Track Lab: extensible data acquisition software for fast pixel detectors, online analysis and automation

TL;DR

The paper addresses the need for transferable, high-performance DAQ software amid rapid hardware evolution in pixel detectors. It introduces Track Lab, a modular, extensible platform built around a core API and plug-in modules, employing a directed-acyclic-graph data flow, a per-actor finite-state machine, multi-threading, and ZeroMQ-based messaging. Key contributions include real-time clustering, filtering, calibration, time-walk compensation, and threshold equalization modules, along with extensive hardware support (Timepix2/3, PMTs, plus translation/rotation stages) and live visualization. The work demonstrates deployment in beam tests, tissue scanning, and detector networks at ATLAS and MoEDAL, and provides cross-platform binaries with a permissive license, enabling widespread adoption and replication.

Abstract

Fast, incremental evolution of physics instrumentation raises the question of efficient software abstraction and transferability of algorithms across similar technologies. This contribution aims to provide an answer by introducing Track Lab, a modern data acquisition program focusing on extensibility and high performance. Shipping with documented API and more than 20 standard modules, Track Lab allows complex analysis pipelines to be constructed from simple, reusable building blocks. Thanks to multi-threaded infrastructure, data can be clustered, filtered, aggregated and plotted concurrently in real-time. In addition, full hardware support for Timepix2, Timepix3 pixel detectors and embedded photomultiplier systems enables such analysis to be carried out online during data acquisition. Repetitive procedures can be automated with support for motorized stages and X-ray tubes. Freely distributed on 7 popular operating systems and 2 CPU architectures, Track Lab is a versatile tool for high energy physics research.

Figures (8)

• Figure 1: Architecture of Track Lab. Boxes correspond to linking units (libraries, executables), arrows indicate dependency relationships. For brevity, only a subset of actual dependencies and modules is displayed.
• Figure 2: Typical topology of a pipeline graph with a single device actor (Timepix3 readout, left) and many actors that modify, visualize and save the data stream. Even though some non-device actors are used multiple times, Track Lab permits this analysis to be efficiently performed in real-time.
• Figure 3: On the left, state diagram of a single actor. The initial state is marked with double border, labeled state transitions can be initiated by the user. States are color-coded to match the user interface (on the right).
• Figure 4: Hardware compatible with Track Lab (not to scale). From the left, Katherine with Timepix3, MicroDAQ, Spectrig, Amptek® Mini-X2, Standa 8MR190-2 rotation stage and Universal Robots UR3e.
• Figure 5: Visualization modules displaying data taken with Timepix3 detectors at the ATLAS experiment. In the left window, clusters filtered by size and roundness are fed into two pixel matrices (one for accepted, other for rejected data). In the right window, two-dimensional histogram of cluster energy and cluster size is plotted next to a series of timeline plots.