To aid the reproducibility of the results in the paper “Classification of Periodic Variables with Cyclic-Permutation Invariant Neural Networks,” we make our aggregation of the following data available. Code used to load the data and generate the results can be found at https://github.com/kmzzhang/periodicnetwork. These datasets have been constructed from publicly available data sources. If you use these datasets, please cite the original papers [1, 2, 3], in addition to ours [TBD]. Others might find this data useful for testing time-series inference techniques. [1] Jayasinghe, T. et al. The ASAS-SN catalogue of variable stars I: The Serendipitous Survey. Monthly Notices of the Royal Astronomical Society 477, 3145–3163 (2018). URL https://a...
Contains fulltext : 35108.pdf (preprint version ) (Open Access) ...
We present an automated classification of stars exhibiting periodic, non-periodic and irregular ligh...
We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. Thes...
To aid the reproducibility of the results in the paper “Improved Classification of Variable Stars wi...
We present a machine learning package for the classification of periodic variable stars. Our package...
Despite the utility of neural networks (NNs) for astronomical time-series classification, the prolif...
Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field ...
Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus...
The EPOCH (EROS-2 periodic variable star classification using machine learning) project ai...
With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can q...
International audienceThe EPOCH (EROS-2 periodic variable star classification using machine learning...
Owing to the current and upcoming extensive surveys studying the stellar variability, accurate and q...
The EPOCH (EROS-2 periodic variable star classification using machine learning) project aims to dete...
Variable stars play a prominent role in our study of the universe and are essential to estimating co...
Context.The fast classification of new variable stars is an important step in making them available ...
Contains fulltext : 35108.pdf (preprint version ) (Open Access) ...
We present an automated classification of stars exhibiting periodic, non-periodic and irregular ligh...
We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. Thes...
To aid the reproducibility of the results in the paper “Improved Classification of Variable Stars wi...
We present a machine learning package for the classification of periodic variable stars. Our package...
Despite the utility of neural networks (NNs) for astronomical time-series classification, the prolif...
Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field ...
Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus...
The EPOCH (EROS-2 periodic variable star classification using machine learning) project ai...
With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can q...
International audienceThe EPOCH (EROS-2 periodic variable star classification using machine learning...
Owing to the current and upcoming extensive surveys studying the stellar variability, accurate and q...
The EPOCH (EROS-2 periodic variable star classification using machine learning) project aims to dete...
Variable stars play a prominent role in our study of the universe and are essential to estimating co...
Context.The fast classification of new variable stars is an important step in making them available ...
Contains fulltext : 35108.pdf (preprint version ) (Open Access) ...
We present an automated classification of stars exhibiting periodic, non-periodic and irregular ligh...
We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. Thes...