Sky Images Research Articles

Covariance-regularized Reconstruction of Data Cubes in Integral Field Spectroscopy and Application to MaNGA Data

Abstract Integral field spectroscopy can map astronomical objects spatially and spectroscopically. Due to instrumental and atmospheric effects, it is common for integral field instruments to yield a sampling of the sky image that is both irregular and wavelength dependent. Most subsequent analysis procedures require a regular, wavelength-independent sampling (for example a fixed rectangular grid), and thus an initial step of fundamental importance is to resample the data onto a new grid. The best possible resampling would produce a well-sampled image, with a resolution equal to that imposed by the intrinsic spatial resolution of the instrument, telescope, and atmosphere, and with no statistical correlations between neighboring pixels. A standard method in the field to produce a regular set of samples from an irregular set of samples is Shepard’s method, but Shepard’s method typically yields images with a degraded resolution and large statistical correlations between pixels. Here we introduce a new method, which improves on Shepard’s method in both these respects. We apply this method to data from the Mapping Nearby Galaxies at Apache Point Observatory survey, part of Sloan Digital Sky Survey IV, demonstrating a full width at half maximum close to that of the intrinsic spatial resolution (and ∼16% better than Shepard’s method) and low statistical correlations between pixels. These results nearly achieve the ideal resampling. This method can have broader applications to other integral field data sets and to other astronomical data sets (such as dithered images) with irregular sampling.

A PVC Geodesic Dome Planetarium

A planetarium is an invaluable tool for teaching introductory astronomy, but one that few astronomy educators have ready access to. Here we describe a do-it-yourself planetarium that can be built with modest funding. There have been other planetarium construction projects described in the literature and online, most of which use cardboard to make multiple faces, which are then fastened together to form a dome that serves as the planetarium projection surface. Our design uses a skeletal dome that supports a hemispherical cloth projection screen. A computer LCD projector, reflecting off a diverging half-dome security mirror on the interior edge of the dome base, serves as the starfield projector. The open-source sky simulation software Stellarium has the necessary features to pre-distort the sky image appropriately so that, after reflection from the diverging mirror, the dome is properly filled.

Solar Irradiance Fluctuation Prediction Methodology Using Artificial Neural Networks

Abstract Renewable energy is an attractive alternative source of energy to fossil fuels, as it can help prevent global warming and air pollution. Solar energy, one of the most promising renewable energy sources, can be converted into electricity using photovoltaic power generation systems. Anywhere on the Earth, solar irradiance generally fluctuates during the day but depends on atmospheric conditions. Thus, when a photovoltaic power generation system is connected to a conventional electricity network, predicting near-future global solar irradiance, especially its drastic increases and decreases, is critical to stabilize the network. In this research, a simple method utilizing artificial neural networks to predict large increases and decreases in global solar irradiance is developed. The red–blue ratio (RBR) values, which are extracted from a set of sampling points in images of the sky, as well as the corresponding global solar irradiance values, are used as the artificial neural network inputs. The direction of the movement of clouds is predicted using RBR data at the sampling points. Then, solar irradiance is predicted using the RBR values along the axis closest to the predicted cloud movement direction and the corresponding solar irradiance measurements. The proposed methodology is able to predict both large increases and decreases in solar irradiance greater than 50 through 100 W/m2 1 min in advance with a 40% prediction error. A significant reduction in computational effort is achieved compared to existing sky image-based methodologies using limited sky image data.

Short-term solar PV forecasting using computer vision: The search for optimal CNN architectures for incorporating sky images and PV generation history

Cloud movement makes short-term forecasting of solar photovoltaic (PV) panel output challenging. A better PV forecast can realize value for both grid operators and commercial or industrial customers with solar assets. In this study, we build convolutional neural network (CNN) based models to forecast power output from PV panels 15 min into the future. Model inputs are the PV power output history and ground-based sky images for the past 15 min. The key challenge is ensuring that due importance is given to each type of input. We systematically explore 28 methods of “fusing” these heterogeneous inputs in our CNN. These methods of fusion (MoF) belong to 4 families. We also systematically explore the many hyperparameters related to model training and tuning. Limited resources preclude an exhaustive search. We apply a three-stage “funnel” approach instead, wherein we narrow our search to the most promising one of these 28 MoF. We find that a two-step autoregression-CNN MoF has the best performance followed closely by a “mix-in” MoF that performs feature expansion and reduction to give appropriate importance to the two types of inputs. The two-step autoregression-CNN model has a forecast skill (FS) of 17.1% relative to smart persistence on the test set comprising 20 complete days (9 sunny, FS = 22%; 11 cloudy, FS = 16.9%). This optimization results in the improvement of FS from 14.1% for a previously published nonoptimized “baseline” model, a CNN wherein the PV history was simply concatenated to the end of the image-sourced vector obtained after convolution, pooling, and flattening operations.

A Cloud Detection Algorithm with Reduction of Sunlight Interference in Ground-Based Sky Images

Cloud detection for ground-based sky images has attracted much attention in cloud-related fields. In this paper, we proposed a cloud detection algorithm that reduced the sunlight interference in the image. The solar location method was introduced to track the sun in the image used for feature calculation, which was suitable for the case where the camera could not be calibrated. Following this, the adjustable red green difference (ARGD) feature using red and green channels was proposed. The red weight in the feature was determined by the layering region division, which classified the degree of sunlight interference in the image, and the sky state, which discriminated whether there was sunlight interference in the image. Finally, a fixed zero threshold was applied to feature images in order to obtain the cloud detection results. Experimental results showed that the proposed algorithm performs better than the other algorithms and can effectively reduce the sunlight interference.

ELIFAN, an algorithm for the estimation of cloud cover from sky imagers

Abstract. In the context of a network of sky cameras installed on atmospheric multi-instrumented sites, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images with a common principle and procedure. ELIFAN was initially developed for a self-made full-sky image system presented in this article and adapted to a set of other systems in the network. It is based on red-to-blue ratio thresholding for the distinction of cloudy and cloud-free pixels of the image and on the use of a cloud-free sky library, without taking account of aerosol loading. Both an absolute (without the use of a cloud-free reference image) and a differential (based on a cloud-free reference image) red-to-blue ratio thresholding are used. An evaluation of the algorithm based on a 1-year-long series of images shows that the proposed algorithm is very convincing for most of the images, with about 97 % of relevance in the process, outside the sunrise and sunset transitions. During those latter periods, however, ELIFAN has large difficulties in appropriately processing the image due to a large difference in color composition and potential confusion between cloud-free and cloudy sky at that time. This issue also impacts the library of cloud-free images. Beside this, the library also reveals some limitations during daytime, with the possible presence of very small and/or thin clouds. However, the latter have only a small impact on the cloud cover estimate. The two thresholding methodologies, the absolute and the differential red-to-blue ratio thresholding processes, agree very well, with departure usually below 8 % except in sunrise–sunset periods and in some specific conditions. The use of the cloud-free image library gives generally better results than the absolute process. It particularly better detects thin cirrus clouds. But the absolute thresholding process turns out to be better sometimes, for example in some cases in which the sun is hidden by a cloud.

Deep learning predictions of galaxy merger stage and the importance of observational realism

ABSTRACT Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps ($87.1{{\ \rm per\ cent}}$ compared to $79.6{{\ \rm per\ cent}}$ accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data ($86.0{{\ \rm per\ cent}}$ with r-only compared to $87.1{{\ \rm per\ cent}}$ with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.

Estimating solar irradiance using sky imagers

Abstract. Ground-based whole-sky cameras are now extensively used for the localized monitoring of clouds. They capture hemispherical images of the sky at regular intervals using a fish-eye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards the short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers colocated with our whole-sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations compared to other related methods using ground-based observations.

Catalogue with visual morphological classification of 32,616 radio galaxies with optical hosts

AbstractWe present the catalogue of Radio sources associated with Optical Galaxies and having Unresolved or Extended morphologies I (ROGUE I). It was generated by cross-matching galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR 7) as well as radio sources from the First Images of Radio Sky at Twenty Centimetre (FIRST) and the National Radio Astronomical Observatory VLA Sky Survey (NVSS) catalogues. We created the largest handmade catalogue of visually classified radio objects and associated with them optical host galaxies, containing 32,616 galaxies with a FIRST core within 3 arcsec of the optical position. All listed objects possess the good quality SDSS DR 7 spectra with the signal-to-noise ratio > 10 and spectroscopic redshifts up to z = 0.6. The radio morphology classification was performed by a visual examination of the FIRST and the NVSS contour maps overlaid on a DSS image, while an optical morphology classification was based on the 120 arcsec snapshot images from SDSS DR 7.The majority of radio galaxies in ROGUE I, i.e. ∼ 93%, are unresolved (compact or elongated), while the rest of them exhibit extended morphologies, such as Fanaroff-Riley (FR) type I, II, and hybrid, wide-angle tail, narrow-angle tail, head-tail sources, and sources with intermittent or reoriented jet activity, i.e. double–double, X–shaped, and Z–shaped. Most of FR IIs have low radio luminosities, comparable to the luminosities of FR Is. Moreover, due to visual check of all radio maps and optical images, we were able to discover or reclassify a number of radio objects as giant, double–double, X–shaped, and Z–shaped radio galaxies. The presented sample can serve as a database for training automatic methods of identification and classification of optical and radio galaxies.

Using convolutional neural networks to automatically select small artificial space objects on optical images of a starry sky

This article discusses the use of convolutional neural networks to solve the problem of automatically selecting moving objects on a moving starry background when their images exhibit speed blur. The article gives the results of testing several networks that have substantially less structural complexity than does the prototype. The estimates obtained for the accuracy and selection rate of several of the networks studied here are evidence that it is promising to use such networks to detect, classify, and estimate the location of two types of objects in the instrument’s coordinate system when resources are severely limited.

Atmospheric cloud algorithms for day/night whole sky imagers.

This paper discusses new atmospheric cloud algorithms that we have developed to detect the presence and location of clouds in images of the sky acquired by day/night whole sky imagers, and to categorize the clouds by opacity. The day/night whole sky imagers that we developed are ground-based sensors that acquire digital imagery of the full sky down to the horizon in several visible spectral bands and a near-infrared spectral band. The instruments acquire accurate data under daylight, moonlight, and starlight conditions. This paper discusses the previously unpublished day and night cloud algorithms. The day cloud algorithm identifies opaque clouds using a red/blue ratio and identifies thin clouds by comparing current red/blue ratios with the ratios anticipated for clear skies. The clear sky ratios include an image-to-image adaptive algorithm feature to adjust for haze amount. The night cloud algorithms are based on determinations of earth-to-space beam transmittances and on comparisons of current radiances with radiance distributions for clear skies and for opaque clouds. They also include an adaptive algorithm feature to adjust for changes in haze amount. In addition, these new night algorithms report the Earth-to-space beam transmittance in selected directions. The algorithms provide a pixel-by-pixel determination of the cloud results with excellent accuracy. This paper discusses the new day and night cloud algorithms and an assessment of their accuracy, as well as a short discussion of the pros and cons of using these wave bands versus other wave bands.

Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer

Observation of the spatial distribution of cloud optical thickness (COT) is useful for the prediction and diagnosis of photovoltaic power generation. However, there is not a one-to-one relationship between transmitted radiance and COT (so-called COT ambiguity), and it is difficult to estimate COT because of three-dimensional (3D) radiative transfer effects. We propose a method to train a convolutional neural network (CNN) based on a 3D radiative transfer model, which enables the quick estimation of the slant-column COT (SCOT) distribution from the image of a ground-mounted radiometrically calibrated digital camera. The CNN retrieves the SCOT spatial distribution using spectral features and spatial contexts. An evaluation of the method using synthetic data shows a high accuracy with a mean absolute percentage error of 18% in the SCOT range of 1–100, greatly reducing the influence of the 3D radiative effect. As an initial analysis result, COT is estimated from a sky image taken by a digital camera, and a high correlation is shown with the effective COT estimated using a pyranometer. The discrepancy between the two is reasonable, considering the difference in the size of the field of view, the space–time averaging method, and the 3D radiative effect.

Progressive steepening of the SNR RX J1713.7–3946 X-ray spectrum from XMM–Newton to INTEGRAL

ABSTRACT In this work, we present the first detailed analysis of the supernova remnant RX J1713.7–3946 in the hard X-ray energy range with the Imager on Board the INTEGRAL Satellite (IBIS) coded-mask telescope onboard the INTEGRAL observatory. The shell-type morphology of the entire remnant is mapped in hard X-rays for the first time and significantly detected up to 50 keV. The IBIS sky image of RX J1713.7–3946, accumulated over 14 yr of operations, demonstrates two extended hard X-ray sources. These sources are spatially consistent with north-west and south-west rims of RX J1713.7–3946 and are also clearly visible at energies below 10 keV with XMM–Newton. This points to a single emission mechanism operating in soft and hard X-rays. The INTEGRAL 17–120 keV spectrum of RX J1713.7–3946 is characterized by a power-law continuum with the photon index of Γ ≈ 3 that is significantly softer than Γ ≈ 2 determined by XMM–Newton in the 1–10 keV energy range, suggesting a progressive steepening of the spectrum with the energy.

The 3.6 Metre Devasthal Optical Telescope:From Inception to Realization

India's largest size 3.6 meter Devashal optical telescope (DOT) was commissioned in the year 2016, though the idea of building it germinated way back in the year 1976. This article provides research accounts as well as glimpses of its nearly four decades of journey. After a decade of site surveys, location of Devasthal in central Himalyan region of Kumaon was identified. Thereafter, a detailed site characterization was conducted and project approvals were obtained. The telescope is designed to be a technologically advanced optical astronomy instrument. It has been demonstrated to resolve a binary star having angular separation of 0.4 arc-sec. After the technical activation of the telescope on March 30, 2016, it has been in regular use for testing various back-end instruments as well as for optical and near-infrared observations of celestial objects. Back-end instruments used for these observations are 4KX4K CCD imager, Faint object imager cum spectrograph and TIFR near-infrared camera-II. A few published science results based on the observations taken with the telescope are also presented. Furthermore, the routine observations find that for a good fraction of observing time the telescope provides sky images of sub-arc-second resolution at optical and near-infrared wavelengths. This indicates that the extreme care taken in the design and construction of the telescope dome building has been rewarding since the as-built thermal mass contributes minimally so as not to degrade the natural atmospheric seeing measured at Devasthal about two decades ago during 1997-1999 using differential image motion monitor. The overall on-site performance of the telescope is found to be excellent and at par with the performance of other similar telescopes located over the globe.

A parallel and automatically tuned algorithm for multispectral image deconvolution

ABSTRACT In the era of big data, radio astronomical image reconstruction algorithms are challenged to estimate clean images given limited computing resources and time. This article is driven by the need for large-scale image reconstruction for the future Square Kilometre Array (SKA), which will become in the next decades the largest low and intermediate frequency radio telescope in the world. This work proposes a scalable wide-band deconvolution algorithm called MUFFIN, which stands for ‘MUlti Frequency image reconstruction For radio INterferometry’. MUFFIN estimates the sky images in various frequency bands, given the corresponding dirty images and point spread functions. The reconstruction is achieved by minimizing a data fidelity term and joint spatial and spectral sparse analysis regularization terms. It is consequently non-parametric w.r.t. the spectral behaviour of radio sources. MUFFIN algorithm is endowed with a parallel implementation and an automatic tuning of the regularization parameters, making it scalable and well suited for big data applications such as SKA. Comparisons between MUFFIN and the state-of-the-art wide-band reconstruction algorithm are provided.

Analysis algorithm for sky type and ice halo recognition in all-sky images

Abstract. Halo displays, in particular the 22∘ halo, have been captured in long time series of images obtained from total sky imagers (TSIs) at various Atmospheric Radiation Measurement (ARM) sites. Halo displays form if smooth-faced hexagonal ice crystals are present in the optical path. We describe an image analysis algorithm for long time series of TSI images which scores images with respect to the presence of 22∘ halos. Each image is assigned an ice halo score (IHS) for 22∘ halos, as well as a photographic sky type (PST), which differentiates cirrostratus (PST-CS), partially cloudy (PST-PCL), cloudy (PST-CLD), or clear (PST-CLR) within a near-solar image analysis area. The color-resolved radial brightness behavior of the near-solar region is used to define the discriminant properties used to classify photographic sky type and assign an ice halo score. The scoring is based on the tools of multivariate Gaussian analysis applied to a standardized sun-centered image produced from the raw TSI image, following a series of calibrations, rotation, and coordinate transformation. The algorithm is trained based on a training set for each class of images. We present test results on halo observations and photographic sky type for the first 4 months of the year 2018, for TSI images obtained at the Southern Great Plains (SGP) ARM site. A detailed comparison of visual and algorithm scores for the month of March 2018 shows that the algorithm is about 90 % reliable in discriminating the four photographic sky types and identifies 86 % of all visual halos correctly. Numerous instances of halo appearances were identified for the period January through April 2018, with persistence times between 5 and 220 min. Varying by month, we found that between 9 % and 22 % of cirrostratus skies exhibited a full or partial 22∘ halo.

Image phase shift invariance based multi-transform-fusion method for cloud motion displacement calculation using sky images

For minute time scale solar photovoltaic (PV) power forecasting, the motion of clouds over PV power plant mainly contribute to the fluctuant and intermittent nature of solar PV power output. Therefore, research on cloud motion displacement (CMD) calculation to realize cloud motion prediction is a key sub-process for minute time scale solar PV power forecasting approaches. Fourier phase correlation theory (FPCT) is widely applied in CMD calculation for its superiority of simplicity and less computation, then an improved algorithm based on image-phase-shift-invariance (IPSI) is proposed to reduce the outlier probability of CMD results. However, at present, the current IPSI algorithm still has limitations and cannot avoid the occurrence of outliers altogether. In this paper, we presented a novel method, termed IPSI based multi-transform-fusion (MTF) method, to further improve the effectiveness compared with traditional FPCT and affine transform based IPSI method. First, three image transform methods satisfying IPSI condition, respectively wavelet transform (WT), affine transform (AT), and convolution transform (CT), are explored. Then the information increment of the transformed sky images using the above three methods is analyzed, respectively. Second, we determine the suitable image transform method for IPSI algorithm under specific cloud condition according to the corresponding information increment. Third, an IPSI based MTF method for CMD calculation in sky images is proposed. The original sky images are transformed through WT, AT, and CT to generate multiple images that maintain the same object motion information, then calculate the CMDs in each generated image. Finally, we apply Gaussian distribution to fit the multiple CMD values and taking its mathematical expectation as final CMD result. Various experimental results in 4 different scenarios show that the performance of the proposed approach is better than FPCT, AT based IPSI, and OF method, by reducing plenty of CMD outliers, thus delivering greater accuracy and robustness.

Storm-time convection dynamics viewed from optical auroras

A series of statistical and event studies have demonstrated that the motion of patches in regions of Patchy Pulsating Aurora (PPA) is very close to, if not exactly, E×B convection. Therefore, 2D maps of PPA motion provide us the opportunity to remotely sense magnetospheric convection with relatively high space and time resolution, subject to uncertainties associated with the mapping between the ionosphere and magnetosphere. In this study, we use THEMIS ASI (All Sky Imager) aurora observations combined with RBSP electric field and magnetic field measurements to explore convection dynamics during storm time. From 0500 UT to 0600 UT on March 19 2015, auroral observations across ~4 h of magnetic local time (MLT) show that increases in the westward velocities of patches are closely related to earthward flow bursts in the inner plasma sheet. Together with the meridian scanning photometer (MSP) data, this suggests that the increase in the westward velocities of PPA patches is caused by earthward-moving ion injection structures carried by the fast earthward flows.

Radiometric calibration methods for day/night whole sky imagers and extinction imagers.

This paper discusses the new radiometric calibration methods and results that we have developed to apply to day/night whole sky imager data and extinction imager data. Day/night whole sky imagers detect the presence and location of clouds and categorize them by opacity. The new day and night cloud algorithms for these instruments are documented elsewhere, and these algorithms depend on the use of the new calibrations we have developed. In this paper, we document our calibration methods for determining the absolute radiance of the whole sky imager scene at each pixel in the images as well as the results of the calibrations. The extinction imagers are new systems we developed that determine beam transmittance and path extinction for horizontal paths through the atmosphere. These instruments and their algorithms are documented elsewhere, and they also depend on the new calibration results. The extinction imager algorithms only require relative radiances, as opposed to absolute radiances. The calibration steps for the two instruments are quite similar, and they include dark correction; linearity calibration, which corrects for nonlinearities in the signal versus flux relationship; absolute calibration (for the whole sky imager); and uniformity calibration, which corrects for pixel-to-pixel nonuniformities. These developments include methods for handling uniformity calibrations in a system using a fisheye lens and the calibration of both visible and short-wave IR systems. This paper presents both the new methods and results in support of the papers documenting the algorithms.

Development of an all-sky imaging system for cloud cover assessment.

This study develops a novel automatic all-sky imaging system, namely, an all-sky camera (ASC) system, for cloud cover assessment. The proposed system does not require conventional solar occulting devices and can capture complete hemispheric sky images. Cloud detection is performed innovatively using a convolutional neural network model (i.e., the optimized U-Net model). Experiments demonstrate that the optimized U-Net model can effectively detect clouds from sky images. In terms of cloud cover, the estimation results of the ASC system exhibit a high correlation with those obtained via manual observation, thereby indicating the applicability of the ASC system in ground-based cloud observation and analysis.