Detecting stellar occultations using machine-learning techniques and smart telescopes powered by citizen scientists

We present an overview of the performance and improvements of the Occultation Detection neural Network framework: ODNet, a convolutional neural network for asteroid occultation detection (Cazeneuve+ 2023). ODNet is the backbone of the occultation detection program of the Unistellar network; an expanding network of 15,000+ smart telescopes distributed around the world that allows citizen scientists to join various observational campaigns and provide invaluable data for multitude of scientific projects.

The original ODNet was built using the TensorFlow architecture and was trained on mostly synthetic data. The purpose of ODNet is to provide an independent and reproducible measure of the likelihood of the occultation event in the observed data set (a set of individual image frames), remove any influence of human analysis, and to allow a fully autonomous detection of occultations in all Unistellar observations in the occultations campaign.

The original results were promising. ODNet was able to analyze raw data from occultation events in minutes with very high precision (91%) and recall (87%). This enabled a completely automated pipeline that is able to handle multiple occultation events per day from multiple locations and observers. However, as more observations were made and different observing configurations, conditions, and telescope models appeared the efficiency and precision of the original model was not satisfactory.

Here, we present improvements to the original ODNet framework by increasing its precision and recall to >95%, ability to handle very short occultation events (<4 frames / 1 second), deal with more diverse observation conditions, and able to deal with datasets from various telescopes. We improved identification of false positives and were able to recover a number of short occultation events in the archival data. We also added a fully automated light curve analysis that provides additional information about the nature of the observation and significance of the event.

Ultimately, we show our latest advancements in our search for a lightweight solution that is based on a YOLOv8 framework (You-only-look-once) that would allow the observer to analyze both predicted and serendipitous occultation events in real time while using processing units on modern mobile devices.