arXiv subjects

Jianzhen Chen

Publications and source records attributed to Jianzhen Chen.

11 recordsLinked to original sources

arXiv2026

Indirect evidence of the $2175\,\mathring{\mathrm{A}}$ extinction bump within the dusty torus of SDSS J141945.50+524648.0

We present a multi-wavelength study of the quasar SDSS J141945.50$+$524648.0 ($z=1.1599$), a member of the newly identified population of quasar-associated $2175\,\mathring{\mathrm{A}}$ dust absorbers. Utilizing JWST/NIRCam observations from the SAPPHIRES survey and archival data, we analyze the prominent $2175\,\mathring{\mathrm{A}}$ extinction bump detected in this object. The bump parameters are highly consistent with the Milky Way extinction curve, implying similar dust properties. Spectral analysis reveals a $\mathrm{Mg\,II}$ absorption doublet near the systemic velocity. Joint fitting of the $\mathrm{Mg\,II}$ and $\mathrm{Fe\,II}$ absorption lines favors a partial-covering model, yielding $C_{f,\rm BEL}=0.11^{+0.12}_{-0.07}$. Together with the small velocity offset ($Δv\approx96~{\rm km\,s^{-1}}$), these results favor an intrinsic absorber rather than an intervening system. Infrared SED modeling and double-peaked $\mathrm{Pa\,α}$ emission indicate consistent orientations of the dusty torus ($θ\approx60^{+7}_{-8}$ deg) and accretion disk ($i=63.7^{+10.9}_{-7.8}$ deg), suggesting a rim-penetrating line of sight through the torus. This configuration naturally explains the infrared emission, continuum extinction, and associated $2175\,\mathring{\mathrm{A}}$ bump, although a contribution from the host-galaxy ISM cannot be excluded. If associated with the torus, the carbonaceous carriers of the $2175\,\mathring{\mathrm{A}}$ feature may survive the intense AGN radiation field through localized shielding in optically thick dusty clumps. These results highlight the role of viewing geometry and dust distribution in regulating dust survival in quasar environments.

astro-ph.GA↗

arXiv2026

Beyond Colors: Probing Redshifts from Galaxy Morphology in Single-band Images with ViT-MDNz

To address the challenge of estimating redshifts when only single-band images are available, this study introduces a deep learning model named ViT-MDNz. Leveraging robust statistical priors learned from large-scale data concerning the correlation between redshift and morphology, the model can directly estimate redshifts and their associated uncertainties from single-band galaxy images. It integrates a Vision Transformer (ViT) to extract deep morphological features and a Mixture Density Network (MDN) to predict the full redshift probability density function. Trained and evaluated on approximately 300,000 single-band images from the DESI Legacy Imaging Surveys (DESI-LS), the model achieves a normalized median absolute deviation $σ_{\rm NMAD} = 0.034$ and an outlier fraction $f_{\rm out} = 2.6\%$ in the $r$-band for redshifts up to $z \lesssim 1$. Evaluations using probability integral transform (PIT) and continuous ranked probability score (CRPS) confirm that the predicted probability density functions are well calibrated and closely match the true distribution. These results demonstrate that competitive redshift estimates can be obtained using morphological features alone, and that incorporating color information further enhances the accuracy and robustness of the estimation. Therefore, ViT-MDNz provides a practical approach for redshift estimation of galaxy samples with limited photometric band coverage, contributing to improved completeness and usability of redshift catalogs for future large-scale surveys such as DESI and LSST.

astro-ph.GA↗

arXiv2025

LSTM-MDNz: Estimating Quasar Photometric Redshifts with an LSTM-Augmented Mixture Density Network

Quasar photometric redshifts are essential for studying cosmology and large-scale structures. However, their complex spectral energy distributions cause significant redshift-color degeneracy, limiting the accuracy of traditional methods. To overcome this, we introduce LSTM-MDNz, a novel end-to-end deep learning model combining long short-term memory networks (LSTM) with mixture density networks (MDN). The model directly uses multi-band photometric fluxes and associated errors as wavelength-ordered sequential inputs, eliminating the need for manual feature engineering while enabling simultaneous point estimation and probability distribution function (PDF) prediction of quasar redshifts. We integrate data from four major sky surveys-SDSS, DESI-LS, WISE, and GALEX-to assemble a sample of over 550,000 spectroscopically confirmed quasars ($0 \leq z_{\mathrm{spec}} \leq 5$) across 14 ultraviolet to infrared bands for model training and testing. Experimental results show that using all 14 bands yields optimal performance, with a normalized median absolute deviation ($σ_{\mathrm{NMAD}}$) of 0.037 and an outlier rate ($f_{\mathrm{out}}$) of 3.5\% on the test set. These values represent reductions of 29\% and 56\%, respectively, compared to the commonly adopted SDSS+WISE band set. Probability integral transform ($\mathrm{PIT}$) and continuous ranked probability score ($\mathrm{CRPS}$) analyses confirm that the predicted PDFs align closely with the true redshift distribution. Band-ablation experiments further highlight the essential role of ultraviolet and infrared data in alleviating color degeneracy and reducing systematic bias. This study demonstrates the effectiveness of multi-band fusion in improving quasar photo-z accuracy and offers a ready-to-use estimation framework for future surveys like LSST, CSST, and Euclid.

astro-ph.GA↗

arXiv2025

BALNet: Deep Learning-Based Detection and Measurement of Broad Absorption Lines in Quasar Spectra

Broad absorption line (BAL) quasars serve as critical probes for understanding active galactic nucleus (AGN) outflows, black hole accretion, and cosmic evolution. To address the limitations of manual classification in large-scale spectroscopic surveys - where the number of quasar spectra is growing exponentially - we propose BALNet, a deep learning approach consisting of a one-dimensional convolutional neural network (1D-CNN) and bidirectional long short-term memory (Bi-LSTM) networks to automatically detect BAL troughs in quasar spectra. BALNet enables both the identification of BAL quasars and the measurement of their BAL troughs. We construct a simulated dataset for training and testing by combining non-BAL quasar spectra and BAL troughs, both derived from SDSS DR16 observations. Experimental results in the testing set show that: (1) BAL trough detection achieves 83.0% completeness, 90.7% purity, and an F1-score of 86.7%; (2) BAL quasar classification achieves 90.8% completeness and 94.4% purity; (3) the predicted BAL velocities agree closely with simulated ground truth labels, confirming BALNet's robustness and accuracy. When applied to the SDSS DR16 data within the redshift range 1.5<z<5.7, at least one BAL trough is detected in 20.4% of spectra. Notably, more than a quarter of these are newly identified sources with significant absorption, 8.8% correspond to redshifted systems, and some narrow/weak absorption features were missed. BALNet greatly improves the efficiency of large-scale BAL trough detection and enables more effective scientific analysis of quasar spectra.

astro-ph.GA↗

arXiv2025

Detection of Quasi-periodic Oscillations in the $γ$-Ray Light Curve of 4FGL J0309.9-6058

In this work, we report, for the first time, a quasi-periodic oscillation (QPO) in the $γ$-ray band of 4FGL J0309.9-6058, also known as PKS 0308-611. We employed three analytical methods (the Lomb-Scargle periodogram, REDFIT, and the weighted wavelet Z-transform) to analyze the QPO signal using \textit{Fermi} $γ$-ray light curve data. The analysis reveals a potential QPO during MJD 57983$-$60503, with a period of approximately 550 days and a maximum local significance of 3.72$σ$ and global significance of 2.72$σ$ derived from the WWZ analysis. To validate this result, we applied Gaussian Process (GP) to the same light curve, which independently confirms the presence of QPO signal consistent with our Fourier-based results. We further extended the analysis to the full duration of the \textit{Fermi} observations, and the results consistently support and strengthen the presence of this QPO signal. Additionally, a time lag between the optical and $γ$-ray bands indicates separate emission regions for these two bands. Given the year-like timescale of the QPO signal and the fact that a QPO signal with local significance over 3$σ$ for full \textit{Fermi}-LAT observed time, we suggest that the QPO is most likely caused by a precessing jet.

astro-ph.HE↗

arXiv2025

Identifying Dust-lane Spheroidal Galaxies in DESI Legacy Imaging Surveys Using Semi-Supervised Methods

Dust-lane spheroidal galaxies (DLSGs) are unique astrophysical systems that exhibit the morphology of early-type galaxies (ETGs) but are distinguished by prominent dust lanes. Recent studies propose that they form through minor mergers between ETGs and gas-rich dwarf galaxies, offering a window into the interstellar medium (ISM) of ETGs and star formation triggered by small-scale interactions. However, their rarity poses a challenge for assembling large, statistically robust samples via manual selection. To overcome this limitation, we employ GC-SWGAN, a semi-supervised learning method developed by \citet{2025ApJS..279...17L}, to systematically identify DLSGs within the DESI Legacy Imaging Surveys (DESI-LS). The methodology involves training a generative adversarial network (GAN) on unlabeled galaxy images to extract morphological features, followed by fine-tuning the model using a small dataset of labeled DLSGs. In our experiments, despite DLSGs constituting only $\sim$ 3.7\% of the test set, GC-SWGAN achieves remarkable performance, with an 87\% recall rate, 84\% accuracy, and an F1 score of 86\%, underscoring its efficacy for DLSG detection. Applying this model to $\sim$ 310,000 DESI-LS galaxies that meet the criteria $m_r < 17.0$ and $0.01 < z < 0.07$ we compile the largest catalog of DLSG candidates to date, identifying 9,482 dust-lane ETGs. A preliminary analysis reveals that these DLSGs exhibit significantly redder $g-r$ colors and higher specific star formation rates compared to non-DLSGs. This catalog enables future studies of ISM properties in ETGs and the role of minor mergers in driving star formation in the nearby universe.

astro-ph.GA↗

arXiv2025

Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning

The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semi-supervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the DESI Legacy Imaging Surveys. We selected over 5,000 confirmed ringed galaxies from the Catalog of Southern Ringed Galaxies (CSRG) and the Northern Ringed Galaxies from the GZ2 catalog (GZ2-CNRG), both verified by morphology expert R. J. Buta, to create an annotated training set. Additionally, we incorporated strictly selected non-ringed galaxy samples from the Galaxy Zoo 2 dataset and utilized unlabelled data from DESI Legacy Surveys to train our model. Through semi-supervised learning, the model significantly reduced reliance on extensive annotated data while enhancing robustness and generalization. On the test set, it demonstrated exceptional performance in identifying ringed galaxies. With a probability threshold of 0.5, the classification accuracy reached 97\%, with precision and recall for ringed galaxies at 94\% and 93\%, respectively. Building on these results, we predicted 750,000 galaxy images from the DESI Legacy Imaging Surveys with r-band apparent magnitudes less than 17.0 and redshifts in the range 0.0005 < z < 0.25, compiling the largest catalog of ringed galaxies to date, containing 62,962 galaxies with ring structures. This catalog provides essential data for subsequent research on the formation mechanisms and evolutionary history of galaxy rings.

astro-ph.GA↗

arXiv2025

Galaxy Morphology Classification via Deep Semi-Supervised Learning with Limited Labeled Data

Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the universe. With galaxy observation data growing exponentially, machine learning has become a core technology for this classification task. However, traditional machine learning methods predominantly rely on supervised learning frameworks, and their dependence on large of labeled samples limits practical applications. To address this challenge, we propose an innovative hybrid semi-supervised model, GC-SWGAN, designed to tackle galaxy morphology classification under conditions of limited labeled data. This model integrates semi-supervised generative adversarial networks (SGAN) with Wasserstein GAN with gradient penalty (WGAN-GP), establishing a multi-task learning framework. Within this framework, the discriminator and classifier are designed independently while sharing part of the architecture. By collaborating with the generator, the model significantly enhances both classification performance and sample generation capabilities, while also improving convergence and stability during training. Experimental results demonstrate that, on the Galaxy10 DECals dataset, GC-SWGAN achieves comparable or even superior classification accuracy (exceeding 75%) using only one-fifth of the labeled samples typically required by conventional fully supervised methods. Under identical labeled conditions, the model displays excellent generalization performance, attaining approximately 84% classification accuracy. Notably, in extreme scenarios where only 10\% of the data is labeled, GC-SWGAN still achieves high classification accuracy (over 68%), fully demonstrating its stability and effectiveness in low-labeled data environments. Furthermore, galaxy images generated by GC-SWGAN are visually similar to real samples.

astro-ph.GA↗

arXiv2025

Cross-Survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis

This study focuses on transforming galaxy images between astronomical surveys, specifically enhancing images from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera Legacy Survey (DECaLS) to achieve quality comparable to the Hyper Suprime-Cam survey (HSC). We proposed a hybrid model called Pix2WGAN, which integrates the pix2pix framework with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to convert low-quality observational images into high-quality counterparts. Our model successfully transformed DECaLS images into pseudo-HSC images, yielding impressive results and significantly enhancing the identification of complex structures, such as galaxy spiral arms and tidal tails, which may have been overlooked in the original DECaLS images. Moreover, Pix2WGAN effectively addresses issues like artifacts, noise, and blurriness in both source and target images. In addition to the basic Pix2WGAN model, we further developed an advanced architecture called Cascaded Pix2WGAN, which incorporates a multi-stage training mechanism designed to bridge the quality gap between SDSS and HSC images, demonstrating similarly promising outcomes. We systematically assessed the similarity between the model-generated pseudo-HSC images and actual HSC images using various metrics, including Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), along with perceptual metrics such as Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID). The results indicate that images transformed by our model outperform both the original SDSS and DECaLS images across nearly all evaluation metrics. Our research is expected to provide significant technical support for astronomical data analysis, cross-survey image integration, and high-precision astrometry.

astro-ph.IM↗

arXiv2024

Revisiting the flaring activity in early 2015 of BL Lacertae object S5 0716+714

In this work, we analyzed multi-wavelength data of the BL Lac object S5 0716+714 to investigate its emission mechanisms during a flaring state observed in early 2015. We examined the temporal behavior and broadband spectral energy distributions (SEDs) during the flare. The size of the $γ$-ray emission region was estimated based on the variability timescale. To explore the multi-wavelength properties of S5 0716+714, we employed three one-zone models: the SSC model, the SSC plus EC model, and the SSC plus pp interactions model, to reproduce the SEDs. Our findings indicate that while the SSC model can describe the SEDs, it requires an extreme Doppler factor. In contrast, the SSC plus EC model successfully fits the SEDs under the assumption of weak external photon fields but requires a high Doppler factor. Additionally, the SSC plus interactions model also reproduces the SEDs, with $γ$-ray emission originating from $π^{0}$ decay. However, this model leads to a jet power that exceeds the Eddington luminosity, which remains plausible due to the flaring state or the presence of a highly collimated jet.

astro-ph.HE↗

arXiv2023

The `blazar sequence' in TeV band

The `blazar sequence' has been proposed for more than 20 years, yet its nature is still unclear. In this work, for the first time, we expand this topic to the TeV band by using a sample of 58 TeV blazars including 48 blazars in the quiescent state and 21 blazars in the flaring state. We investigate the correlation between the TeV luminosity, which has been compensated for attenuation from extragalactic background light, and the synchrotron peak frequency. We note that there is no correlation between TeV luminosity and peak frequency in the quiescent state and a strong anti-correlation in the flaring state for the observed value. However, there is a strong positive correlation in both the quiescent state and the flaring state for the intrinsic value. This indicates that the blazar sequence is shown in the flaring state rather than in the quiescent state for the observed value and the blazar sequence is not present in both two states after removing the beaming effect. In addition, to confirm whether the beaming effect results in the blazar sequence, we compare the \textit{Fermi} $γ$-ray luminosity between the quiescent state and the flaring state. We find the \textit{Fermi} $γ$-ray luminosity in the flaring state is greater than that in the quiescent state and the Doppler factor in the flaring state is greater. We suggest the blazar sequence in the flaring state may be due to the stronger beaming effect.

astro-ph.HE↗