM, with dead time expected to be $(4.2 \pm 1.3)\%$. The effect of multi-bit errors in block RAM was found to be almost negligible in COMET Phase-I. In addition, multiple solutions have already been proposed in order to suppress these errors further. Soft errors observed in the multi-gigabit transfer links were measured to be of two orders of magnitude less impact compared to the unrecoverable errors in configuration RAM. We concluded that the COTTRI system meets the trigger requirement in COMET Phase-I.

The electronics, online trigger system and data acquisition system of the J-PARC E16 experiment

The J-PARC E16 experiment was proposed to investigate the restoration of chiral symmetry at the normal nuclear density. E16 will systematically measure in-medium mass of vector mesons at J-PARC Hadron Experimental Facility using a 30-GeV proton beam with an intensity of 2 x 1010 protons per pulse. The E16 spectrometer was designed to detect e+e- from slowly moving vector meson, particularly the ϕ meson. The detector system consists of GEM tracker, Gas Cerenkov detector based on GEM and electromagnetic calorimeter made of lead-glass, whose number of channels reaches about 100,000 in total. The readout electronics, trigger system and data acquisition system for the detectors have been developed, for which level-1 trigger rate of 1-2 kHz is required under several 10 MHz interaction rate. The preparation is underway for the first beam time in 2017.

Design, characterization, and sensitivity of the supernova trigger system at Daya Bay

Providing an early warning of galactic supernova explosions from neutrino signals is important in studying supernova dynamics and neutrino physics. A dedicated supernova trigger system has been designed and installed in the data acquisition system at Daya Bay and integrated into the worldwide Supernova Early Warning System (SNEWS). Daya Bay’s unique feature of eight identically-designed detectors deployed in three separate experimental halls makes the trigger system naturally robust against cosmogenic backgrounds, enabling a prompt analysis of online triggers and a tight control of the false-alert rate. The trigger system is estimated to be fully sensitive to 1987A-type supernova bursts throughout most of the Milky Way. The significant gain in sensitivity of the eight-detector configuration over a mass-equivalent single detector is also estimated. The experience of this online trigger system is applicable to future projects with spatially distributed detectors.

Fast Tracker: a hardware real time track finder for the ATLAS trigger system

The Fast Tracker (FTK) is an integral part of the trigger upgrade program of the ATLAS detector at the Large Hadron Collider (LHC). As the LHC luminosity approaches its design level of 1034 cm−2s−1, the combinatorial problem posed by charged particle tracking becomes increasingly difficult due to the swelling of multiple interactions per bunch crossing. The FTK is a highly-parallel hardware system intended to provide high-quality tracks with transverse momentum above 1 GeV in real time for online trigger system. The FTK system's design, based on a mixture of advanced technologies, and the expected physics performance will be presented.

Level 1 Trigger System for the Belle II Experiment

The super-KEKB B factory, currently under construction at the KEK High Energy Accelerator Research Organization in Japan, has a goal of producing 50 ab-1 of integrated luminosity, thus allowing the Belle II experiment to study rare decays of B mesons, D mesons, and τ leptons. Such large statistics on these decays provide an experimental probe of physics beyond the Standard Model. An online trigger system is indispensable to Belle II to reduce the number of beam background events associated with high electron and positron beam currents, as well as to enhance physics-oriented events. For this purpose, we have designed the Belle II online trigger system with two kinds of primary Level 1 trigger components: a track trigger and an energy trigger. The track trigger is composed of 2-dimensional and 3-dimensional tracking algorithms, and the energy trigger implements algorithms based on total energy, isolated clusters, and identification of Bhabha events. In addition, precise event trigger timing and muon tagging information are provided by the time-of-propagation detector and iron flux return muon detector, respectively.

The Configuration System of the ATLAS Trigger

The ATLAS detector at CERN's LHC will be exposed to proton-proton collisions at a rate of 40 MHz. To reduce the data rate, only potentially interesting events are selected by a three-level trigger system. The first level is implemented in custom-made electronics, with an output rate to less than 100 kHz. The second and third level are software triggers with a final output rate of 100 to 200 Hz. A system has been designed and implemented that holds and records the full configuration information of all three trigger levels at a centrally maintained location. This system provides fast access to consistent configuration information of the online trigger system for the purpose of data taking as well as to all parts of the offline trigger simulation. The use of relational database technology provides a means of reliable recording of the trigger configuration history over the lifetime of the experiment. In addition to the online system, tools for flexible browsing and manipulation of trigger configurations, and for their distribution across the ATLAS reconstruction sites have been developed. The usability of this design has been demonstrated in dedicated configuration tests of the ATLAS level-1 Central Trigger and of a 600-node software trigger computing farm. Further tests on a computing cluster which is part of the final high level trigger system were also successful.

Muon identification with the event filter of the ATLAS experiment at CERN LHC

The Large Hadron Collider at CERN offers unprecedented challenges to the design and construction of detectors and trigger/data acquisition systems. For ATLAS, a three level trigger system has been developed to extract interesting physics signatures with a 10/sup 6/ rate reduction. To accomplish this, components of physics analysis traditionally deferred to off-line physics analysis must be embedded within the on-line trigger system. For the Muon trigger, the specific off-line algorithms MOORE (Muon Object Oriented REconstruction) and MuId (Muon Identification) have been adopted so far for the on-line use, imposing an operation in a Bayesian-like environment where only specific hypotheses must be validated. After a short review of the ATLAS trigger, the paper shows the general strategy of the Muon Identification and Selection accessing the full event data, or being seeded from results derived at a previous stage of the trigger chain.

Online trigger system to select particles generated in high multiplicity environments

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2020 Impact Factor: 0.515The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.