Related papers: Mock Observations for the CSST Mission: HSTDM--Syn…
Recent breakthroughs in synthetic data generation approaches made it possible to produce highly photorealistic images which are hardly distinguishable from real ones. Furthermore, synthetic generation pipelines have the potential to…
The Hubble Space Telescope (HST) has been our most prolific tool to study exoplanet atmospheres. As the age of JWST begins, there is a wealth of HST archival data that is useful to strengthen our inferences from JWST. Notably, HST/STIS and…
The Hilbert-Huang transform (HHT) consists of empirical mode decomposition (EMD), which is a template-free method that represents the combination of different intrinsic modes on a time-frequency map (i.e., the Hilbert spectrum). The…
With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from…
The James Webb Space Telescope (JWST) has unveiled unexpectedly massive galaxy candidates at high redshifts, challenging standard $\Lambda$CDM cosmological predictions. In this work, we study the predictions of more realistic dark matter…
The Space-based multi-band astronomical Variable Objects Monitor (SVOM) mission was launched in June 2024. It is a joint Sino-French collaboration designed to detect, localize, and study gammaray bursts (GRBs) and other high-energy…
With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical…
In multivariate time series (MTS) forecasting, many deep learning based methods have been proposed for modeling dependencies at multiple spatial (inter-variate) or temporal (intra-variate) scales. However, existing methods may fail to model…
The distinction between stars and galaxies is a fundamental problem in the field of celestial classification. This issue has become challenging for these ongoing and upcoming digital surveys, which will produce terabytes and even petabytes…
This paper presents CSST-PSFNet, a deep learning method for high-fidelity point spread function (PSF) reconstruction developed for the Chinese Space Station Survey Telescope (CSST). The model integrates a residual neural network, a…
Scattering transforms are a new type of summary statistics recently developed for the study of highly non-Gaussian processes, which have been shown to be very promising for astrophysical studies. In particular, they allow one to build…
Synthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in…
Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained significant interest. However, current methods often overlook…
HERMES (High Energy Rapid Modular Ensemble of Satellites) is an innovative mission aiming to observe transient high-energy events such as gamma-ray bursts (GRBs) through a constellation of CubeSats hosting a broadband X and gamma-ray…
The Chinese Spectral RadioHeliograph (CSRH) is a synthetic aperture radio interferometer built in Inner Mongolia, China. As a solar-dedicated interferometric array, CSRH is capable of producing high quality radio images at frequency range…
Spatial-temporal forecasting and imputation are important for real-world intelligent systems. Most existing methods are tailored for individual forecasting or imputation tasks but are not designed for both. Additionally, they are less…
Meteors are important phenomenon reflecting many properties of interplanetary dust particles. The study of their origin, mass distribution, and orbit evolution all require large data volume, which can only be obtained using large meteor…
We forecast the constraint on the Hu-Sawicki $f(R)$ model from the photometric survey operated by the Chinese Space Station Survey Telescope (CSST). The simulated $3\times2$pt data of galaxy clustering, weak lensing, and galaxy-galaxy…
The Large Synoptic Survey Telescope (LSST) has been designed in order to satisfy several different scientific objectives that can be addressed by a ten-year synoptic sky survey. However, LSST will also provide a large amount of data that…
The Chinese Space Station Optical Survey (CSS-OS) is a mission to explore the vast universe. This mission will equip a 2-meter space telescope to perform a multi-band NUV-optical large area survey (over 40% of the sky) and deep survey (~1%…