CSST Dense Star Field Preparation:A Framework for Astrometry and Photometry for Dense Star Field Images Obtained by the China Space Station Telescope(CSST)

The China Space Station Telescope(CSST)is a telescope with 2 m diameter,obtaining images with high quality through wide-field observations.In its first observation cycle,to capture time-domain observation data,the CSST is proposed to observe the Galactic halo across different epochs.These data have significant potential for the study of properties of stars and exoplanets.However,the density of stars in the Galactic center is high,and it is a well-known challenge to perform astrometry and photometry in such a dense star field.This paper presents a deep learning-based framework designed to process dense star field images obtained by the CSST,which includes photometry,astrometry,and classifications of targets according to their light curve periods.With simulated CSST observation data,we demonstrate that this deep learning framework achieves photometry accuracy of 2%and astrometry accuracy of 0.03 pixel for stars with moderate brightness mag=24(i band),surpassing results obtained by traditional methods.Additionally,the deep learning based light curve classification algorithm could pick up celestial targets whose magnitude variations are 1.7 times larger than magnitude variations brought by Poisson photon noise.We anticipate that our framework could be effectively used to process dense star field images obtained by the CSST.

Dynamic ocean inverse modeling based on differentiable rendering

Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation.To bridge the technical gap between virtual and real environments,we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans,taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised manner.First,we infer hierarchical geometry using two networks,which are optimized via the differentiable renderer.We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model.Then,ocean dynamics can be evolved using the reconstructed wave components.Through extensive experiments,we verify that our new method yields satisfactory results for both geometry reconstruction and wave estimation.Moreover,the new framework has the inverse modeling potential to facilitate a host of graphics applications,such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.

Erratum to: Dynamic ocean inverse modeling based on differentiable rendering

Erratum to Xueguang Xie,Yang Gao,Fei Hou,Aimin Hao&Hong Qin.Dynamic ocean inverse modeling based on differentiable rendering.Computational Visual Media Vol.10,No.2,279–294,2024.https://doi10.1007/s41095-023-0338-4 The authors apologize for a hidden error in the article.It is that the images in Figs.14(a)and 14(d)were mistakenly presented as left–right mirror images.The authors have flipped them to ensure that the figures now correspond correctly with others in the subfigures(b,c,e,f).The accurate version of Fig.14 is provided as below.