arXiv subjects

Shihong Liu

Publications and source records attributed to Shihong Liu.

10 recordsLinked to original sources

arXiv2026

A Direct DESI--SDSS Two-Fibre Test of Aperture Bias in Optical Emission-Line Diagnostics

Fixed angular apertures sample different physical regions of nearby galaxies and can therefore bias optical emission-line diagnostics. We use 20,545 high-confidence emission-line galaxies observed by both the Dark Energy Spectroscopic Instrument (DESI) and the Sloan Digital Sky Survey (SDSS) as a two-fibre experiment, in which the DESI 1.5 arcsec fibre is nested within the SDSS 3 arcsec fibre. To isolate aperture effects from line-measurement systematics, we refit every spectrum using the same eMILES stellar-continuum and emission-line model while preserving the wavelength-dependent line-spread function of each spectrum. This common refit yields 20,200 quality-controlled matched galaxies. Relative to SDSS, the smaller DESI aperture produces small but highly significant offsets: $\Delta\log({\rm N2})=+0.0067\pm0.0002$, $\Delta\log({\rm S2})=-0.0148\pm0.0003$, $\Delta\log({\rm O3})=-0.0246\pm0.0006$, $\Delta{\rm O3N2}=-0.0327\pm0.0007$, and $\Delta\log({\rm H}\alpha/{\rm H}\beta)=-0.0195\pm0.0003$, where $\Delta$ denotes DESI minus SDSS. These offsets persist in Baldwin--Phillips--Terlevich-selected star-forming galaxies and in a near-concentric subset. Their amplitudes vary across the redshift distribution and retain a measurable dependence on apparent fibre coverage after redshift is controlled, showing that redshift and relative aperture coverage jointly modulate the aperture terms. We conclude that, for high-S/N emission-line galaxies, DESI--SDSS comparisons carry measurable, diagnostic-dependent fibre-aperture terms even when continuum modelling, emission-line fitting, and spectral-resolution treatment are held fixed.

astro-ph.GA↗

arXiv2026

Electron Densities of Typical Low-Mass Galaxies at z~2-7 from Stacked JWST/NIRSpec Spectra

Direct electron-density measurements at high redshift are usually limited to galaxies with individually strong density-sensitive doublets, and therefore may not trace the average interstellar medium of ordinary low-mass galaxies. We stack public JWST/NIRSpec medium-resolution spectra from the DAWN JWST Archive to measure [SII]-based electron densities $n_e$ for low-mass galaxies at $2 5$ have a higher normalization, $n_{e,0}=211^{+36}_{-31}\ {\rm cm^{-3}}$, showing that individual-doublet samples select a denser subset. Stacking archival JWST spectra therefore provides a direct route to measuring the average gas density of low-mass galaxies below the individual-doublet detection threshold.

astro-ph.GA↗

arXiv2026

Stellar Surface Density Modulates MgII Cool-gas Outflow Absorption in DESI Star-forming Galaxies

Galaxy outflows are usually ordered by stellar mass and star-formation rate (SFR), but the same feedback budget may couple differently to gas in diffuse and compact galaxies. We use Dark Energy Spectroscopic Instrument (DESI) Data Release 1 stacked spectra of massive star-forming galaxies at $0.35<z<1.0$ to test whether stellar surface density, $\Sigma_\star=M_\star/(2\pi R_e^2)$, is an independent empirical coordinate of down-the-barrel singly ionized magnesium (MgII) cool-gas absorption. In AGN-clean samples matched in stellar mass, and in a stricter sample matched in both stellar mass and a Balmer-line SFR proxy, the MgII outflow equivalent width (EW) rises monotonically with $\Sigma_\star$ in every redshift bin. From the lowest to highest $\Sigma_\star$ tertile, EW increases by 0.37-0.61 Angstrom, while the absolute outflow velocity changes only weakly. DESI therefore shows that cool-gas outflow strength in massive star-forming galaxies is not set only by how much stellar mass or star formation a galaxy has, but also by how tightly the galaxy is built. The structural dependence points to changes in the absorbing velocity distribution and/or the effective covering fraction of cool outflowing gas.

astro-ph.GA↗

arXiv2026

A DESI Calibration of the [O II]--[S II] Electron-density Offset in Integrated Star-forming Galaxies

The [O II]$\lambda\lambda3726,3729$ and [S II]$\lambda\lambda6716,6731$ doublets are widely used as low-ionization electron-density diagnostics in galaxy spectra and are often treated as interchangeable when only one of them is accessible. We test this assumption using the DESI DR1 Emission Line Catalog. For star-forming galaxies with fiducial emission lines, [O II] yields systematically higher electron densities than [S II], with a median offset of 0.228 dex. The binned median calibration is $\log n_e({\rm OII})=(0.752^{+0.182}_{-0.097})\log n_e({\rm SII}) +(0.832^{+0.231}_{-0.422})$. The offset is larger in galaxies with higher stellar mass, H$\alpha$ star-formation rate, dust attenuation, and $N2\equiv\log([{\rm NII}]\lambda6583/{\rm H}\alpha)$, an empirical gas-phase metallicity proxy, and smaller in galaxies with higher $\log O_{32}\equiv\log\{[{\rm OIII}]\lambda5007/ ([{\rm OII}]\lambda3726+\lambda3729)\}$, an ionization proxy; no significant trend is found with specific star-formation rate. These trends are consistent with [O II] and [S II] sampling different low-ionization gas phases in integrated spectra, with [S II] more strongly weighted toward lower-density diffuse or outer gas. Our results show that [O II]- and [S II]-based densities should not be mixed without empirical calibration in studies of ISM pressure, nebular density, and their evolution across galaxy samples and redshift.

astro-ph.GA↗

arXiv2026

DELNet: Continuous All-in-One Weather Removal via Dynamic Expert Library

All-in-one weather image restoration methods are valuable in practice but depend on pre-collected data and require retraining for unseen degradations, leading to high cost. We propose DELNet, a continual learning framework for weather image restoration. DELNet integrates a judging valve that measures task similarity to distinguish new from known tasks, and a dynamic expert library that stores experts trained on different degradations. For new tasks, the valve selects top-k experts for knowledge transfer while adding new experts to capture task-specific features; for known tasks, the corresponding experts are directly reused. This design enables continuous optimization without retraining existing models. Experiments on OTS, Rain100H, and Snow100K demonstrate that DELNet surpasses state-of-the-art continual learning methods, achieving PSNR gains of 16\%, 11\%, and 12\%, respectively. These results highlight the effectiveness, robustness, and efficiency of DELNet, which reduces retraining cost and enables practical deployment in real-world scenarios.

cs.CV↗

arXiv2026

Updated Metallicity Diagnostics for Precision Oxygen Abundance Measurements in High-redshift Galaxies with JWST

Recent work has demonstrated that widely used strong-line oxygen abundance indicators, such as O3N2, $\rm R23$, and $\widehat{\rm R}$, suffer from large uncertainties when applied to high-redshift galaxies. We show that this loss of precision primarily arises because, at fixed \Oabund, galaxies span a wide dynamic range in ionization parameter and nitrogen enrichment. Here we develop updated indicators that explicitly incorporate both effects via the proxies O32 and N2O2. We define ${\rm R}_{\rm u}\equiv \rm R23+\alpha_1 O32+\alpha_2 N2O2$, $\widehat{\rm R}_{\rm u}\equiv \rm \widehat{R}+\beta_1 O32+\beta_2 N2O2$, and ${\rm O}_{\rm u}\equiv \rm O3N2+\gamma_1 O32+\gamma_2 N2O2$, and calibrate \Oabund~as low-order polynomials in each composite indicator. Applied to a JWST sample with $T_{\rm e}$-method abundances, the updated indicators substantially tighten the correlations with \Oabund, boosting adjusted coefficients of determination from $\mathbb{R}^2\lesssim 0$ (classical indicators) to $\mathbb{R}^2\gtrsim 0.5$ for the full sample and to $\sim 0.7$ at $z>2$. The residuals reveal a redshift evolution in the mapping between \Oabund, strong lines, ionization, and nitrogen enrichment, with a pivotal turning point near the cosmic noon ($z\sim 2$). Our calibrations provide a practical, physically grounded path to precise metallicity measurements in the JWST era and a firmer basis for quantifying early chemical enrichment and feedback.

astro-ph.GA↗

arXiv2025

The Internal Kinematics, Stellar Population, and Gas-phase Properties of The Pseudobulge in An Ultra-diffuse Galaxy: AGC721966

Leveraging spectroscopic data from the Sloan Digital Sky Survey, we conduct a comprehensive analysis of the central stellar velocity dispersion, stellar population properties, star formation history, and gas-phase chemical abundances in AGC721966, a unique ultra-diffuse galaxy (UDG) harboring a pseudobulge. Our findings reveal that the pseudobulge formed in the early universe but underwent a recent episode of rejuvenated star formation. The system exhibits a mass-weighted (light-weighted) stellar population age of $\tau_{\star}\sim 7.4\pm2.5$ ($2.9\pm1.5$)~Gyr, a stellar metallicity of [M/H]$\sim -0.62\pm0.26$ ($-0.55\pm0.20$), an $\alpha$-element enhancement of [$\alpha$/Fe]$\sim 0.36\pm0.09$ ($0.37\pm0.07$), and a gas-phase oxygen abundance of \Oabund$\sim 8.15\pm0.03$. The central stellar velocity dispersion is measured as $\sigma_{\rm c}\sim 57.9\pm15.7$~km/s. These results provide robust evidence supporting the early halo-halo merging formation scenario proposed by \cite{Rong25}, while unequivocally ruling out the ``failed'' $L^{\star}$ formation model, at least for AGC721966. Furthermore, through systematic application of the baryonic Tully-Fisher relation, we establish that these pseudobulge-hosting UDGs are neither misidentified nuclear star cluster-bearing dwarf galaxies nor bulge-dominated massive galaxies, thereby affirming their distinct evolutionary pathway.

astro-ph.GA↗

arXiv2024

Halo Spin Depends on The Distance to Cosmic Filament

We employ a semi-analytical methodology to estimate the dark matter halo spin of HI-rich galaxies in the Arecibo Legacy Fast Alfa Survey and investigate the relationship between halo spin and the proximity of galaxies to cosmic filaments. We exclude galaxies with low HI signal-to-noise ratios, those potentially influenced by velocity dispersions, and those affiliated with galaxy clusters/groups. Additionally, we apply a mass-weighting technique to ensure consistent mass distribution across galaxy samples at varying distances from filaments. Our analysis reveals, for the first time, a subtle yet statistically significant correlation between halo spin and filament distance in observational data, indicating higher spins closer to filaments. This suggests that the tidal forces exerted by filaments may impact the spin of dark matter halos.

astro-ph.GA↗

arXiv2024

Strong Correlation between Galactic HI-to-stellar Mass Ratio And Halo Spin Explored by HI-rich Galaxies

Using a semi-analytic approach, we estimate halo spins for a large sample of HI-rich galaxies from the Arecibo Legacy Fast Alfa Survey and examine the correlation between HI mass fractions and halo spins. Our analysis reveals a strong correlation between halo spin and the HI-to-stellar mass ratio in both low-mass and massive galaxy samples. This finding suggests a universal formation scenario: higher halo spin reduces angular momentum loss and gas condensation, leading to lower star formation rates and weaker feedback, which in turn helps retain gas within dark matter halos.

astro-ph.GA↗

arXiv2024

Language Models as Black-Box Optimizers for Vision-Language Models

Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically, we adopt an automatic hill-climbing procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit gradient direction in textual feedback for a more efficient search. In addition, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly, we apply our framework to optimize the state-of-the-art black-box VLM (DALL-E 3) for text-to-image generation, prompt inversion, and personalization.

cs.CL↗