Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder

Convolutional Deep Denoising Autoencoders for Radio Astronomical Images

We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthetic images of state-of-the-art radio telescopes, with the goal of detecting the faint, diffused radio sources predicted to characterise the radio cosmic web. In our application, denoising is intended to address both the reduction of random instrumental noise and the minimisation of additional spurious artefacts like the sidelobes, resulting from the aperture synthesis technique. The effectiveness and the accuracy of the method are analysed for different kinds of corrupted input images, together …

Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder

Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the gravitational-wave signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals in the publicly available LIGO O1 time series strain d…

Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders

Gravitational wave astronomy is a rapidly growing field of modern astrophysics, with observations being made frequently by the LIGO detectors. Gravitational wave signals are often extremely weak and the data from the detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary noise, often containing transient disturbances which can obscure real signals. Traditional denoising methods, such as principal component analysis and dictionary learning, are not optimal for dealing with this non-Gaussian noise, especially for low signal-to-noise ratio gravitational wave signals. Furthe…

Unsupervised feature-learning for galaxy SEDs with denoising autoencoders

With the increasing number of deep multi-wavelength galaxy surveys, the spectral energy distribution (SED) of galaxies has become an invaluable tool for studying the formation of their structures and their evolution. In this context, standard analysis relies on simple spectro-photometric selection criteria based on a few SED colors. If this fully supervised classification already yielded clear achievements, it is not optimal to extract relevant information from the data. In this article, we propose to employ very recent advances in machine learning, and more precisely in feature learning, to d…

利用U-Net变分自编码器进行图像去噪以增强星系分类

Enhancing Galaxy Classification with U-Net Variational Autoencoders for Image Denoising

本研究利用U-Net变分自编码器对星系图像去噪，在EFIGI数据集上训练，通过PSNR和SSIM评估，并用于多种分类模型，显著提升分类性能，可推广至其他天文应用。

Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method

When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth. However, this assumption can be seriously violated in practice since the unresolved or mis-subtracted foreground sources, which are further complicated by the frequency-dependent beam effects of interferometers, will generate significant fluctuations along the frequency dimension. To address this issue, we propose a novel deep-learning-based method that uses a 9-layer convolutional denoising autoencoder (CDAE) to separate t…