The fast co-adding algorithm of QCT

Ping, Yiding; Zhang, Chen

doi:10.1016/j.asr.2017.05.018

Cited by 8 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These properties make WFSATs light-weighted and low-cost. With remote control, WFSATs are widely used in optical observations for time domain astronomy (Burd et al 2005;Ma et al 2007;Ping & Zhang 2017;Ratzloff et al 2019;Sun & Yu 2019). Meanwhile since WFSATs are normally working automatically and no wavefront sensors are installed in them, they are hard to be maintained timely.…”

Section: Introductionmentioning

confidence: 99%

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

Jia

et al. 2020

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

The point spread function reflects the state of an optical telescope and it is important for data post-processing methods design. 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 to use the 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 pure data based point spread function modelling method, 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 as 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 can map any star images obtained by wide field small aperture telescopes directly to its point spread function, which could be used to design data post-processing or optical system alignment methods.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Jia

et al. 2020

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

show abstract

“…Telescopes with wide FoV (normally greater than 1 • ) and small apertures (less than 2 metre) are called WFSATs. They are low-cost, light-weighted and commonly used for wide field sky surveys in time domain astronomy (Burd et al 2005;Ma et al 2007;Yuan et al 2008;Cui et al 2008;Pablo et al 2016;Ping & Zhang 2017;Ratzloff et al 2019;Sun & Yu 2019). Because apertures of WFSATs are small, they are normally used to observe bright targets.…”

Section: Introductionmentioning

confidence: 99%

Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data

Jia,

Wu,

et al. 2020

Preprint

View full text Add to dashboard Cite

The point spread function (PSF) reflects states of a telescope and plays an important role in development of smart data processing methods. However, for wide field small aperture telescopes (WFSATs), estimating PSF in any position of the whole field of view (FoV) is hard, because aberrations induced by the optical system are quite complex and the signal to noise ratio of star images is often too low for PSF estimation. In this paper, we further develop our deep neural network (DNN) based PSF modelling method and show its applications in PSF estimation. During the telescope alignment and testing stage, our method collects system calibration data through modification of optical elements within engineering tolerances (tilting and decentering). Then we use these data to train a DNN. After training, the DNN can estimate PSF in any field of view from several discretely sampled star images. We use both simulated and experimental data to test performance of our method. The results show that our method could successfully reconstruct PSFs of WFSATs of any states and in any positions of the FoV. Its results are significantly more precise than results obtained by the compared classic method -Inverse Distance Weight (IDW) interpolation. Our method provides foundations for developing of smart data processing methods for WFSATs in the future.

show abstract

“…Wide field small aperture telescope (WFSAT) is a kind of telescope that can obtain images with a large field of view in a cost effective way. [1][2][3][4][5][6][7] WFSATs are widely used to detect bright transits for fast sky survey. Besides, because WFSATs are low cost, it would be possible to build a telescope array with several WFSATs to scan sky continuously, which can further increase sky coverage and observation cadence.…”

Section: Introductionmentioning

confidence: 99%

Smart obervation method with wide field small aperture telescopes for real time transient detection

Jia¹,

Liu²,

Sun³

et al. 2020

Preprint

View full text Add to dashboard Cite

Wide field small aperture telescopes (WFSATs) are commonly used for fast sky survey. Telescope arrays composed by several WFSATs are capable to scan sky several times per night. Huge amount of data would be obtained by them and these data need to be processed immediately. In this paper, we propose ARGUS (Astronomical taRGets detection framework for Unified telescopes) for real-time transit detection. The ARGUS uses a deep learning based astronomical detection algorithm implemented in embedded devices in each WFSATs to detect astronomical targets. The position and probability of a detection being an astronomical targets will be sent to a trained ensemble learning algorithm to output information of celestial sources. After matching these sources with star catalog, ARGUS will directly output type and positions of transient candidates. We use simulated data to test the performance of ARGUS and find that ARGUS can increase the performance of WFSATs in transient detection tasks robustly.

show abstract

The fast co-adding algorithm of QCT

Cited by 8 publications

References 7 publications

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

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

Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data

Smart obervation method with wide field small aperture telescopes for real time transient detection

Contact Info

Product

Resources

About