Xiaotong Guo, Huijie Zhao, Shuwei Shao, Xudong Li, Baochang Zhang
Self-supervised monocular depth estimation methods have been increasingly given much attention due to the benefit of not requiring large, labelled datasets. Such self-supervised methods require high-quality salient features and consequently suffer from severe performance drop for indoor scenes, where low-textured regions dominant in the scenes are almost indiscriminative. To address the issue, we propose a self-supervised indoor monocular depth estimation framework called $\mathrm{F^2Depth}$. A self-supervised optical flow estimation network is introduced to supervise depth learning. To improve optical flow estimation performance in low-textured areas, only some patches of points with more discriminative features are adopted for finetuning based on our well-designed patch-based photometric loss. The finetuned optical flow estimation network generates high-accuracy optical flow as a supervisory signal for depth estimation. Correspondingly, an optical flow consistency loss is designed. Multi-scale feature maps produced by finetuned optical flow estimation network perform warping to compute feature map synthesis loss as another supervisory signal for depth learning. Experimental results on the NYU Depth V2 dataset demonstrate the effectiveness of the framework and our proposed losses. To evaluate the generalization ability of our $\mathrm{F^2Depth}$, we collect a Campus Indoor depth dataset composed of approximately 1500 points selected from 99 images in 18 scenes. Zero-shot generalization experiments on 7-Scenes dataset and Campus Indoor achieve $δ_1$ accuracy of 75.8% and 76.0% respectively. The accuracy results show that our model can generalize well to monocular images captured in unknown indoor scenes.
Xiaohan Wang, Zhan Zhao, Hongmou Zhang, Xiaotong Guo, Jinhua Zhao
Ridesharing is recognized as one of the key pathways to sustainable urban mobility. With the emergence of Transportation Network Companies (TNCs) such as Uber and Lyft, the ridesharing market has become increasingly fragmented in many cities around the world, leading to efficiency loss and increased traffic congestion. While an integrated ridesharing market (allowing sharing across TNCs) can improve the overall efficiency, how such benefits may vary across TNCs based on actual market characteristics is still not well understood. In this study, we extend a shareability network framework to quantify and explain the efficiency benefits of ridesharing market integration using available TNC trip records. Through a case study in Manhattan, New York City, the proposed framework is applied to analyze a real-world ridesharing market with 3 TNCs$-$Uber, Lyft, and Via. It is estimated that a perfectly integrated market in Manhattan would improve ridesharing efficiency by 13.3%, or 5% of daily TNC vehicle hours traveled. Further analysis reveals that (1) the efficiency improvement is negatively correlated with the overall demand density and inter-TNC spatiotemporal unevenness (measured by network modularity), (2) market integration would generate a larger efficiency improvement in a competitive market, and (3) the TNC with a higher intra-TNC demand concentration (measured by clustering coefficient) would benefit less from market integration. As the uneven benefits may deter TNCs from collaboration, we also illustrate how to quantify each TNC's marginal contribution based on the Shapley value, which can be used to ensure equitable profit allocation. These results can help market regulators and business alliances to evaluate and monitor market efficiency and dynamically adjust their strategies, incentives, and profit allocation schemes to promote market integration and collaboration.
Xiaotong Guo, Qiusheng Gu, Jun Xu, Guanwen Fang, Xue Ge, Yongyun Chen, Xiaoling Yu, Nan Ding
Jan 13, 2023·astro-ph.GA·PDF We presented the multiwavelength analysis of a heavily obscured active galactic nucleus (AGN) in NGC 449. We first constructed a broadband X-ray spectrum using the latest NuSTAR and XMM-Newton data. Its column density ($\simeq 10^{24} \rm{cm}^{-2}$) and photon index ($Γ\simeq 2.4$) were reliably obtained by analyzing the broadband X-ray spectrum. However, the scattering fraction and the intrinsic X-ray luminosity could not be well constrained. Combined with the information obtained from the mid-infrared (mid-IR) spectrum and spectral energy distribution (SED) fitting, we derived its intrinsic X-ray luminosity ($\simeq 8.54\times 10^{42} \ \rm{erg\ s}^{-1}$) and scattering fraction ($f_{\rm{scat}}\simeq 0.26\%$). In addition, we also derived the following results: (1). The mass accretion rate of central AGN is about $2.54 \times 10^{-2} \rm{M}_\odot\ \rm{yr}^{-1}$, and the Eddington ratio is $8.39\times 10^{-2}$; (2). The torus of this AGN has a high gas-to-dust ratio ($N_{\rm H}/A_{\rm V}=8.40\times 10^{22}\ \rm{cm}^{-2}\ \rm{mag}^{-1}$); (3). The host galaxy and the central AGN are both in the early stage of co-evolution.
Xiaotong Guo, Qiusheng Gu, Nan Ding, E. Contini, Yongyun Chen
Dec 22, 2019·astro-ph.GA·PDF The physical parameters of galaxies and/or AGNs can be derived by fitting their multi-band spectral energy distributions (SEDs). By using CIGALE code, we perform multi-band SED fitting (from ultraviolet to infrared) for 791 X-ray sources (518 AGNs and 273 normal galaxies) in the 7 Ms Chandra Deep Field-south survey (CDFS). We consider the contributions from AGNs and adopt more accurate redshifts than published before. Therefore, more accurate star formation rates (SFRs) and stellar masses (M$_*$) are derived. We classify the 518 AGNs into type-I and type-II based on their optical spectra and their SEDs. Moreover, six AGN candidates are selected from the 273 normal galaxies based on their SEDs. Our main results are as follows: (1) the host galaxies of AGNs have larger M$_*$ than normal galaxies, implying that AGNs prefer to host in massive galaxies; (2) the specific star formation rates (sSFRs) of AGN host galaxies are different from those of normal galaxies, suggesting that AGN feedback may play an important role in the star formation activity; (3) we find that the fraction of optically obscured AGNs in CDFS decreases with the increase of intrinsic X-ray luminosity, which is consistent with previous studies;(4) the host galaxies of type-I AGNs tend to have lower M$_*$ than type-II AGNs, which may suggest that dust in the host galaxy may also contribute to the optical obscuration of AGNs.
Xiaotong Guo, Guanwen Fang, Haicheng Feng, Rui Zhang
The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates (SFRs) are the most fundamental. We develop a novel tool, \textit{Multi-Layer Perceptron for Predicting Galaxy Parameters} (MLP-GaP), that uses a machine-learning (ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multi-band catalogs. We first adopt a mock dataset generated by the \textit{Code Investigating GALaxy Emission} (CIGALE) for training and testing datasets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training dataset. The results of the test performed on the mock dataset show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from SED fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.
Xiaotong Guo, Qiusheng Gu, Nan Ding, Xiaoling Yu, Yongyun Chen
Even in deep X-ray surveys, Compton-thick active galactic nuclei (CT AGNs, ${\rm N_H} \geqslant 1.5~\times~10^{24}~{\rm cm}^{-2}$) are difficult to be identified due to X-ray flux suppression and their complex spectral shape. However, the study of CT AGNs is vital for understanding the rapid growth of black holes and the origin of cosmic X-ray background. In the local universe, the fraction of CT AGNs accounts for 30% of the whole AGN population. We may expect a higher fraction of CT AGNs in deep X-ray surveys, however, only 10% of AGNs have been identified as CT AGNs in the 7 Ms \textit{Chandra} Deep Field-South (CDFS) survey. In this work, we select 51 AGNs with abundant multi-wavelength data. Using the method of the mid-infrared (mid-IR) excess, we select hitherto unknown 8 CT AGN candidates in our sample. Seven of these candidates can confirm as CT AGN based on the multi-wavelength identification approach, and a new CT AGN (XID 133) is identified through the mid-IR diagnostics. We also discuss the X-ray origin of these eight CT AGNs and the reason why their column densities were underestimated in previous studies. We find that the multi-wavelength approaches of selecting CT AGNs are highly efficient, provided the high quality of observational data. We also find that CT AGNs have a higher Eddington ratio than non-CT AGNs, and that both CT AGNs and non-CT AGNs show similar properties of host galaxies.
Xiaotong Guo, Samitha Samaranayake
We consider the Single School Routing Problem (SSRP) where students from a single school are picked up by a fleet of school buses, subject to a set of constraints. The constraints that are typically imposed for school buses are bus capacity, a maximum student walking distance to a pickup point, and a maximum commute time for each student. This is a special case of the Vehicle Routing Problem (VRP) with a common destination. We propose a decomposition approach for solving this problem based on the existing notion of a shareability network, which has been used recently in the context of dynamic ridepooling problems. Moreover, we come up with a simplified formulation for solving the SSRP by introducing the connection between the SSRP and the weighted set covering problem (WSCP). To scale this method to large-scale problem instances, we propose i) a node compression method for the shareability network based decomposition approach, and ii) heuristic-based edge pruning techniques that perform well in practice. We show that the compressed problem leads to an Integer Linear Program (ILP) of reduced dimensionality that can be solved efficiently using off-the-shelf ILP solvers. Numerical experiments on the synthetic Boston Public School (BPS) instances are conducted to evaluate the performance of our approach. Meanwhile, our proposed SSRP formulation allows a natural extension for introducing alternate transportation modes to students, which effectively reduces the number of buses needed for each school and leads to a 15\% cost reduction on average. Moreover, two state-of-art large-scale SSRP solving techniques are compared with our proposed approaches on benchmark networks and our methods outperform both techniques under a single school setting.
Xiaotong Guo, Baichuan Mo, Haris N. Koutsopoulos, Shenhao Wang, Jinhua Zhao
Public transit systems are the backbone of urban mobility systems in the era of urbanization. The design of transit schedules is important for the efficient and sustainable operation of public transit. However, previous studies usually assume fixed demand patterns and ignore uncertainties in demand, which may generate transit schedules that are vulnerable to demand variations. To address demand uncertainty issues inherent in public transit systems, this paper adopts both stochastic programming (SP) and robust optimization (RO) techniques to generate robust transit schedules against demand uncertainty. A nominal (non-robust) optimization model for the transit frequency setting problem (TFSP) under a single transit line setting is first proposed. The model is then extended to SP-based and RO-based formulations to incorporate demand uncertainty. The large-scale origin-destination (OD) matrices for real-world transit problems make the optimization problems hard to solve. To efficiently generate robust transit schedules, a Transit Downsizing (TD) approach is proposed to reduce the dimensionality of the problem. We prove that the optimal objective function of the problem after TD is close to that of the original problem (i.e., the difference is bounded from above). The proposed models are tested with real-world transit lines and data from the Chicago Transit Authority (CTA). Compared to the current transit schedule implemented by CTA, the nominal TFSP model without considering demand uncertainty reduces passengers' wait times while increasing in-vehicle travel times. After incorporating demand uncertainty, both stochastic and robust TFSP models reduce passengers' wait times and in-vehicle travel times simultaneously. The robust TFSP model produces transit schedules with better in-vehicle travel times and worse wait times for passengers compared to the stochastic TFSP model.
Xiaotong Guo, Jinhua Zhao
This paper addresses the pressing challenge of urban mobility in the context of growing urban populations, changing demand patterns for urban mobility, and emerging technologies like Mobility-on-Demand (MoD) platforms and Autonomous Vehicle (AV). As urban areas swell and demand pattern changes, the integration of Autonomous Mobility-on-Demand (AMoD) systems with existing public transit (PT) networks presents great opportunities to enhancing urban mobility. We propose a novel optimization framework for solving the Transit-Centric Multimodal Urban Mobility with Autonomous Mobility-on-Demand (TCMUM-AMoD) at scale. The system operator (public transit agency) determines the network design and frequency settings of the PT network, fleet sizing and allocations of AMoD system, and the pricing for using the multimodal system with the goal of minimizing passenger disutility. Passengers' mode and route choice behaviors are modeled explicitly using discrete choice models. A first-order approximation algorithm is introduced to solve the problem at scale. Using a case study in Chicago, we showcase the potential to optimize urban mobility across different demand scenarios. To our knowledge, ours is the first paper to jointly optimize transit network design, fleet sizing, and pricing for the multimodal mobility system while considering passengers' mode and route choices.
Xiaotong Guo, Baichuan Mo, Qingyi Wang
In response to the Amazon Last-Mile Routing Challenge, Team Permission Denied proposes a hierarchical Travelling Salesman Problem (TSP) optimization with a customized cost matrix. The higher level TSP solves for the zone sequence while the lower level TSP solves the intra-zonal stop sequence. The cost matrix is modified to account for routing patterns beyond the shortest travel time. Lastly, some post-processing is done to edit the sequence to match commonly observed routing patterns, such as when travel times are similar, drivers usually start with stops with more packages than those with fewer packages. The model is tested on 1223 routes that are randomly selected out of the training set and the score is 0.0381. On the 13 routes in the given model apply set, the score was 0.0375.
Hongmou Zhang, Xiaotong Guo, Jinhua Zhao
On-demand mobility sharing, provided by one or several transportation network companies (TNCs), is realized by real-time optimization algorithms to connect trips among tens of thousands of drivers and fellow passengers. In a market of mobility sharing comprised of TNCs, there are two competing principles, the economies of network scale and the healthy competition between TNCs, which can lead to "segmentation" of market. To understand the substantiality and relationship of the two competing principles, we need to answer how much efficiency loss is generated due to the segmentation of market, and which factors are related to it. Here we show how four critical factors of market structure and characteristics of mobility sharing services -- density of trips (thickness), maximum detour allowed for sharing (tightness), market shares (unevenness), and spatial segregation of the TNCs (dissolvedness) -- are associated with the efficiency loss, represented as the difference in vehicle miles traveled (VMT) under different market structures. We found that 1) while VMT shows a simple power function with thickness, the corresponding exponent term can be expressed as a non-monotonic function with tightness -- essentially showing how economies and diseconomies of scale in this market arise, and appearing a very similar form to the Lennard--Jones model in inter-molecular potentials; and 2) the efficiency loss is higher when unevenness is closer to 0.5 (50-50 market share) and dissolvedness is larger. Our results give a comprehensive analysis of how the inefficiency of market segmentation is generated, and how potentially it may be avoided through market mechanism design.
Rui Zhang, Xiaotong Guo, Qiusheng Gu, Guanwen Fang, Jun Xu, Hai-Cheng Feng, Yongyun Chen, Rui Li, Nan Ding, Hongtao Wang
May 27, 2025·astro-ph.GA·PDF Compton-thick active galactic nuclei (CT-AGNs), which are defined by column density $\mathrm{N_H} \geqslant 1.5 \times 10^{24} \ \mathrm{cm}^{-2}$, emit feeble X-ray radiation, even undetectable by X-ray instruments. Despite this, the X-ray emissions from CT-AGNs are believed to be a substantial contributor to the cosmic X-ray background (CXB). According to synthesis models of AGNs, CT-AGNs are expected to make up a significant fraction of the AGN population, likely around 30% or more. However, only $\sim$11% of AGNs have been identified as CT-AGNs in the Chandra Deep Field-South (CDFS). To identify hitherto unknown CT-AGNs in the field, we used a Random Forest algorithm for identifying them. First, we build a secure classified subset of 210 AGNs to train and evaluate our algorithm. Our algorithm achieved an accuracy rate of 90% on the test set after training. Then, we applied our algorithm to an additional subset of 254 AGNs, successfully identifying 67 CT-AGNs within this group. This result significantly increased the fraction of CT-AGNs in the CDFS, which is closer to the theoretical predictions of the CXB. Finally, we compared the properties of host galaxies between CT-AGNs and non-CT-AGNs and found that the host galaxies of CT-AGNs exhibit higher levels of star formation activity.
Xiaotong Guo, Qiusheng Gu, Guanwen Fang, Shiying Lu, Fen Lyu, Yongyun Chen, Nan Ding, Mengfei Zhang, Xiaoling Yu, Hongtao Wang
Compton-thick active galactic nuclei (CT-AGNs), defined by column density $\mathrm{N_H} \geqslant 1.5 \times 10^{24} \ \mathrm{cm}^{-2}$, are so heavily absorbed that their X-ray emission is often feeble, even undetectable by X-ray instruments. Nevertheless, their radiation is expected to be a substantial contributor to the cosmic X-ray background (CXB), predicting that CT-AGNs should comprise at least $\sim$30% of the total AGN population. In the Cosmological Evolution Survey (COSMOS), the identified CT-AGN fraction falls far below theoretical expectations, indicating that a substantial population of CT-AGNs is hidden due to their low photon counts or their flux below the current flux limits of X-ray instruments. This work focuses on identifying CT-AGNs hidden in mid-infrared (MIR)-selected AGNs. First, we selected a sample of 1,104 MIR-selected AGNs that were covered but individually undetected by X-ray. Next, we reduced the X-ray data in the COSMOS and analyzed multiwavelength data in our sample to derive the key physical parameters required for CT-AGN identification. Using MIR diagnostics, we first find out 7 to 23 CT-AGN candidates. Their subsequent X-ray stacking analysis reveals a clear detection at $>3σ$ significance in the soft band and only $>1σ$ significance in the hard band. We fit the stacked soft- and hard-band fluxes with a physical model and confirm that these sources are absorbed by Compton-thick material. However, CT-AGNs constitute only 2.1% (23/1104) of our sample, significantly below the fraction predicted by CXB synthesis models, indicating that a considerable population of CT-AGNs remains missed by our selection. A comparison of host-galaxy properties between CT-AGNs and non-CT-AGNs reveals no significant differences.
Xiaotong Guo, Hanyong Xu, Dingyi Zhuang, Yunhan Zheng, Jinhua Zhao
The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.
Xiaotong Guo, Qindong Zheng, Jinshi Zhao, Bing Li, Eric Morgan Yeatman
Advances in flexible catheters pave the way for minimally invasive diagnosis and treatment of luminal organs and tubular structures through endoluminal interventions. A key challenge is in establishing non-constraining pressure monitoring at the interfaces between medical catheters and intraluminal anatomy exhibiting curvilinear contours, structural variability, and time-dependent physiological motion. This work presents a scalable and multi-purpose pressure sensing system for multidirectional monitoring of tissue interactions, establishing a robust solution for deploying diagnostic and therapeutic instruments in various types of endoluminal interventions. This approach provides an integrated system encompassing pressure sensors, catheters, and signal acquisition devices. A poly (vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) film is miniaturized and configured into a multiplexed piezoelectric-based pressure sensor, providing flexibility and scalability in conforming to medical catheters with curved surfaces. The catheter is fabricated with a cost-effective and highly scalable fiber drawing technology, establishing a means of fast prototyping catheters with bespoke structures for sensor integration and medical instrument integration. The system achieves enhanced pressure detection sensitivity and a comparable sensing range, compared with state-of-the-art catheter-integrated sensors. Through in-vitro phantom studies, the system performs precise multi-directional sensing within various clinical endoluminal scenarios, showing its potential in digitalizing tissue interactions during endoluminal interventions.
Xiaotong Guo, Ao Qu, Hongmou Zhang, Peyman Noursalehi, Jinhua Zhao
In the governance of the shared mobility market of a city or of a metropolitan area, there are two conflicting principles: 1) the healthy competition between multiple platforms, such as between Uber and Lyft in the United States, and 2) economies of network scale, which leads to higher chances for trips to be matched, and thus higher operation efficiency, but which also implies monopoly. The current shared mobility markets, as observed in different cities in the world, are either monopolistic, or largely segmented by multiple platforms, the latter with significant efficiency loss. How to keep the competition between platforms, but to reduce the efficiency loss due to segmentation with new market designs is the focus of this paper. We first propose a theoretical framework of shared mobility market segmentation and then propose four market structure designs thereupon. The framework and four designs are first discussed as an abstract model, without losing generality, thus not constrained to any specific city. High-level perspectives and detailed mechanisms for each proposed market structure are both examined. Then, to assess the real-world performance of these market structure designs, we used a ride-sharing simulator with real-world ride-hailing trip data from New York City to simulate. The proposed market designs can reduce the total vehicle-miles traveled (VMT) by 6\% while serving more customers with 8.4\% fewer total number of trips. In the meantime, customers receive better services with on-average 5.4\% shorter waiting time. At the end of the paper, the feasibility of implementation for each proposed market structure is discussed.
Xiaotong Guo, Qiusheng Gu, Guanwen Fang, Yongyun Chen, Nan Ding, Xiaoling Yu, Hongtao Wang
Compton-thick active galactic nuclei (CT-AGNs), characterized by a significant absorption with column densities of $\mathrm{N_H}\geqslant 1.5\times 10^{24} \ \mathrm{cm}^{-2}$, emit feeble X-ray radiation and are even undetectable by X-ray instruments, making them difficult to identify. X-ray radiation from AGNs is the predominant source of the cosmic X-ray background (CXB). Based on AGN synthesis models for the CXB, the fraction of CT-AGNs should constitute a substantial portion of AGN population, approximately 30\% or more. The fraction of CT-AGNs discovered in the Cosmological Evolution Survey (COSMOS) is significantly lower than this value. This means that many CT-AGNs may be hidden in AGNs that exhibit low photon counts or that have not been detected by X-ray instruments. This work focuses on identifying CT-AGNs hidden in AGNs with low photon counts. Firstly, we selected 440 AGNs with abundant multiwavelength data as our sample. Secondly, we analyzed multiwavelength data, extracting crucial physical parameters required for the CT-AGN diagnosis. Finally, we used multiwavelength approaches to identify CT-AGNs. We have successfully identified 18 CT-AGNs in our sample. Among the CT-AGNs, four AGNs show discrepant results across different diagnostic methods. We discuss the potential reasons behind these diagnostic discrepancies. We explore the impact of estimating [O~III]$λ~5007$ luminosities based on [O~II]$λ~3727$ luminosities for the CT-AGN diagnosis. We have also found that the properties of host galaxies for CT-AGNs and non-CT-AGNs do not show significant discrepancies.
Shiying Lu, Yizhou Gu, Guanwen Fang, Qirong Yuan, Min Bao, Xiaotong Guo
Jun 11, 2020·astro-ph.GA·PDF We investigate the differences in the stellar population properties, the structure, and the environment between massive compact star-forming galaxies (cSFGs) with or without active galactic nucleus (AGN) at $2<z<3$ in the five 3D-HST/CANDELS fields. In a sample of 221 massive cSFGs, we constitute the most complete AGN census so far, identifying 66 AGNs by the X-ray detection, the mid-infrared color criterion, and/or the SED fitting, while the rest (155) are non-AGNs. Further dividing these cSFGs into two redshift bins, i.e., $2<z<2.5$ and $2.5 \leq z<3$, we find that in each redshift bin the cSFGs with AGNs have similar distributions of the stellar mass, the specific star formation rate, and the ratio of $L_{\rm IR}$ to $L_{\rm UV}$ to those without AGNs. After having performed a two-dimensional surface brightness modeling for those cSFGs with X-ray-detected AGNs (37) to correct for the influence of the central point-like X-ray AGN on measuring the structural parameters of its host galaxy, we find that in each redshift bin the cSFGs with AGNs have comparable distributions of all concerned structural parameters, i.e., the Sersic index, the 20\%-light radius, the Gini coefficient, and the concentration index, to those without AGNs. With a gradual consumption of available gas and dust, the structure of cSFGs, indicated by the above structural parameters, seem to be slightly more concentrated with decreasing redshift. At $2<z<3$, the similar environment between cSFGs with and without AGNs suggests that their AGN activities are potentially triggered by internal secular processes, such as gravitational instabilities or/and dynamical friction.
Linghua Xie, Nicola R. Napolitano, Xiaotong Guo, Crescenzo Tortora, Haicheng Feng, Antonios Katsianis, Rui Li, Sirui Wu, Mario Radovich, Leslie K. Hunt, Yang Wang, Lin Tang, Baitian Tang, Zhiqi Huang
The Kilo Degree Survey (KiDS) is currently the only sky survey providing optical ($ugri$) plus near-infrared (NIR, $ZYHJK_S$) seeing matched photometry over an area larger than 1000 $\rm deg^2$. This is obtained by incorporating the NIR data from the VISTA Kilo Degree Infrared Galaxy (VIKING) survey, covering the same KiDS footprint. As such, the KiDS multi-wavelength photometry represents a unique dataset to test the ability of stellar population models to return robust photometric stellar mass ($M_*$) and star-formation rate (SFR) estimates. Here we use a spectroscopic sample of galaxies for which we possess $u g r i Z Y J H K_s$ ``gaussianized'' magnitudes from KiDS data release 4. We fit the spectral energy distribution from the 9-band photometry using: 1) three different popular libraries of stellar {population} templates, 2) single burst, simple and delayed exponential star-formation history models, and 3) a wide range of priors on age and metallicity. As template fitting codes we use two popular softwares: LePhare and CIGALE. We investigate the variance of the stellar masses and the star-formation rates from the different combinations of templates, star formation recipes and codes to assess the stability of these estimates and define some ``robust'' median quantities to be included in the upcoming KiDS data releases. As a science validation test, we derive the mass function, the star formation rate function, and the SFR-$M_*$ relation for a low-redshift ($z<0.5$) sample of galaxies, that result in excellent agreement with previous literature data. The final catalog, containing $\sim290\,000$ galaxies with redshift $0.01<z<0.9$, is made publicly available.
Chen Yongyun, Gu Qiusheng, Fan Junhui, Yu xiaoling, Ding Nan, Guo Xiaotong, Xiong Dingrong
Sep 23, 2022·astro-ph.HE·PDF Under the coronal magnetic field, we estimate the maximal jet power of the Blandford-\Znajek (BZ) mechanism, Blandford-\Payne (BP) mechanism, and hybrid model. The jet power of the BZ and Hybrid model mechanisms depends on the spin of a black hole, while the jet power of the BP mechanism does not depend on the spin of a black hole. At high black hole spin, the jet power of the hybrid model is greater than that of the BZ and BP mechanisms. We find that the jet power of almost all gamma-\ray narrow line Seyfert 1 galaxies (gamma-\NLS1s) can be explained by the hybrid model. However, one source with jet power 0.1~\1 Eddington luminosity can not be explained by the hybrid model. We suggest that the magnetic field dragged inward by the accretion disk with magnetization-\driven outflows may accelerate the jets in this gamma-\NLS1.