arXiv subjects

Yisheng Qiu

Publications and source records attributed to Yisheng Qiu.

17 recordsLinked to original sources

arXiv2026

The ALMA-QUARKS survey: Investigating Thermal Feedback of Massive Protostars in Hot Molecular Cores

We identify a sample of 83 spatially resolved hot molecular cores (HMCs) in the QUARKS survey, aiming at investigating thermal feedback from massive stars. Using CH$_3$CN\,(12--11) line emission together with 1.3\,mm continuum data we derive the radial temperature, volume density and \ch3cn{} abundance profiles for the 83 HMCs. Based on the envelope temperature and density profiles, we compute the luminosities of the embedded massive protostars with \radmc{} radiation transfer model. The derived luminosities are comparable (within $\sim1$ dex) to the bolometric luminosities of their natal clumps and show strong correlations with several core-scale properties, including the HMC mass ($Log[ M_\mathrm{env}] = 1.01\,Log [L_\star] - 4.80$), the inner core radius (the flat radius of Plummer-like volume density profile) ($Log[a] = 0.46\,Log[L_\star] + 0.52$) and the central density $ (Log[n_c] = -0.55 Log[L_\star] +10.47) $. These empirical relations provide useful observational constraints for physical models of protostellar objects. Importantly, we find a strong positive correlation between the massive protostellar luminosity and the local thermal Jeans mass. The derived Jeans masses, $M_\mathrm{Jeans}$, exceed the HMC masses $M_\mathrm{env}$, with the average $M_\mathrm{Jeans}$ being two times larger than the average $M_\mathrm{env}$. This provides observational evidence that thermal feedback from massive protostars can effectively suppress further fragmentation of HMCs, thereby promoting massive star formation. In addition, the positive correlation between massive protostellar luminosity and natal clump mass suggests that more massive clumps preferentially host more luminous protostars, leading to stronger thermal feedback.

astro-ph.GA↗

arXiv2026

Spectuner-D1: Spectral Line Fitting of Interstellar Molecules Using Deep Reinforcement Learning

Spectral lines from interstellar molecules provide crucial insights into the physical and chemical conditions of the interstellar medium. Traditional spectral line analysis relies heavily on manual intervention, which becomes impractical when handling the massive datasets produced by modern facilities like ALMA. To address this challenge, we introduce a novel deep reinforcement learning framework to automate spectral line fitting. Using observational data from ALMA, we train a neural network that maps both molecular spectroscopic data and observed spectra to physical parameters such as excitation temperature and column density. The neural network predictions can serve as initial estimates and be further refined using a local optimizer. Our method achieves consistent fitting results compared to global optimization with multiple runs, while reducing the number of forward modeling runs by an order of magnitude. We apply our method to pixel-level fitting for an observation of the G327.3-0.6 hot core and validate our results using XCLASS. We perform the fitting for typical complex organic molecules of hot cores, including CH$_3$OH, CH$_3$OCHO, CH$_3$OCH$_3$, C$_2$H$_5$CN, and C$_2$H$_3$CN. For a 100 $\times$ 100 region covering 5 GHz bandwidth, the fitting process requires 4.9 to 41.9 minutes using a desktop with 16 cores and one consumer-grade GPU card.

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arXiv2025

A semi-analytical mock galaxy catalog for the CSST extragalactic surveys from the Jiutian simulations

We introduce a mock galaxy catalog built for the CSST extragalactic surveys using the primary runs of the Jiutian $N$-body simulation suites. The catalogs are built by coupling the GAlaxy Evolution and Assembly (GAEA) semi-analytical model of galaxy formation with merger trees extracted from the simulations using the Hierarchical Bound-Tracing (HBT+) algorithm. The spectral energy distributions (SEDs) and broadband magnitudes are computed using the neural-network-based stellar population synthesizer StarDuster, which is trained on radiative transfer simulations to account for detailed galaxy geometry in modeling dust obscuration. Galaxy light-cones up to $z=5$ are subsequently generated with the BLiC light-cone builder which interpolates the properties of galaxies over time using an optimized interpolation scheme. The resulting catalogs exhibit good convergence in many statistical properties of the galaxy population produced from two different resolution simulations. The catalogs reproduce a number of observed galaxy properties across a range of galaxy mass and redshift, including the stellar mass functions, the luminosity function, gas mass fraction, galaxy size-mass relation and galaxy clustering. We also present the photometric and redshift distributions of galaxies expected to be observed in the CSST surveys.

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arXiv2025

The ALMA-QUARKS survey: Hot Molecular Cores are a long-standing phenomenon in the evolution of massive protostars

We present an analysis of the QUARKS survey sample, focusing on protoclusters where Hot Molecular Cores (HMCs, traced by CH3CN(12--11)) and UC HII regions (traced by H30α/H40α) coexist. Using the high-resolution, high-sensitivity 1.3 mm data from the QUARKS survey, we identify 125 Hot Molecular Fragments (HMFs), which represent the substructures of HMCs at higher resolution. From line integrated intensity maps of CH3CN(12--11) and H30α, we resolve the spatial distribution of HMFs and UC HII regions. By combining with observations of CO outflows and 1.3 mm continuum, we classify HMFs into four types: HMFs associated with jet-like outflow, with wide-angle outflow, with non-detectable outflow, and shell-like HMFs near UC HII regions. This diversity possibly indicates that the hot core could be polymorphic and long-standing phenomenon in the evolution of massive protostars. The separation between HMFs and H30α/H40αemission suggests that sequential high-mass star formation within young protoclusters is not likely related to feedback mechanisms.

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arXiv2025

Gas-phase Molecules in Protoplanetary Nebulae with the 21 μm Emission Feature II. Carbon monosulfide

The carrier of the 21 $μ$m emission feature discovered in a handful of protoplanetary nebulae (PPNe) is one of the most intriguing enigmas in circumstellar chemistry. Investigating the gas-phase molecules in PPNe could yield important hints for understanding the 21 $μ$m feature. In this paper, we report observations of the CS $J = 5 \to 4$ line at 245 GHz and the CO $J = 1 \to 0$ line at 115 GHz toward seven PPNe exhibiting the 21 $μ$m feature. We find that CS is extremely scarce in these PPNe and the CS line is only detected in one source, IRAS Z02229+6208. Based on the assumption of local thermal equilibrium and negligible optical depth, we derive that the CS column densities and fractional abundances relative to H$_{2}$ are $N$(CS) < 9.1 ${\times}$ 10$^{13}$cm$^{-2}$ and $f$(CS) < 8.1 ${\times}$ 10$^{-7}$. A comparison of the CS abundances across different circumstellar envelopes reveals that the variations in CS abundance are complex, depending not only on the evolutionary stages but also on the properties of individual objects.

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arXiv2025

Application of Physics-Informed Neural Networks in Removing Telescope Beam Effects

This study introduces {\tt{PI-AstroDeconv}}, a physics-informed semi-supervised learning method specifically designed for removing beam effects in astronomical telescope observation systems. The method utilizes an encoder-decoder network architecture and combines the telescope's point spread function or beam as prior information, while integrating fast Fourier transform accelerated convolution techniques into the deep learning network. This enables effective removal of beam effects from astronomical observation images. {\tt{PI-AstroDeconv}} can handle multiple PSFs or beams, tolerate imprecise measurements to some extent, and significantly improve the efficiency and accuracy of image deconvolution. Therefore, this architecture is particularly suitable for astronomical data processing that does not rely on annotated data. To validate the reliability of the architecture, we used the SKA Science Data Challenge 3a datasets and compared it with the $\tt{CLEAN}$ deconvolution method at the 21-cm power spectrum level. The results demonstrate that our algorithm not only restores details and reduces blurriness in celestial images at the pixel level but also more accurately recovers the true neutral hydrogen power spectrum at the power spectrum level.

astro-ph.IM↗

arXiv2025

The Jiutian simulations for the CSST extra-galactic surveys

We provide an overview of the Jiutian simulations, a hybrid simulation suite for the China Space Survey Telescope (CSST) extragalactic surveys. It consists of four complementary modules: the primary runs with high resolutions with the fiducial concordance cosmology, the emulator runs exploring the parameter uncertainties around the fiducial cosmology, the reconstruction runs intended for recovering the observed Universe position by position, and the extension runs employing extended cosmologies beyond the standard model. For the primary runs, two independent pipelines are adopted to construct subhaloes and merger trees. On top of them, four sets of mock galaxy light-cone catalogs are produced from semi-analytical models and subhalo abundance matching, providing a variety of observational properties including galaxy SED, emission lines, lensing distortions, and mock images. The 129 emulator runs are used to train the CSST emulator, achieving one percent accuracy in predicting the matter power spectrum over $k\leq 10h{\rm Mpc}^{-1}$ and $z\leq 2$. The reconstruction runs employ a number of subgrid baryonic models to predict the evolution and galaxy population resembling certain regions in the real Universe with constrained initial conditions, enabling controlled investigation of galaxy formation on top of structure formation. The extension runs cover models with warm dark matter, $f(R)$ gravity, interacting dark energy, and nonzero neutrino masses, revealing differences in the cosmic structure under alternative cosmological models. We introduce the specifications for each run, the data products derived from them, the corresponding pipeline developments, and present some main tests. Using the primary runs, we also show that the subhalo peak mass functions of different levels are approximately universal. These simulations form a comprehensive and open library for CSST surveys and beyond.

astro-ph.CO↗

arXiv2025

Spectuner: A Framework for Automated Line Identification of Interstellar Molecules

Interstellar molecules, which play an important role in astrochemistry, are identified using observed spectral lines. Despite the advent of spectral analysis tools in the past decade, the identification of spectral lines remains a tedious task that requires extensive manual intervention, preventing us from fully exploiting the vast amounts of data generated by large facilities such as ALMA. This study aims to address the aforementioned issue by developing a framework of automated line identification. We introduce a robust spectral fitting technique applicable for spectral line identification with minimal human supervision. Our method is assessed using published data from five line surveys of hot cores, including W51, Orion-KL, Sgr B2(M), and Sgr B2(N). By comparing the identified lines, our algorithm achieves an overall recall of ~ 74% - 93%, and an average precision of ~ 78% - 92%. Our code, named Spectuner, is publicly available on GitHub.

astro-ph.GA↗

arXiv2024

CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)

Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine learning algorithms and applied to cut-out-centered galaxies. However, according to the design and survey strategy of optical surveys by CSST, preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images. Moreover, given that multi-color images of strong lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong lensing systems in images with any number of channels. As evaluated using CSST mock data based on an Semi-Analytic Model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. 61 new strong lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.

astro-ph.IM↗

arXiv2024

Forklens: Accurate weak-lensing shear measurement with deep learning

Weak gravitational lensing is one of the most important probes of the nature of dark matter and dark energy. In order to extract cosmological information from next-generation weak lensing surveys (e.g., Euclid, Roman, LSST, and CSST) as much as possible, accurate measurements of weak lensing shear are required. There are existing algorithms to measure the weak lensing shear on imaging data, which have been successfully applied in previous surveys. In the meantime, machine learning (ML) has been widely recognized in various astrophysics applications in modeling and observations. In this work, we present a fully deep-learning-based approach to measuring weak lensing shear accurately. Our approach comprises two modules. The first one contains a convolutional neural network (CNN) with two branches for taking galaxy images and point spread function (PSF) simultaneously, and the output of this module includes the galaxy's magnitude, size, and shape. The second module includes a multiple-layer neural network (NN) to calibrate weak-lensing shear measurements. We name the program Forklens and make it publicly available online. Applying Forklens to CSST-like mock images, we achieve consistent accuracy with traditional approaches (such as moment-based measurement and forward model fitting) on the sources with high signal-to-noise ratios (S/N, > 20). For the sources with S/N < 10, Forklens exhibits an $\sim 36\%$ higher Pearson coefficient on galaxy ellipticity measurements. After adopting galaxy weighting, the shear measurements with Forklens deliver accuracy levels to $0.2\%$. The whole procedure of Forklens is automated and costs about $0.7$ milliseconds per galaxy, which is appropriate for adequately taking advantage of the sky coverage and depth of the upcoming weak lensing surveys.

astro-ph.CO↗

arXiv2024

PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution

In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task. However, deconvolution presents a classical and challenging inverse computation problem. In scenarios where the beam or PSF is complex or inaccurately measured, such as in interferometric arrays and certain radio telescopes, the resultant blurry images are often challenging to interpret visually or analyze using traditional physical detection methods. We argue that traditional methods frequently lack specific prior knowledge, thereby leading to suboptimal performance. To address this issue and achieve image deconvolution and reconstruction, we propose an unsupervised network architecture that incorporates prior physical information. The network adopts an encoder-decoder structure while leveraging the telescope's PSF as prior knowledge. During network training, we introduced accelerated Fast Fourier Transform (FFT) convolution to enable efficient processing of high-resolution input images and PSFs. We explored various classic regression networks, including autoencoder (AE) and U-Net, and conducted a comprehensive performance evaluation through comparative analysis.

astro-ph.IM↗

arXiv2023

Thermal and Reionisation History within a Large-Volume Semi-Analytic Galaxy Formation Simulation

We predict the 21-cm global signal and power spectra during the Epoch of Reionisation using the MERAXES semi-analytic galaxy formation and reionisation model, updated to include X-ray heating and thermal evolution of the intergalactic medium. Studying the formation and evolution of galaxies together with the reionisation of cosmic hydrogen using semi-analytic models (such as MERAXES) requires N-body simulations within large volumes and high mass resolutions. For this, we use a simulation of side-length $210~h^{-1}$ Mpc with $4320^3$ particles resolving dark matter haloes to masses of $5\times10^8~h^{-1}~M_\odot$. To reach the mass resolution of atomically cooled galaxies, thought to be the dominant population contributing to reionisation, at $z=20$ of $\sim 2\times10^7~h^{-1}~M_\odot$, we augment this simulation using the DARKFOREST Monte-Carlo merger tree algorithm (achieving an effective particle count of $\sim10^{12}$). Using this augmented simulation we explore the impact of mass resolution on the predicted reionisation history as well as the impact of X-ray heating on the 21-cm global signal and the 21-cm power spectra. We also explore the cosmic variance of 21-cm statistics within $70^{3}$ $h^{-3}$ Mpc$^3$ sub-volumes. We find that the midpoint of reionisation varies by $Δz\sim0.8$ and that the cosmic variance on the power spectrum is underestimated by a factor of $2-4$ at $k\sim 0.1-0.4$ Mpc$^{-1}$ due to the non-Gaussian nature of the 21-cm signal. To our knowledge, this work represents the first model of both reionisation and galaxy formation which resolves low-mass atomically cooled galaxies while simultaneously sampling sufficiently large scales necessary for exploring the effects of X-rays in the early Universe.

astro-ph.CO↗

arXiv2022

Can we constrain galaxy geometry parameters using spatially integrated SED fitting?

Sophisticated spectral energy distribution (SED) models describe dust attenuation and emission using geometry parameters. This treatment is natural since dust effects are driven by the underlying star-dust geometry in galaxies. An example is the Starduster SED model, which divides a galaxy into a stellar disk, a stellar bulge, and a dust disk. This work utilises the Starduster SED model to study the efficacy of inferring geometry parameters using spatially integrated SED fitting. Our method fits the SED model to mock photometry produced by combining a semi-analytic model with the same SED model. Our fitting results imply that the disk radius can be constrained, while the inclination angle, dust disk to stellar disk radius ratio, bulge radius and intrinsic bulge to total luminosity ratio are unconstrained, even though 21 filters from UV to FIR are used. We also study the impact of S/N, finding that the increase of S/N (up to 80) brings limited improvements to the results. We provide a detailed discussion to explain these findings, and point out the implications for models with more general geometry.

astro-ph.GA↗

arXiv2022

Starduster: A multi-wavelength SED model based on radiative transfer simulations and deep learning

We present Starduster, a supervised deep learning model that predicts the multi-wavelength SED from galaxy geometry parameters and star formation history by emulating dust radiative transfer simulations. The model is comprised of three specifically designed neural networks, which take into account the features of dust attenuation and emission. We utilise the Skirt radiative transfer simulation to produce data for the training data of neural networks. Each neural network can be trained using ~ 4000 - 5000 samples. Compared with the direct results of the Skirt simulation, our deep learning model produces ~ 0.005 mag and ~ 0.1 - 0.2 mag errors for dust attenuation and emission respectively. As an application, we fit our model to the observed SEDs of IC4225 and NGC5166. Our model can reproduce the observations, and provide reasonable measurements of the inclination angle and stellar mass. However, some predicted geometry parameters are different from an image-fitting study. Our analysis implies that including a constraint at (rest-frame) ~ 40 micron could alleviate the degeneracy in the parameter space for both IC4225 and NGC5166, leading to broadly consistent results with the image-fitting predictions. Our SED code is publicly available and can be applied to both SED-fitting and SED-modelling of galaxies from semi-analytic models.

astro-ph.GA↗

arXiv2020

An efficient hybrid method to produce high resolution large volume dark matter simulations for semi-analytic models of reionisation

Resolving faint galaxies in large volumes is critical for accurate cosmic reionisation simulations. While less demanding than hydrodynamical simulations, semi-analytic reionisation models still require very large N-body simulations in order to resolve the atomic cooling limit across the whole reionisation history within box sizes $\gtrsim 100 \, h^{-1} {\rm Mpc}$. To facilitate this, we extend the mass resolution of N-body simulations using a Monte Carlo algorithm. We also propose a method to evolve positions of Monte Carlo halos, which can be an input for semi-analytic reionisation models. To illustrate, we present an extended halo catalogue that reaches a mass resolution of $M_\text{halo} = 3.2 \times 10^7 \, h^{-1} \text{M}_\odot$ in a $105 \, h^{-1} {\rm Mpc}$ box, equivalent to an N-body simulation with $\sim 6800^3$ particles. The resulting halo mass function agrees with smaller volume N-body simulations with higher resolution. Our results also produce consistent two-point correlation functions with analytic halo bias predictions. The extended halo catalogues are applied to the \textsc{meraxes} semi-analytic reionisation model, which improves the predictions on stellar mass functions, star formation rate densities and volume-weighted neutral fractions. Comparison of high resolution large volume simulations with both small volume or low resolution simulations confirms that both low resolution and small volume simulations lead to reionisation ending too rapidly. Lingering discrepancies between the star formation rate functions predicted with and without our extensions can be traced to the uncertain contribution of satellite galaxies.

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arXiv2019

Dark-ages Reionization and Galaxy Formation Simulation -- XIX: Predictions of infrared excess and cosmic star formation rate density from UV observations

We present a new analysis of high-redshift UV observations using a semi-analytic galaxy formation model, and provide self-consistent predictions of the infrared excess (IRX) -- $β$ relations and cosmic star formation rate density. We combine the Charlot & Fall dust attenuation model with the Meraxes semi-analytic model, and explore three different parametrisations for the dust optical depths, linked to star formation rate, dust-to-gas ratio and gas column density respectively. A Bayesian approach is employed to statistically calibrate model free parameters including star formation efficiency, mass loading factor, dust optical depths and reddening slope directly against UV luminosity functions and colour-magnitude relations at z ~ 4-7. The best-fit models show excellent agreement with the observations. We calculate IRX using energy balance arguments, and find that the large intrinsic scatter in the IRX -$β$ plane is driven by the specific star formation rate. Additionally, the difference among the three dust models suggests a factor of two systematic uncertainty in the dust-corrected star formation rate when using the Meurer IRX - $β$ relation at z > 4.

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arXiv2018

Dependence of galaxy clustering on UV-luminosity and stellar mass at $z \sim 4 - 7$

We investigate the dependence of galaxy clustering at $z \sim 4 - 7$ on UV-luminosity and stellar mass. Our sample consists of $\sim$ 10,000 Lyman-break galaxies (LBGs) in the XDF and CANDELS fields. As part of our analysis, the $M_\star - M_{\rm UV}$ relation is estimated for the sample, which is found to have a nearly linear slope of $d\log_{10} M_\star / d M_{\rm UV} \sim 0.44$. We subsequently measure the angular correlation function and bias in different stellar mass and luminosity bins. We focus on comparing the clustering dependence on these two properties. While UV-luminosity is only related to recent starbursts of a galaxy, stellar mass reflects the integrated build-up of the whole star formation history, which should make it more tightly correlated with halo mass. Hence, the clustering segregation with stellar mass is expected to be larger than with luminosity. However, our measurements suggest that the segregation with luminosity is larger with $\simeq 90\%$ confidence (neglecting contributions from systematic errors). We compare this unexpected result with predictions from the \textsc{Meraxes} semi-analytic galaxy formation model. Interestingly, the model reproduces the observed angular correlation functions, and also suggests stronger clustering segregation with luminosity. The comparison between our observations and the model provides evidence of multiple halo occupation in the small scale clustering.

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