Astronomical Images Research Articles

AstroSR: A Data Set of Galaxy Images for Astronomical Superresolution Research

In the past decade, various sky surveys with a wide range of wavelengths have been conducted, resulting in an explosive growth of survey data. There may be overlapping regions between different surveys, but the data quality and brightness are different. The translation of data quality between different surveys provides benefits for studying the properties of galaxies in specific regions that high-quality surveys have not yet covered. In this paper, we create a data set for analyzing the quality transformation of different surveys, AstroSR, using the galaxy images from overlapping regions from the Subaru/Hyper Suprime-Cam (HSC) and the Sloan Digital Sky Survey (SDSS). In addition, we use superresolution (SR) techniques to improve the quality of low-resolution images in the AstroSR and explore whether the proposed data set is suitable for SR. We try four representative models: EDSR, RCAN, ENLCN, and SRGAN. Finally, we compare the evaluation metrics and visual quality of the above methods. SR models trained with AstroSR successfully generate HSC-like images from SDSS images, which enhance the fine structure present in the SDSS images while retaining important morphological information and increasing the brightness and signal-to-noise. Improving the resolution of astronomical images by SR can improve the size and quality of the sky surveys. The data set proposed in this paper provides strong data support for the study of galaxy SR and opens up new research possibilities in astronomy. The data set is available online at https://github.com/jiaweimmiao/AstroSR.

Meta Shack–Hartmann wavefront sensor with large sampling density and large angular field of view: phase imaging of complex objects

Shack–Hartmann wavefront sensors measure the local slopes of an incoming wavefront based on the displacement of focal spots created by a lenslet array, serving as key components for adaptive optics for astronomical and biomedical imaging. Traditionally, the challenges in increasing the density and the curvature of the lenslet have limited the use of such wavefront sensors in characterizing slowly varying wavefront structures. Here, we develop a metasurface-enhanced Shack–Hartmann wavefront sensor (meta SHWFS) to break this limit, considering the interplay between the lenslet parameters and the performance of SHWFS. We experimentally validate the meta SHWFS with a sampling density of 5963 per mm2 and a maximum acceptance angle of 8° which outperforms the traditional SFWFS by an order of magnitude. Furthermore, to the best of our knowledge, we demonstrate the first use of a wavefront sensing scheme in single-shot phase imaging of highly complex patterns, including biological tissue patterns. The proposed approach opens up new opportunities in incorporating exceptional light manipulation capabilities of the metasurface platform in complex wavefront characterization.

A distributed approach for persistent homology computation on a large scale

Persistent homology (PH) is a powerful mathematical method to automatically extract relevant insights from images, such as those obtained by high-resolution imaging devices like electron microscopes or new-generation telescopes. However, the application of this method comes at a very high computational cost that is bound to explode more because new imaging devices generate an ever-growing amount of data. In this paper, we present PixHomology, a novel algorithm for efficiently computing zero-dimensional PH on 2D images, optimizing memory and processing time. By leveraging the Apache Spark framework, we also present a distributed version of our algorithm with several optimized variants, able to concurrently process large batches of astronomical images. Finally, we present the results of an experimental analysis showing that our algorithm and its distributed version are efficient in terms of required memory, execution time, and scalability, consistently outperforming existing state-of-the-art PH computation tools when used to process large datasets.

Learning from small data sets: Patch‐based regularizers in inverse problems for image reconstruction

AbstractThe solution of inverse problems is of fundamental interest in medical and astronomical imaging, geophysics as well as engineering and life sciences. Recent advances were made by using methods from machine learning, in particular deep neural networks. Most of these methods require a huge amount of data and computer capacity to train the networks, which often may not be available. Our paper addresses the issue of learning from small data sets by taking patches of very few images into account. We focus on the combination of model‐based and data‐driven methods by approximating just the image prior, also known as regularizer in the variational model. We review two methodically different approaches, namely optimizing the maximum log‐likelihood of the patch distribution, and penalizing Wasserstein‐like discrepancies of whole empirical patch distributions. From the point of view of Bayesian inverse problems, we show how we can achieve uncertainty quantification by approximating the posterior using Langevin Monte Carlo methods. We demonstrate the power of the methods in computed tomography, image super‐resolution, and inpainting. Indeed, the approach provides also high‐quality results in zero‐shot super‐resolution, where only a low‐resolution image is available. The article is accompanied by a GitHub repository containing implementations of all methods as well as data examples so that the reader can get their own insight into the performance.

Innovative educational and research center for monitoring forest resources of Siberia based on laser and microwave aerospace imaging

The possibilities of creating an innovative educational and scientific center for monitoring forest resources in Siberia on the basis of the Department of Space Facilities and Technologies of the Siberian State University of Science and Technology named after Mikhail Fedorovich Reshetnev are discussed, with the aim of training highly qualified engineering personnel and conducting promising scientific research in the field of monitoring, modeling, forecasting and management of forest resources. Methodological solutions and algorithms for three-dimensional modeling of forest structure and dynamics based on laser scanning data, digital aerial and space photography are proposed. These methods contribute to operational monitoring and can significantly reduce the cost of monitoring the condition and use of forest resources over the vast territory of Siberia. Remote sensing data is presented in the form of a geotransformed database and digital photo map, compatible in formats with computer-aided design systems and with the main geographic information systems – ArcView, ArcINFO, MapINFO. The innovative monitoring center will be used for operational state control and monitoring of forest management, the state of forest lands, forest management and forest inventory, solving problems of ecology and environmental management, geoecology, formation of a forest resource inventory, aerospace methods for studying natural resources and territories, information technology. Solving these problems will allow for the training of highly qualified specialists. The center's specialists plan to create information technologies for remote sensing of natural objects with the aim of import substitution of foreign software products. The main scientific directions of the created center: development and research of methods for system analysis of large-scale multidimensional remote sensing data based on nonparametric decision-making algorithms and parallel computing technologies; testing hypotheses about the distributions of large-volume remote sensing data based on nonparametric nuclear-type pattern recognition algorithms; detection of compact groups of large-volume remote sensing data corresponding to unimodal fragments of the joint probability density of multivariate random variables.

SuperBIT Superpressure Flight Instrument Overview and Performance: Near-diffraction-limited Astronomical Imaging from the Stratosphere

SuperBIT was a 0.5 m near-UV to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multiband images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument’s technical requirements, achieving a telescope pointing stability of 0.″34 ± 0.″10, a focal plane image stability of 0.″055 ± 0.″027, and a point-spread function FWHM of ∼0.″35 over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction-limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34 m near-UV to near-infrared balloon-borne telescope.

Mitigating Noise Current in 2D Perovskite Single Crystal Photodetectors for Imaging under Black‐Light Condition

AbstractWeak‐light imaging plays a pivotal role in various fields such as astronomical photography, military nighttime surveillance, and biomedical imaging. The capability of photodetectors (PDs) in detecting weak‐light relies heavily on minimizing their noise current. In this study, the weak‐light detection performances of PDs constructed from PEA2MAn‐1PbnI3n+1 (n = 1, 2, 3) 2D perovskite single crystals are presented. Among these, the n = 1 PD showcases incredibly low noise current that induces an ultra‐low detection limit of 14 pW cm−2 under 532 nm light illumination, and a high detectivity of 3.25 × 1015 Jones. The n = 1 PD also meets imaging requirements even under black‐level illumination conditions of 75 pW cm−2. The investigation reveals that decreasing n value corresponds to an increase in the PEA+ ratios of cations, resulting in reduced defects and enhanced ion migration activation energy and exciton binding energy. These reduce noise current of devices from electron/hole, ion, and exciton dynamic behaviors. Notably, inhibiting ion migration can significantly improve the stability of the noise current baseline and facilitate the stable detection of weak signals. This study underscores the potential of 2D perovskites for advancing weak‐light imaging technologies, offering valuable insights for future development in this field.

End-to-end simulations of photonic phase correctors for adaptive optics systems.

Optical beams and starlight distorted by atmospheric turbulence can be corrected with adaptive optics systems to enable efficient coupling into single-mode fibers. Deformable mirrors, used to flatten the wavefront in astronomical telescopes, are costly, sensitive, and complex mechanical components that require careful calibration to enable high-quality imaging in astronomy, microscopy, and vision science. They are also impractical to deploy in large numbers for non-imaging applications like free-space optical communication. Here, we propose a photonic integrated circuit capable of spatially sampling the wavefront collected by the telescope and co-phasing the subapertures to maximize the flux delivered to an output single-mode fiber as the integrated photonic implementation of a deformable mirror. We present the results of end-to-end simulations to quantify the performance of the proposed photonic solution under varying atmospheric conditions toward realizing an adaptive optics system without a deformable mirror for free-space optical receivers.

Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning

Low-surface-brightness (LSB) galaxies play a crucial role in our understanding of galaxy evolution and dark matter cosmology. However, efficiently detecting them in large-scale surveys is challenging, due to their dim appearance. In this study, we propose a two-step detection method based on deep learning to address this issue. First, an object detection model called GalCenterNet was designed to detect LSB galaxy candidates in astronomical images. The model was trained using a data set of 665 Sloan Digital Sky Survey (SDSS) images, which contained 667 LSB galaxies. On the test set, the model achieved an accuracy of 95.05% and a recall of 96.00%. Next, an anomaly detection technique known as Deep Support Vector Data Description was applied to identify abnormal sources, thus refining the LSB candidates. By applying the two-step detection method to SDSS images, we have obtained a sample of 37,536 LSB galaxy candidates. This wide-area sample contains diverse and abundant LSB galaxies, which are valuable for studying the properties of LSB galaxies and the role that the environment plays in their evolution. The proposed detection method enables end-to-end detection from the SDSS images to the final detection results. This approach will be further employed to efficiently identify objects in the upcoming Chinese Survey Space Telescope sky survey.

Breaking Boundaries: A Universal Wavefront Reconstruction Approach for High-resolution Solar Imaging

This Letter proposes a universal wavefront reconstruction approach based on a coupled data set and neural network, aiming to overcome the limitations of current algorithms in terms of universality and wavefront sensing accuracy for variable imaging objects. First, a novel data set, Multi-Object Wavefront Coupling Dataset (MOCD-Dataset), is developed to provide diverse data and enable the network to learn universal wavefront features. Next, a new universal wavefront reconstruction network called Object-Independent Wavefront Decoupling Network (OIWD-Net) is introduced, aiming to separate imaging object information from multiple variable images. Our algorithm eliminates the need for specialized wavefront sensors, has a simple system, high light energy utilization, and does not require customized models for each different type of imaging objects, making it highly practical. By combining the MOCD-Dataset and the OIWD-Net, excellent accuracy in wavefront reconstruction of different imaging objects has been achieved. This research provides a new solution for high-resolution image restoration in fields such as solar structure observation and astronomical high-resolution imaging.

AstroDLLC: Efficiently Reducing Storage and Transmission Costs for Massive Solar Observation Data via Deep Learning-based Lossless Compression

Effective data compression technology is essential for addressing data storage and transmission needs, especially given the escalating volume and complexity of data generated by contemporary astronomy. In this study, we propose utilizing deep learning-based lossless compression techniques to improve compression efficiency. We begin with a qualitative and quantitative analysis of the temporal and spatial redundancy in solar observation data. Based on this analysis, we introduce a novel deep learning-based framework called AstroDLLC for the lossless compression of astronomical solar images. AstroDLLC first segments high-resolution images into blocks to ensure that deep learning model training does not rely on high-computation power devices. It then addresses the non-normality of the partitioned data through simple reversible computational methods. Finally, it utilizes Bit-swap to train deep learning models that capture redundant features across multiple image frames, thereby enhancing compression efficiency. Comprehensive evaluations using data from the New Vacuum Solar Telescope reveal that AstroDLLC achieves a maximum compression ratio of 3.00 per image, surpassing Gzip, RICE, and other lossless technologies. The performance of AstroDLLC underscores its potential to address data compression challenges in astronomy.

A radio astronomy image restoration algorithm based on compressed sensing framework

A radio astronomy image restoration algorithm based on compressed sensing framework

A novel edge computing approach to astronomical image data processing based on sCMOS camera using SoC

The ever-growing deluge of astronomical data challenges traditional server-based processing, hindering real-time analysis and scientific discovery. This paper proposes a novel approach: edge computing directly on an sCMOS camera using a System-on-Chip (SoC) architecture currently developed at Creotech Instruments. We present a custom-designed camera equipped with an FPGA-based SoC, enabling on-board preprocessing and feature extraction of astronomical images. This significantly reduces data transmission, minimizes latency, and empowers real-time decision-making for critical observations. We showcase the camera's capabilities through real-world scenarios, demonstrating its usability in astronomy.

Programming the scalable optical learning operator with spatial-spectral optimization

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic counterparts and with reduced energy consumption. Here, we use the optical reservoir computing framework we have previously described (Scalable Optical Learning Operator or SOLO [1]) to program the spatial-spectral output of the light after nonlinear propagation in a multimode fiber. The novelty in the current paper is that the system is programmed through an output sampling scheme, similar to that used in hyperspectral imaging in astronomy. Linear and nonlinear computations are performed by light in the multimode fiber and the high dimensional spatial-spectral information at the fiber output is optically programmed before it reaches the camera. We then used a digital computer to classify the programmed output of the multi-mode fiber using a simple, single layer network. When combining front-end programming and the proposed spatial-spectral programming, we were able to achieve 89.9 % classification accuracy on the dataset consisting of chest X-ray images from COVID-19 patients. At the same time, we obtained a decrease of 99 % in the number of tunable parameters compared to an equivalently performing digital neural network. These results show that the performance of programmed SOLO is comparable with cutting-edge electronic computing platforms, albeit with a much-reduced number of electronic operations.

Vertical Distribution of Optical Turbulence at the Peak Terskol Observatory and Mount Kurapdag

Atmospheric turbulence characteristics are essential in determining the quality of astronomical images and implementing adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol observatory (43.27472°N 42.50083°E, 3127 m a.s.l.) using the Era-5 reanalysis and scintillation measurements are investigated. For the closest reanalysis grid node to the observatory, vertical profiles of the structural constant of the air refractive index turbulent fluctuations Cn2 were obtained. The calculated Cn2(z) vertical profiles are compared with the vertical distribution of turbulence intensity obtained from tomographic measurements with a Shack–Hartmann sensor. The atmospheric coherence length at the location of Terskol Peak was estimated. Using a combination of atmospheric models and paramaterization schemes of turbulence, Cn2(z) profiles at Mt. Kurapdag were obtained. The values of atmospheric coherence length at Peak Terskol are compared with estimated values of this length at the ten astronomical sites, including Ali, Lenghu and Daocheng.

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluate image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point-spread-function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep-learning-based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.

Resolution Enhancement of SOHO/MDI Magnetograms

Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency. The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field variation. Within the realm of astronomical data processing, super-resolution (SR) reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution (LR) and high-resolution (HR) images, thereby achieving higher-resolution astronomical images. This paper is an application study in high-dimensional nonlinear regression. Deep learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms, thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed information. For this study, a data set comprising 9717 pairs of data from 2010 April to 2011 February was used as the training set, 1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test set. After data preprocessing, we randomly cropped 128 × 128 sub-images as the LR cases from the full-disk MDI magnetograms, and the corresponding 512 × 512 sub-images as HR ones from the HMI full-disk magnetograms for model training. The tests conducted have shown that the study successfully produced reliable 4× SR reconstruction of full-disk MDI magnetograms. The MESR model’s results (0.911) were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient values. Furthermore, the method achieved the best PSNR, SSIM, MAE and RMSE values, indicating that the MESR model can effectively reconstruct magnetograms.

SatStreaks: Towards Supervised Learning for Delineating Satellite Streaks from Astronomical Images

SatStreaks: Towards Supervised Learning for Delineating Satellite Streaks from Astronomical Images

Solar multiobject multiframe blind deconvolution with a spatially variant convolution neural emulator

The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field of view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multiobject multiframe blind deconvolution algorithm for solar images. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding the artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude.

Surveying image segmentation approaches in astronomy

Image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is not only time-consuming but also susceptible to biases introduced by human intervention. As a result, automated segmentation methods have become essential for achieving robust and consistent results in astronomical studies. This review begins by summarizing traditional and classical segmentation methods widely used in astronomical tasks. Despite the significant improvements these methods have brought to segmentation outcomes, they fail to meet astronomers’ expectations, requiring additional human correction, further intensifying the labor-intensive nature of the segmentation process. The review then focuses on the transformative impact of machine learning, particularly deep learning, on segmentation tasks in astronomy. It introduces state-of-the-art machine learning approaches, highlighting their applications and the remarkable advancements they bring to segmentation accuracy in both astronomical images and data cubes. As the field of machine learning continues to evolve rapidly, it is anticipated that astronomers will increasingly leverage these sophisticated techniques to enhance segmentation tasks in their research projects. In essence, this review serves as a comprehensive guide to the evolution of segmentation methods in astronomy, emphasizing the transition from classical approaches to cutting-edge machine learning methodologies. We encourage astronomers to embrace these advancements, fostering a more streamlined and accurate segmentation process that aligns with the ever-expanding frontiers of astronomical exploration.