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Modal expansion method applied to thin deformable primary mirror surface control for orbiting astronomical observatory
Two methods are described for graphically displaying and numerically analyzing surveys of the celestial sphere as conducted by astronomical satellites. The methods are applicable to the design phase of a mission, for planning during operations, and for post-mission display of results. Both methods have been tested in use, including application to the Infrared Astronomical Satellite (IRAS).
Procedures are given for calculating thermal fields and associated thermal deformations in astronomical mirrors. A technique is described for thermal strain simulation when complex thermal fields develop in astronomical mirrors. Thermal strains in pyroceramic, quartz and pyrex mirrors can be effectively determined at temperatures ranging from -70 to 150 C by this technique.
We describe a compression method for floating-point astronomical images that gives compression ratios of 6 - 10 while still preserving the scientifically important information in the image. The pixel values are first preprocessed by quantizing them into scaled integer intensity levels, which removes some of the uncompressible noise in the image. The integers are then losslessly compressed using the fast and efficient Rice algorithm and stored in a portable FITS format file. Quantizing an image more coarsely gives greater image compression, but it also increases the noise and degrades the precision of the photometric and astrometric measurements in the quantized image. Dithering the pixel values during the quantization process greatly improves the precision of measurements in the more coarsely quantized images. We perform a series of experiments on both synthetic and real astronomical CCD images to quantitatively demonstrate that the magnitudes and positions of stars in the quantized images can be measured with the predicted amount of precision. In order to encourage wider use of these image compression methods, we have made available a pair of general-purpose image compression programs, called fpack and funpack, which can be used to compress any FITS format image.
A proposed method of automated, precise alignment of a ground-based astronomical telescope would eliminate the need for initial manual alignment. The method, based on automated identification of known stars and other celestial objects in the telescope field of view, would also eliminate the need for an initial estimate of the aiming direction. The method does not require any equipment other than a digital imaging device such as a charge-coupled-device digital imaging camera and control computers of the telescope and camera, all of which are standard components in professional astronomical telescope systems and in high-end amateur astronomical telescope systems. The method could be implemented in software running in the telescope or camera control computer or in an external computer communicating with the telescope pointing mount and camera control computers.
A Hadamard-transform spectrometer was used to obtain a spectrum of Jupiter from 880-770/cm. Three ammonia absorption features stood out at 870, 851, and 833/cm. The general shape of the spectrum implied an atmosphere with a monotonically decreasing temperature profile up to the 125 K level. Transmission profiles of the earth's atmosphere were taken between 16 microns and 25 for five consecutive nights under varying amounts of atmospheric water and air mass. There are many saturated lines, but nightly variations were fairly constant and agreed well with a theoretical profile. These results show that the Hadamard-transform technique is a useful method for obtaining astronomical spectra.
Eleven of the 15 stars on Serkowski's (1974) list of 'Standard Stars with Large Interstellar Polarization' were investigated to determine whether the orientation of the plane of their linear polarization showed any dependence on wavelength. Nine of the eleven stars exhibited a statistically significant wavelength dependence of position angle when measured with an accuracy of about 0.1 deg standard deviation. For the majority of these stars, the effect is caused primarily by intrinsic polarization. The calibration of polarimeter position angles in a celestial coordinate frame must evidently be done at the 0.1 deg level of accuracy by using only carefully selected standard stars or by using other astronomical or laboratory methods.
Effective remote observing requires sending large images over long distances. The usual approach to the transfer problem is to require high bandwidth transmission links, which are expensive to install and operate. An alternative approach is to use existing low-bandwidth connections, such as phone lines or the Internet, in a highly efficient manner by compressing the images. The combined use of existing low-cost infrastructure and standard networking software means that remote observing can be made practical even for small observatories with limited network resources. The authors have implemented such a scheme based on the H-transform compression method developed for astronomical images, which are often resistant to compression because they are noisy. The H-transform can be used for either lossy or lossless compression, and compression factors of at least 10 can be achieved with no noticeable losses in the astrometric or photometric properties of the compressed images. The H-transform allows us to organize the information in an image so that the 'useful' information can be sent first, followed by the noise, which makes up the bulk of the transmission. The receiver can invert a partially received set of H-coefficients, creating an image that improves with time. The H-transform is particularly well-suited to this style of incremental reconstruction, because the spatially localized nature of the basis functions of the H-transorm prevents the appearance of artifacts such as ringing around point sources and edges. The authors' implementation uses the WIYN Telescope Control System's TCP-based communications protocol. An 800x800 16-bit astronomical image was sent over a 2400 baud connection, which would normally take about 71 minutes; after only 60 seconds, the partially received H-transform produced an image that did not differ appreciably from the original. This poster presents a quantification of the efficiencies, as well as examples of images reconstructed from partial data.
Methods are described for calculating significant factors in the observational environment of orbiting astronomical satellites. These factors must be considered in the process of scheduling observations and in data reduction. Subroutines which perform these calculations are described.
No abstract available
The Optical Properties of Astronomical Silicates with Infrared Techniques (OPASI-T) program utilizes multiple instruments to provide spectral data over a wide range of temperature and wavelengths. Experimental methods include Vector Network Analyzer (VNA) and Fourier Transform Spectroscopy (FTS) transmission, and reflection/scattering measurements. From this data, we can determine the optical parameters for the index of refraction, n, and the absorption coefficient, k. The analysis of the laboratory transmittance data for each sample type is based upon different mathematical models, which are applied to each data set according to their degree of coherence. Presented here are results from iron silicate dust grain analogs, in several sample preparations and at temperatures ranging from 5-300 K, across the infrared and millimeter portion of the spectrum (from 2.5-10,000 m or 4,000-1 cm(exp−1).
A method is described for algebraic image restoration capable of treating astronomical images. For a typical 500 x 500 image, direct algebraic restoration would require the solution of a 250,000 x 250,000 linear system. The block iterative approach is used to reduce the problem to solving 4900 121 x 121 linear systems. The algorithm was implemented on the Goddard Massively Parallel Processor, which can solve a 121 x 121 system in approximately 0.06 seconds. Examples are shown of the results for various astronomical images.
The different methods of reducing measurements on photographic plates by the overlap methods are compared. An attempt is made to distinguish the methods accepted by most astronomers from those not generally agreed upon and from those that are still being studied. Questions examined are: organization of future works, improvement of AGK2 and AGK3, and solutions for the astrographic catalog, as well as considerations of some empirical methods.
The Phillips-Twomey method for solving simultaneous integral equations is applied to the problem of removing blur effects arising from atmospheric and instrumental sources from astronomical observations made with photoelectric area-scanning instruments. Details of the method and the effects of observational errors on the quality of the results are discussed in the context of applications to actual observations of stars and the planet Saturn.
The coverage of the celestial sphere or the surface of the earth with a narrow-field instrument onboard a satellite can be described by a set of swaths on the sphere. A transect is a curve on this sphere constructed to sample the coverage. At each point on the transect the number of times that the field-of-view of the instrument has passed over the point is recorded. This information is conveniently displayed as an integer-valued histogram over the length of the transect. The effectiveness of the transect method for a particular observing plan and the best placement of the transects depends upon the structure of the set of observations. Survey missions are usually characterized by a somewhat parallel alignment of the instrument swaths. Using autocorrelation and cross-correlation functions among the histograms the structure of a survey has been analyzed into two components, and each is illustrated by a simple mathematical model. The complex, all-sky survey to be performed by the Infrared Astronomical Satellite (IRAS) is synthesized in some detail utilizing the objectives and constraints of that mission. It is seen that this survey possesses the components predicted by the simple models and this information is useful in characterizing the properties of the IRAS survey and the placement of the transects as a function of celestial latitude and certain structural properties of the coverage.
In this paper, we present a new algorithm for estimating the Point Spread Function (PSF) in wide-field astronomical images with extreme source crowding. Robust and accurate PSF estimation in crowded astronomical images dramatically improves the fidelity of astrometric and photometric measurements extracted from wide-field sky monitoring imagery. Our radically new approach utilizes convolutional sparse representations to model the continuous functions involved in the image formation. This approach avoids the need to detect and precisely localize individual point sources that is shared by existing methods. In experiments involving simulated astronomical imagery, it significantly outperforms the recent alternative method with which it is compared.
Frequency histograms and the 'power spectrum analysis' (PSA) method, the latter developed by Yu & Peebles (1969), have been widely employed as techniques for establishing the existence of periodicities. We provide a formal analysis of these two classes of methods, including controlled numerical experiments, to better understand their proper use and application. In particular, we note that typical published applications of frequency histograms commonly employ far greater numbers of class intervals or bins than is advisable by statistical theory sometimes giving rise to the appearance of spurious patterns. The PSA method generates a sequence of random numbers from observational data which, it is claimed, is exponentially distributed with unit mean and variance, essentially independent of the distribution of the original data. We show that the derived random processes is nonstationary and produces a small but systematic bias in the usual estimate of the mean and variance. Although the derived variable may be reasonably described by an exponential distribution, the tail of the distribution is far removed from that of an exponential, thereby rendering statistical inference and confidence testing based on the tail of the distribution completely unreliable. Finally, we examine a number of astronomical examples wherein these methods have been used giving rise to widespread acceptance of statistically unconfirmed conclusions.
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.