scholarly journals Variability and transient search in the SUDARE–VOICE field: a new method to extract the light curves

2020 ◽  
Vol 493 (3) ◽  
pp. 3825-3837
Author(s):  
Dezi Liu ◽  
Wenqiang Deng ◽  
Zuhui Fan ◽  
Liping Fu ◽  
Giovanni Covone ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Point Spread Function ◽  
Forthcoming Paper ◽  
Light Curves ◽  
New Method ◽  
Imaging Data ◽  
Point Spread ◽  
Spread Function ◽  
Function Modelling ◽  
The Difference

ABSTRACT The VLT Survey Telescope (VST) Optical Imaging of the CDFS and ES1 Fields Survey, in synergy with the SUDARE survey, is a deep optical ugri imaging of the CDFS and ES1 fields using the VST. The observations for the CDFS field comprise about 4.38 deg2 down to r ∼ 26 mag. The total on-sky time spans over 4 yr in this field, distributed over four adjacent sub-fields. In this paper, we use the multiepoch r-band imaging data to measure the variability of the detected objects and search for transients. We perform careful astrometric and photometric calibrations and point spread function modelling. A new method, referring to as differential running-average photometry, is proposed to measure the light curves of the detected objects. With the method, the difference of PSFs between different epochs can be reduced, and the background fluctuations are also suppressed. Detailed uncertainty analysis and detrending corrections on the light curves are performed. We visually inspect the light curves to select variable objects, and present some objects with interesting light curves. Further investigation of these objects in combination with multiband data will be presented in our forthcoming paper.

Brain PET imaging optimization with time of flight and point spread function modelling

2015 ◽  
Vol 31 (8) ◽  
pp. 948-955 ◽  
Author(s):  
Elena Prieto ◽  
Josep M. Martí-Climent ◽  
Verónica Morán ◽  
Lidia Sancho ◽  
Benigno Barbés ◽  
...  
Keyword(s):  
Pet Imaging ◽  
Point Spread Function ◽  
Time Of Flight ◽  
Brain Pet ◽  
Point Spread ◽  
Spread Function ◽  
Function Modelling

scholarly journals On point spread function modelling: towards optimal interpolation

2011 ◽  
Vol 419 (3) ◽  
pp. 2356-2368 ◽  
Author(s):  
Joel Bergé ◽  
Sedona Price ◽  
Adam Amara ◽  
Jason Rhodes
Keyword(s):  
Point Spread Function ◽  
Optimal Interpolation ◽  
Point Spread ◽  
Spread Function ◽  
Function Modelling

scholarly journals Revisiting KELT-19Ab, WASP-156b, and WASP-121b in the TESS Era

2022 ◽  
Vol 163 (2) ◽  
pp. 42
Author(s):  
Fan Yang ◽  
Ranga-Ram Chary ◽  
Ji-Feng Liu
Keyword(s):  
Point Spread Function ◽  
Sampling Interval ◽  
General Purpose ◽  
Radius Ratio ◽  
Point Spread ◽  
Spread Function ◽  
Significant Contamination ◽  
The Difference ◽  
General Purpose Software

Abstract We present a re-analysis of transit depths of KELT-19Ab, WASP-156b, and WASP-121b, including data from the Transiting Exoplanet Survey Satellite (TESS). The large ∼21″ TESS pixels and point-spread function result in significant contamination of the stellar flux by nearby objects. We use Gaia data to fit for and remove this contribution, providing general-purpose software for this correction. We find all three sources have a larger inclination, compared to earlier work. For WASP-121b, we find significantly smaller values (13.°5) of the inclination when using the 30 minute cadence data compared to the 2 minute cadence data. Using simulations, we demonstrate that the radius ratio of exoplanet to star (R p /R *) is biased small relative to data taken with a larger sampling interval although oversampling corrections mitigate the bias. This is particularly important for deriving subpercent transit differences between bands. We find the radius ratio of exoplanet to star (R p /R *) in the TESS band is 7.5σ smaller than previous work for KELT-19Ab, but consistent to within ∼2σ for WASP-156b and WASP-121b. The difference could be due to specific choices in the analysis, not necessarily due to the presence of atmospheric features. The result for KELT-19Ab possibly favors a haze-dominated atmosphere. We do not find evidence for the ∼0.95 μm water feature contaminating transit depths in the TESS band for these stars but show that with photometric precision of 500 ppm and with a sampling of about 200 observations across the entire transit, this feature could be detectable in a more narrow z-band.

scholarly journals Atmospheric point spread function interpolation for weak lensing in short exposure imaging data

2012 ◽  
Vol 427 (3) ◽  
pp. 2572-2587 ◽  
Author(s):  
C. Chang ◽  
P. J. Marshall ◽  
J. G. Jernigan ◽  
J. R. Peterson ◽  
S. M. Kahn ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Short Exposure ◽  
Weak Lensing ◽  
Imaging Data ◽  
Point Spread ◽  
Spread Function

scholarly journals Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

2020 ◽  
Vol 493 (1) ◽  
pp. 651-660 ◽  
Author(s):  
Peng Jia ◽  
Xiyu Li ◽  
Zhengyang Li ◽  
Weinan Wang ◽  
Dongmei Cai
Keyword(s):  
Point Spread Function ◽  
Wide Field ◽  
Calibration Data ◽  
Post Processing ◽  
Denoising Autoencoder ◽  
Small Aperture ◽  
Point Spread ◽  
Spread Function ◽  
Function Modelling ◽  
Star Images

ABSTRACT The point spread function reflects the state of an optical telescope and it is important for the design of data post-processing methods. For wide-field small-aperture telescopes, the point spread function is hard to model because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose the use of a denoising autoencoder, a type of deep neural network, to model the point spread function of wide-field small-aperture telescopes. The denoising autoencoder is a point spread function modelling method, based on pure data, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them realizations of the point spread function (i.e. simulated star images). Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and it can map any star images obtained by wide-field small-aperture telescopes directly to its point spread function. This could be used to design data post-processing or optical system alignment methods.

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