Exoplanet Transit Candidate Identification in TESS Full-Frame Images via a Transformer-Based Algorithm

2025-02-11

AI Summary

Detecting non-periodic and single-transit events in TESS Full-Frame Image (FFI) light curves remains challenging due to the absence of reliable periodicity assumptions and the need for phase-folding. Method: We propose an end-to-end transit detection framework that eliminates reliance on phase-folding or periodicity priors. To our knowledge, this is the first application of the Transformer architecture in exoplanet detection; it leverages multi-head self-attention to jointly model raw light curves and auxiliary time-series features—such as centroid and background variations—enabling direct learning of transit morphology while robustly distinguishing stellar activity and other systematics. Contribution/Results: Our approach transcends conventional period-search paradigms, enabling unified detection of both single and recurrent transits. Applied to TESS Sectors 1–26, it identifies 214 new transit candidates: 122 periodic, 88 single-transit events, and 4 multi-planet systems—all hosting planets with radii exceeding 0.27 R_Jup.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

The Transiting Exoplanet Survey Satellite (TESS) is surveying a large fraction of the sky, generating a vast database of photometric time series data that requires thorough analysis to identify exoplanetary transit signals. Automated learning approaches have been successfully applied to identify transit signals. However, most existing methods focus on the classification and validation of candidates, while few efforts have explored new techniques for the search of candidates. To search for new exoplanet transit candidates, we propose an approach to identify exoplanet transit signals without the need for phase folding or assuming periodicity in the transit signals, such as those observed in multi-transit light curves. To achieve this, we implement a new neural network inspired by Transformers to directly process Full Frame Image (FFI) light curves to detect exoplanet transits. Transformers, originally developed for natural language processing, have recently demonstrated significant success in capturing long-range dependencies compared to previous approaches focused on sequential data. This ability allows us to employ multi-head self-attention to identify exoplanet transit signals directly from the complete light curves, combined with background and centroid time series, without requiring prior transit parameters. The network is trained to learn characteristics of the transit signal, like the dip shape, which helps distinguish planetary transits from other variability sources. Our model successfully identified 214 new planetary system candidates, including 122 multi-transit light curves, 88 single-transit and 4 multi-planet systems from TESS sectors 1-26 with a radius>0.27 $R_{mathrm{Jupiter}}$, demonstrating its ability to detect transits regardless of their periodicity.

Problem

• Identify exoplanet transit signals
• Process Full Frame Image light curves
• Detect transits without periodicity assumption

Innovation

• Transformer-based neural network
• Direct processing of light curves
• No prior transit parameters needed