arXiv subjects

Haicheng Feng

Publications and source records attributed to Haicheng Feng.

12 recordsLinked to original sources

arXiv2026

The Intermediate-Mass Black Hole Reverberation Mapping Project: Scientific Overview and Sample Characteristics

Recent discoveries with the James Webb Space Telescope of massive black holes at high redshift have highlighted fundamental questions about black hole seed formation and the coevolution of black holes with their host galaxies. Because the initial seed population cannot yet be observed directly, nearby intermediate-mass black holes provide a complementary fossil record of black hole formation and early growth. Motivated by this opportunity, we present the Intermediate-Mass Black Hole Reverberation Mapping (IMBH-RM) project and construct a homogeneous Sloan Digital Sky Survey sample of active broad-line IMBHs by uniformly reanalyzing literature candidates with consistent spectral decomposition and black hole mass estimation. Our sample contains 192 reliable IMBH candidates at $z\lesssim0.3$ with $\log(M_{\rm BH}/M_\odot)<6$, including four particularly compelling sources with $\log(M_{\rm BH}/M_\odot)<5$. The primary goal of IMBH-RM is to obtain reliable black hole masses from direct measurements and characteristic sizes of the broad-line region and accretion disk for a carefully selected subsample. These measurements will provide robust low-mass anchors for calibrating single-epoch black hole mass estimates and extending black hole--galaxy scaling relations into the IMBH regime. By building a statistically meaningful reverberation-mapped sample spanning $10^4-10^6\,M_\odot$, we aim to constrain the local IMBH mass distribution and place observational constraints on competing black hole seed formation scenarios. The future Multi-Channel Imager aboard the Chinese Space-station Survey Telescope provides a particularly promising platform for achieving these goals.

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arXiv2026

Constraints on the Intranight Optical Variability of Intermediate-Mass Black Hole Candidates

Intermediate-mass black holes (IMBHs) provide a unique regime for studying accretion variability at the low-mass end of the black hole population, yet their intranight optical variability (INOV) remains poorly constrained. We present a systematic investigation of INOV in an optically selected sample of IMBH candidates using high-cadence observations from the Zwicky Transient Facility (ZTF). From a parent sample of 1,447 broad H$\alpha$-selected candidates, we identify 64 IMBH candidates (median $f_{\mathrm{AGN}}\sim0.06$) with 163 intranight monitoring sessions. Apparent INOV signals identified by conventional ZTF PSF-fit photometry are largely associated with seeing-dependent changes in the relative contributions of compact nuclear and extended host components, which can mimic intrinsic short-timescale variability. In contrast, no robust INOV is detected with difference-image analysis. An ensemble structure function spanning $\Delta t\sim0.003$--$1600$ days reveals long-term variability in a small subsample of sources, whereas intrinsic variability remains unresolved at intranight timescales. Monte Carlo simulations further show that ZTF-like single-night monitoring has a low INOV recovery probability ($\sim1.2%$) for the variability amplitudes inferred from the long-term analysis. The recovery probability is primarily controlled by source brightness, AGN contribution, intrinsic variability amplitude, and photometric precision. These results demonstrate that the absence of detected INOV does not imply the absence of rapid accretion variability, but can reflect the limited detectability of low-amplitude signals under current observing capabilities. Our findings highlight the importance of robust photometric methodologies for future high-cadence variability studies of low-mass accreting black holes.

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arXiv2026

The Impact of Elliptical Broad-Line Regions on Reverberation-Based Black Hole Mass Estimates

The virial factor $f$ is critical for accurate supermassive black hole (SMBH) mass measurements using reverberation mapping (RM) and the radius--luminosity ($R$--$L$) relation, yet its value remains highly uncertain. While traditional models assume axisymmetric broad-line region (BLR) geometries, growing evidence suggests that BLRs may possess more complex, asymmetric structures. We systematically investigate the impact of elliptical-disk BLR geometries on SMBH mass determinations through comprehensive numerical simulations. By computing emission-line profiles, emissivity-weighted time lags, and the corresponding virial factor $f$ over a wide range of eccentricities, orientations, and inclinations, we find that even in purely virialized systems, geometric effects alone can cause $f$ to vary by more than an order of magnitude and can mimic observational signatures typically attributed to radiation pressure. Additionally, local broadening introduces further systematic uncertainties in velocity width measurements, biasing $f$ by up to a factor of $\sim$3. Asymmetric BLR configurations also induce a scatter of $\sim$0.18 dex in the $R$--$L$ relation due to projection effects, comparable to the intrinsic scatter observed in RM studies. These results challenge the conventional attribution of RM uncertainties to non-virial motions or radiation pressure, and instead highlight the fundamental role of BLR geometry in SMBH mass measurements.

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arXiv2025

Spectral Hardening Reveals Afterglow Emergence in Long-Duration Fast X-ray Transients: A Case Study of GRB 250404A/EP250404a

The prompt emission and afterglow phases of gamma-ray bursts (GRBs) have been extensively studied, yet the transition between these two phases remains inadequately characterized due to limited multiwavelength observational coverage. Among the recent growing samples of fast X-ray transients observed by Einstein Probe (EP), a subgroup of GRBs are captured with long-duration X-ray emission, potentially containing featured evolution from prompt emission to the afterglow phase. In this Letter, we present a detailed analysis of GRB 250404A/EP250404a, a bright fast X-ray transient detected simultaneously by EP and the Fermi Gamma-ray Burst Monitor in X-rays and gamma rays. Its continuous X-ray emission reveals a long-duration tail, accompanied by distinct spectral evolution manifested by the spectral index $\alpha_{\rm X}$ with an initial softening, followed by an evident hardening, eventually reaching a plateau at the value of $\sim$ -2. Early optical and near-infrared observations enable broadband modeling with forward- and reverse-shock components, confirming that the X-ray hardening signals the emergence of the external-shock afterglow. From this spectral hardening we infer that the prompt phase in soft X-rays lasted $\sim300\;\mathrm{s}$, which is more than 3 times longer than the gamma-ray $T_{90}$. This well-tracked soft-hard-flat spectral pattern provides a clear indication of afterglow emergence from the fading prompt emission and offers a practical criterion for identifying a distinct population of GRBs among fast X-ray transients, even when the detection of the gamma-ray counterpart or obvious temporal break is absent.

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arXiv2025

The Intermediate-Mass Black Hole Reverberation Mapping Project: First Detection of Mid-Infrared Lags in Prototypical IMBHs in NGC 4395 and POX 52

The search for robust evidence of intermediate-mass black holes (IMBHs) is crucial for understanding black hole seeding process and the formation of supermassive black holes in the early Universe. NGC 4395 and POX 52 are two prototypical IMBH hosts, both exhibiting multi-line evidence of low-mass black hole activity. Here, we report the first detection of mid-infrared (MIR) lags in response to optical variability, with measurements of $3.0^{+2.4}_{-1.9}$ days for NGC 4395 and $35.2^{+14.2}_{-11.7}$ days for POX~52 at $3.4$ $\mu$m, respectively, using archival optical data and observations from the Wide-field Infrared Survey Explorer (WISE). This detection provides the first reverberation evidence of low-mass black hole activity in POX 52. The time lags of these two low-mass, low-luminosity active galactic nuclei (AGNs) generally follow the extent of the $R_{\rm dust}-L_{\rm 5100}$ relation found in higher-mass AGNs. Based on an empirical relation between the broad-line region and dusty torus size, we constrain the black hole mass of POX 52 to log($M_{\rm BH}$/$M_\odot$) = 5.5 $\pm$ 0.37 (systemic and statistical errors), confirming its IMBH nature. Furthermore, long-term optical continuum monitoring of POX 52 reveals a mild inter-band lag of $\lesssim$ 1 day. However, no significant intranight variability was detected during its one-night, high-cadence monitoring, which we attribute to the longer duty cycle of fast variability in POX 52 compared to that in NGC 4395.

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arXiv2025

Optical+NIR analysis of a Newly Confirmed Einstein ring at z$\sim$1 from the Kilo-Degree Survey: Dark matter fraction, total and dark matter density slope and IMF

We report the spectroscopic confirmation of a bright blue Einstein ring in the Kilo Degree Survey (KiDS) footprint: the Einstein ``blue eye''. Spectroscopic data from X-Shooter at the Very Large Telescope (VLT) show that the lens is a typical early-type galaxy (ETG) at $z_l=0.9906$, while the background source is a Ly$\alpha$ emitter at $z_s=2.823$. The reference lens modeling was performed on a high-resolution $Y-$band adaptive-optics image from HAWK-I at VLT. Assuming a singular isothermal ellipsoid (SIE) total mass density profile, we inferred an Einstein radius $R_{Ein}=10.47 \pm 0.06$ kpc. The average slope of the total mass density inside the Einstein radius, as determined by a joint analysis of lensing and isotropic Jeans equations is $\gamma_{tot}=2.14^{+0.06}_{-0.07}$, showing no systematic deviation from the slopes of lower redshift galaxies, This can be the evidence of ETGs developing through dry mergers plus moderate dissipationless accretion. Stellar population analysis with 8-band ($gri$ZYJHK$s$) photometries from KiDS and VIKING shows that the total stellar mass of the lens is $M*=(3.95\pm 0.35)\times 10^{11} M_\odot$ (Salpeter Initial Mass Function, IMF), implying a dark matter fraction inside the effective radius to be $f_{\rm DM}=0.307\pm 0.151$. We finally explored the dark matter halo slope and found a strong degeneracy with the dynamic stellar mass. Dark matter adiabatic contraction is needed to explain the posterior distribution of the slope unless IMF heavier than Salpeter is assumed.

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arXiv2024

Multi-Layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): stellar masses and star formation rates

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.

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arXiv2024

Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous datasets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a Convolutional Neural Network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5 (KiDS DR5). Our approach involves three key stages: firstly, we pre-selected ten million bright objects (with $r$-band $\tt{MAG\_AUTO} < 22$), excluding stars from the dataset; secondly, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false positive rate (FPR) of $\sim$1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; thirdly, human inspections were performed for further selections, and then 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online. We have also included an excellent quad candidate in the appendix, discovered serendipitously during the fine-tuning process of the CNN.

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arXiv2024

Optical Continuum Reverberation Mapping of a Candidate IMBH in a Nearby Seyfert 1 Galaxy

To investigate the short-term variability and determine the size of the optical continuum emitting region of intermediate-mass black holes (IMBHs), we carried out high-cadence, multiband photometric monitoring of a Seyfert 1 galaxy J0249-0815 across two nights, together with a one-night single-band preliminary test. The presence of the broad Ha component in our target was confirmed by recent Paloma spectroscopic observations, 23 years after the Sloan Digital Sky Survey, ruling out the supernovae origin of the broad Ha line. The photometric experiment was primarily conducted utilizing four-channel imagers MuSCAT 3 and 4 mounted on 2 m telescopes within the Las Cumbres Observatory Global Telescope Network. Despite the expectation of variability, we observed no significant variation (<1.4%) on timescales of 6 to 10 hr. This nondetection is likely due to substantial host galaxy light diluting the subtle active galactic nucleus (AGN) variability. Additionally, we cannot rule out that the target was in a relatively quiescent state without intranight variability during our monitoring, owing to the stochastic nature of AGN variations. To enhance the possibility of detecting subtle variability signals and lag in future IMBH reverberation campaigns, it may be beneficial to select targets with a higher AGN-to-host flux ratio, and conduct dual-band preliminary tests and tailored simulations.

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arXiv2023

Toward a stellar population catalog in the Kilo Degree Survey: the impact of stellar recipes on stellar masses and star formation rates

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.

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arXiv2022

Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. As a first application of this tool, we estimate photo-z of a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on $\sim140 000$ galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG$\_$AUTO$<21$) and low redshift ($z < 0.8$) systems, however, we could use $\sim$ 6500 galaxies in the range $0.8 < z < 3$ to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the 9-band magnitudes and colours, from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD=0.014 for lower redshift and NMAD=0.041 for higher redshift galaxies) and low fraction of outliers ($0.4$\% for lower and $1.27$\% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a $\sim 10-35$% improvement in precision at different redshifts and a $\sim$ 45% reduction in the fraction of outliers. We finally discuss that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to $0.3$\%.

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arXiv2018

Using deep Residual Networks to search for galaxy-Lyα emitter lens candidates based on spectroscopic-selection

More than one hundred galaxy-scale strong gravitational lens systems have been found by searching for the emission lines coming from galaxies with redshifts higher than the lens galaxies. Based on this spectroscopic-selection method, we introduce the deep Residual Networks (ResNet, a kind of deep Convolutional Neural Networks) to search for the galaxy-Ly$α$ emitter (LAE) lens candidates by recognizing the Ly$α$ emission lines coming from high redshift galaxies ($2 < z < 3$) in the spectra of early-type galaxies (ETGs) at middle redshift ($z\sim 0.5$). The spectra of the ETGs come from the Data Release 12 (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey \uppercase\expandafter{\romannumeral3} (SDSS-\uppercase\expandafter{\romannumeral3}). In this paper, we first build a 28 layers ResNet model, and then artificially synthesize 150,000 training spectra, including 140,000 spectra without Ly$α$ lines and 10,000 ones with Ly$α$ lines, to train the networks. After 20 training epochs, we obtain a near-perfect test accuracy at 0.9954. The corresponding loss is 0.0028 and the completeness is 93.6\%. We finally apply our ResNet model to our predictive data with 174 known lens candidates. We obtain 1232 hits including 161 of the 174 known candidates (92.5\% discovery rate). Apart from the hits found in other works, our ResNet model also find 536 new hits. We then perform several subsequent selections on these 536 hits and present 5 most believable lens candidates.

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