arXiv subjects

Shaohua Zhang

Publications and source records attributed to Shaohua Zhang.

At least 19 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

Enhancing Photometric Redshift Estimation for LSST with a Hybrid LSTM-Mixture Density Network

Accurate photometric redshift (photo-$z$) estimation and robust uncertainty quantification are essential for the LSST to achieve its precision cosmology goals. Traditional machine learning algorithms are largely restricted to point estimates, struggling to characterize the multimodal nature of redshift PDFs and the degeneracies within the color-redshift space. To address this, we present and validate the LSTM-MDNz architecture, which integrates sequential feature extraction with flexible probability density modeling to enhance both prediction accuracy and uncertainty calibration across a broad redshift range, thereby meeting the stringent data quality requirements necessitated by next-generation cosmological analysis. The LSTM-MDNz framework treats multi-band photometry as wavelength-ordered sequences, utilizing LSTM networks to capture non-linear evolutionary correlations across the SED. A Mixture Density Network (MDN) is then employed to explicitly model posterior PDFs via Gaussian mixture models (GMMs). Performance is evaluated on the HSC GalaxiesML dataset (which serves as a small-scale proxy for next-generation surveys like LSST) and benchmarked against the BNN architecture established by Jones et al. (2024). The proposed model consistently outperforms the BNN baseline, achieving a $\sim 10\%$ improvement in point-estimation accuracy (specifically across RMSE, MAE, scatter, and $σ_{\text{NMAD}}$) and a $\sim 20\%$ reduction in the rates of both general and catastrophic outliers. A uniform probability integral transform (PIT) distribution confirms well-calibrated probabilistic outputs. Furthermore, the PDF-based confidence metric $z_{\text{conf}}$ enables high-purity catalog construction: excluding just approximately $4\%$ of extremely low-confidence ($z_{\text{conf}} < 0.05$) samples reduces the overall outlier rate by $\sim 48\%$.

astro-ph.GA↗

arXiv2026

Chasing the neutrino blazar candidates II: SED modeling with hadronic model

Blazars are promising candidates for high energy neutrino sources, yet the physical origin of their neutrino emission remains uncertain. In this work, we extend our previous study by modeling the broadband spectral energy distributions (SEDs) of 103 neutrino blazar candidates (NBCs) within a hadronic framework. To estimate the maximum possible neutrino output, we adopt an assumption in which the high energy emission is dominated by p gamma interactions and the contribution from leptonic inverse Compton scattering is strongly suppressed. From the SED modeling, we constrain nine key parameters describing the emission region and particle energy distributions. We perform a partial correlation analysis to investigate the relationship between neutrino luminosity and electromagnetic emission, and we found a weak or moderate correlation between optical R band and neutrino emission. Our model predicts prominent proton synchrotron emission peaking in the MeV band for most sources, with 99 out of 103 NBCs exhibiting proton synchrotron peaks within 0.1 to 100 MeV, highlighting the MeV band as a key window for distinguishing between leptonic and hadronic scenarios. Based on the model-predicted maximum neutrino fluxes, we find that three NBCs are potentially detectable by IceCube, while up to 22, 45, and 62 sources may be detectable by KM3NeT, NEON, and TRIDENT, respectively. These results provide testable predictions for future multi-messenger observations and offer new insights into the composition and radiation mechanisms of blazar jets.

astro-ph.HE↗

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↗

arXiv2026

Inverse orbital Hall effect induced terahertz emission enabled by a ferromagnet with quenched orbital moment in Fe/Pt/W trilayers

The inverse orbital Hall effect (IOHE) has recently attracted considerable attention as an emerging mechanism for terahertz (THz) emission based on ultrafast angular-momentum-to-charge conversion. Most experimental studies have focused on materials with strong spin-orbit coupling or pronounced orbital character, where sizable orbital Hall responses are expected. Elemental ferromagnets such as Fe are generally regarded as quenched orbital sources and are not expected to exhibit orbital-dominated THz emission. Here, we report a pronounced enhancement of THz emission in Fe/Pt/W trilayer heterostructures, despite the absence of detectable orbital contributions in the corresponding Fe/Pt and Fe/W bilayers. Thickness-dependent measurements reveal long-distance signal persistence, systematic delay accumulation, and pronounced pulse broadening with increasing W thickness. These features are inconsistent with diffusive spin transport and indicate that orbital angular momentum transport in the W layer, converted into charge current via the IOHE, becomes a dominant channel for THz emission in the trilayer configuration. Our results demonstrate that strong IOHE can emerge in heterostructures incorporating a quenched orbital ferromagnet, providing an effective route to enhance spintronic THz emitters through orbital Hall physics.

cond-mat.mtrl-sci↗

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

Optimizing Text Search: A Novel Pattern Matching Algorithm Based on Ukkonen's Approach

In the realm of computer science, the efficiency of text-search algorithms is crucial for processing vast amounts of data in areas such as natural language processing and bioinformatics. Traditional methods like Naive Search, KMP, and Boyer-Moore, while foundational, often fall short in handling the complexities and scale of modern datasets, such as the Reuters corpus and human genomic sequences. This study rigorously investigates text-search algorithms, focusing on optimizing Suffix Trees through methods like Splitting and Ukkonen's Algorithm, analyzed on datasets including the Reuters corpus and human genomes. A novel optimization combining Ukkonen's Algorithm with a new search technique is introduced, showing linear time and space efficiencies, outperforming traditional methods like Naive Search, KMP, and Boyer-Moore. Empirical tests confirm the theoretical advantages, highlighting the optimized Suffix Tree's effectiveness in tasks like pattern recognition in genomic sequences, achieving 100% accuracy. This research not only advances academic knowledge in text-search algorithms but also demonstrates significant practical utility in fields like natural language processing and bioinformatics, due to its superior resource efficiency and reliability.

cs.DS↗

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

Memory Retrieval and Consolidation in Large Language Models through Function Tokens

The remarkable success of large language models (LLMs) stems from their ability to consolidate vast amounts of knowledge into the memory during pre-training and to retrieve it from the memory during inference, enabling advanced capabilities such as knowledge memorization, instruction-following and reasoning. However, the mechanisms of memory retrieval and consolidation in LLMs remain poorly understood. In this paper, we propose the function token hypothesis to explain the workings of LLMs: During inference, function tokens activate the most predictive features from context and govern next token prediction (memory retrieval). During pre-training, predicting the next tokens (usually content tokens) that follow function tokens increases the number of learned features of LLMs and updates the model parameters (memory consolidation). Function tokens here roughly correspond to function words in linguistics, including punctuation marks, articles, prepositions, and conjunctions, in contrast to content tokens. We provide extensive experimental evidence supporting this hypothesis. Using bipartite graph analysis, we show that a small number of function tokens activate the majority of features. Case studies further reveal how function tokens activate the most predictive features from context to direct next token prediction. We also find that during pre-training, the training loss is dominated by predicting the next content tokens following function tokens, which forces the function tokens to select the most predictive features from context.

cs.CL↗

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

The averaged broadband spectral energy distribution study of Fermi bright BL Lac objects

The physics-determined broadband spectral energy distributions (SEDs) of blazars have been widely used to study the property during their flaring/outburst states, while the non-flaring state takes up most of their lifetime and the general property of blazars has been barely discussed. In this work, for the first time, we used the archival data and employed the physics-determined SED processing method to form approximately average-state SEDs for 513 \textit{Fermi} bright BL Lacs. In general, we found that the magnetic field ($B$) is weaker than those obtained for flaring/outburst state by nearly one order of magnitude, and the dissipation region size ($R$) is larger than those obtained for flaring/outburst state, suggesting that the dissipation region could be more extend and less magnetized. A correlation between the synchrotron-self Compton (SSC) peak frequency ($\log ν_{\rm ssc}$) against the synchrotron peak frequency ($\log ν_{\rm sy}$) suggest that the inverse Compton scattering of HBLs suffer a significant Klein-Nishina (KN) suppression, we quantified the condition of KN suppression by determining the critical synchrotron peak frequency ($ν_{\rm sy}^{\rm c}$) and found 359 out of 513 sources in our sample suffer KN suppression. Furthermore, our analysis of the relationship between synchrotron curvature ($1/b_{\rm sy}$) and $\log ν_{\rm sy}$ indicates that the energy-dependent probability acceleration (EDPA) mechanism may dominate the particle acceleration in BL Lac jets.

astro-ph.HE↗

arXiv2025

See Different, Think Better: Visual Variations Mitigating Hallucinations in LVLMs

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in visual understanding and multimodal reasoning. However, LVLMs frequently exhibit hallucination phenomena, manifesting as the generated textual responses that demonstrate inconsistencies with the provided visual content. Existing hallucination mitigation methods are predominantly text-centric, the challenges of visual-semantic alignment significantly limit their effectiveness, especially when confronted with fine-grained visual understanding scenarios. To this end, this paper presents ViHallu, a Vision-Centric Hallucination mitigation framework that enhances visual-semantic alignment through Visual Variation Image Generation and Visual Instruction Construction. ViHallu introduces visual variation images with controllable visual alterations while maintaining the overall image structure. These images, combined with carefully constructed visual instructions, enable LVLMs to better understand fine-grained visual content through fine-tuning, allowing models to more precisely capture the correspondence between visual content and text, thereby enhancing visual-semantic alignment. Extensive experiments on multiple benchmarks show that ViHallu effectively enhances models' fine-grained visual understanding while significantly reducing hallucination tendencies. Furthermore, we release ViHallu-Instruction, a visual instruction dataset specifically designed for hallucination mitigation and visual-semantic alignment. Code is available at https://github.com/oliviadzy/ViHallu.

cs.CV↗

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

MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs

We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems.

cs.AI↗

arXiv2025

Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine

Generating high-quality geometry problems is both an important and challenging task in education. Compared to math word problems, geometry problems further emphasize multi-modal formats and the translation between informal and formal languages. In this paper, we introduce a novel task for geometry problem generation and propose a new pipeline method: the Symbolic Deduction Engine-based Geometry Problem Generation framework (SDE-GPG). The framework leverages a symbolic deduction engine and contains four main steps: (1) searching a predefined mapping table from knowledge points to extended definitions, (2) sampling extended definitions and performing symbolic deduction, (3) filtering out unqualified problems, and (4) generating textual problems and diagrams. Specifically, our method supports to avoid inherent biases in translating natural language into formal language by designing the mapping table, and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function. With obtained formal problems, they are translated to natural language and the accompanying diagrams are automatically drew by rule-based methods. We conduct experiments using real-world combinations of knowledge points from two public datasets. The results demonstrate that the SDE-GPG can effectively generate readable, solvable and controllable geometry problems.

cs.AI↗

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

The Quasar-associated 2175 Å Dust Absorbers in the SDSS DR16 Quasar Catalog

We present, for the first time, a systematic study of quasar-associated 2175 Å dust absorbers using spectroscopic data from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16). By analyzing the optical spectra and multi-band magnitudes of 557,674 quasars in the redshift range of $0.7 \le z \le 2.4$, we identify 843 absorbers that share the same redshifts as quasars and are believed to originate from dust in the quasar nuclei, the host galaxies, or their surrounding environments. These absorbers exhibit weak bump strengths ($A\rm_{bump}=0.49\pm0.15~μm^{-1}$) and narrow widths ($γ\rm=0.81\pm0.14~μm^{-1}$), while their peak positions span a broad range from $x_0 = 4.2$ to $4.84~ μm^{-1}$. Their average extinction curves resemble those of the Large Magellanic Cloud (LMC) but exhibit a shallower slope. In broad absorption line (BAL) quasars, the absorption bumps show systematic shifts in peak positions. Although further confirmation is needed, this may suggest environmental differences in dust grain properties. We find a statistically significant negative correlation between bump strength and redshift, suggesting possible evolution in dust properties. These findings highlight the changing composition and physical conditions of dust in quasar environments, likely influenced by factors such as metallicity, radiation fields, and dust processing mechanisms. Future studies incorporating ultraviolet and infrared data will be essential for refining the dust evolution models. Machine learning techniques and high-resolution spectroscopic follow-ups could enhance sample completeness and provide deeper insights into the chemical properties of the dust absorbers.

astro-ph.GA↗