Related papers: Advances in Deep Space Exploration via Simulators …
Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to automate the…
In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify.…
Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the…
Since the start of the Wide Angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by…
The detection of exoplanets with the radial velocity method consists in detecting variations of the stellar velocity caused by an unseen sub-stellar companion. Instrumental errors, irregular time sampling, and different noise sources…
Two new interplanetary technologies have advanced in the past decade to the point where they may enable exciting, affordable missions that reach further and faster deep into the outer regions of our solar system: (i) small and capable…
Compared to bright star searches, surveys for transiting planets against fainter (V=12-18) stars have the advantage of much higher sky densities of dwarf star primaries, which afford easier detection of small transiting bodies. Furthermore,…
Strong variations of any kind and causes within a stellar light curve may prohibit the detection of transits, particularly of faint or shallow transits caused by small planets passing in front of the stellar disk. The success of future…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
Traditionally, a simulation of a dense stellar system required choosing an initial model, running an integrator, and analyzing the output. Almost all of the effort went into writing a clever integrator that could handle binaries, triples…
We present a method that enables wide field ground-based telescopes to scan the sky for sub-second stellar variability. The method has operational and image processing components. The operational component is to take star trail images. Each…
We present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real-time identify the host galaxies of extragalactic transients. The proposed algorithm receives as input…
The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a…
Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a space device, e.g. a satellite, is becoming more accessible to…
Long range observations in the field of astronomy have opened up our understanding of the Solar System, the Galaxy and the wider Universe. In this paper we discuss the idea of direct in-situ reconnaissance of nearby stellar systems, using…
Deep learning tasks are often complicated and require a variety of components working together efficiently to perform well. Due to the often large scale of these tasks, there is a necessity to iterate quickly in order to attempt a variety…
Our ability to explore the cosmos by direct contact has been limited to a small number of lunar and interplanetary missions. However, the NASA Starlight program points a path forward to send small, relativistic spacecraft far outside our…
Ultra-faint dwarf galaxies, which can be detected as resolved satellite systems of the Milky Way, are critical to understanding galaxy formation, evolution, and the nature of dark matter, as they are the oldest, smallest, most metal-poor,…
The WFIRST microlensing mission will measure precise light curves and relative parallaxes for millions of stars, giving it the potential to characterize short-period transiting planets all along the line of sight and into the galactic…
Due to poor observational constraints on the low-mass end of the subhalo mass function, the detection of dark matter (DM) subhalos on sub-galactic scales would provide valuable information about the nature of DM. Stellar wakes, induced by…