TRANSCENDENCE - A TRANSit CaptureENgine for DEtection and Neural networkCharacterization of Exoplanets

The discovery and characterization of exoplanets has become a central goal of modern astronomy. While space-based missions such as Kepler, K2, and TESS have revolutionized the search for transiting exoplanets by producing vast amounts of stellar photometric data, the process of identifying and confirming planetary transits within these light curves remains a time-intensive task, still heavily reliant on manual inspection and traditional search algorithms. These conventional methods, such as Box Least Squares (BLS), tend to struggle with noisy signals, low signal-to-noise ratios, and sparse transit events, resulting in a significant number of missed detections and false positives.

To address these limitations, we present TRANSCENDENCE — a machine learning-based pipeline designed to automate and enhance both the detection and characterization of exoplanetary transits. At the core of our approach is a hybrid architecture combining convolutional neural networks (CNNs) with recurrent layers (LSTMs), trained using supervised learning techniques on light curves augmented with synthetic transits. This architecture allows for both denoising and classification of transit-like signals, effectively distinguishing them from instrumental noise and stellar variability.

A crucial aspect of the pipeline's development is the preparation of a robust training dataset. Because real transit events are rare compared to the total volume of light curve data, we enhance our dataset by injecting simulated planetary transits into real TESS light curves. This procedure involves generating synthetic transit signals based on realistic astrophysical parameters, including limb darkening effects modeled with a quadratic law and parameterized using stellar characteristics from the TESS Input Catalog. The simulated transits are produced using the \texttt{batman} library and injected at random phases into real photometric data, preserving the noise characteristics of the original observations. This ensures that the resulting dataset maintains high astrophysical realism while achieving a roughly balanced distribution of transit and non-transit examples, which is essential for effective model training.

In total, we use light curves from approximately 450,000 stars across 30 TESS sectors, selected based on the availability and quality of stellar parameters and limb darkening coefficients. To further enhance model robustness, the injected planetary parameters are sampled from empirically informed distributions, with a deliberate skew toward smaller planets (e.g., 50% of injected planets have radii between 0.5 and 4 Earth radii). This encourages the model to improve its sensitivity to shallow transit signals that are commonly overlooked by traditional methods.

TRANSCENDENCE demonstrates strong performance in both detection and characterization tasks. It consistently identifies planets larger than 2 Earth radii with high accuracy and maintains a low false positive rate between 5% and 10%. Smaller planets are also detected, albeit with reduced reliability, which is consistent with the intrinsic challenges posed by weak transit signals. Importantly, the pipeline operates with minimal manual intervention and offers significant computational efficiency, positioning it as a promising tool for large-scale, automated exoplanet discovery in current and future survey missions.