arXiv subjects

Yun Qi Li

Publications and source records attributed to Yun Qi Li.

5 recordsLinked to original sources

arXiv2026

Observing Co-Located Neutral and Ionized Gas-Phase Iron Depletion in the Magellanic Clouds

Depletion is the observed phenomenon where gas-phase elemental abundances are reduced through accretion onto dust grains. We measure neutral gas-phase elemental abundances (S, Fe) in the Magellanic Clouds along 33 sightlines using high-resolution UV spectroscopy (HST/COS and HST/STIS), and compare them to ionized gas-phase abundances (S, Fe) adopted from the literature for six co-located H\,\textsc{ii} regions (with the furthest separation of $\lesssim3'$, 50 pc). Comparing S abundances show that S is minimally depleted in the H\,\textsc{ii} regions and surrounding diffuse ISM. However, we find that the gas-phase Fe abundances in H\,\textsc{ii} regions can be lower than those of the neighboring neutral ISM by 0.3 to 2 dex. This difference is likely an offset in the amount of Fe depleted into dust grains. As accretion of gas-phase Fe is likely not effective at the temperatures of the H\,\textsc{ii} regions, Fe depletion into solid form would have occurred in the dense atomic or molecular clouds prior to star formation. Stronger depletion in the H\,\textsc{ii} regions shows that Fe-bearing grains survive destruction in the first few million years following ionization. Our observations highlight that Fe depletion in H\,\textsc{ii} regions can be a useful tracer of Fe depletion in dense molecular clouds, which are challenging to observe directly via UV absorption.

astro-ph.GA↗

arXiv2025

Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models

Redshift measures the distance to galaxies and underlies our understanding of the origin of the Universe and galaxy evolution. Spectroscopic redshift is the gold-standard method for measuring redshift, but it requires about $1000$ times more telescope time than broad-band imaging. That extra cost limits sky coverage and sample size and puts large spectroscopic surveys out of reach. Photometric redshift methods rely on imaging in multiple color filters and template fitting, yet they ignore the wealth of information carried by galaxy shape and structure. We demonstrate that a diffusion model conditioned on continuous redshift learns this missing joint structure, reproduces known morphology-$z$ correlations. We verify on the HyperSuprime-Cam survey, that the model captures redshift-dependent trends in ellipticity, semi-major axis, Sérsic index, and isophotal area that these generated images correlate closely with true redshifts on test data. To our knowledge this is the first study to establish a direct link between galaxy morphology and redshift. Our approach offers a simple and effective path to redshift estimation from imaging data and will help unlock the full potential of upcoming wide-field surveys.

astro-ph.GA↗

arXiv2024

GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning

We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam Survey PDR2 in five imaging filters ($g,r,i,z,y$) with spectroscopically confirmed redshifts as ground truth. Such a dataset is important for machine learning applications because it is uniform, consistent, and has minimal outliers but still contains a realistic range of signal-to-noise ratios. We make this dataset public to help spur development of machine learning methods for the next generation of surveys such as Euclid and LSST. The aim of GalaxiesML is to provide a robust dataset that can be used not only for astrophysics but also for machine learning, where image properties cannot be validated by the human eye and are instead governed by physical laws. We describe the challenges associated with putting together a dataset from publicly available archives, including outlier rejection, duplication, establishing ground truths, and sample selection. This is one of the largest public machine learning-ready training sets of its kind with redshifts ranging from 0.01 to 4. The redshift distribution of this sample peaks at redshift of 1.5 and falls off rapidly beyond redshift 2.5. We also include an example application of this dataset for redshift estimation, demonstrating that using images for redshift estimation produces more accurate results compared to using photometry alone. For example, the bias in redshift estimate is a factor of 10 lower when using images between redshift of 0.1 to 1.25 compared to photometry alone. Results from dataset such as this will help inform us on how to best make use of data from the next generation of galaxy surveys.

astro-ph.CO↗

arXiv2024

A Dust-Scattering Model for M1-92: A Revised Estimate of the Mass Distribution and Inclination

Preplanetary nebulae (PPNe) are formed from mass-ejecting late-stage AGB stars. Much of the light from the star gets scattered or absorbed by dust particles, giving rise to the observed reflection nebula seen at visible and near-IR wavelengths. Precursors to planetary nebulae (PNe), PPNe generally have not yet undergone any ionization by UV radiation from the still-buried stellar core. Bipolar PPNe are a common form of observed PPNe. This study lays the groundwork for future dynamical studies by reconstructing the dust density distribution of a particularly symmetric bipolar PPN, M1-92 (Minkowski's Footprint, IRAS 19343$+$2926). For this purpose, we develop an efficient single-scattering radiative transfer model with corrections for double-scattering. Using a V-band image from the Hubble Space Telescope (HST), we infer the dust density profile and orientation of M1-92. These results indicate that M1-92's slowly expanding equatorial torus exhibits an outer radial cutoff in its density, which implicates the influence of a binary companion during the formation of the nebula.

astro-ph.SR↗

arXiv2024

Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models

Generative models producing images have enormous potential to advance discoveries across scientific fields and require metrics capable of quantifying the high dimensional output. We propose that astrophysics data, such as galaxy images, can test generative models with additional physics-motivated ground truths in addition to human judgment. For example, galaxies in the Universe form and change over billions of years, following physical laws and relationships that are both easy to characterize and difficult to encode in generative models. We build a conditional denoising diffusion probabilistic model (DDPM) and a conditional variational autoencoder (CVAE) and test their ability to generate realistic galaxies conditioned on their redshifts (galaxy ages). This is one of the first studies to probe these generative models using physically motivated metrics. We find that both models produce comparable realistic galaxies based on human evaluation, but our physics-based metrics are better able to discern the strengths and weaknesses of the generative models. Overall, the DDPM model performs better than the CVAE on the majority of the physics-based metrics. Ultimately, if we can show that generative models can learn the physics of galaxy evolution, they have the potential to unlock new astrophysical discoveries.

astro-ph.IM↗