Related papers: Detrending Exoplanetary Transit Light Curves with …

High precision lightcurves combined with eclipse mapping techniques can reveal the horizontal and vertical structure of a planet's thermal emission and the dynamics of hot Jupiters. Someday, they even may reveal the surface maps of rocky…

Although transit spectroscopy is a powerful method for studying the composition, thermal properties and dynamics of exoplanet atmospheres, only a few transiting terrestrial exoplanets will be close enough to allow significant transit…

The emergence of Long Short-Term Memory (LSTM) solves the problems of vanishing gradient and exploding gradient in traditional Recurrent Neural Networks (RNN). LSTM, as a new type of RNN, has been widely used in various fields, such as text…

In the coming decades, research in extrasolar planets aims to advance two goals: 1) detecting and characterizing low-mass planets increasingly similar to the Earth, and 2) improving our understanding of planet formation. We present a new…

Precise and timely traffic flow prediction plays a critical role in developing intelligent transportation systems and has attracted considerable attention in recent decades. Despite the significant progress in this area brought by deep…

Planetary magnetic fields can affect the predicted mass loss rate for close-in planets that experience large amounts of UV irradiation. In this work, we present a method to detect the magnetic fields of close-in exoplanets undergoing…

Transmission spectroscopy can be used to constrain the properties of exoplanetary atmospheres. During a transit, the light blocked from the atmosphere of the planet leaves an imprint in the light coming from the star. This has been shown…

To prepare for the analyses of the future PLATO light curves, we develop a deep learning model, Panopticon, to detect transits in high precision photometric light curves. Since PLATO's main objective is the detection of temperate Earth-size…

A simple transit model is described along with tests of this model against published results for 4 exoplanet systems (Kepler-1, 2, 8, and 77). Data from the Kepler mission are used. The Markov Chain Monte Carlo (MCMC) method is applied to…

Accurate, precise, and computationally efficient removal of unwanted activity that exists as a combination of periodic, quasi-periodic, and non-periodic systematic trends in time-series photometric data is a critical step in exoplanet…

The transit timing variation technique (TTV) has been widely used to detect and characterize multiple planetary systems. Due to the observational biases imposed mainly by the photometric conditions and instrumentation and the high…

We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory…

Recurrent neural networks (RNNs) have many advantages over more traditional system identification techniques. They may be applied to linear and nonlinear systems, and they require fewer modeling assumptions. However, these neural network…

Transiting planets manifest themselves by a periodic dimming of their host star by a fixed amount. On the other hand, light curves of transiting circumbinary (CB) planets are expected to be neither periodic nor to have a single depth while…

Lyman-$\alpha$ transits have been detected from several nearby exoplanets and are one of our best insights into the atmospheric escape process. However, due to ISM absorption, we typically only observe the transit signature in the…

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~85% 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…

Context. Kepler observations have revealed a class of short period exoplanets, of which Kepler-1520 b is the prototype, which have comet-like dust tails thought to be the result of small, rocky planets losing mass. The shape and…

We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of identifying and removing problematic epochs in the light…

We introduce a new approach for disfluency detection using a Bidirectional Long-Short Term Memory neural network (BLSTM). In addition to the word sequence, the model takes as input pattern match features that were developed to reduce…

Traditionally, ground-based spectrophotometric observations probing transiting exoplanet atmospheres have employed a linear map between comparison and target star light curves (e.g. via differential spectrophotometry) to correct for…