English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Numerous spectra can be obtained from sky surveys such as the Sloan Digital Sky Survey and the Large Sky Area Multi-Object Fibre Spectroscopic Telescope. However, a considerable fraction of such spectra, which are also valuable for astronomical research, are of low quality, possessing characteristics such as low signal-to-noise ratio (low-S/N). Principal component analysis is widely used to process these low-S/N spectra, but it is not efficient enough to describe the non-linear properties within the spectra. Wavelets are often used to denoise the low-S/N spectra. However, as is well known, the most optimal wavelet basis for each type of spectra needs to be determined; therefore, wavelet analysis is very difficult to use in practice. Restricted Boltzmann machine is a non-linear algorithm that performs poorly when applied to low-S/N spectra. Denoising Convolutional Neural Networks (DnCNN) is a promising denoiser, however, its performance is unsatisfactory due to the lack of suitable noise model. To better exploit the spectra with low-S/N, we propose a new method that can be used to obtain better denoised spectra when compared to those obtained using other methods. A new method called the Spectra Generative Adversarial Nets (Spectra-GANs) is introduced. Spectra-GANs is simply a feedforward neural network that learns the difference between the input vector and the target by minimizing the loss function. It can be used in spectral denoising. The performance of Spectra-GANs is better than those of other methods with regard to denoising the spectra, especially with regard to extremely low-S/N spectral processing. Thus, Spectra-GANs proposed herein is a suitable alternative to previously used methods in spectral denoising.

Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra