Directional iDBSCAN to detect cosmic-ray tracks for the CYGNO experiment

The CYGNO experiment aims to study rare events related to the search for low-mass dark matter and solar neutrino events. One of the main components of background comes from cosmic rays that generate long tracks in the detector's images. The interaction of such particles with the gas releases a variable energy profile along its trajectory to form tracks with multiple cores that can be easily reconstructed erroneously by being split into more than one cluster. Thus, this work offers a newly adapted version of the well-known density-based spatial clustering of applications with noise (DBSCAN) algorithm, called iDDBSCAN, which exploits the directional characteristics of the clusters found by the DBSCAN to improve its clustering efficiency when dealing with multi-core tracks. This paper provides a detailed explanation of this algorithm, covering its parameter validation and evaluating its influence when integrated into the experiment's event selection routine. To generate background events, data acquisition was performed with the detector installed in an overground laboratory, leaving it exposed to natural radiation. To produce signals in the energy range of interest for the experiment, a 55Fe radioactive source was used. The achieved results showed that the iDDBSCAN algorithm is capable of improving the background rejection of the experiment, through a more accurate reconstruction of the tracks produced by natural radiation such as cosmic rays, without deteriorating its signal detection efficiency and energy estimation

Noise assessment of CMOS active pixel sensors for the CYGNO Experiment

Active Pixel sensors play a crucial role in enabling successful low-light scientific experiments due to their inherent advantages and capabilities. Such devices not only offer high spatial resolution but also feature individual pixels with integrated amplifiers, allowing for direct signal amplification at the pixel level. This results in reduced readout noise and improved signal-to-noise ratio (SNR), which are particularly vital when dealing with limited photon counts in low-light environments. This holds particularly true for scientific CMOS (sCMOS) sensors, acknowledged as an advanced evolution of Active Pixel sensors. However, despite their advantages, such sensors can still exhibit limitations such as higher cost and presence of noise artifacts that should be closely investigated. In particular, CYGNO project fits in a global effort aimed at direct detection of Dark Matter particles. CYGNO collaboration intends to build a detector based on a Time Projection Chamber making use of Gas Electron Multipliers for the amplification of ionization electrons. The GEM multiplication process produces photons that can be readout by a high-resolution sCMOS sensor. Such detection system is being designed to have enough sensitivity to detect low-energy particles and to measure released energy with enough granularity so to reconstruct direction and energy profile along their trajectories. The image sensor has an important role in the detector performance, having a direct impact on the SNR of the experiment. This work proposes a study on the performance of three different sCMOS sensors with respect to their sensitivity to low-energy particles and their intrinsic noise, which are of the utmost importance for various scientific experiments