Gaussian process regression for astronomical time series

S Aigrain, D Foreman-Mackey - Annual Review of Astronomy …, 2023 - annualreviews.org
The past two decades have seen a major expansion in the availability, size, and precision of
time-domain data sets in astronomy. Owing to their unique combination of flexibility …

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

M Botifoll, I Pinto-Huguet, J Arbiol - Nanoscale Horizons, 2022 - pubs.rsc.org
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

F Lanusse - Publications of the Astronomical Society of Australia, 2023 - cambridge.org
The amount and complexity of data delivered by modern galaxy surveys has been steadily
increasing over the past years. New facilities will soon provide imaging and spectra of …

Machine learning for observational cosmology

K Moriwaki, T Nishimichi… - Reports on Progress in …, 2023 - iopscience.iop.org
An array of large observational programs using ground-based and space-borne telescopes
is planned in the next decade. The forthcoming wide-field sky surveys are expected to …

First impressions: early-time classification of supernovae using host-galaxy information and shallow learning

A Gagliano, G Contardo… - The Astrophysical …, 2023 - iopscience.iop.org
Substantial effort has been devoted to the characterization of transient phenomena from
photometric information. Automated approaches to this problem have taken advantage of …

Searching for changing-state agns in massive data sets. i. applying deep learning and anomaly-detection techniques to find agns with anomalous variability behaviors

P Sánchez-Sáez, H Lira, L Martí… - The Astronomical …, 2021 - iopscience.iop.org
The classic classification scheme for active galactic nuclei (AGNs) was recently challenged
by the discovery of the so-called changing-state (changing-look) AGNs. The physical …

Parsnip: Generative models of transient light curves with physics-enabled deep learning

K Boone - The Astronomical Journal, 2021 - iopscience.iop.org
We present a novel method to produce empirical generative models of all kinds of
astronomical transients from data sets of unlabeled light curves. Our hybrid model, which we …

Deep attention-based supernovae classification of multiband light curves

Ó Pimentel, PA Estévez, F Förster - The Astronomical Journal, 2022 - iopscience.iop.org
In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are
relatively uncommon objects compared to other classes of variable events. Along with this …

The ROAD to discovery: Machine-learning-driven anomaly detection in radio astronomy spectrograms

M Mesarcik, AJ Boonstra, M Iacobelli… - Astronomy & …, 2023 - aanda.org
Context. As radio telescopes increase in sensitivity and flexibility, so do their complexity and
data rates. For this reason, automated system health management approaches are …

Inferencing Progenitor and Explosion Properties of Evolving Core-collapse Supernovae from Zwicky Transient Facility Light Curves

BM Subrayan, D Milisavljevic, TJ Moriya… - The Astrophysical …, 2023 - iopscience.iop.org
We analyze a sample of 45 Type II supernovae from the Zwicky Transient Facility public
survey using a grid of hydrodynamical models in order to assess whether theoretically …