Efficient physics signal selectors for the first trigger level of the Belle II experiment based on machine learning

A neural network based z-vertex trigger is developed for the first level trigger of the upgraded flavor physics experiment Belle II at the high luminosity B factory SuperKEKB in Tsukuba, Japan. Using the hit and drift time information from the central drift chamber, a pool of expert neural networks estimates the 3D track parameters of the single tracks found by a 2D Hough finder. The neural networks are already implemented on parallel FPGA hardware for real time data processing and running pipelined in the online first level trigger of Belle II. Due to the anticipated high luminosity of up to 8 × 10³⁵ cm⁻²s⁻¹, Belle II will have to face severe levels of background tracks with vertices displaced along the beamline. The neural z-vertex algorithm presented in this thesis allows to reject displaced background tracks such that the requirements of the standard track trigger can be strongly relaxed. Especially for physics decay channels with a low track multiplicity in the final states, like τ pair production, or initial state radiation events with reduced center of mass energies, the trigger efficiencies can be significantly increased. As an upgrade of the present 2D Hough finder in the neural network preprocessing, a model independent 3D track finder is developed that uses the additional stereo hit information of the drift chamber. Thus, the trigger efficiencies improve for tracks in the phase space of low transverse momenta and shallow polar angles. Since the cross sections of the physics signal events typically increase towards shallow polar angles, this enlarged acceptance of the track trigger provides a substantial gain in the signal efficiencies. By using an adapted pool of expert networks, the enlarged phase space provided by the 3D finder can be efficiently covered. Studies on simulated MC background, on simulated initial state radiation events, and on recorded data from early Belle II runs demonstrate the high performance of the novel trigger algorithms. With the 3D finder an increase of the track finding rate of about 50 % is confirmed for signal tracks, while displaced background tracks are actively suppressed prior to the neural network. Based on z-vertex cuts on the tracks processed by the neural networks, a two track event efficiency of more than 99 % can be achieved with a purity of around 80 %

Online Estimation of Particle Track Parameters based on Neural Networks for the Belle II Trigger System

The Belle II particle accelerator experiment is experiencing substantial background from outside of the interaction point. To avoid taking data representing this background, track parameters are estimated within the pipelined and dead time-free level 1 trigger system of the experiment and used to suppress such events. The estimation of a particle track\u27s origin with respect to the z-Axis, which is along the beamline, is performed by the neural z-Vertex trigger. This system is estimating the origin or z-Vertex using a trained multilayer perceptron, leveraging the advantages of training to current circumstances of operation. In order fulfil the requirements set by the overall trigger system it has to provide the estimation within an overall latency of 5 us while matching a refresh rate of up to 31.75 for new track estimations. The focus of this contribution is this system\u27 current status. For this both implementation and integration into the level 1 trigger will be presented, supported by first data taken during operation as well as figures of merit such as latency and resource consumption. In addition its upgrade plan for the near future will be presented. The center of these is a Hough based track finding approach that uses Bayes theorem for training the weighting of track candidates. Characteristics of this system\u27s current prototypical implementation on FPGAs as well as present plants towards integration for future operation will be presented

A neural network z-vertex trigger for Belle II

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D ($r - \varphi$) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.Comment: Proceedings of the 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Preprint, reviewed version (only minor corrections

Status of the BELLE II Pixel Detector

The Belle II experiment at the super KEK B-factory (SuperKEKB) in Tsukuba, Japan, has been collecting $e^+e^−$ collision data since March 2019. Operating at a record-breaking luminosity of up to $4.7×10^{34} cm^{−2}s^{−1}$, data corresponding to $424 fb^{−1}$ has since been recorded. The Belle II VerteX Detector (VXD) is central to the Belle II detector and its physics program and plays a crucial role in reconstructing precise primary and decay vertices. It consists of the outer 4-layer Silicon Vertex Detector (SVD) using double sided silicon strips and the inner two-layer PiXel Detector (PXD) based on the Depleted P-channel Field Effect Transistor (DePFET) technology. The PXD DePFET structure combines signal generation and amplification within pixels with a minimum pitch of $(50×55) μm^2$. A high gain and a high signal-to-noise ratio allow thinning the pixels to $75 μm$ while retaining a high pixel hit efficiency of about $99$. As a consequence, also the material budget of the full detector is kept low at $≈0.21$$\frac{X}{X_0}$ per layer in the acceptance region. This also includes contributions from the control, Analog-to-Digital Converter (ADC), and data processing Application Specific Integrated Circuits (ASICs) as well as from cooling and support structures. This article will present the experience gained from four years of operating PXD; the first full scale detector employing the DePFET technology in High Energy Physics. Overall, the PXD has met the expectations. Operating in the intense SuperKEKB environment poses many challenges that will also be discussed. The current PXD system remains incomplete with only 20 out of 40 modules having been installed. A full replacement has been constructed and is currently in its final testing stage before it will be installed into Belle II during the ongoing long shutdown that will last throughout 2023

Track vertex reconstruction with neural networks at the first level trigger of Belle II

The track trigger is one of the main components of the Belle II first level trigger, taking input from the Central Drift Chamber (CDC). It consists of several stages, first combining hits to track segments, followed by a 2D track finding in the transverse plane and finally a 3D track reconstruction. The results of the track trigger are the track multiplicity, the momentum vector of each track and the longitudinal displacement of the origin or production vertex of each track (“z-vertex”). The latter allows to reject background tracks from outside of the interaction region and thus to suppress a large fraction of the machine background. This contribution focuses on the track finding stage using Hough transforms and on the z-vertex reconstruction with neural networks. We describe the algorithms and show performance studies on simulated events

The Neuro-Z-Vertex Trigger of the Belle II Experiment

A neural network z vertex trigger is planned for the upcoming Belle II detector at the SuperKEKB collider. This neural algorithm is based on a single track 3D parameter estimation using only hit and drift time information from the central drift chamber. Due to the high luminosity (L = 8 × 1035 cm−2 s−1) Belle II will have to face high levels of beam induced background, making a z vertex reconstruction at the first level trigger mandatory. Using the neural z vertex algorithm, the requirements of the standard track trigger can be strongly relaxed. By this, the trigger efficiencies, especially for low multiplicity events, e.g. τ pair production, can be significantly increased. This contribution presents the foreseen neural network trigger setup and the preceding 2D track finder. Special focus is put on the proposal and evaluation of a possible 3D upgrade of the 2D track finder. Additionally, details are given on a dedicated setup for the upcoming cosmic ray test