Apr 28, 2020 · We present a successful deep learning approach that learns directly from imaging data. Our method models explicitly the spatio-temporal patterns ...
We train these deep neural networks using 1.3 million real astronomical images from the Catalina Real-Time Transient Survey to classify the sequences into five ...
Oct 2, 2020 · Our approach leverages both the spatial and temporal information to improve the classification of nearly 7000 image sequence into different ...
The TAO-Net (for Transient Astronomical Objects Network) architecture outperforms the results from random forest classification on light curves by 10 ...
We train these deep neural networks using 1.3 million real astronomical images from the Catalina Real-Time Transient Survey to classify the sequences into five ...
We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of ...
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks · Physics, Computer Science. Monthly Notices of the Royal Astronomical Society.
May 23, 2024 · In this work, we propose a deep learning-based classification model of astronomical objects using alerts reported by the Zwicky Transient Facility (ZTF) survey.
Astronomy is currently facing new challenges because of the large amount and high rate of data being produced by large CCD cameras.
In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ...