Related papers: From stellar light to astrophysical insight: autom…
The advent of next-generation radio telescopes is set to transform radio astronomy by producing massive data volumes that challenge traditional processing methods. Deep learning techniques have shown strong potential in automating radio…
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread…
The disciplines of asteroseismology and extrasolar planet science overlap methodically in the branch of high-precision photometric time series observations. Light curves are, amongst others, useful to measure intrinsic stellar variability…
Satellite data has the potential to inspire a seismic shift for machine learning -- one in which we rethink existing practices designed for traditional data modalities. As machine learning for satellite data (SatML) gains traction for its…
Since its emergence two decades ago, astrophotonics has found broad application in scientific instruments at many institutions worldwide. The case for astrophotonics becomes more compelling as telescopes push for AO-assisted,…
As we enter the final year of the second extended mission of the Transiting Exoplanet Survey Satellite (TESS), it is time to reflect on what the TESS mission has contributed to the advancement of astronomy. Thousands of papers based on TESS…
We introduce a new method of searching for and characterizing extra-solar planets. We show that by monitoring the center-of-light motion of microlensing alerts using the next generation of high precision astrometric instruments the…
Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most…
With the rapid advancements in observational technologies and the widespread implementation of large-scale sky surveys, diverse electromagnetic wave data (e.g., optical and infrared) and non-electromagnetic wave data (e.g., gravitational…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
It has recently been demonstrated that deep learning has significant potential to automate parts of the exoplanet detection pipeline using light curve data from satellites such as Kepler \cite{borucki2010kepler} \cite{koch2010kepler} and…
Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as…
With long data sets available for asteroseismology from space missions, it is sometimes necessary to deal with time series that have large gaps. This is becoming particularly relevant for TESS, which is revisiting many fields on the sky…
Astronomy produces extremely large data sets from ground-based telescopes, space missions, and simulation. The volume and complexity of these rich data sets require new approaches and advanced tools to understand the information contained…
The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the…
A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to…
We discuss how recent advances in observations, theory and numerical simulations have allowed the stellar community to progress in its understanding of stellar convection, rotation and magnetism and to assess the degree to which the Sun and…
Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated…
The launch of NASA's Kepler space telescope in 2009 revolutionized the quality and quantity of observational data available for asteroseismic analysis. While Kepler was able to detect solar-like oscillations in hundreds of main-sequence and…
After decades of efforts, optical long-baseline interferometry has become a mainstream observational technique in terms of operation robustness and user friendliness. Interferometry has opened a new observational window, enabling…