Alexander Belov

In this article, we present cutting-edge machine learning-based techniques for the detection and reconstruction of meteors and space debris in the Mini-EUSO experiment, a detector installed on board of the International Space Station, and pointing toward the Earth. We base our approach on a recent technique, the STACKing method plus Convolutional Neural Network (STACK-CNN), originally developed as an online trigger in an orbiting remediation system to detect space debris. Our proposed method, the refined-STACKing method plus convolutional neural network (R-Stack-CNN), makes the STACKing method plus convolutional neural network (STACK-CNN) more robust, thanks to a random forest that learns the temporal development of these events in the camera. We prove the flexibility of our method by showing that it is sensitive to any space object that moves linearly in the field of view. First, we search small space debris, never observed by Mini-EUSO. Due to the limiting statistics, also in this case, no debris were found. However, since meteors produce signals similar to space debris but they are much more frequent, the R-Stack-CNN is adapted to identify such events while avoiding the numerous false positives of the Stack-CNN. Results from real data show that the R-Stack-CNN is able to find more meteors than a classical thresholding method and a new method of two neural networks. We also show that the method is also able to accurately reconstruct speed and direction of meteors with simulated data.

Spectroscopic measurements of aurora emissions provide valuable insights into the altitude of electron atmospheric penetration and their maximum energy. To achieve this, the photometers used in the PAIPS (Pulsating Aurora Imaging Photometers System) project are equipped with spectrometers. These spectrometers enable the measurement of auroral emissions in narrow spectral lines with a temporal resolution of milliseconds. In this study, we present two cases of PsA (Pulsating Aurora) measurements in the 337 nm and 391 nm spectral lines. We demonstrate that during quiet geomagnetic conditions the ratio of night sky emissions in these bands is close to one and significantly increases during substorms. We propose and implement a special procedure for estimating this ratio. Our findings reveal that the intensity of emissions in both spectral lines correlates with the AL index of geomagnetic activity. However, the ratio between the emissions fluctuates around constant values over time and does not undergo significant changes throughout the entire PsA event, which can last for more than an hour.

Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet (UV) radiation in the nocturnal atmosphere of Earth from the International Space Station. Meteors are among multiple phenomena that manifest themselves not only in the visible range but also in the UV. We present two simple artificial neural networks that allow for recognizing meteor signals in the Mini-EUSO data with high accuracy in terms of a binary classification problem. We expect that similar architectures can be effectively used for signal recognition in other fluorescence telescopes, regardless of the nature of the signal. Due to their simplicity, the networks can be implemented in onboard electronics of future orbital or balloon experiments.

Mini-EUSO (Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory) is a telescope observing the Earth from the International Space Station since 2019. The instrument employs a Fresnel-lens optical system and a focal surface composed of 36 multi-anode photomultiplier tubes, 64 channels each, for a total of 2304 channels with single photon counting sensitivity. Mini-EUSO also contains two ancillary cameras to complement measurements in the near infrared and visible ranges. The scientific objectives of the mission range from the search for extensive air showers generated by Ultra-High Energy Cosmic Rays (UHECRs) with energies above 10$^{21}$ eV, the search for nuclearites and Strange Quark Matter (SQM), up to the study of atmospheric phenomena such as Transient Luminous Events (TLEs), meteors and meteoroids. Mini-EUSO can map the night-time Earth in the near UV range (between 290-430 nm) with a spatial resolution of about 6.3 km (full field of view of 44{\deg}) and a maximum temporal resolution of 2.5 $\mu$s, observing our planet through a nadir-facing UV-transparent window in the Russian Zvezda module. The detector saves triggered transient phenomena with a sampling rate of 2.5 $\mu$s and 320 $\mu$s, as well as continuous acquisition at 40.96 ms scale. In this paper we discuss the detector response and the flat-fielding and calibration procedures. Using the 40.96 ms data, we present $\simeq$6.3 km resolution night-time Earth maps in the UV band, and report on various emissions of anthropogenic and natural origin. We measure ionospheric airglow emissions of dark moonless nights over the sea and ground, studying the effect of clouds, moonlight, and artificial (towns, boats) lights. In addition to paving the way forward for the study of long-term variations of natural and artificial light, we also estimate the observation live-time of future UHECR detectors.