Trend Filtering -- II. Denoising Astronomical Signals with Varying Degrees of Smoothness
Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A. C. Croft, Larry Wasserman
备注：
Part 2 of 2, Link to Part 1: arXiv:1908.07151; 15 pages, 7 figures
发表信息：
Trend filtering -- II. Denoising astronomical signals with varying degrees of smoothness, Monthly Notices of the Royal Astronomical Society, Volume 492, Issue 3, March 2020, Pages 4019 - 4032
Trend filtering---first introduced into the astronomical literature in Paper I of this series---is a state-of-the-art statistical tool for denoising one-dimensional signals that possess varying degrees of smoothness. In this work, we demonstrate the broad utility of trend filtering to observational astronomy by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The observations we discuss are (1) the Lyman-$α$ forest of quasar spectra; (2) more general spectroscopy of quasars, galaxies, and stars; (3) stellar light curves with planetary transits; (4) eclipsing binary light curves; and (5) supernova light curves. We study the Lyman-$α$ forest in the greatest detail---using trend filtering to map the large-scale structure of the intergalactic medium along quasar-observer lines of sight. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra; and (2) constructing observational spectral/light-curve templates. We also briefly discuss the utility of trend filtering as a tool for one-dimensional data reduction and compression.
In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the workload of data processing. Developing automated tools, such as convolutional neural networks (CNN), for denoising has become a promising area of research. We investigate the feasibility of CNN-based self-supervised learning algorithms (e.g., Noise2Noise) for denoising astronomical images. We experimented with Noise2Noise on simulated noisy astronomical data. We evaluate the results based on the accuracy of recoverin…
Astronomical images suffer a constant presence of multiple defects that are consequences of the intrinsic properties of the acquisition equipments, and atmospheric conditions. One of the most frequent defects in astronomical imaging is the presence of additive noise which makes a denoising step mandatory before processing data. During the last decade, a particular modeling scheme, based on sparse representations, has drawn the attention of an ever growing community of researchers. Sparse representations offer a promising framework to many image and signal processing tasks, especially denoising…
The problem of denoising a one-dimensional signal possessing varying degrees of smoothness is ubiquitous in time-domain astronomy and astronomical spectroscopy. For example, in the time domain, an astronomical object may exhibit a smoothly varying intensity that is occasionally interrupted by abrupt dips or spikes. Likewise, in the spectroscopic setting, a noiseless spectrum typically contains intervals of relative smoothness mixed with localized higher frequency components such as emission peaks and absorption lines. In this work, we present trend filtering, a modern nonparametric statistical…
Istituto di Radio Astronomia, INAF（意大利国家天体物理研究所射电天文研究所）；Hamburger Sternwarte（汉堡天文台）；Universitá di Bologna（博洛尼亚大学）
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 …
Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China（自动化系，北京信息科学与技术国家研究中心，清华大学，北京，中国）；Department of Astronomy, Tsinghua University, Beijing, China（天文系，清华大学，北京，中国）；Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China（脑与认知科学研究院，清华大学，北京，中国）；Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo, Kashiwa, Japan（宇宙物理与数学科学研究所（WPI），东京大学，日光，日本）；National Astronomical Observatory of Japan, Tokyo, Japan（日本国家天文台，东京，日本）；Institute for Cosmic Ray Research, The University of Tokyo, Kashiwa, Japan（宇宙射线研究所，东京大学，日光，日本）；College of AI, Tsinghua University, Beijing, China（人工智能学院，清华大学，北京，中国）；School of Mathematics and Physics, Qinghai University, Xining, China（数学与物理学院，青海大学，西宁，中国）；Tsinghua Wuxi Institute of Applied Technology, Wuxi, China（清华大学无锡应用技术研究院，无锡，中国）
maria goes NIFTy: Gaussian Process-Based Reconstruction and Denoising of Simulated (Sub-)Millimetre Single-Dish Telescope Data
Technical University of Munich（慕尼黑工业大学）；Excellence Cluster ORIGINS（ORIGINS卓越集群）；Leiden Observatory, Leiden University（莱顿大学莱顿天文台）；Yale University（耶鲁大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；European Southern Observatory（欧洲南方天文台）；Institute of Space Sciences (ICE, CSIC)（空间科学研究所（ICE，CSIC））
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
National Research Council Herzberg Astronomy an Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada（国家研究委员会赫兹伯格天文学与天体物理学研究中心，5071号西萨安奇路，维多利亚，BC V9E 2E7，加拿大）；Department of Computer Science, , University of British Columbia, Vancouver, BC, V6T 1Z4, Canada（计算机科学系，不列颠哥伦比亚大学，温哥华，BC，V6T 1Z4，加拿大）
Astrophysical light curves are particularly challenging data objects due to the intensity and variety of noise contaminating them. Yet, despite the astronomical volumes of light curves available, the majority of algorithms used to process them are still operating on a per-sample basis. To remedy this, we propose a simple Transformer model -- called Denoising Time Series Transformer (DTST) -- and show that it excels at removing the noise and outliers in datasets of time series when trained with a masked objective, even when no clean targets are available. Moreover, the use of self-attention ena…
Rotating radio transients (RRATs) are sporadically emitting pulsars detectable only through searches for single pulses. While over 100 RRATs have been detected, only a small fraction (roughly 20\%) have phase-connected timing solutions, which are critical for determining how they relate to other neutron star populations. Detecting more pulses in order to achieve solutions is a key to understanding their physical nature. Astronomical signals collected by radio telescopes contain noise from many sources, making the detection of weak pulses difficult. Applying a denoising method to raw time serie…
In this work, we propose a new method of computing the Karhunen-Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of astrophysical and artificial signals via Monte Carlo simulations. We applied our novel method to a real data study-case: the Voyager 1 telemetry signal. We evaluated the KLT performance in an astrophysical context: our technique provides a…
We report the results of the high precision photometric follow-up observations of five transiting hot jupiters - WASP-33b, WASP-50b, WASP-12b, HATS-18b and HAT-P-36b. The observations are made from the 2m Himalayan Chandra Telescope at Indian Astronomical Observatory, Hanle and the 1.3m J. C. Bhattacharyya Telescope at Vainu Bappu Observatory, Kavalur. This exercise is a part of the capability testing of the two telescopes and their back-end instruments. Leveraging the large aperture of both the telescopes used, the images taken during several nights were used to produce the transit light curv…
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Guangzhou University（广州大学）
