scholarly journals Bayesian Fitting of Multi-Gaussian Expansion Models to Galaxy Images

2021 ◽  
Vol 923 (1) ◽  
pp. 124
Author(s):  
Tim B. Miller ◽  
Pieter van Dokkum
Keyword(s):  
Surface Brightness ◽  
Hubble Space Telescope ◽  
Computation Time ◽  
Large Samples ◽  
Point Spread ◽  
Spread Function ◽  
Analysis Of Results ◽  
Validation Tests ◽  
Degraded Images ◽  
Python Package

Abstract Fitting parameterized models to images of galaxies has become the standard for measuring galaxy morphology. This forward-modeling technique allows one to account for the point-spread function to effectively study semi-resolved galaxies. However, using a specific parameterization for a galaxy’s surface brightness profile can bias measurements if it is not an accurate representation. Furthermore, it can be difficult to assess systematic errors in parameterized profiles. To overcome these issues we employ the Multi-Gaussian expansion (MGE) method of representing a galaxy’s profile together with a Bayesian framework for fitting images. MGE flexibly represents a galaxy’s profile using a series of Gaussians. We introduce a novel Bayesian inference approach that uses pre-rendered Gaussian components, which greatly speeds up computation time and makes it feasible to run the fitting code on large samples of galaxies. We demonstrate our method with a series of validation tests. By injecting galaxies, with properties similar to those observed at z ∼ 1.5, into deep Hubble Space Telescope observations we show that it can accurately recover total fluxes and effective radii of realistic galaxies. Additionally we use degraded images of local galaxies to show that our method can recover realistic galaxy surface brightness and color profiles. Our implementation is available in an open source python package imcascade, which contains all methods needed for the preparation of images, fitting, and analysis of results.

scholarly journals Surface Brightness Profiles of 68 Globular Clusters Derived From Electronic Camera Recordings

1980 ◽  
Vol 85 ◽  
pp. 459-460
Author(s):  
Gerald E. Kron ◽  
Katherine C. Gordon ◽  
Anthony V. Hewitt
Keyword(s):  
Point Spread Function ◽  
Globular Clusters ◽  
Surface Brightness ◽  
Naval Observatory ◽  
Point Spread ◽  
Spread Function ◽  
Spacial Information ◽  
Cluster Profiles ◽  
Star Images ◽  
The U.S

Images of 68 globular clusters have been recorded in 125 exposures made with the electronic camera of the U.S. Naval Observatory on the 24-inch, 40-inch and 61-inch reflecting telescopes at the Flagstaff Station. The images were electronically malfocussed to allow the integration of light from the fainter cluster stars without saturation of the central portions of the brighter star images. Spacial information thus lost was partly regained by subsequent linear deconvolution of the cluster profiles by means of a star profile used as the point spread function.

scholarly journals Imaging of Magellanic Cloud Planetary Nebulae by the Hubble Space Telescope

1993 ◽  
Vol 155 ◽  
pp. 212-212
Author(s):  
M. A. Dopita ◽  
S. J. Meatheringham ◽  
P. R. Wood ◽  
H. C. Ford ◽  
R. C. Bohlin ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Hubble Space Telescope ◽  
Planetary Nebulae ◽  
Magellanic Cloud ◽  
Space Telescope ◽  
Point Spread ◽  
Spread Function ◽  
Made In

We have obtained Hubble Space Telescope (HST) Planetary Camera (PC) images of a number of Magellanic Cloud planetary nebulae. The objects, except for SMP 83 were observed as part of the Cycle I GO program. The observations were made in the [O III] λ5007Å line. The object SMP 83, was observed as part of the GTO program, and in this case observations were also made in the Hα line using the F650N filter. In order to characterise the point spread function, a star was placed at the same point on the chip as the PN. This allowed us to determine the diameters of barely resolved PN in an accurate manner, by convolving the PSF with a function until it matched the appearance of the PN image. The results are given in Table 1.

Adaptive restoration method of multi-frame turbulence-degraded images based on stochastic point spread function

2015 ◽  
Vol 8 (3) ◽  
pp. 368-377
Author(s):  
朱瑞飞 ZHU Rei-fei ◽  
魏群 WEI Qun ◽  
王超 WANG Chao ◽  
贾宏光 JIA Hong-guang ◽  
吴海龙 WU Hai-long
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Adaptive Restoration ◽  
Spread Function ◽  
Restoration Method ◽  
Degraded Images

scholarly journals Structural Parameters of Star Clusters: Signal to Noise Effects

2015 ◽  
Vol 24 (3) ◽  
Author(s):  
D. Narbutis ◽  
A. Bridžius ◽  
D. Semionov
Keyword(s):  
Surface Brightness ◽  
Structural Parameters ◽  
Model Fitting ◽  
Star Clusters ◽  
Brightness Distribution ◽  
Star Cluster ◽  
Signal To Noise ◽  
Point Spread ◽  
Spread Function ◽  
The Impact

AbstractWe study the impact of photometric signal to noise on the accuracy of derived structural parameters of unresolved star clusters using MCMC model fitting techniques. Star cluster images were simulated as a smooth surface brightness distribution following a King profile convolved with a point spread function. The simulation grid was constructed by varying the levels of sky background and adjusting the cluster’s flux to a specified signal to noise. Poisson noise was introduced to a set of cluster images with the same input parameters at each node of the grid. Model fitting was performed using “

Blind Image Deblurring for Multiply Image Frames Based on an Iterative Algorithm

2016 ◽  
Vol 13 (10) ◽  
pp. 6531-6538
Author(s):  
Jia Ge ◽  
Peng Xianrong ◽  
Zhang Jianlin ◽  
Fu Chengyu
Keyword(s):  
Blind Deconvolution ◽  
Object Image ◽  
Current Frame ◽  
Blind Image Deblurring ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Iterative Solutions ◽  
Degraded Images ◽  
Blind Image

A novel algorithm based on an iterative and nonnegative algorithm has been developed for performing blind deconvolution on multiply degraded image frames. The algorithm naturally preserves the nonnegative constraint on the iterative solutions of blind deconvolution and can produce a restored image of high resolution. Furthermore, benefited from the interframe information, the neighbouring frame can be seen as degenerated from the same object image and different point spread function (PSF), so utilizing the result of the last frame to the initial estimate of the current frame can reduce iterative times and enhance the efficiency of the algorithm, meanwhile, the algorithm is free from the instability of numerical computation. Results of applying the algorithm to simulated and real degraded images are reported.

scholarly journals Deep learning for studies of galaxy morphology

2016 ◽  
Vol 12 (S325) ◽  
pp. 191-196 ◽  
Author(s):  
D. Tuccillo ◽  
M. Huertas-Company ◽  
E. Decencière ◽  
S. Velasco-Forero
Keyword(s):  
Deep Learning ◽  
Hubble Space Telescope ◽  
Wide Field ◽  
Human Intervention ◽  
Convolutional Networks ◽  
Point Spread ◽  
Profile Fitting ◽  
Spread Function ◽  
High Level ◽  
Infrared Survey

AbstractEstablishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.

scholarly journals The effects of seeing on Sèrsic profiles

2001 ◽  
Vol 321 (2) ◽  
pp. 269-276 ◽  
Author(s):  
I. Trujillo ◽  
J. A. L. Aguerri ◽  
J. Cepa ◽  
C. M. Gutiérrez
Keyword(s):  
Point Spread Function ◽  
Surface Brightness ◽  
Analytical Form ◽  
The Real ◽  
Point Spread ◽  
Central Depression ◽  
Spread Function ◽  
Gaussian Point

Abstract The effects of seeing on the parameters of the Sèrsic profile are studied in an analytical form using a Gaussian point spread function. The surface brightness of Sèrsic profiles is proportional (in magnitudes) to r1/n. The parameter n serves to classify the type of profile and is related to the central luminosity concentration. It is the parameter most affected by seeing; furthermore, the value of n that can be measured is always smaller than the real one. It is shown that the luminosity density of the Sèrsic profile with n less than 0.5 has a central depression, which is physically unlikely. Also, the intrinsic ellipticity of the sources has been taken into account and we show that the parameters are dependent when the effects of seeing are non-negligible. Finally, a prescription for correcting raw effective radii, central intensities and n parameters is given.

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