14 citations · 23 across the 8 of their papers we have counts for
8 papers
From Observation to Ground Truth: Reconstruction Losses of Galaxy Imaging Under Heteroscedastic Noise
Renhao Ye, Shiyin Shen, Quanfeng Xu
Deep learning is widely used to analyze galaxy images, and the reconstruction loss determines which image features a model prioritizes during training. Mean squared error (MSE) and…
From DESI to Euclid: A Generative Bridge to Improve Measurements of Galaxy Structure
Renhao Ye, Shiyin Shen
Ground-based seeing imprints size-dependent biases on galaxy structural parameters, yet the high-resolution space-based imaging needed to improve these measurements currently cover…
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption
Renhao Ye, Shiyin Shen, Rafael S. de Souza +6
The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey (BASS), and the Mayall z-b…
Capivara: A Spectral-based Segmentation Method for IFU Data Cubes
Rafael S. de Souza, Luis G. Dahmer-Hahn, Shiyin Shen +7
We present capivara, a fast and scalable spectral-based segmentation package designed to study astrophysical properties within distinct structural components of galaxies. This spec…
Systematic analysis of jellyfish galaxy candidates in Fornax, Antlia, and Hydra from the S-PLUS survey: A self-supervised visual identification aid
Yash Gondhalekar, Ana L. Chies-Santos, Rafael S. de Souza +22
We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wid…
Galmoss: A package for GPU-accelerated Galaxy Profile Fitting
Mi Chen, Rafael S. de Souza, Quanfeng Xu +5
We introduce galmoss, a python-based, torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss meets the high computat…
From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
Quanfeng Xu, Shiyin Shen, Rafael S. de Souza +7
We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA). We demonstr…
qrpca: A Package for Fast Principal Component Analysis with GPU Acceleration
Rafael S. de Souza, Xu Quanfeng, Shiyin Shen +2
We present qrpca, a fast and scalable QR-decomposition principal component analysis package. The software, written in both R and python languages, makes use of torch for internal m…