EXO-INSIGHT: An Explainable Multi-Modal Deep Learning Framework for Enhanced Exoplanet Detection

Because of the rapid increase in astronomical ob-servation missions, a huge amount of data is generated, which should be analyzed to detect exoplanets that may be present in the data. Space-based telescopes such as the Kepler mission and the Transiting Exoplanet Survey Satellite (TESS) have generated large amounts of data in the form of photometric light curves and astronomical data [1], [2]. The current study introduces an explainable multi-modal deep learning framework named EXO-INSIGHT to improve exoplanet detection by analyzing temporal and spatial astronomical data. The framework analyzes the changes in the brightness of stars from light curve sequences using temporal learning models, whereas it also analyzes astro-nomical images to extract features using CNNs. The features from these two modalities are combined using a feature fusion mechanism to improve the reliability of the exoplanet detection process. Moreover, the framework also includes explainability techniques to highlight the relevant region of images and relevant segments of light curves to provide insights into the detection process [13]. The framework is implemented in the form of a web-based platform to provide researchers with a facility to upload their data and obtain results from the framework with explanations for the prediction results provided by the framework. The framework is expected to provide reliable results for analyzing large amounts of astronomical data for exoplanet detection.