Speaker
Description
Physicists continue to invest significant effort in the search for dark matter using increasingly large and sensitive detectors. ARGO is a next generation experiment in conceptual development designed to push sensitivity through advanced photodetection and large-scale instrumentation. The detection medium is a ~400-tonne mass of low-background argon inside an acrylic vessel. To capture the scintillation light, the 200 m$^2$ outer surface will be covered in Single Photon Avalanche Diodes (SPAD) with a digital readout. The high potential granularity (mm-scale) of SPAD arrays results in channels numbering in the millions, requiring a new approach for the readout and data acquisition. We will present a data acquisition architecture exploiting distributed real-time artificial intelligence to identify signals of interest and extract the relevant properties, such as position, energy and probable particle type.
| Keyword-1 | Data acquisition system |
|---|---|
| Keyword-2 | Dark matter |
| Keyword-3 | Edge Machine Learning |