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.

This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. While the GPFC method has broad applicability across period ranges, this research specifically focuses on detecting ultra-short-period planets with orbital periods less than one day. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves $97%$ training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers $100\%$ of known ultra-short-period planets in $\textit{Kepler}$ light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with $\textit{Kepler}$ and other space transit missions such as K2, TESS and future PLATO and Earth 2.0.

Since the discovery of the first hot Jupiter orbiting a solar-type star, 51 Peg, in 1995, more than 4000 exoplanets have been identified using various observational techniques. The formation process of these sub-Earths remains elusive, and acquiring additional samples is essential for investigating this unique population. In our study, we employ a novel GPU Phase Folding algorithm combined with a Convolutional Neural Network, termed the GPFC method, on Kepler photometry data. This method enhances the transit search speed significantly over the traditional Box-fitting Least Squares method, allowing a complete search of the known KOI photometry data within hours using a commercial GPU card. To date, we have identified five promising sub-Earth short-period candidates: K00446.c, K01821.b, K01522.c, K03404.b, and K04978.b. A closer analysis reveals the following characteristics: K00446.c orbits a K dwarf on a 0.645091-day period. With a radius of $0.461R_\oplus$, it ranks as the second smallest USP discovered to date. K01821.b is a sub-Earth with a radius of $0.648R_\oplus$, orbiting a G dwarf over a 0.91978-day period. It is the second smallest USP among all confirmed USPs orbiting G dwarfs in the NASA Archive. K01522.c has a radius of $0.704 R_\oplus$ and completes an orbit around a Sun-like G dwarf in 0.64672 days; K03404.b, with a radius of $0.738 R_\oplus$, orbits a G dwarf on a 0.68074-day period; and K04978.b, with its planetary radius of $0.912 R_\oplus$, orbits a G dwarf, completing an orbit every 0.94197 days. Three of our finds, K01821.b, K01522.c and K03404.b, rank as the smallest planets among all confirmed USPs orbiting G dwarfs in the Kepler dataset. The discovery of these small exoplanets underscores the promising capability of the GPFC method for searching for small, new transiting exoplanets in photometry data from Kepler, TESS, and future space transit missions.

Most existing exoplanets are discovered using validation techniques rather than being confirmed by complementary observations. These techniques generate a score that is typically the probability of the transit signal being an exoplanet (y(x)=exoplanet) given some information related to that signal (represented by x). Except for the validation technique in Rowe et al. (2014) that uses multiplicity information to generate these probability scores, the existing validation techniques ignore the multiplicity boost information. In this work, we introduce a framework with the following premise: given an existing transit signal vetter (classifier), improve its performance using multiplicity information. We apply this framework to several existing classifiers, which include vespa (Morton et al. 2016), Robovetter (Coughlin et al. 2017), AstroNet (Shallue & Vanderburg 2018), ExoNet (Ansdel et al. 2018), GPC and RFC (Armstrong et al. 2020), and ExoMiner (Valizadegan et al. 2022), to support our claim that this framework is able to improve the performance of a given classifier. We then use the proposed multiplicity boost framework for ExoMiner V1.2, which addresses some of the shortcomings of the original ExoMiner classifier (Valizadegan et al. 2022), and validate 69 new exoplanets for systems with multiple KOIs from the Kepler catalog.

Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematically. In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNN), have been used to classify transit signals of candidate exoplanets automatically. However, CNNs have some drawbacks; for example, they require many layers to capture dependencies on sequential data, such as light curves, making the network so large that it eventually becomes impractical. The self-attention mechanism is a DL technique that attempts to mimic the action of selectively focusing on some relevant things while ignoring others. Models, such as the Transformer architecture, were recently proposed for sequential data with successful results. Based on these successful models, we present a new architecture for the automatic classification of transit signals. Our proposed architecture is designed to capture the most significant features of a transit signal and stellar parameters through the self-attention mechanism. In addition to model prediction, we take advantage of attention map inspection, obtaining a more interpretable DL approach. Thus, we can identify the relevance of each element to differentiate a transit signal from false positives, simplifying the manual examination of candidates. We show that our architecture achieves competitive results concerning the CNNs applied for recognizing exoplanetary transit signals in data from the TESS telescope. Based on these results, we demonstrate that applying this state-of-the-art DL model to light curves can be a powerful technique for transit signal detection while offering a level of interpretability.

This summer I was invited to take part in the 2018 NASA Frontier Development Lab, along with a small team including Michele Sasdelli (University of Adelaide), and a pair of planetary scientists, Megan Ansdel (University of California at Berkeley) and Hugh Osborn (Laboratoire d'Astrophysique de Marseille). Our team composed of both machine learning and planetary scientists, was challenged over the course of 8 weeks to combine our expert knowledge in order to improve the methods behind one of the most exciting frontiers of science: exoplanet discovery. Here I discuss some of the challenges of applying machine learning to real-world scientific data, in particular noisy and sparse periodic time-series data. Our knowledge of exoplanets, or planets that exist outside our Solar System, has advanced drastically over the last few decades. In fact, until relatively recently one could have called exoplanets a theoretical concept.