Machine learning in astronomy: A practical overview
D Baron - arXiv preprint arXiv:1904.07248, 2019 - arxiv.org
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 …
the development of data-driven science as a useful companion to the common model-driven …
A review on interpretable and explainable artificial intelligence in hydroclimatic applications
This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable
Artificial Intelligence (XAI) models for data imputations and numerical or categorical …
Artificial Intelligence (XAI) models for data imputations and numerical or categorical …
Probabilistic random forest: A machine learning algorithm for noisy data sets
Abstract Machine learning (ML) algorithms have become increasingly important in the
analysis of astronomical data. However, because most ML algorithms are not designed to …
analysis of astronomical data. However, because most ML algorithms are not designed to …
Groundwater prediction using machine-learning tools
Predicting groundwater availability is important to water sustainability and drought
mitigation. Machine-learning tools have the potential to improve groundwater prediction …
mitigation. Machine-learning tools have the potential to improve groundwater prediction …
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
Galaxy morphology is a fundamental quantity, which is essential not only for the full
spectrum of galaxy-evolution studies, but also for a plethora of science in observational …
spectrum of galaxy-evolution studies, but also for a plethora of science in observational …
Sustainable groundwater management using stacked LSTM with deep neural network
Groundwater is a vital water resource and plays a major role in human life, production,
irrigation, and development of the country based on economically. Due to irregular rainfall …
irrigation, and development of the country based on economically. Due to irregular rainfall …
Improving the reliability of photometric redshift with machine learning
In order to answer the open questions of modern cosmology and galaxy evolution theory,
robust algorithms for calculating photometric redshifts (photo-z) for very large samples of …
robust algorithms for calculating photometric redshifts (photo-z) for very large samples of …
Kids-squad-ii. machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
V Khramtsov, A Sergeyev, C Spiniello… - Astronomy & …, 2019 - aanda.org
Context. The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) is aimed at
finding as many previously undiscovered gravitational lensed quasars as possible in the …
finding as many previously undiscovered gravitational lensed quasars as possible in the …
Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
SJ Nakoneczny, M Bilicki, A Pollo, M Asgari… - Astronomy & …, 2021 - aanda.org
We present a catalog of quasars with their corresponding redshifts derived from the
photometric Kilo-Degree Survey (KiDS) Data Release 4. We achieved it by training machine …
photometric Kilo-Degree Survey (KiDS) Data Release 4. We achieved it by training machine …
Photometric redshifts from SDSS images with an interpretable deep capsule network
Studies of cosmology, galaxy evolution, and astronomical transients with current and next-
generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space …
generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space …