Software solution for detecting asteroids using machine learning techniques

Abstract

To accurately predict potential future impacts with the Earth, it is crucial to continuously examine the area around it for Near Earth Objects (NEOs) and particularly Near Earth Asteroids (NEAs). Large data sets of astronomical images must be analyzed in order to accomplish this task. NEARBY [1] offers such a processing and analysis platform based on Cloud computing. Despite the fact that this method is automated, the results are validated by human observers after potential asteroids have been identified from the raw data. It is crucial that the amount of candidate objects does not outweigh the available human resources. We believe we can maximize the advantages of having access to enormous amounts of data in the field of astronomy by combining artificial intelligence with the use of high-performance distributed processing infrastructures like Cloud-based solutions. This research is carried out as part of the CERES project which aims to design and put into practice a software solution that can classify objects found in astronomical images. The objective is to identify and recognize asteroids. We use machine learning techniques to develop an asteroid classification model in order to achieve this goal. It is essential to reduce the number of false negative findings. The major objective of the current paper is to assess how well deep CNNs perform when it comes to categorizing astronomical objects, particularly asteroids. We will compare the outcomes of several of the most well-known deep convolutional neural networks (CNNs), including InceptionV3, Xception, InceptionResNetV2, and ResNet152V2. These cutting-edge classification CNNs are used to investigate the best approach to this specific classification challenge, either through full-training or through fine-tuning.Acknowledgment: This work was partially 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. This research was partially supported by the project 38 PFE in the frame of the programme PDI-PFE-CDI 2021.