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Time series foundation models (TSFMs) are increasingly being adopted as highly-capable general-purpose time series representation learners. Although their training corpora are vast, they exclude astronomical time series data. Observations…

Robust fast methods to classify variable light curves in large sky surveys are becoming increasingly important. While it is relatively straightforward to identify common periodic stars and particular transient events (supernovae, novae,…

In the present era of large scale surveys, big data presents new challenges to the discovery process for anomalous data. Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly,…

Asteroseismology has grown from its beginnings three decades ago to a mature field teeming with discoveries and applications. This phenomenal growth has been enabled by space photometry with precision $10-100$ times better than ground-based…

In this paper, we study stellar light curves from the TESS satellite (Transiting Exoplanet Survey Satellite) for the presence of stellar flares. The main aim is to detect stellar flares using two-minutes cadence data and to perform…

With the advent of dedicated photometric space missions, the ability to rapidly process huge catalogues of stars has become paramount. Bellinger and Angelou et al. (2016) recently introduced a new method based on machine learning for…

The Kepler space telescope has revolutionised our knowledge about exoplanets and stars and is continuing to do so in the K2 mission. The exquisite photometric precision, together with the long, uninterrupted observations opened up a new way…

The Gaia mission has delivered hundreds of thousands of variable star light curves in multiple wavelengths. Recent work demonstrates that these light curves can be used to identify (non-)radial pulsations in the OBAF-type stars, despite the…

The large amounts of astrophysical data being provided by existing and future instrumentation require efficient and fast analysis tools. Transfer learning is a new technique promising higher accuracy in the derived data products, with…

Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods…

It has been demonstrated that the time variability of a star's brightness at different frequencies can be used to infer its surface gravity, radius, mass, and age. With large samples of light curves now available from Kepler and K2, and…

During its two year prime mission the Transiting Exoplanet Survey Satellite (TESS) will perform a time-series photometric survey covering over 80% of the sky. This survey comprises observations of 26 24 x 96 degree sectors that are each…

Future surveys such as the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will observe an order of magnitude more astrophysical transient events than any previous survey before. With this deluge of photometric data,…

Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy. Some of these transients are the explosive deaths of…

During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…

Space-based missions such as Kepler, and soon TESS, provide large datasets that must be analyzed efficiently and systematically. Recent work by Shallue & Vanderburg (2018) successfully used state-of-the-art deep learning models to…

Foundational models have emerged as a powerful paradigm in deep learning field, leveraging their capacity to learn robust representations from large-scale datasets and effectively to diverse downstream applications such as classification.…

Precise measurements of stellar parameters are required in order to develop our theoretical understanding of stellar structure. These measurements enable errors and uncertainties to be quantified in theoretical models and constrain the…

Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the…

We present an update on the variable star survey performed on the TESS 30 min Full Frame Image (FFI) data reported by our first two papers in this series. This update includes a contamination analysis in order to identify false positives…