arXiv subjects

Yue Shen

Publications and source records attributed to Yue Shen.

At least 109 records · Page 6Linked to original sources

arXiv2024

A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): Broad-line AGN at $z=4-5$ revealed by JWST/NIRCam WFSS

Low-luminosity AGNs with low-mass black holes (BHs) in the early universe are fundamental to understanding the BH growth and their co-evolution with the host galaxies. Utilizing JWST NIRCam Wide Field Slitless Spectroscopy (WFSS), we perform a systematic search for broad-line ${\rm Hα}$ emitters (BHAEs) at $z\approx 4-5$ in 25 fields of the ASPIRE (A SPectroscopic survey of biased halos In the Reionization Era) project, covering a total area of 275 arcmin$^2$. We identify 16 BHAEs with FWHM of the broad components spanning from $\sim$ 1000 km s$^{-1}$ to 3000 km s$^{-1}$. Assuming the broad linewidths arise due to Doppler broadening around BHs, the implied BH masses range from $10^7$ to $10^{8}~M_\odot$, with broad ${\rm Hα}$-converted bolometric luminosity of $10^{44.5}-10^{45.5}$ erg s$^{-1}$ and Eddington ratios of $0.07-0.47$. The spatially extended structure of the F200W stacked image may trace the stellar light from the host galaxies. The ${\rm Hα}$ luminosity function indicates an increasing AGN fraction towards the higher ${\rm Hα}$ luminosities. We find possible evidence for clustering of BHAEs: two sources are at the same redshift with a projected separation of 519 kpc; one BHAE appears as a composite system residing in an overdense region with three close companion ${\rm Hα}$ emitters. Three BHAEs exhibit blueshifted absorption troughs indicative of the presence of high-column-density gas. We find the broad-line and photometrically selected BHAE samples exhibit different distributions in the optical continuum slopes, which can be attributed to their different selection methods. The ASPIRE broad-line ${\rm Hα}$ sample provides a good database for future studies of faint AGN populations at high redshift.

astro-ph.GA↗

arXiv2024

Fast and Flexible Inference Framework for Continuum Reverberation Mapping using Simulation-based Inference with Deep Learning

Continuum reverberation mapping (CRM) of active galactic nuclei (AGN) monitors multiwavelength variability signatures to constrain accretion disk structure and supermassive black hole (SMBH) properties. The upcoming Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) will survey tens of millions of AGN over the next decade, with thousands of AGN monitored with almost daily cadence in the deep drilling fields. However, existing CRM methodologies often require long computation time and are not designed to handle such large amount of data. In this paper, we present a fast and flexible inference framework for CRM using simulation-based inference (SBI) with deep learning to estimate SMBH properties from AGN light curves. We use a long-short-term-memory (LSTM) summary network to reduce the high-dimensionality of the light curve data, and then use a neural density estimator to estimate the posterior of SMBH parameters. Using simulated light curves, we find SBI can produce more accurate SMBH parameter estimation with $10^3-10^5$ times speed up in inference efficiency compared to traditional methods. The SBI framework is particularly suitable for wide-field RM surveys as the light curves will have identical observing patterns, which can be incorporated into the SBI simulation. We explore the performance of our SBI model on light curves with irregular-sampled, realistic observing cadence and alternative variability characteristics to demonstrate the flexibility and limitation of the SBI framework.

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arXiv2024

Understanding the Broad-line Region of Active Galactic Nuclei with Photoionization. I. the Moderate-Accretion Regime

Over three decades of reverberation mapping (RM) studies on local broad-line active galactic nuclei (AGNs) have measured reliable black-hole (BH) masses for $> 100$ AGNs. These RM measurements reveal a significant correlation between the Balmer broad-line region size and the AGN optical luminosity (the $R-L$ relation). Recent RM studies for AGN samples with more diverse BH accretion parameters (e.g., mass and Eddington ratio) reveal a substantial intrinsic dispersion around the average $R-L$ relation, suggesting variations in the overall spectral energy distribution shape as functions of accretion parameters. Here we perform a detailed photoionization investigation of expected broad-line properties as functions of accretion parameters, using the latest models for the AGN continuum implemented in {\tt qsosed}. We compare theoretical predictions with observations of a sample of 67 $z\lesssim0.5$ reverberation-mapped AGNs with both rest-frame optical and UV spectra in the moderate-accretion regime (Eddington ratio $λ_{\rm Edd}\equiv L/L_{\rm Edd}<0.5$). The UV/optical line strengths and their dependences on accretion parameters can be reasonably well reproduced by the locally-optimally-emitting cloud (LOC) photoionization models. We provide quantitative recipes that use optical/UV line flux ratios to infer the ionizing continuum, which is not directly observable. In addition, photoionization models with universal values of ionization parameter ($\log U_{\rm H}=-2$) and hydrogen density ($\log n({\rm H})=12$) can qualitatively reproduce the observed global $R-L$ relation for the current AGN sample. However, such models fail to reproduce the observed trend of decreasing BLR size with $L/L_{\rm Edd}$ at fixed optical luminosity, which may imply that the gas density increases with the accretion rate.

astro-ph.GA↗

arXiv2024

Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs

In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this issue is how to transform natural languages into practical structured logical languages, i.e., the structured understanding of marketer demands. In practical scenarios, the demands of non-expert marketers are often abstract and diverse. Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue. To stimulate the LLMs' reasoning ability, the chain-of-thought (CoT) prompting method is widely used, but existing methods still have some limitations in our scenario: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and concrete questions, making LLMs ineffective when the marketers' demands are abstract and diverse. (2) Previous methods are often implemented in closed-source models or excessively large models, which is not suitable in industrial practical scenarios. Based on these, we propose ARALLM (i.e., Analogical Reasoning Augmented Large Language Models) consisting of two modules: Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task Model Distillation. Part of our data and code can be found at https://github.com/alipay/Analogic-Reasoning-Augmented-Large-Language-Model.

cs.CL↗

arXiv2024

The SDSS-V Black Hole Mapper Reverberation Mapping Project: CIV BAL Acceleration in the Quasar SBS 1408+544

We present the results of an investigation of a highly variable CIV broad absorption-line feature in the quasar SBS 1408+544 (z=2.337) that shows a significant shift in velocity over time. This source was observed as a part of the Sloan Digital Sky Survey Reverberation Mapping Project and the SDSS-V Black Hole Mapper Reverberation Mapping Project, and has been included in two previous studies, both of which identified significant variability in a high-velocity CIV broad absorption line (BAL) on timescales of just a few days in the quasar rest frame. Using ~130 spectra acquired over eight years of spectroscopic monitoring with SDSS, we have determined that this BAL is not only varying in strength, but is also systematically shifting to higher velocities. Using cross-correlation methods, we measure the velocity shifts (and corresponding acceleration) of the BAL on a wide range of timescales, measuring an overall velocity shift of delta v = -683 (+89, -84) km s-1 over the 8-year monitoring period. This corresponds to an average rest-frame acceleration of a=1.04 (+0.14, -0.13) cm s-2, though the magnitude of the acceleration on shorter timescales is not constant throughout. We place our measurements in the context of BAL-acceleration models and examine various possible causes of the observed velocity shift.

astro-ph.GA↗

arXiv2024

Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts

In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge intensive tasks, where retrieval augmented generation (RAG) can be of help. Nevertheless, existing retrieval augmented models typically use similarity as a bridge between queries and documents and follow a retrieve then read procedure. In this work, we argue that similarity is not always the panacea and totally relying on similarity would sometimes degrade the performance of retrieval augmented generation. To this end, we propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented Generation framework. To begin with, beyond existing similarity oriented thought, we embrace a small scale utility model that draws supervision from an LLM for utility oriented thought and further come up with a smarter model by comprehensively combining the similarity and utility oriented thoughts. Furthermore, given the fact that the retrieved document set tends to be huge and using them in isolation makes it difficult to capture the commonalities and characteristics among them, we propose to make an LLM as a task adaptive summarizer to endow retrieval augmented generation with compactness-oriented thought. Finally, with multi layered thoughts from the precedent stages, an LLM is called for knowledge augmented generation. Extensive experiments on knowledge-intensive tasks have demonstrated the superiority of MetRag.

cs.LG↗

arXiv2024

Unified Hallucination Detection for Multimodal Large Language Models

Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such hallucinations in MLLMs has, therefore, become a vital aspect of model evaluation and the safeguarding of practical application deployment. Prior research in this domain has been constrained by a narrow focus on singular tasks, an inadequate range of hallucination categories addressed, and a lack of detailed granularity. In response to these challenges, our work expands the investigative horizons of hallucination detection. We present a novel meta-evaluation benchmark, MHaluBench, meticulously crafted to facilitate the evaluation of advancements in hallucination detection methods. Additionally, we unveil a novel unified multimodal hallucination detection framework, UNIHD, which leverages a suite of auxiliary tools to validate the occurrence of hallucinations robustly. We demonstrate the effectiveness of UNIHD through meticulous evaluation and comprehensive analysis. We also provide strategic insights on the application of specific tools for addressing various categories of hallucinations.

cs.CL↗

arXiv2024

Active Galactic Nuclei and Host Galaxies in COSMOS-Web. II. First Look at the Kpc-scale Dual and Offset AGN Population

Kpc-scale dual and offset Active Galactic Nuclei (AGNs) are signposts of accreting supermassive black holes (SMBHs) triggered during late-stage galaxy mergers, offering crucial insights into the coevolution of SMBHs and galaxies. However, robustly confirmed systems at high redshift (e.g., $z>1$) are scarce and biased towards the most luminous and unobscured systems. In this study, we systematically search for kpc-scale (projected separation $<15$ kpc) dual and offset AGNs around 571 moderate-luminosity, X-ray-selected AGNs including the obscured population, utilizing deep HST ACS/F814W and multiband JWST NIRCam imaging from the COSMOS-Web survey. We identify 59 dual and 30 offset AGN candidates in late stage major mergers based on spatially-resolved spectral energy distribution analyses. This translates to $\sim28$ and $\sim10$ bona-fide dual and offset AGNs using a probabilistic pair counting scheme to minimize chance superpositions. Notably, the fraction of dual and offset AGNs among moderate-luminosity ($L_{\rm bol}\sim10^{43}-10^{46}\ \rm erg\ s^{-1}$), obscured AGNs is nearly two orders of magnitude higher than that of the most luminous, unobscured quasar pairs. We find tentative evidence for an increasing pair fraction among AGNs with redshift (from a few percent at $z\sim 0.5$ to $\sim22.9_{-17.7}^{+27.5}\%$ at $z\sim4.5$) and a higher occurrence rate of dual over offset AGNs. There is no pileup of dual/offset AGNs below $\sim 2~{\rm kpc}$ separations. These results generally align with predictions from the ASTRID and Horizon-AGN cosmological simulations when matching sample selection criteria, implying a high probability of both BHs being active simultaneously in late-stage major mergers.

astro-ph.GA↗

arXiv2024

Exploring Changing-look Active Galactic Nuclei with the Sloan Digital Sky Survey V: First Year Results

"Changing-look" active galactic nuclei (CL-AGNs) challenge our basic ideas about the physics of accretion flows and circumnuclear gas around supermassive black holes. Using first-year Sloan Digital Sky Survey V (SDSS-V) repeated spectroscopy of nearly 29,000 previously known AGNs, combined with dedicated follow-up spectroscopy, and publicly available optical light curves, we have identified 116 CL-AGNs where (at least) one broad emission line has essentially (dis-)appeared, as well as 88 other extremely variable systems. Our CL-AGN sample, with 107 newly identified cases, is the largest reported to date, and includes $\sim0.4\%$ of the AGNs reobserved in first-year SDSS-V operations. Among our CL-AGNs, 67% exhibit dimming while 33% exhibit brightening. Our sample probes extreme AGN spectral variability on months to decades timescales, including some cases of recurring transitions on surprisingly short timescales ($\lesssim 2$ months in the rest frame). We find that CL events are preferentially found in lower-Eddington-ratio ($f_{Edd}$) systems: Our CL-AGNs have a $f_{Edd}$ distribution that significantly differs from that of a carefully constructed, redshift- and luminosity-matched control sample (Anderson-Darling test yielding $p_{\rm AD}\approx 6\times10^{-5}$; median $f_{Edd}\approx0.025$ vs. $0.043$). This preference for low $f_{Edd}$ strengthens previous findings of higher CL-AGN incidence at lower $f_{Edd}$, found in smaller samples. Finally, we show that the broad MgII emission line in our CL-AGN sample tends to vary significantly less than the broad H$β$ emission line. Our large CL-AGN sample demonstrates the advantages and challenges in using multi-epoch spectroscopy from large surveys to study extreme AGN variability and physics.

astro-ph.GA↗

arXiv2024

The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results

We present the final data from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project, a precursor to the SDSS-V Black Hole Mapper Reverberation Mapping program. This data set includes 11-year photometric and 7-year spectroscopic light curves for 849 broad-line quasars over a redshift range of 0.1 =0.62+-0.07 for the line dispersion measured from the RMS spectrum. The intrinsic scatter of individual virial factors is 0.31+-0.07 dex, indicating a factor of two systematic uncertainty in RM black hole masses. Our lag measurements reveal significant R-L relations for Hbeta and MgII at high redshift, consistent with the latest measurements based on heterogeneous samples. While we are unable to robustly constrain the slope of the R-L relation for CIV given the limited dynamical range in luminosity, we found substantially larger scatter in CIV lags at fixed L1350. Using the SDSS-RM lag sample, we derive improved single-epoch (SE) mass recipes for Hbeta, MgII and CIV, which are consistent with their respective RM masses as well as between the SE recipes from two different lines, over the luminosity range probed by our sample. The new Hbeta and MgII recipes are approximately unbiased estimators at given RM masses, but there are systematic biases in the CIV recipe. The intrinsic scatter of SE masses around RM masses is ~0.45 dex for Hbeta and MgII, increasing to ~0.58 dex for CIV.

astro-ph.GA↗

arXiv2024

Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm

Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural language. Ensuring the safe and reliable clinical applications, the evaluation of LLMs indeed becomes critical for better mitigating the potential risks, e.g., hallucinations. However, current evaluation methods heavily rely on labor-intensive human participation to achieve human-preferred judgements. To overcome this challenge, we propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services, e.g., disease diagnosis and treatment. The evaluation paradigm contains three basic elements: metric, data, and algorithm. Specifically, inspired by professional clinical practice pathways, we formulate a LLM-specific clinical pathway (LCP) to define the clinical capabilities that a doctor agent should possess. Then, Standardized Patients (SPs) from the medical education are introduced as the guideline for collecting medical data for evaluation, which can well ensure the completeness of the evaluation procedure. Leveraging these steps, we develop a multi-agent framework to simulate the interactive environment between SPs and a doctor agent, which is equipped with a Retrieval-Augmented Evaluation (RAE) to determine whether the behaviors of a doctor agent are in accordance with LCP. The above paradigm can be extended to any similar clinical scenarios to automatically evaluate the LLMs' medical capabilities. Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach, providing more insights for LLMs' safe and reliable deployments in clinical practice.

cs.CL↗

arXiv2024

VODKA-JWST: A 3.8 kpc dual quasar at cosmic noon in a powerful starburst galaxy with JWST/MIRI IFU

Dual quasars, two active supermassive black holes at galactic scales, represent crucial objects for studying the impact of galaxy mergers and quasar activity on the star formation rate (SFR) within their host galaxies, particularly at cosmic noon when SFR peaks. We present JWST/MIRI mid-infrared integral field spectroscopy of J074922.96+225511.7, a dual quasar with a projected separation of 3.8 kilo-parsec at a redshift $z$ of 2.17. We detect spatially extended [Fe II] 5.34$\rm μ$m and polycyclic aromatic hydrocarbon (PAH) 3.3$μ$m emissions from the star formation activity in its host galaxy. We derive the SFR of 10$^{3.0\pm0.2}$ M$_{\odot}$ yr$^{-1}$ using PAH 3.3$μ$m, which is five times higher than that derived from the cutoff luminosity of the infrared luminosity function for galaxies at $z\sim2$. While the SFR of J0749+2255 agrees with that of star-forming galaxies of comparable stellar mass at the same redshifts, its molecular gas content falls short of expectations based on the molecular Kennicutt-Schmidt law. This discrepancy may result from molecular gas depletion due to the longer elevated stage of star formation, even after the molecular gas reservoir is depleted. We do not observe any quasar-driven outflow that impacts PAH and [Fe II] in the host galaxy based on the spatially resolved maps. From the expected flux in PAH-based star formation, the [Fe II] line likely originates from the star-forming regions in the host galaxy. Our study highlights the stardust nature of J0749+2255, indicating a potential connection between the dual quasar phase and intense star formation activities.

astro-ph.GA↗

arXiv2024

Tip of the iceberg: overmassive black holes at 4<z<7 found by JWST are not inconsistent with the local $\mathcal{M}_{\rm BH}$-$\mathcal{M}_\star$ relation

JWST is revealing a new remarkable population of high-redshift ($z\gtrsim4$), low-luminosity Active Galactic Nuclei (AGNs) in deep surveys and detecting the host galaxy stellar light in the most luminous and massive quasars at $z\sim 6$ for the first time. Latest results claim supermassive black holes (SMBHs) in these systems to be significantly more massive than expected from the local BH mass - stellar mass ($\mathcal{M}_{\rm BH} - \mathcal{M}_\star$) relation and that this is not due to sample selection effects. Through detailed statistical modeling, we demonstrate that the coupled effects of selection biases (i.e., finite detection limit and requirements on detecting broad lines) and measurement uncertainties in $\mathcal{M}_{\rm BH}$ and $\mathcal{M}_\star$ can in fact largely account for the reported offset and flattening in the observed $\mathcal{M}_{\rm BH} - \mathcal{M}_\star$ relation toward the upper envelope of the local relation, even for those at $\mathcal{M}_{\rm BH} < 10^8\,M_{\odot}$. We further investigate the possible evolution of the $\mathcal{M}_{\rm BH} - \mathcal{M}_\star$ relation at $z\gtrsim 4$ with careful treatment of observational biases and consideration of the degeneracy between intrinsic evolution and dispersion in this relation. The bias-corrected intrinsic $\mathcal{M}_{\rm BH} - \mathcal{M}_\star$ relation in the low-mass regime suggests that there might be a large population of low-mass BHs (${\rm log}\,\mathcal{M}_{\rm BH} \lesssim 5$), possibly originating from lighter seeds, remaining undetected or unidentified even in the deepest JWST surveys. These results have important consequences for JWST studies of BH seeding and the coevolution between SMBHs and their host galaxies at the earliest cosmic times.

astro-ph.GA↗

arXiv2024

High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam

High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4m Blanco telescope. Since Feb 2019, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the $(u)gri(z)$ bands, over a collective sky area of $\sim 38$ deg${\rm ^2}$. The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependences on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first $\sim 3.5$ years of light curves during 2019A--2022A.

astro-ph.GA↗

arXiv2024

Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.

cs.IR↗

arXiv2024

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However, these models face a significant challenge when it comes to sensitive applications, such as reasoning over medical knowledge and answering medical questions in a physician-like manner. Prior studies attempted to overcome this challenge by increasing the model size (>100B) to learn more general medical knowledge, while there is still room for improvement in LLMs with smaller-scale model sizes (<100B). In this work, we start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a medical beginner towards a medical expert (called AntGLM-Med-10B), which leverages a 3-stage optimization procedure, i.e., general medical knowledge injection, medical domain instruction tuning, and specific medical task adaptation. Our contributions are threefold: (1) We specifically investigate how to adapt a pre-trained general LLM in medical domain, especially for a specific medical task. (2) We collect and construct large-scale medical datasets for each stage of the optimization process. These datasets encompass various data types and tasks, such as question-answering, medical reasoning, multi-choice questions, and medical conversations. (3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs. Remarkably, by combining the above approaches, our AntGLM-Med-10B model can outperform the most of LLMs on PubMedQA, including both general and medical LLMs, even when these LLMs have larger model size.

cs.CL↗

arXiv2024

RJUA-QA: A Comprehensive QA Dataset for Urology

We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. RJUA-QA is derived from realistic clinical scenarios and aims to facilitate LLMs in generating reliable diagnostic and advice. The dataset contains 2,132 curated Question-Context-Answer pairs, corresponding about 25,000 diagnostic records and clinical cases. The dataset covers 67 common urological disease categories, where the disease coverage exceeds 97.6\% of the population seeking medical services in urology. Each data instance in RJUA-QA comprises: (1) a question mirroring real patient to inquiry about clinical symptoms and medical conditions, (2) a context including comprehensive expert knowledge, serving as a reference for medical examination and diagnosis, (3) a doctor response offering the diagnostic conclusion and suggested examination guidance, (4) a diagnosed clinical disease as the recommended diagnostic outcome, and (5) clinical advice providing recommendations for medical examination. RJUA-QA is the first medical QA dataset for clinical reasoning over the patient inquiries, where expert-level knowledge and experience are required for yielding diagnostic conclusions and medical examination advice. A comprehensive evaluation is conducted to evaluate the performance of both medical-specific and general LLMs on the RJUA-QA dataset. Our data is are publicly available at \url{https://github.com/alipay/RJU_Ant_QA}.

cs.CL↗

arXiv2023

Gemini Near-infrared Spectroscopy of High-Redshift Fermi Blazars: Jetted Black Holes in the Early Universe Were Overly Massive

Jetted active galactic nuclei (AGNs) are the principal extragalactic $γ$-ray sources. Fermi-detected high-redshift ($z>3$) blazars are jetted AGNs thought to be powered by massive, rapidly spinning supermassive black holes (SMBHs) in the early universe ($<2$ Gyr). They provide a laboratory to study early black hole (BH) growth and super-Eddington accretion -- possibly responsible for the more rapid formation of jetted BHs. However, previous virial BH masses of $z>3$ blazars were based on C IV in the observed optical, but C IV is known to be biased by strong outflows. We present new Gemini/GNIRS near-IR spectroscopy for a sample of nine $z>3$ Fermi $γ$-ray blazars with available multi-wavelength observations that maximally sample the spectral energy distributions (SEDs). We estimate virial BH masses based on the better calibrated broad H$β$ and/or Mg II . We compare the new virial BH masses against independent mass estimates from SED modeling. Our work represents the first step in campaigning for more robust virial BH masses and Eddington ratios for high-redshift Fermi blazars. Our new results confirm that high-redshift Fermi blazars indeed host overly massive SMBHs as suggested by previous work, which may pose a theoretical challenge for models of the rapid early growth of jetted SMBHs.

astro-ph.HE↗