Abstract Machine learning (automated processes that learn by example in order to classify,
predict, discover, or generate new data) and artificial intelligence (methods by which a …

Astronomy is experiencing a rapid growth in data size and complexity. This change fosters
the development of data-driven science as a useful companion to the common model-driven …

Abstract The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey
to routinely monitor the whole sky with a cadence of∼ 2–3 d down to V≲ 17 mag. ASAS-SN …

The variable stars in the VSX catalogue are derived from a multitude of inhomogeneous
data sources and classification tools. This inhomogeneity complicates our understanding of …

ABSTRACT We present EPIC Variability Extraction and Removal for Exoplanet Science
Targets (EVEREST), an open-source pipeline for removing instrumental noise from K2 light …

We present k2sc (K2 Systematics Correction), a python pipeline to model instrumental
systematics and astrophysical variability in light curves from the K2 mission. k2sc uses …

We present an update to the EVEREST K2 pipeline that addresses various limitations in the
previous version and improves the photometric precision of the light curves. We develop a …

We present the first near all-sky yield of oscillating red giants from the prime mission data of
NASA's Transiting Exoplanet Survey Satellite (TESS). We apply machine learning toward …

Context. Rotation period measurements of stars observed with the Kepler mission have
revealed a lack of stars at intermediate rotation periods, accompanied by a decrease of …

We present 197 planet candidates discovered using data from the first year of the NASA K2
mission (Campaigns 0–4), along with the results of an intensive program of photometric …