Star clusters were historically considered simple stellar populations, with all stars sharing the same age and initial chemical composition. However, the presence of chemical anomalies in globular clusters (GCs), called multiple stellar populations (MPs), has challenged star formation theories in dense environments. Literature studies show that mass, metallicity, and age are likely controlling parameters for the manifestation of MPs. Identifying the limit between clusters with/without MPs in physical parameter space is crucial to reveal the driving mechanism behind their presence. In this study, we look for MP signals in Whiting 1, traditionally considered a young GC. Using the Magellan telescope, we obtained low-resolution spectra within $\rm \lambda\lambda = 3850-5500 Å$ for eight giants of Whiting 1. We measured the C and N abundances from the CN and CH spectral indices. C and N abundances have variations comparable with their measurement errors ($\sim0.1$ dex), suggesting that MPs are absent from Whiting 1. Combining these findings with literature studies, we propose a limit in the metallicity vs. cluster compactness index parameter space, which relatively clearly separates star clusters with/without MPs (GCs/open clusters). This limit is physically motivated. On a larger scale, the galactic environment determines cluster compactness and metallicity, leading to metal-rich, diffuse, old clusters formed ex situ. Our proposed limit also impacts our understanding of the formation of the Sagittarius dwarf galaxy: star clusters formed after the first starburst (age$\lesssim 8-10$ Gyr). These clusters are simple stellar populations because the enriched galactic environment is no longer suitable for MP formation.

Galaxy-galaxy strong gravitational lensing (GGSL) is a powerful probe for the formation and evolution of galaxies and cosmology, while the sample size of GGSLs leads to considerable uncertainties and potential bias. The China Space Station Telescope (CSST, to be launched in late 2026) will conduct observations across 17,500 square degrees of the sky, capturing images in the $ugriz$ bands with a spatial resolution comparable to that of the Hubble Space Telescope. We ran a set of Monte Carlo simulations to predict that the CSST's wide-field survey will observe $\sim$160,000 galaxy-galaxy strong lenses over its lifespan, increasing the number of existing galaxy-galaxy strong lens samples by three orders of magnitude. This is comparable to the capabilities of the $\it Euclid$ telescope but with the added benefit of additional color information. Specifically, the CSST can detect strong lenses with Einstein radii about $0.64\pm0.42^{"}$, corresponding to the velocity dispersions of $217.19 \pm 50.55 \, \text{km/s}$. These lenses exhibit a median magnification of $\sim$5. The apparent magnitude of the unlensed sources in the g-band is $25.87 \pm 1.19$. The signal-to-noise ratio of the lensed images covers a range of $\sim 20$ to $\sim 1000$, allowing us to determine the Einstein radius with an accuracy ranging from $\sim 1 \%$ to $\sim 0.1 \%$, ignoring various modeling systematics. Our estimates indicate that CSST can observe rare systems like double source-plane and spiral galaxy lenses. The above selection functions of the CSST strong lensing observation help optimize the strategy of finding and modeling GGSLs.

We create the first large-scale mock spectroscopic survey of gas absorption sightlines traversing the interstellar medium (ISM), circumgalactic medium (CGM), and intergalactic medium (IGM) surrounding galaxies of virtual Universes. That is, we create mock, or synthetic, absorption spectra by drawing lines-of-sight through cosmological hydrodynamical simulations, using a new mesh-free Voronoi ray-tracing algorithm. The result is the Synthetic Absorption Line Spectral Almanac (SALSA), which is publicly released on a feature-rich online science platform (this http URL). It spans a range of ions, transitions, instruments, observational characteristics, assumptions, redshifts, and simulations. These include, but are not limited to: (ions) HI, OI, CI, MgI, MgII, FeII, SiII, CaII, ZnII, SiIII, SiIV, NV, CII, CIV, OVI; (instruments) SDSS-BOSS, KECK-HIRES, UVES, COS, DESI, 4MOST, WEAVE, XSHOOTER; (model choices) with/without dust depletion, noise, quasar continua, foregrounds; (redshift) from z=0 to z~6; (ancillary data) integrated equivalent widths, column densities, distances and properties of nearby galaxies; (simulations) IllustrisTNG including TNG50, TNG-Cluster, EAGLE, and SIMBA. This scope is not fixed, and will grow and evolve with community interest and requests over time -- suggestions are welcome. The resulting dataset is generic and broadly applicable, enabling diverse science goals such as: (i) studies of the underlying physical gas structures giving rise to particular absorption signatures, (ii) galaxy-absorber and halo-absorber correlations, (iii) virtual surveys and survey strategy optimization, (iv) stacking experiments and the identification of faint absorption features, (v) assessment of data reduction methods and completeness calculations, (vi) inference of physical properties from observables, and (vii) apples-to-apples comparisons between simulations and data.

While Large Language Models (LLMs) have become the predominant paradigm for automated code generation, current single-model approaches fundamentally ignore the heterogeneous computational strengths that different models exhibit across programming languages, algorithmic domains, and development stages. This paper challenges the single-model convention by introducing a multi-stage, performance-guided orchestration framework that dynamically routes coding tasks to the most suitable LLMs within a structured generate-fix-refine workflow. Our approach is grounded in a comprehensive empirical study of 17 state-of-the-art LLMs across five programming languages (Python, Java, C++, Go, and Rust) using HumanEval-X benchmark. The study, which evaluates both functional correctness and runtime performance metrics (execution time, mean/max memory utilization, and CPU efficiency), reveals pronounced performance heterogeneity by language, development stage, and problem category. Guided by these empirical insights, we present PerfOrch, an LLM agent that orchestrates top-performing LLMs for each task context through stage-wise validation and rollback mechanisms. Without requiring model fine-tuning, PerfOrch achieves substantial improvements over strong single-model baselines: average correctness rates of 96.22% and 91.37% on HumanEval-X and EffiBench-X respectively, surpassing GPT-4o's 78.66% and 49.11%. Beyond correctness gains, the framework delivers consistent performance optimizations, improving execution time for 58.76% of problems with median speedups ranging from 17.67% to 27.66% across languages on two benchmarks. The framework's plug-and-play architecture ensures practical scalability, allowing new LLMs to be profiled and integrated seamlessly, thereby offering a paradigm for production-grade automated software engineering that adapts to the rapidly evolving generative AI landscape.

We present CAPERS-LRD-z9, a little red dot (LRD) which we confirm to be a $z=9.288$ broad-line AGN (BLAGN). First identified as a high-redshift LRD candidate from PRIMER NIRCam photometry, follow-up NIRSpec/PRISM spectroscopy of CAPERS-LRD-z9 from the CANDELS-Area Prism Epoch of Reionization Survey (CAPERS) has revealed a broad $3500$ km s$^{-1}$ H$\beta$ emission line and narrow [O III]$\lambda\lambda4959,5007$ lines, indicative of a BLAGN. Based on the broad H$\beta$ line, we compute a canonical black-hole mass of $\log(M_{\textrm{BH}}/M_{\odot})=7.58\pm0.15$, although full consideration of systematic uncertainties yields a conservative range of 6.65<\log(M_{\textrm{BH}}/M_{\odot})<8.50. These observations suggest that either a massive black hole seed, or a lighter stellar remnant seed undergoing periods of super-Eddington accretion, is necessary to grow such a massive black hole in $\lesssim500$ Myr of cosmic time. CAPERS-LRD-z9 exhibits a strong Balmer break, consistent with a central AGN surrounded by dense ($\sim 10^{10}\textrm{ cm}^{-3}$) neutral gas. We model CAPERS-LRD-z9 using CLOUDY to fit the emission red-ward of the Balmer break with a dense gas-enshrouded AGN, and bagpipes to fit the rest-ultraviolet emission as a host-galaxy stellar population. This upper limit on the stellar mass of the host galaxy (<10^9\,{\rm M_\odot}) implies that the black-hole to stellar mass ratio may be extremely large, possibly >5\% (although systematic uncertainties on the black-hole mass prevent strong conclusions). However, the shape of the UV continuum differs from typical high-redshift star-forming galaxies, indicating that this UV emission may also be of AGN origin, and hence the true stellar mass of the host may be still lower.

Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on textual data within issues, neglecting visual data. However, this visual data is crucial for resolving issues as it conveys additional knowledge that text alone cannot. We propose CodeV, the first approach to leveraging visual data to enhance the issue-resolving capabilities of LLMs. CodeV resolves each issue by following a two-phase process: data processing and patch generation. To evaluate CodeV, we construct a benchmark for visual issue resolving, namely Visual SWE-bench. Through extensive experiments, we demonstrate the effectiveness of CodeV, as well as provide valuable insights into leveraging visual data to resolve GitHub issues.

We present the distance priors from the finally released $Planck~ \textrm{TT,TE,EE}+\textrm{lowE}$ data in 2018. The uncertainties are around $40\%$ smaller than those from $Planck$ 2015 TT$+$lowP. In order to check the validity of these new distance priors, we adopt the distance priors to constrain the cosmological parameters in different dark energy models, including the $\Lambda$CDM model, the $w$CDM model and the CPL model, and conclude that the distance priors provide consistent constraints on the relevant cosmological parameters compared to those from the full $Planck$ 2018 data release.

Integrated photonic circuits are foundational for versatile applications, where high-performance traveling-wave optical resonators are critical. Conventional whispering-gallery mode microresonators (WGMRs) confine light in closed-loop waveguide paths, thus inevitably occupy large footprints. Here, we report an ultracompact high loaded Q silicon photonic WGMR in an open curved path instead. By leveraging spatial mode multiplexing, low-loss mode converter-based photonic routers enable reentrant photon recycling in a single non-closed waveguide. The fabricated device achieves a measured loaded Q-factor of 1.78*10^5 at 1554.3 nm with a 1.05 nm free spectral range in a ultracompact footprint of 0.00137 mm^2-6*smaller than standard WGMRs while delivering 100*higher Q-factor than photonic crystal counterparts. This work pioneers dense integration of high-performance WGMR arrays through open-path mode recirculation.

This work aims to analyze how hyperons affect neutrino radiation properties in nucleonic direct URCA processes, expecting to provide useful references for finding evidence of the existence of hyperons in astronomical observations. This analysis is carried out using the GM1 and NL3 parameter sets under the SU(6) and SU(3) flavor symmetries in the relativistic mean field theory framework. Combined with the inferred mass and radius values of PSRs J1231-1411, J0030+0451, and J0740+6620, our results show that nucleonic direct Urca processes are absent in PSR J1231-1411 due to momentum conservation violation. In hyperon-containing PSR J0030+0451 (NL3 parameter set), the nucleonic direct Urca processes involving $e^{-}$/ $\mu^{-}$ would occur. A large inferred mass span induces hyperon fraction variations, affecting neutrino emissivity. If the inferred mass of PSR J0030+0451 exceeds approximately 1.8 $M_{\odot}$, the neutrino luminosity of the nucleonic direct Urca processes under the SU(3) flavor symmetry remains nearly the same as that in npe$\mu$ matter, without depending on hyperons. However, it exhibits an obvious hyperon dependence under the SU(6) spin-flavor symmetry. For hyperon-containing J0740+6620, the nucleonic direct Urca processes under the SU(3) flavor symmetry in GM1 parameter set predicts faster neutrino luminosity decline with hyperonic fraction than npe$\mu$ matter, and under the SU(6) spin-flavor symmetry in NL3 parameter set it shows monotonic decreasing trend. The research shows that hyperonic fraction significantly affect the neutrino radiation properties of the nucleonic direct URCA processes in neutron stars. Different-mass pulsars (e.g., PSRs J1231-1411, J0030+0451, J0740+6620) exhibit the distinct nucleonic direct URCA processes behaviors, dependent on inferred masses/radii, parameter sets, and theoretical models.

Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feature extraction using different network architectures, the integration of multimodal information, and the adoption of unsupervised or semi-supervised learning strategies. However, despite extensive research, MRI reconstruction remains a challenging problem that has yet to be fully resolved. This survey provides a systematic review of MRI reconstruction methods, covering key aspects such as data acquisition and preprocessing, publicly available datasets, single and multi-modal reconstruction models, training strategies, and evaluation metrics based on image reconstruction and downstream tasks. Additionally, we analyze the major challenges in this field and explore potential future directions.

Stellar abundances for a large number of stars are key information for the study of Galactic formation history. Large spectroscopic surveys such as DESI and LAMOST take median-to-low resolution ($R\lesssim5000$) spectra in the full optical wavelength range for millions of stars. However, line blending effect in these spectra causes great challenges for the elemental abundances determination. Here we employ the DD-PAYNE, a data-driven method regularised by differential spectra from stellar physical models, to the DESI EDR spectra for stellar abundance determination. Our implementation delivers 15 labels, including effective temperature $T_{\rm eff}$, surface gravity $\log g$, microturbulence velocity $v_{\rm mic}$, and abundances for 12 individual elements, namely C, N, O, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Ni. Given a spectral signal-to-noise ratio of 100 per pixel, internal precision of the label estimates are about 20 K for $T_{\rm eff}$, 0.05 dex for $\log~g$, and 0.05 dex for most elemental abundances. These results are agree with theoretical limits from the Cr\'amer-Rao bound calculation within a factor of two. The Gaia-Enceladus-Sausage that contributes the majority of the accreted halo stars are discernible from the disk and in-situ halo populations in the resultant [Mg/Fe]-[Fe/H] and [Al/Fe]-[Fe/H] abundance spaces. We also provide distance and orbital parameters for the sample stars, which spread a distance out to $\sim$100 kpc. The DESI sample has a significant higher fraction of distant (or metal-poor) stars than other existed spectroscopic surveys, making it a powerful data set to study the Galactic outskirts. The catalog is publicly available.