Enhancement of the NEARBY automated asteroid detection platform with a machine learning-based classifier

The detection of asteroids involves the processing of sequences of astronomical images. The main challenges arise from the huge volume of data that should be processed in a reasonable amount of time. To address this, we developed the NEARBY platform [1], [2] for efficiently automatic detection of asteroids in sequence of astronomical images. This platform encompasses multidimensional data processing capabilities, human-verified visual analysis, and cloud-based adaptability. This paper outlines the enhancements we have made to this automated asteroid detection system by integrating a machine learning-based classifier known as the CERES module. The integration of the CERES module [3] into the NEARBY platform substantially enhances its performance by automatically reducing the number of false positive detections. Consequently, this leads to a more reliable and efficient system for asteroid identification, while also reducing the time and effort required by human experts to validate detected candidates (asteroids). The experiments highlight these improvements and their significance in advancing the field of asteroid tracking. Additionally, we explore the applicability of the asteroid classification model, initially trained using images from a specific telescope, across different telescopes.

Acknowledgment:

• This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-0796, within PNCDI III. (the development of the dataset and CNN models)
• This research was partially supported by the project 38 PFE in the frame of the programme PDI-PFE-CDI 2021.

References:

• Bacu, V., Sabou, A., Stefanut, T., Gorgan, D., Vaduvescu, O., NEARBY platform for detecting asteroids in astronomical images using cloud-based containerized applications, 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 371-376
• Stefanut, T., Bacu, V., Nandra, C., Balasz, D., Gorgan, D., Vaduvescu, O., NEARBY Platform: Algorithm for automated asteroids detection in astronomical images, 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 365-369
• Bacu, V.; Nandra, C.; Sabou, A.; Stefanut, T.; Gorgan, D. Assessment of Asteroid Classification Using Deep Convolutional Neural Networks. Aerospace 2023, 10, 752. https://doi.org/10.3390/aerospace10090752