arXiv subjects

Zefeng Li

Publications and source records attributed to Zefeng Li.

At least 37 records · Page 2Linked to original sources

arXiv2024

The properties of the interstellar medium in dusty, star-forming galaxies at $z \sim 2-4$: The shape of the CO spectral line energy distributions

The molecular gas in the interstellar medium (ISM) of star-forming galaxy populations exhibits diverse physical properties. We investigate the $^{12}$CO excitation of twelve dusty, luminous star-forming galaxies at $z \sim 2-4$ by combining observations of the $^{12}$CO from $J_{\rm up} = 1$ to $J_{\rm up} = 8$. The spectral line energy distribution (SLED) has a similar shape to NGC 253, M82, and local ULIRGs, with much stronger excitation than the Milky Way inner disc. By combining with resolved dust continuum sizes from high-resolution $870$-$μ$m ALMA observations and dust mass measurements determined from multi-wavelength SED fitting, we measure the relationship between the $^{12}$CO SLED and probable physical drivers of excitation: star-formation efficiency, the average intensity of the radiation field $\langle U\rangle$, and the star-formation rate surface density. The primary driver of high-$J_{\rm up}$ $^{12}$CO excitation in star-forming galaxies is star-formation rate surface density. We use the ratio of the CO($3-2$) and CO($6-5$) line fluxes to infer the CO excitation in each source and find that the average ratios for our sample are elevated compared to observations of low-redshift, less actively star-forming galaxies and agree well with predictions from numerical models that relate the ISM excitation to the star-formation rate surface density. The significant scatter in the line ratios of a factor $\approx 3$ within our sample likely reflects intrinsic variations in the ISM properties which may be caused by other effects on the excitation of the molecular gas, such as cosmic ray ionization rates and mechanical heating through turbulence dissipation.

astro-ph.GA↗

arXiv2024

Quantifying azimuthal variations within the interstellar medium of z ~ 0 spiral galaxies with the TYPHOON survey

Most star formation in the local Universe occurs in spiral galaxies, but their origin remains an unanswered question. Various theories have been proposed to explain the development of spiral arms, each predicting different spatial distributions of the interstellar medium. This study maps the star formation rate (SFR) and gas-phase metallicity of nine spiral galaxies with the TYPHOON survey to test two dominating theories: density wave theory and dynamic spiral theory. We discuss the environmental effects on our galaxies, considering reported environments and merging events. Taking advantage of the large field of view covering the entire optical disk, we quantify the fluctuation of SFR and metallicity relative to the azimuthal distance from the spiral arms. We find higher SFR and metallicity in the trailing edge of NGC~1365 (by 0.117~dex and 0.068~dex, respectively) and NGC~1566 (by 0.119~dex and 0.037~dex, respectively), which is in line with density wave theory. NGC~2442 shows a different result with higher metallicity (0.093~dex) in the leading edge, possibly attributed to an ongoing merging. The other six spiral galaxies show no statistically significant offset in SFR or metallicity, consistent with dynamic spiral theory. We also compare the behaviour of metallicity inside and outside the co-rotation radius (CR) of NGC~1365 and NGC~1566. We find comparable metallicity fluctuations near and beyond the CR of NGC~1365, indicating gravitational perturbation. NGC~1566 shows the greatest fluctuation near the CR, in line with the analytic spiral arms. Our work highlights that a combination of mechanisms explains the origin of spiral features in the local Universe.

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

The mass-metallicity and fundamental metallicity relations in non-AGN and AGN-host galaxies

Galaxies' stellar masses, gas-phase oxygen abundances (metallicity), and star formation rates (SFRs) obey a series of empirical correlations, most notably the mass-metallicity relation (MZR) and fundamental metallicity relation (FZR), which relates oxygen abundance to a combination of stellar mass and SFR. However, due to the difficulty of measuring oxygen abundances and SFRs in galaxies that host powerful active galactic nuclei (AGN), to date it is unknown to what extent AGN-host galaxies also follow these correlations. In this work, we apply Bayesian methods to the MaNGA integral field spectrographic (IFS) survey that allow us to measure oxygen abundances and SFRs in AGN hosts, and use these measurements to explore how the MZR and FZR differ between galaxies that do and do not host AGN. We find similar MZRs at stellar masses above $10^{10.5} \mathrm{M}_\odot$, but that at lower stellar masses AGN hosts show up to $\sim 0.2$ dex higher oxygen abundances. The offset in the FZR is significantly smaller, suggesting that the larger deviation in the MZR is a result of AGN-host galaxies having systematically lower SFRs at fixed stellar mass. However, within the AGN-host sample there is little correlation between SFR and oxygen abundance. These findings support a scenario in which an AGN can halt efficient gas accretion, which drives non-AGN host galaxies to both higher SFR and lower oxygen abundance, resulting in the galaxy evolving off the star-forming main sequence (SFMS). As a consequence, as the SFR declines for an individual system its metallicity remains mostly unchanged.

astro-ph.GA↗

arXiv2024

GroundingGPT:Language Enhanced Multi-modal Grounding Model

Multi-modal large language models have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize capturing global information within each modality while neglecting the importance of perceiving local information across modalities. Consequently, these models lack the ability to effectively understand the fine-grained details of input data, limiting their performance in tasks that require a more nuanced understanding. To address this limitation, there is a compelling need to develop models that enable fine-grained understanding across multiple modalities, thereby enhancing their applicability to a wide range of tasks. In this paper, we propose GroundingGPT, a language enhanced multi-modal grounding model. Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input. It demonstrates precise identification and localization of specific regions in images or moments in videos. To achieve this objective, we design a diversified dataset construction pipeline, resulting in a multi-modal, multi-granularity dataset for model training. The code, dataset, and demo of our model can be found at https: //github.com/lzw-lzw/GroundingGPT.

cs.CV↗

arXiv2024

Cosmological evolution of metallicity correlation functions from the Auriga simulations

We study the cosmological evolution of the two-point correlation functions of galactic gas-phase metal distributions using the 28 simulated galaxies from the Auriga Project. Using mock observations of the $z = 0$ snapshots to mimic our past work, we show that the correlation functions of the simulated mock observations are well matched to the correlation functions measured from local galaxy surveys. This comparison suggests that the simulations capture the processes important for determining metal correlation lengths, the key parameter in metallicity correlation functions. We investigate the evolution of metallicity correlations over cosmic time using the true simulation data, showing that individual galaxies undergo no significant systematic evolution in their metal correlation functions from $z\sim 3$ to today. In addition, the fluctuations in metal correlation length are correlated with but lag ahead fluctuations in star formation rate. This suggests that re-arrangement of metals within galaxies occurs at a higher cadence than star formation activity, and is more sensitive to the changes of environment, such as galaxy mergers, gas inflows / outflows, and fly-bys.

astro-ph.GA↗

arXiv2023

The MAGPI Survey: Effects of Spiral Arms on Different Tracers of the Interstellar Medium and Stellar Populations at z~0.3

Spiral structures are important drivers of the secular evolution of disc galaxies, however, the origin of spiral arms and their effects on the development of galaxies remain mysterious. In this work, we present two three-armed spiral galaxies at z~0.3 in the Middle Age Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey. Taking advantage of the high spatial resolution (~0.6'') of the Multi-Unit Spectroscopic Unit (MUSE), we investigate the two-dimensional distributions of different spectral parameters: Halpha, gas-phase metallicity, and D4000. We notice significant offsets in Halpha (~0.2 dex) as well as gas-phase metallicities (~0.05 dex) among the spiral arms, downstream and upstream of MAGPI1202197197 (SG1202). This observational signature suggests the spiral structure in SG1202 is consistent with arising from density wave theory. No azimuthal variation in Halpha or gas-phase metallicities is observed in MAGPI1204198199 (SG1204), which can be attributed to the tighter spiral arms in SG1204 than SG1202, coming with stronger mixing effects in the disc. The absence of azimuthal D4000 variation in both galaxies suggests the stars at different ages are well-mixed between the spiral arms and distributed around the disc regions. The different azimuthal distributions in Halpha and D4000 highlight the importance of time scales traced by various spectral parameters when studying 2D distributions in spiral galaxies. This work demonstrates the feasibility of constraining spiral structures by tracing interstellar medium (ISM) and stellar population at z~0.3, with a plan to expand the study to the full MAGPI survey.

astro-ph.GA↗

arXiv2023

MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots

Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI) services due to their exceptional proficiency in understanding and generating human-like text. LLM chatbots, in particular, have seen widespread adoption, transforming human-machine interactions. However, these LLM chatbots are susceptible to "jailbreak" attacks, where malicious users manipulate prompts to elicit inappropriate or sensitive responses, contravening service policies. Despite existing attempts to mitigate such threats, our research reveals a substantial gap in our understanding of these vulnerabilities, largely due to the undisclosed defensive measures implemented by LLM service providers. In this paper, we present Jailbreaker, a comprehensive framework that offers an in-depth understanding of jailbreak attacks and countermeasures. Our work makes a dual contribution. First, we propose an innovative methodology inspired by time-based SQL injection techniques to reverse-engineer the defensive strategies of prominent LLM chatbots, such as ChatGPT, Bard, and Bing Chat. This time-sensitive approach uncovers intricate details about these services' defenses, facilitating a proof-of-concept attack that successfully bypasses their mechanisms. Second, we introduce an automatic generation method for jailbreak prompts. Leveraging a fine-tuned LLM, we validate the potential of automated jailbreak generation across various commercial LLM chatbots. Our method achieves a promising average success rate of 21.58%, significantly outperforming the effectiveness of existing techniques. We have responsibly disclosed our findings to the concerned service providers, underscoring the urgent need for more robust defenses. Jailbreaker thus marks a significant step towards understanding and mitigating jailbreak threats in the realm of LLM chatbots.

cs.CR↗

arXiv2023

SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction

Training specific deep learning models for particular tasks is common across various domains within seismology. However, this approach encounters two limitations: inadequate labeled data for certain tasks and limited generalization across regions. To address these challenges, we develop SeisCLIP, a seismology foundation model trained through contrastive learning from multi-modal data. It consists of a transformer encoder for extracting crucial features from time-frequency seismic spectrum and an MLP encoder for integrating the phase and source information of the same event. These encoders are jointly pre-trained on a vast dataset and the spectrum encoder is subsequently fine-tuned on smaller datasets for various downstream tasks. Notably, SeisCLIP's performance surpasses that of baseline methods in event classification, localization, and focal mechanism analysis tasks, employing distinct datasets from different regions. In conclusion, SeisCLIP holds significant potential as a foundational model in the field of seismology, paving the way for innovative directions in foundation-model-based seismology research.

physics.geo-ph↗

arXiv2023

Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)

Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdependent tasks of phase picking, association, and location. Although deep learning methods have been successfully applied to earthquake monitoring, they mostly address the tasks separately and ignore the geographic relationships among stations. Here, we propose a graph neural network that operates directly on multi-station seismic data and achieves simultaneous phase picking, association, and location. Particularly, the inter-station and inter-task physical relationships are informed in the network architecture to promote accuracy, interpretability, and physical consistency among cross-station and cross-task predictions. When applied to data from the Ridgecrest region and Japan regions, this method showed superior performance over previous deep learning-based phase-picking and localization methods. Overall, our study provides for the first time a prototype self-consistent all-in-one system of simultaneous seismic phase picking, association, and location, which has the potential for next-generation autonomous earthquake monitoring.

physics.geo-ph↗

arXiv2022

Spatial metallicity distribution statistics at $\lesssim 100$ pc scales in the AMUSING++ nearby galaxy sample

We analyse the spatial statistics of the 2D gas-phase oxygen abundance distributions in a sample of 219 local galaxies. We introduce a new adaptive binning technique to enhance the signal-to-noise ratio of weak lines, which we use to produce well-filled metallicity maps for these galaxies. We show that the two-point correlation functions computed from the metallicity distributions after removing radial gradients are in most cases well described by a simple injection-diffusion model. Fitting the data to this model yields the correlation length $l_{\rm corr}$, which describes the characteristic interstellar medium mixing length scale. We find typical correlation lengths $l_{\rm corr} \sim 1$ kpc, with a strong correlation between $l_{\rm corr}$ and stellar mass, star formation rate, and effective radius, a weak correlation with Hubble type, and significantly elevated values of $l_{\rm corr}$ in interacting or merging galaxies. We show that the trend with star formation rate can be reproduced by a simple transport+feedback model of interstellar medium turbulence at high star formation rate, and plausibly also at low star formation rate if dwarf galaxy winds have large mass-loading factors. We also report the first measurements of the injection width that describes the initial radii over which supernova remnants deposit metals. Inside this radius the metallicity correlation function is not purely the product of a competition between injection and diffusion. We show that this size scale is generally smaller than 60 pc.

astro-ph.GA↗

arXiv2021

Detection of metallicity correlations in 100 nearby galaxies

In this paper we analyse the statistics of the 2D gas-phase oxygen abundance distributions of 100 nearby galaxies drawn from the Calar Alto Legacy Integral Field spectroscopy Area survey. After removing the large-scale radial metallicity gradient, we compute the two-point correlation functions of the resulting metallicity fluctuation maps. We detect correlations in the majority of our targets, which we show are significantly in excess of what is expected due to beam-smearing, and are robust against the choice of metallicity diagnostic. We show that the correlation functions are generally well-fit by the predictions of a simple model for stochastic metal injection coupled with diffusion, and from the model we show that, after accounting for the effects of both beam smearing and noise, the galaxies in our sample have characteristic correlation lengths of $\sim1$ kpc. Correlation lengths increase with both stellar mass and star formation rate, but show no significant variation with Hubble type, barredness, or merging state. We also find no evidence for a theoretically-predicted relationship between gas velocity dispersion and correlation length, though this may be due to the small dynamic range in gas velocity dispersion across our sample. Our results suggest that measurements of 2D metallicity correlation functions can be a powerful tool for studying galaxy evolution.

astro-ph.GA↗

arXiv2020

The Sloan Digital Sky Survey Reverberation Mapping Project: Photometric g and i Light Curves

The Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) program monitors 849 active galactic nuclei (AGN) both spectroscopically and photometrically. The photometric observations used in this work span over four years and provide an excellent baseline for variability studies of these objects. We present the photometric light curves from 2014 to 2017 obtained by the Steward Observatory's Bok telescope and the CFHT telescope with MegaCam. We provide details on the data acquisition and processing of the data from each telescope, the difference imaging photometry used to produce the light curves, and the calculation of a variability index to quantify each AGN's variability. We find that the Welch-Stetson J-index provides a useful characterization of AGN variability and can be used to select AGNs for further study.

astro-ph.GA↗

arXiv2020

The Third Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a wide and deep imaging survey to cover a 5400 deg$^2$ area in the Northern Galactic Cap with the 2.3m Bok telescope using two filters ($g$ and $r$ bands). The Mosaic $z$-band Legacy Survey (MzLS) covers the same area in $z$ band with the 4m Mayall telescope. These two surveys will be used for spectroscopic targeting of the Dark Energy Spectroscopic Instrument (DESI). The BASS survey observations were completed in 2019 March. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations between 2015 January and 2019 March. The median astrometric precision relative to {\it Gaia} positions is about 17 mas and the median photometric offset relative to the PanSTARRS1 photometry is within 5 mmag. The median $5σ$ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for $g$, $r$, and $z$ bands, respectively. The photometric depth within the survey area is highly homogeneous, with the difference between the 20\% and 80\% depth less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at \url{http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home}.

astro-ph.GA↗

arXiv2019

Ultra-luminous quasars at redshift $z>4.5$ from SkyMapper

The most luminous quasars at high redshift harbour the fastest-growing and most massive black holes in the early Universe. They are exceedingly rare and hard to find. Here, we present our search for the most luminous quasars in the redshift range from $z=4.5$ to $5$ using data from SkyMapper, Gaia and WISE. We use colours to select likely high-redshift quasars and reduce the stellar contamination of the candidate set with parallax and proper motion data. In $\sim$12,500~deg$^2$ of Southern sky, we find 92 candidates brighter than $R_p=18.2$. Spectroscopic follow-up has revealed 21 quasars at $z\ge 4$ (16 of which are within $z=[4.5,5]$), as well as several red quasars, BAL quasars and objects with unusual spectra, which we tentatively label OFeLoBALQSOs at redshifts of $z\approx 1$ to $2$. This work lifts the number of known bright $z\ge 4.5$ quasars in the Southern hemisphere from 10 to 26 and brings the total number of quasars known at $R_p<18.2$ and $z\ge 4.5$ to 42.

astro-ph.GA↗

arXiv2019

The Sloan Digital Sky Survey Reverberation Mapping Project: Improving Lag Detection with an Extended Multi-Year Baseline

We investigate the effects of extended multi-year light curves (9-year photometry and 5-year spectroscopy) on the detection of time lags between the continuum variability and broad-line response of quasars at z>~1.5, and compare with the results using 4-year photometry+spectroscopy presented in a companion paper. We demonstrate the benefits of the extended light curves in three cases: (1) lags that are too long to be detected by the shorter-duration data but can be detected with the extended data; (2) lags that are recovered by the extended light curves but missed in the shorter-duration data due to insufficient light curve quality; and (3) lags for different broad line species in the same object. These examples demonstrate the importance of long-term monitoring for reverberation mapping to detect lags for luminous quasars at high-redshift, and the expected performance of the final dataset from the Sloan Digital Sky Survey Reverberation Mapping project that will have 11-year photometric and 7-year spectroscopic baselines.

astro-ph.GA↗

arXiv2019

The Sloan Digital Sky Survey Reverberation Mapping Project: Initial CIV Lag Results from Four Years of Data

We present reverberation-mapping lags and black-hole mass measurements using the CIV 1549 broad emission line from a sample of 349 quasars monitored as a part of the Sloan Digital Sky Survey Reverberation Mapping Project. Our data span four years of spectroscopic and photometric monitoring for a total baseline of 1300 days. We report significant time delays between the continuum and the CIV 1549 emission line in 52 quasars, with an estimated false-positive detection rate of 10%. Our analysis of marginal lag measurements indicates that there are on the order of 100 additional lags that should be recoverable by adding more years of data from the program. We use our measurements to calculate black-hole masses and fit an updated CIV radius-luminosity relationship. Our results significantly increase the sample of quasars with CIV RM results, with the quasars spanning two orders of magnitude in luminosity toward the high-luminosity end of the CIV radius-luminosity relation. In addition, these quasars are located at among the highest redshifts (z~1.4-2.8) of quasars with black hole masses measured with reverberation mapping. This work constitutes the first large sample of CIV reverberation-mapping measurements in more than a dozen quasars, demonstrating the utility of multi-object reverberation mapping campaigns.

astro-ph.GA↗

arXiv2019

Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan earthquake

The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more reliable phase detection and picking. However, many less studied regions lack a significant amount of labeled events needed for traditional CNN approaches. In this paper, we present a CNN-based Phase- Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets. When trained on 30,146 labeled phases and applied to one-month of continuous recordings during the aftershock sequences of the 2008 MW 7.9 Wenchuan Earthquake in Sichuan, China, CPIC detects 97.5% of the manually picked phases in the standard catalog and predicts their arrival times with a five-times improvement over the ObsPy AR picker. In addition, unlike other CNN-based approaches that require millions of training samples, when the off-line training set size of CPIC is reduced to only a few thousand training samples the accuracy stays above 95%. The online implementation of CPIC takes less than 12 hours to pick arrivals in 31-day recordings on 14 stations. In addition to the catalog phases manually picked by analysts, CPIC finds more phases for existing events and new events missed in the catalog. Among those additional detections, some are confirmed by a matched filter method while others require further investigation. Finally, when tested on a small dataset from a different region (Oklahoma, US), CPIC achieves 97% accuracy after fine tuning only the fully connected layer of the model. This result suggests that the CPIC developed in this study can be used to identify and pick P/S arrivals in other regions with no or minimum labeled phases.

physics.geo-ph↗