Speaker
Description
The spherical proportional counter (SPC) is a versatile gaseous detector with a variety of physics applications from fast neutron spectroscopy to rare events searches such as the direct detection of dark matter. The electric field established by the spherical geometry of the detector grants typical single-anode SPCs sensitivity to the radial position of ionising events via the drift of primary electrons in the gas. The development of the ACHINOS multi-anode sensor, with individual anode read-out, improves on this by giving the detector additional sensitivity to the angular position of these events, determined by which anodes receive signal. This enables the 3D reconstruction of event initial positions, particularly for point-like interactions such as nuclear or electron recoils. Extending this further, timing differences between anode signals can give the SPC sensitivity to the direction of ionising particles in track-like events, including ionisation trails by cosmic muons, allowing potential for full track reconstruction. In this work we present the latest developments in SPC track reconstruction algorithms in the 11- and 60-anode ACHINOS configurations, employing machine learning techniques such as neural networks, trained on the multi-anode read-out signals of simulated muon events. The reconstruction of cosmic muon data is also discussed.