arXiv subjects

Yijia Li

Publications and source records attributed to Yijia Li.

At least 19 recordsLinked to original sources

arXiv2026

Multi-Trigger Crypto CAT Bonds with On-Chain Settlement: Valuation and Optimal Design

Cryptocurrencies have experienced repeated large-scale losses from protocol exploits and exchange breaches, exposing insurers and investors to severe operational risks. This paper develops an equilibrium pricing framework for catastrophe bonds tailored to the cryptocurrency ecosystem. We introduce a double-trigger structure that jointly captures short-term catastrophic shocks and longer-term systemic deterioration. To model the multi-risk environment, we incorporate dual dependence, combining dependence across triggers with multivariate dependence among financial risk factors through vine copulas. Beyond expected prices, we characterize the full distribution of discounted cash flows and return rates, enabling risk-sensitive metrics such as Value-at-Risk and Tail Value-at-Risk. Furthermore, we propose an on-chain settlement architecture where calibrated payout functions are embedded directly into smart contracts. This design eliminates basis risk associated with settlement delays and minimizes the agency costs inherent in traditional intermediation. Our results demonstrate that multi-trigger crypto CAT bonds offer a statistically robust and economically efficient vehicle for transferring systemic digital asset risks to capital markets.

stat.AP↗

arXiv2026

An Empirical Study of Speculative Decoding on Software Engineering Tasks

Large Language Models (LLMs) have become widely used for Software Engineering (SE) tasks, spanning from function-level code generation to complex repository-level workflows. However, the high latency of autoregressive inference remains a significant bottleneck, hindering their deployment in interactive environments. While Speculative Decoding (SD) offers a promising technique for lossless acceleration, prior research on long-context repository-level tasks and complex agentic interactions remains limited. To bridge this gap, we present the first systematic empirical study to evaluate the effectiveness of SD in SE tasks. We systematically benchmark a comprehensive spectrum of strategies, encompassing both model-based and model-free methods, across representative generation, editing, and repair scenarios. Our empirical results indicate that SD demonstrates clear potential for accelerating inference, particularly for smaller models that achieve higher speedups than those of their larger counterparts. We find that the effectiveness of SD methods varies across different task scenarios. Model-based approaches are well-suited for code generation, whereas model-free methods are better adapted to repository-level repair and editing scenarios. Furthermore, we observe that the repetitiveness of SE tasks improves the performance of model-free methods. In contrast to natural language tasks, the higher predictability of SE tasks allows for more aggressive hyperparameters. Our findings are summarized as guidelines to help increase inference efficiency for SE scenarios.

cs.SE↗

arXiv2026

Antenna Elements' Trajectory Optimization for Throughput Maximization in Continuous-Trajectory Fluid Antenna-Aided Wireless Communications

Fluid antenna (FA) systems offer novel spatial degrees of freedom (DoFs) with the potential for significant performance gains. Compared to existing works focusing solely on optimizing FA positions at discrete time instants, we introduce the concept of continuous-trajectory fluid antenna (CTFA), which explicitly considers the antenna element's movement trajectory across continuous time intervals and incorporates the inherent kinematic constraints present in practical FA implementations. Accordingly, we formulate the total throughput maximization problem in CTFA-aided wireless communication systems, addressing the joint optimization of continuous antenna trajectories in conjunction with the transmit covariance matrices under kinematic constraints. To effectively solve this non-convex problem with highly coupled optimization variables, we develop an iterative algorithm based on block coordinate descent (BCD) and majorization-minimization (MM) principles with the aid of the weighted minimum mean square error (WMMSE) method. Finally, numerical results are presented to validate the efficacy of the proposed algorithms and to quantify the substantial total throughput advantages afforded by the conceived CTFA-aided system compared to conventional fixed-position antenna (FPA) benchmarks and alternative approaches employing simplified trajectories.

eess.SP↗

arXiv2025

Ionizing Photon Production Efficiencies and Chemical Abundances at Cosmic Dawn Revealed by Ultra-Deep Rest-Frame Optical Spectroscopy of JADES-GS-z14-0

JWST has discovered an early period of galaxy formation that was more vigorous than expected, which has challenged our understanding of the early Universe. In this work, we present the longest spectroscopic integration ever acquired by JWST/MIRI. This spectrum covers the brightest rest-frame optical nebular emission lines for the luminous galaxy JADES-GS-z14-0 at $z > 14$. Most notably, we detect $[\mathrm{OIII}] \lambda\lambda 4959,5007$ at $\approx 11 \sigma$ and $\mathrm{H}\alpha$ at $\approx 4 \sigma$ with these ultra-deep observations. These lines reveal that JADES-GS-z14-0 has low dust attenuation with a recent star-formation rate of $\mathrm{SFR} \approx 10 \pm 2\ M_{\odot} / \mathrm{yr}$, star-formation rate surface density of $\Sigma_{\mathrm{SFR}} \approx 23 \pm 5\ M_{\odot}/\mathrm{yr}/\mathrm{kpc}^{2}$, and ionizing photon production efficiency of $\xi_{\mathrm{ion}} \approx 10^{25.3 \pm 0.1}\ \mathrm{Hz/erg}$. Using standard strong-line diagnostics, we infer a gas-phase oxygen abundance of $[\mathrm{O/H}] \approx -1.1 \pm 0.4$ ($\approx 10\%\ Z_{\odot}$), carbon-to-oxygen ratio of $[\mathrm{C/O}] \approx -0.4 \pm 0.4$, ionization parameter of $\mathrm{log}_{10}(U) \gtrsim -2.4$, and density of $n_{\mathrm{H}} \approx 720 \pm 210\ \mathrm{cm}^{-3}$. Using detailed photoionization modeling, we instead derive $[\mathrm{O/H}] \approx -0.3_{-0.4}^{+0.4}$ ($\approx 50\%\ Z_{\odot}$) and $\mathrm{log}_{10}(U) \approx -1.5_{-0.4}^{+0.3}$. The inferred properties of JADES-GS-z14-0 are similar to those measured for similarly luminous galaxies at $z > 10$ with previous MIRI/Spectroscopy, such as GHZ2/GLASSz12, GN-z11, and MACS0647-JD1. Existing simulations are unable to reproduce the empirical and inferred properties of JADES-GS-z14-0. This work demonstrates an important step toward understanding the formation of the first stars and heavy elements in the Universe. [Abridged]

astro-ph.GA↗

arXiv2025

NL2CA: Auto-formalizing Cognitive Decision-Making from Natural Language Using an Unsupervised CriticNL2LTL Framework

Cognitive computing models offer a formal and interpretable way to characterize human's deliberation and decision-making, yet their development remains labor-intensive. In this paper, we propose NL2CA, a novel method for auto-formalizing cognitive decision-making rules from natural language descriptions of human experience. Different from most related work that exploits either pure manual or human guided interactive modeling, our method is fully automated without any human intervention. The approach first translates text into Linear Temporal Logic (LTL) using a fine-tuned large language model (LLM), then refines the logic via an unsupervised Critic Tree, and finally transforms the output into executable production rules compatible with symbolic cognitive frameworks. Based on the resulted rules, a cognitive agent is further constructed and optimized through cognitive reinforcement learning according to the real-world behavioral data. Our method is validated in two domains: (1) NL-to-LTL translation, where our CriticNL2LTL module achieves consistent performance across both expert and large-scale benchmarks without human-in-the-loop feed-backs, and (2) cognitive driving simulation, where agents automatically constructed from human interviews have successfully learned the diverse decision patterns of about 70 trials in different critical scenarios. Experimental results demonstrate that NL2CA enables scalable, interpretable, and human-aligned cognitive modeling from unstructured textual data, offering a novel paradigm to automatically design symbolic cognitive agents.

cs.AI↗

arXiv2025

Two-Dimensional Pinching-Antenna Systems: Modeling and Beamforming Design

Recently, the pinching-antenna system (PASS) has emerged as a promising architecture owing to its ability to reconfigure large-scale path loss and signal phase by activating radiation points along a dielectric waveguide. However, existing studies mainly focus on line-shaped PASS architectures, whose limited spatial flexibility constrains their applicability in multiuser and indoor scenarios. In this paper, we propose a novel two-dimensional (2D) pinching-antenna system (2D-PASS) that extends the conventional line-shaped structure into a continuous dielectric waveguide plane, thereby forming a reconfigurable radiating plane capable of dynamic beam adaptation across a 2D spatial domain. An optimization framework is developed to maximize the minimum received signal-to-noise ratio (SNR) among user equipments (UEs) by adaptively adjusting the spatial configuration of pinching antennas (PAs), serving as an analog beamforming mechanism for dynamic spatial control. For the continuous-position scenario, a particle swarm optimization (PSO)-based algorithm is proposed to efficiently explore the nonconvex search space, while a discrete variant is introduced to accommodate practical hardware constraints with limited PA placement resolution. Simulation results demonstrate that the proposed 2D-PASS substantially improves the minimum SNR compared with conventional line-shaped PASS and fixed-position antenna (FPA) benchmarks, while maintaining robustness under varying user distributions and distances.

eess.SP↗

arXiv2025

The Blue Jay Survey: Deep JWST Spectroscopy for a Representative Sample of Galaxies at Cosmic Noon

We present the Blue Jay survey, a Cycle-1 JWST program aimed at studying the stellar and gas content of galaxies at Cosmic Noon. The survey consists of deep spectroscopy for 153 targets observed over two pointings in the COSMOS field using the NIRSpec micro-shutter assembly (MSA). We employ the three medium-resolution gratings G140M, G235M, and G395M, with exposure times of 13 hours, 3.2 hours, and 1.6 hours, respectively. We thus obtain full coverage of the 1-5 micron range, corresponding to the entire rest-frame optical wavelength range. The sample is carefully selected to provide a census of galaxies over the redshift range 1.7 < z < 3.5 above a redshift-dependent minimum stellar mass that ranges from 10^8.7 Msun to 10^9.3 Msun.The Blue Jay sample is representative of the entire galaxy population at these redshifts, without strong biases in color, star formation rate, or other properties. The sizes of massive galaxies at these redshifts are comparable to the NIRSpec shutters, which requires custom strategies for designing and reducing the observations. Since the standard A-B nod subtraction leads to flux self-subtraction, we construct a master background from empty shutters and subtract it from each of the science spectra. This, in turn, allows for the use of shorter slitlets consisting of only two shutters per galaxy instead of the usual three, with a substantial increase in the multiplexing of the NIRSpec MSA. We measure multi-band photometry using archival JWST and HST observations in two different ways: in a large elliptical aperture encompassing the entire source and from the exact area in the sky where the NIRSpec 1D spectrum is extracted. This enables self-consistent fits of spectroscopic and photometric data. The Blue Jay dataset, which we publicly release, represents the ideal sample for studying the stellar populations, neutral gas, and ionized gas in Cosmic Noon galaxies.

astro-ph.GA↗

arXiv2025

Light Future: Multimodal Action Frame Prediction via InstructPix2Pix

Predicting future motion trajectories is a critical capability across domains such as robotics, autonomous systems, and human activity forecasting, enabling safer and more intelligent decision-making. This paper proposes a novel, efficient, and lightweight approach for robot action prediction, offering significantly reduced computational cost and inference latency compared to conventional video prediction models. Importantly, it pioneers the adaptation of the InstructPix2Pix model for forecasting future visual frames in robotic tasks, extending its utility beyond static image editing. We implement a deep learning-based visual prediction framework that forecasts what a robot will observe 100 frames (10 seconds) into the future, given a current image and a textual instruction. We repurpose and fine-tune the InstructPix2Pix model to accept both visual and textual inputs, enabling multimodal future frame prediction. Experiments on the RoboTWin dataset (generated based on real-world scenarios) demonstrate that our method achieves superior SSIM and PSNR compared to state-of-the-art baselines in robot action prediction tasks. Unlike conventional video prediction models that require multiple input frames, heavy computation, and slow inference latency, our approach only needs a single image and a text prompt as input. This lightweight design enables faster inference, reduced GPU demands, and flexible multimodal control, particularly valuable for applications like robotics and sports motion trajectory analytics, where motion trajectory precision is prioritized over visual fidelity.

cs.CV↗

arXiv2025

Pinching-Antenna-Assisted Index Modulation: Channel Modeling, Transceiver Design, and Performance Analysis

In this paper, a novel pinching-antenna assisted index modulation (PA-IM) scheme is proposed for improving the spectral efficiency without increasing the hardware complexity, where the information bits are conveyed not only by the conventional M-ary quadrature amplitude modulation (QAM) symbols but also by the indices of pinching antenna (PA) position patterns. To realize the full potential of this scheme, this paper focuses on the comprehensive transceiver design, addressing key challenges in signal detection at the receiver and performance optimization at thetransmitter. First, a comprehensive channel model is formulated for this architecture, which sophisticatedly integrates the deterministic in-waveguide propagation effects with the stochastic nature of wireless channels, including both largescale path loss and small-scale fading. Next, to overcome the prohibitive complexity of optimal maximum likelihood (ML) detection, a low-complexity box-optimized sphere decoding (BOSD) algorithm is designed, which adaptively prunes the search space whilst preserving optimal ML performance. Furthermore, an analytical upper bound on the bit error rate (BER) is derived and validated by the simulations. Moreover, a new transmit precoding method is designed using manifold optimization, which minimizes the BER by jointly optimizing the complex-valued precoding coefficients across the waveguides for the sake of maximizing the minimum Euclidean distance of all received signal points. Finally, the simulation results demonstrate that the proposed PA-IM scheme attains a significant performance gain over its conventional counterparts and that the overall BER of the pinching-antenna system is substantially improved by the proposed precoding design.

eess.SP↗

arXiv2025

A Cosmic Miracle: A Remarkably Luminous Galaxy at $z_{\rm{spec}}=14.44$ Confirmed with JWST

JWST has revealed a stunning population of bright galaxies at surprisingly early epochs, $z>10$, where few such sources were expected. Here we present the most distant example of this class yet -- MoM-z14, a luminous ($M_{\rm{UV}}=-20.2$) source in the COSMOS field at $z_{\rm{spec}}=14.44^{+0.02}_{-0.02}$ that expands the observational frontier to a mere 280 million years after the Big Bang. The redshift is confirmed with NIRSpec/PRISM spectroscopy through a sharp Lyman-$\alpha$ break and $\sim3\sigma$ detections of five rest-UV emission lines. The number density of bright $z_{\rm{spec}}\sim14-15$ sources implied by our "Mirage or Miracle" survey spanning $\sim350$ arcmin$^{2}$ is $>100\times$ larger ($182^{+329}_{-105}\times$) than pre-JWST consensus models. The high EWs of UV lines ($\sim15{-}35$ \AA) signal a rising star-formation history, with a $\sim10\times$ increase in the last 5 Myr ($\rm{SFR_{\rm{5Myr}}}/\rm{SFR_{\rm{50Myr}}}=9.9^{+3.0}_{-5.8}$). The source is extremely compact (circularized $r_{\rm{e}} = 74^{+15}_{-12}$ pc), and yet elongated ($b/a=0.25^{+0.11}_{-0.06}$), suggesting an AGN is not the dominant source of UV light. The steep UV slope ($\beta=-2.5^{+0.2}_{-0.2}$) implies negligible dust attenuation and a young stellar population. The absence of a strong damping wing provides tentative evidence that the immediate surroundings of MoM-z14 may be partially ionized at a redshift where virtually every reionization model predicts a $\sim100\%$ neutral fraction. The nitrogen emission and highly super-solar [N/C]$>1$ hint at an abundance pattern similar to local globular clusters that may have once hosted luminous supermassive stars. Since this abundance pattern is also common among the most ancient stars born in the Milky Way, we may be directly witnessing the formation of such stars in dense clusters, connecting galaxy evolution across the entire sweep of cosmic time.

astro-ph.GA↗

arXiv2025

A foundation model for human-AI collaboration in medical literature mining

Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature. The model is trained on 633,759 instruction data points in LEADSInstruct, curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. We showed that LEADS demonstrates consistent improvements over four cutting-edge generic large language models (LLMs) on six tasks. Furthermore, LEADS enhances expert workflows by providing supportive references following expert requests, streamlining processes while maintaining high-quality results. A study with 16 clinicians and medical researchers from 14 different institutions revealed that experts collaborating with LEADS achieved a recall of 0.81 compared to 0.77 experts working alone in study selection, with a time savings of 22.6%. In data extraction tasks, experts using LEADS achieved an accuracy of 0.85 versus 0.80 without using LEADS, alongside a 26.9% time savings. These findings highlight the potential of specialized medical literature foundation models to outperform generic models, delivering significant quality and efficiency benefits when integrated into expert workflows for medical literature mining.

cs.CL↗

arXiv2024

Widespread rapid quenching at cosmic noon revealed by JWST deep spectroscopy

Massive quiescent galaxies in the young universe are expected to be quenched rapidly, but it is unclear whether they all experience starbursts before quenching and what physical mechanism drives rapid quenching. We study 14 massive quiescent galaxies ($\log(M_\star/M_\odot) > 10$) at $z\sim2$ selected from a representative sample of the Blue Jay survey. We reconstruct their star formation histories by fitting spectral energy distribution models to the JWST/NIRSpec $R\sim1000$ spectra. We find that massive quiescent galaxies can be split into three categories with roughly equal numbers of galaxies according to their SFHs: 1) Relatively old galaxies quenched at early epochs; 2) Galaxies that are rapidly and recently quenched after a flat or bursty formation history (depending on the assumed prior); 3) Galaxies that are rapidly and recently quenched after a major starburst. Most recently quenched galaxies show neutral gas outflows, probed by blueshifted $\rm Na\,I\,D$ absorption, and ionized gas emission, with line ratios consistent with active galactic nucleus (AGN) diagnostics. This suggests that AGN activity drives multi-phase gas outflows, leading to rapid quenching. By tracing back the SFHs of the entire sample, we predict the number density of massive quiescent galaxies at $z=4-6$: $n=(1.5-6.0)\times10^{-5}\,\rm Mpc^{-3}$. The two old massive quiescent galaxies in our sample appear to have extremely early formation and quenching ($z\gtrsim6$), possibly descendants of early post-starbursts at $z>3$. These galaxies still show neutral gas reservoirs and weak H$α$ emission, perhaps because the ejective AGN feedback that caused rapid quenching has weakened over time.

astro-ph.GA↗

arXiv2024

All the Little Things in Abell 2744: $>$1000 Gravitationally Lensed Dwarf Galaxies at $z=0-9$ from JWST NIRCam Grism Spectroscopy

Dwarf galaxies hold the key to crucial frontiers of astrophysics, however, their faintness renders spectroscopy challenging. Here we present the JWST Cycle 2 survey, All the Little Things (ALT, PID 3516), which is designed to seek late-forming Pop III stars and the drivers of reionization at $z\sim6-7$. ALT has acquired the deepest NIRCam grism spectroscopy yet (7-27 hr), at JWST's most sensitive wavelengths (3-4 $μ$m), covering the powerful lensing cluster Abell 2744. Over the same 30 arcmin$^2$, ALT's ultra-deep F070W+F090W imaging ($\sim$30 mag) enables selection of very faint sources at $z>6$. We demonstrate the success of ALT's novel ``butterfly" mosaic to solve spectral confusion and contamination, and introduce the ``Allegro" method for emission line identification. By collecting spectra for every source in the field of view, ALT has measured precise ($R\sim1600$) redshifts for 1630 sources at $z=0.2-8.5$. This includes one of the largest samples of distant dwarf galaxies: [1015, 475, 50] sources less massive than the SMC, Fornax, and Sculptor with $\log(M_{*}/M_{\odot})<$[8.5, 7.5, 6.5]. We showcase ALT's discovery space with: (i) spatially resolved spectra of lensed clumps in galaxies as faint as $M_{\rm{UV}}\sim-15$; (ii) large-scale clustering -- overdensities at $z$=[2.50, 2.58, 3.97, 4.30, 5.66, 5.77, 6.33] hosting massive galaxies with striking Balmer breaks; (iii) small-scale clustering -- a system of satellites around a Milky Way analog at $z\sim6$; (iv) spectroscopically confirmed multiple images that help constrain the lensing model underlying all science in this legacy field; (v) sensitive star-formation maps based on dust-insensitive tracers such as Pa$α$; (vi) direct spectroscopic discovery of rare sources such as AGN with ionized outflows. These results provide a powerful proof of concept for how grism surveys maximize the potential of strong lensing fields.

astro-ph.GA↗

arXiv2024

Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

cs.CV↗

arXiv2024

No top-heavy stellar initial mass function needed: the ionizing radiation of GS9422 can be powered by a mixture of AGN and stars

JWST is producing high-quality rest-frame optical and UV spectra of faint galaxies at $z>4$ for the first time, challenging models of galaxy and stellar populations. One galaxy recently observed at $z=5.943$, GS9422, has nebular line and UV continuum emission that appears to require a high ionizing photon production efficiency. This has been explained with an exotic stellar initial mass function (IMF), 10-30x more top-heavy than a Salpeter IMF (Cameron et al. 2023). Here we suggest an alternate explanation to this exotic IMF. We use a new flexible neural net emulator for CLOUDY, Cue, to infer the shape of the ionizing spectrum directly from the observed emission line fluxes. By describing the ionizing spectrum with a piece-wise power-law, Cue is agnostic to the source of the ionizing photons. Cue finds that the ionizing radiation from GS9422 can be approximated by a double power law characterized by $\frac{Q_\mathrm{HeII}}{Q_\mathrm{H}} = -1.5$, which can be interpreted as a combination of young, metal-poor stars and a low-luminosity active galactic nucleus (AGN) with $F_ν \propto λ^ {2}$ in a 65%/35% ratio. This suggests a significantly lower nebular continuum contribution to the observed UV flux (24%) than a top-heavy IMF ($\gtrsim80$%), and hence, necessitates a damped Lyman-$α$ absorber (DLA) to explain the continuum turnover bluewards of $\sim1400$ Angstrom. While current data cannot rule out either scenario, given the immense impact the proposed top-heavy IMF would have on models of galaxy formation, it is important to propose viable alternative explanations and to further investigate the nature of peculiar high-z nebular emitters.

astro-ph.GA↗

arXiv2024

Unravelling the role of merger histories in the population of In situ stars: linking IllustrisTNG cosmological simulation to H3 survey

We undertake a comprehensive investigation into the distribution of in~situ stars within Milky Way-like galaxies, leveraging TNG50 simulations and comparing their predictions with data from the H3 survey. Our analysis reveals that 28% of galaxies demonstrate reasonable agreement with H3, while only 12% exhibit excellent alignment in their profiles, regardless of the specific spatial cut employed to define in~situ stars. To uncover the underlying factors contributing to deviations between TNG50 and H3 distributions, we scrutinise correlation coefficients among internal drivers(e.g., virial radius, star formation rate [SFR]) and merger-related parameters (such as the effective mass-ratio, mean distance, average redshift, total number of mergers, average spin-ratio and maximum spin alignment between merging galaxies). Notably, we identify significant correlations between deviations from observational data and key parameters such as the median slope of virial radius, mean SFR values, and the rate of SFR change across different redshift scans. Furthermore, positive correlations emerge between deviations from observational data and parameters related to galaxy mergers. We validate these correlations using the Random Forest Regression method. Our findings underscore the invaluable insights provided by the H3 survey in unravelling the cosmic history of galaxies akin to the Milky Way, thereby advancing our understanding of galactic evolution and shedding light on the formation and evolution of Milky Way-like galaxies in cosmological simulations.

astro-ph.GA↗

arXiv2024

AGN Feedback in Quiescent Galaxies at Cosmic Noon Traced by Ionized Gas Emission

We analyze ionized gas emission lines in deep rest-frame optical spectra of 16 quiescent galaxies at redshift $1.7<z<3.5$ observed with JWST/NIRSpec by the Blue Jay survey. Robust detection of emission lines in $75\%$ of the sample indicates the presence of ongoing ionizing sources in this passive population. The H$\alpha$ line luminosities confirm that the population is quiescent, with star formation rates that are at least ten times lower than the main sequence of star formation. The quiescent sample is clearly separate from the star-forming population in line diagnostic diagrams, and occupies a region usually populated by active galactic nuclei (AGN). Analysis of the observed line ratios, equivalent widths, and velocity dispersions leads us to conclude that in most cases the gas is ionized by AGN activity, despite the lack of X-ray detections. A subset of the sample also hosts ionized and/or neutral outflows. Our results show, for the first time using a representative sample, that low luminosity AGN are extremely common among quiescent galaxies at high redshift. These low luminosity AGN may play a key role in quenching star formation and in maintaining massive galaxies quiescent from Cosmic Noon to $z\sim0$.

astro-ph.GA↗

arXiv2024

Star Formation Shut Down by Multiphase Gas Outflow in a Galaxy at a Redshift of 2.45

Large-scale outflows driven by supermassive black holes are thought to play a fundamental role in suppressing star formation in massive galaxies. However, direct observational evidence for this hypothesis is still lacking, particularly in the young universe where star formation quenching is remarkably rapid, thus requiring effective removal of gas as opposed to slow gas heating. While outflows of ionized gas are commonly detected in massive distant galaxies, the amount of ejected mass is too small to be able to suppress star formation. Gas ejection is expected to be more efficient in the neutral and molecular phases, but at high redshift these have only been observed in starbursts and quasars. Here we report JWST spectroscopy of a massive galaxy experiencing rapid quenching at redshift z=2.445. We detect a weak outflow of ionized gas and a powerful outflow of neutral gas, with a mass outflow rate that is sufficient to quench the star formation. Neither X-ray or radio activity are detected; however, the presence of a supermassive black hole is suggested by the properties of the ionized gas emission lines. We thus conclude that supermassive black holes are able to rapidly suppress star formation in massive galaxies by efficiently ejecting neutral gas.

astro-ph.GA↗