arXiv subjects

Ruibiao Luo

Publications and source records attributed to Ruibiao Luo.

3 recordsLinked to original sources

arXiv2025

Discovery of a Low-mass Strong-lens System in SMACS J0723.3-7327

We report the discovery of an intriguing, low-mass galaxy-scale strong-lens system in the SMACS J0723.3-7327 galaxy cluster. By modeling James Webb Space Telescope imaging and Very Large Telescope Multi-Unit Spectroscopic Explorer spectroscopic data, we find that the lens is cluster member galaxy at $z=0.397$ with an Einstein radius of $0^{\prime \prime}.424$ $\pm$ $0^{\prime \prime}.012$, stellar mass of $M_* = (3.3 \pm 0.8) \times 10^{10} M_\odot$, half-light radius of $\sim 1$ kpc, and central stellar velocity dispersion of $140 \pm 6$ km s$^{-1}$. This lens galaxy is one of the few strong lens galaxies known to date that have stellar mass as low as $M_* \sim 10^{10.5} M_\odot$, offering an exceptional opportunity to peek into the population of low-mass galaxies that has largely remained unexplored in the context of strong-lensing studies. This strong lens system can also assist in assessing the systematic uncertainty in the lens modeling of cluster member galaxies.

astro-ph.GA↗

arXiv2024

Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations

We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train the GGSL-Unet, we show it can correctly model the input image noise and extract the lens signal. However, the most important result of this work is that the GGSL-UNet can accurately reconstruct real ground-based lensing systems from the Kilo Degree Survey (KiDS) in one second. We also test the GGSL-UNet on space-based (HST) lenses from BELLS GALLERY, and obtain comparable accuracy of standard lens modeling tools. Finally, we calculate the magnitudes from the reconstructed deflector and source images and use this to derive photometric redshifts (photo-z), with the photo-z of the deflector well consistent with spectroscopic ones. This first work, demonstrates the great potential of the generative network for lens finding, image denoising, source segmentation, and decomposing and modeling of strong lensing systems. For the upcoming ground- and space-based surveys, the GGSL-UNet can provide high-quality images as well as geometry and redshift information for precise lens modeling, in combination with classical MCMC modeling for best accuracy in the galaxy-galaxy strong lensing analysis.

astro-ph.GA↗

arXiv2022

Galaxy-galaxy lensing in the VOICE deep survey

The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg$^2$ of the Chandra Deep Field South (CDFS) area, enables both shape measurement and photometric redshift estimation to be the two essential quantities for weak lensing analysis. The depth of $mag_{AB}$ is up to 26.1 (5$σ$ limiting) in $r$-band. We estimate the Excess Surface Density (ESD; $ΔΣ$) based on galaxy-galaxy measurements around galaxies at lower redshift (0.10<$z_l$<0.35) while we select the background sources to be at higher redshift ranging from 0.3 to 1.5. The foreground galaxies are divided into two major categories according to their colour (blue/red), each of which has been further divided into high/low stellar mass bins. Then the halo masses of the samples are estimated by modelling the signals, and the posterior of the parameters are samples via Mote Carlo Markov Chain (MCMC) process. We compare our results with the existing Stellar-to-Halo Mass Relation (SHMR) and find that the blue low stellar mass bin (median $M_*=10^{8.31}M_\odot$) deviates from the SHMR relation whereas all other three samples agrees well with empirical curves. We interpret this discrepancy as the effect of a low star formation efficiency of the low-mass blue dwarf galaxy population dominated in the VOICE-CDFS area.

astro-ph.CO↗