Related papers: A Search for Exoplanet Candidates in TESS 2-min Li…
The treatment of systematic noise is a significant aspect of transit exoplanet data processing due to the signal strength of systematic noise relative to a transit signal. Typically the standard approach to transit detection is to estimate…
The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~75% of the sky throughout its two year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for…
In this work, we explore several ways to detect possible exocomet transits in the TESS (The Transiting Exoplanet Survey Satellite) light curves. The first one has been presented in our previous work, a machine learning approach based on the…
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…
Precise physical properties of the known transiting exoplanets are essential for their precise atmospheric characterization using modern and upcoming instruments. Leveraging the large volume of high SNR photometric follow-up data from TESS,…
Transiting exoplanets in multi-planet systems exhibit non-Keplerian orbits as a result of the gravitational influence from companions which can cause the times and durations of transits to vary. The amplitude and periodicity of the transit…
The TESS follow-up of a large number of known transiting exoplanets provide unique opportunity to study their physical properties more precisely. Being a space-based telescope, the TESS observations are devoid of any noise component…
The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep learning techniques such as neural networks have proved effective at differentiating…
Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well proven object…
This study introduces an approach to detecting exocomet transits in the dataset of the Transiting Exoplanet Survey Satellite (TESS), specifically within its Sector 1. Given the limited number of exocomet transits detected in the observed…
Continuous data releases throughout the TESS primary mission will provide unique opportunities for the exoplanet community at large to contribute to maximizing TESS's scientific return via the discovery and validation of transiting planets.…
Context: The TESS team periodically issues a new list of transiting exoplanet candidates based on the analysis of the accumulating light curves obtained by the satellite. The list includes the estimated epochs, periods, and durations of the…
Photometric surveys such as Kepler have the precision to identify exoplanet and eclipsing binary candidates from only a single transit. K2, with its 75d campaign duration, is ideally suited to detect significant numbers of single-eclipsing…
We present a study of the detection efficiency for the TESS mission, focusing on the yield of longer-period transiting exoplanets ($P > 25$ days). We created the Transit Investigation and Recoverability Application (TIaRA) pipeline to use…
The Transiting Exoplanet Survey Satellite (TESS) will observe $\sim$150~million stars brighter than $T_{\rm mag} \approx 16$, with photometric precision from 60~ppm to 3~percent, enabling an array of exoplanet and stellar astrophysics…
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…
The Transiting Exoplanet Survey Satellite (TESS) is currently concluding its 2-year primary science mission searching 85% of the sky for transiting exoplanets. TESS has already discovered well over one thousand TESS objects of interest…
The discovery of circumbinary planets (CBPs) has advanced our understanding of planet formation and dynamical evolution in complex environments. However, the population of such planets remains small, leading their underlying physical…
We present ExoMiner++, an enhanced deep learning model that builds on the success of ExoMiner to improve transit signal classification in 2-minute TESS data. ExoMiner++ incorporates additional diagnostic inputs, including periodogram, flux…
We examine the ability of the Transiting Exoplanet Survey Satellite (TESS) to detect and improve our understanding of planetary systems in the Kepler field. By modeling the expected transits of all confirmed and candidate planets detected…