arXiv subjects

Hai-Nan Lin

Publications and source records attributed to Hai-Nan Lin.

At least 19 recordsLinked to original sources

arXiv2025

Unveiling the distribution and redshift dependence of host galaxy dispersion measures using localized fast radio bursts

Fast Radio Bursts (FRBs) are enigmatic radio pulses whose origins are poorly understood. The dispersion measure of host galaxy (${\rm DM_{host}}$) provides critical insights into the local environment of FRB sources. In this study, we analyze a sample of 117 well-localized FRBs to investigate the statistical properties of ${\rm DM_{host}}$ and its potential correlations with host galaxy parameters, including redshift, stellar mass, star formation rate (SFR), and galaxy age. Our results reveal that ${\rm DM_{host}}$ is consistent with a log-normal distribution, with the mean $\mu_{\rm host}=5.03\pm 0.02$ and standard deviation $\sigma_{\rm host} = 0.96\pm 0.03$, which corresponds to a median value ${\rm Med}({\rm DM_{host}})=\exp(\mu_{\rm host})=153\pm 3~{\rm pc~cm^{-3}}$. We find a moderate positive correlation between ${\rm DM_{host}}$ and redshift, but no statistically significant correlations are found between ${\rm DM_{host}}$ and stellar mass, SFR, or galaxy age. Our findings highlight the importance of ${\rm DM_{host}}$ as a diagnostic tool for unraveling FRB origins, and underscore the need for future FRB surveys with deep multiwavelength host galaxy follow-up.

astro-ph.GA↗

arXiv2025

Revised classification of the CHIME fast radio bursts with machine learning

Fast radio bursts (FRBs) are short-duration and energetic radio transients of unknown origin. Observationally, they are commonly categorized into repeaters and non-repeaters. However, this binary classification may be influenced by observational limitations such as sensitivity and time coverage of telescopes. In this work, we employ unsupervised machine learning techniques to re-examine the CHIME/FRB catalog, with the goal of identifying intrinsic groupings in the FRB population without relying on preassigned labels. Using t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) for clustering, we find that the FRB sample separates naturally into two major clusters. One cluster contains nearly all known repeaters but is contaminated by some apparently non-repeaters, while the other cluster is dominated by non-repeaters. This suggests that certain FRBs previously labeled as non-repeaters may share intrinsic similarities with repeaters. The mutual information analysis reveals that rest-frame frequency width and peak frequency are the most informative features governing the clustering structure. Even when reducing the input space to just these two features, the classification remains robust.

astro-ph.IM↗

arXiv2024

Quantifying the memory and dynamical stability of magnetar bursts

The time series of energy and waiting time of magnetar bursts carry important information about the source activity. In this paper, we investigate the memory and dynamical stability of magnetar bursts from four soft gamma repeater (SGR) sources: SGR 1806$-$20, SGR 1900+14, SGR J1935+2154 and SGR J1550$-$5418. Based on the rescaled range analysis, we quantify the memory in magnetar bursts for the first time and find that there exists long-term memory in the time series of both waiting time and energy. We investigate the dynamical stability in the context of randomness and chaos. For all the four SGR samples, we find that the waiting time is not completely random, but the energy of two SGRs is consistent with a total random organization. Furthermore, both waiting time and energy exhibits weak chaos. We also find no significant difference between SGRs and repeating fast radio bursts (FRBs) in the randomness-chaos phase space. The statistical similarity between SGRs and repeating FRBs hints that there may be potential physical connection between these two phenomena.

astro-ph.HE↗

arXiv2024

The cosmic distance duality relation in light of the time-delayed strong gravitational lensing

The cosmic distance duality relation (DDR), which links the angular diameter distance and the luminosity distance, is a cornerstone in modern cosmology. Any deviation from DDR may indicate new physics beyond the standard cosmological model. In this paper, we use four high-precision time-delayed strong gravitational lensing (SGL) systems provided by the H0LiCOW to test the validity of DDR. To this end, we directly compare the angular diameter distances from these SGL systems and the luminosity distances from the latest Pantheon+ compilation of SNe Ia. In order to reduce the statistical errors arising from redshift matching, the Gaussian process method is applied to reconstruct the distance-redshift relation from the Pantheon+ dataset. We parameterize the possible violation of DDR in three different models. It is found that all results confirm the validity of DDR at 1$\sigma$ confidence level. Additionally, Monte Carlo simulations based on the future LSST survey indicate that the precision of DDR could reach $10^{-2}$ level with 100 SGL systems.

astro-ph.CO↗

arXiv2024

Quantifying the randomness and scale invariance of the repeating fast radio bursts

The statistical properties of energy and waiting time carry essential information about the source of repeating fast radio bursts (FRBs). In this paper, we investigate the randomness of energy and waiting time using four data samples from three extremely active repeating FRBs observed by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We report the deviation from complete randomness of the burst activity using three statistics, i.e., Hurst exponent, Pincus index and non-Gaussian probability density distribution of fluctuations. First, the Hurst exponent greater than 0.5 reveals that there is long-term memory in the time series of energy and waiting time. Second, the deviation of the Pincus index from 1.0 manifests that the time series is not completely random. Finally, the fluctuations of energy and waiting time follow the scale-invariant $q$-Gaussian distribution. All these statistical properties imply that, although the time series of repeating FRBs seems to be irregular, they are not completely random, similar to the features of self-organized criticality.

astro-ph.HE↗

arXiv2024

Time delay of fast radio burst population with respect to the star formation history

In spite of significant progress in the research of fast radio bursts (FRBs) in recent decade, their origin is still under extensive debate. Investigation on the population of FRBs can provide new insight into this interesting problem. In this paper, based on the first CHIME/FRB catalog, we construct a Bayesian framework to analyze the FRB population, with the selection effect of the CHIME telescope being properly taken into account. The energy function is modeled as the power-law with an exponential cutoff. Four redshift distribution models are considered, i.e., the star formation history (SFH) model, and three time-delayed models (Gaussian delay, log-normal delay, and power-law delay). The free parameters are simultaneously constrained using Bayesian inference method, and the Bayesian information criterion (BIC) is used in model comparison. According to BIC, the log-normal delay model fits the data best. The power-law delay model and Gaussian delay model can also give reasonable fits, although they are not as good as the log-normal delay model. However, the SFH model is strongly disfavored compared with the three time-delayed models. The energy function is tightly constrained and is almost independent of the redshift models, with the best-fitting power-law index $\alpha\approx 1.8$, and cut-off energy $\log(E_c/{\rm erg})\approx 42$. The FRB population shows on average $3\sim 5$ billion years time delay with respect to the SFH. Therefore, the hypothesis that the FRB population traces the SFH is conclusively ruled out.

astro-ph.HE↗

arXiv2024

Revised Constraints on the fast radio burst population from the first CHIME/FRB catalog

In this paper, we investigate the FRB population using the first CHIME/FRB catalog. We first reconstruct the extragalactic dispersion measure -- redshift relation ($\mathrm{DM_E} - z$ relation) from well-localized FRBs, then use it to infer redshift and isotropic energy of the first CHIME/FRB catalog. The intrinsic energy distribution is modeled by the power law with an exponential cutoff, and the selection effect of the CHIME telescope is modeled by a two-parametric function of specific fluence. For the intrinsic redshift distribution, the star formation history (SFH) model, as well as other five SFH-related models are considered. We construct the joint likelihood of fluence, energy and redshift, and all the free parameters are constrained simultaneously using Bayesian inference method. The Bayesian information criterion (BIC) is used to choose the model that best matches the observational data. For comparison, we fit our models with two data samples, i.e. the Full sample and the Gold sample. The power-law index and cutoff energy are tightly constrained to be $1.8 \lesssim α\lesssim 1.9$ and $\mathrm{log}(E_c/{\rm erg}) \approx 42$, which are almost independent of the redshift distribution model and the data sample we choose. The parameters involving the selection effect strongly depends on the data sample, but are insensitive to the redshift distribution model. According to BIC, the pure SFH model is strongly disfavored by both the Full sample and Gold sample. For the rest five SFH-related redshift distribution models, most of them can match the data well if the parameters are properly chosen. Therefore, with the present data, it is still premature to draw a conclusive conclusion on the FRB population.

astro-ph.HE↗

arXiv2023

Consistency of Pantheon+ supernovae with a large-scale isotropic universe

We investigate the possible anisotropy of the universe using the most up-to-date type Ia supernovae, i.e. the Pantheon+ compilation. We fit the full Pantheon+ data with the dipole-modulated $Λ$CDM model, and find that it is well consistent with a null dipole. We further divide the full sample into several subsamples with different high-redshift cutoff $z_c$. It is shown that the dipole appears at $2σ$ confidence level only if $z_c\leq 0.1$, and in this redshift region the dipole is very stable, almost independent of the specific value of $z_c$. For $z_c=0.1$, the dipole amplitude is $D=1.0_{-0.4}^{+0.4}\times 10^{-3}$, pointing towards $(l,b)=(334.5_{\ -21.6^{\circ}}^{\circ +25.7^{\circ}},16.0_{\ -16.8^{\circ}}^{\circ +27.1^{\circ}})$, which is about $65^{\circ}$ away from the CMB dipole. This implies that the full Pantheon+ is consistent with a large-scale isotropic universe, but the low-redshift anisotropy couldn't be purely explained by the peculiar motion of the local universe.

astro-ph.CO↗

arXiv2023

The temporally evolving energy and waiting time statistics of two repeating fast radio bursts

Based on two very large samples of repeating fast radio bursts (FRBs), i.e. FRB 20121102A and FRB 20201124A observed by the FAST telescope, we study the statistical properties of energy and waiting time. The bent power-law (BPL) model, thresholded power-law (TPL) model and Band function are used to fit the distribution of energy, and the BPL model and exponential (EXP) model are used to fit the distribution of waiting time. It is found that no single model can fit the distribution of energy or waiting time well in the full range. To investigate the possible temporal evolution, we divide the full samples into several subsamples according to the observing sessions. We find that the distribution of energy for all subsamples can be well fitted by both BPL model and TPL model, while the distribution of waiting time for all subsamples can be well fitted by both BPL model and EXP model. Importantly, for the distribution of energy, the BPL index $β$ of all the subsamples is almost invariant, but the median value parameter $x_b$ varies significantly. Similar situation happens in the distribution of waiting time. Furthermore, for the distribution of waiting time, the occurrence rate parameter $λ$ in EXP model varies significantly. These features show that there may be a common emission mechanism for repeating FRBs, but the burst energy and occurrence rate are temporally evolving.

astro-ph.HE↗

arXiv2023

Inferring redshift and energy distributions of fast radio bursts from the first CHIME/FRB catalog

We reconstruct the extragalactic dispersion measure \ -- redshift relation (${\rm DM_E}-z$ relation) from well-localized fast radio bursts (FRBs) using Bayesian inference method. Then the ${\rm DM_E}-z$ relation is used to infer the redshift and energy of the first CHIME/FRB catalog. We find that the distributions of extragalactic dispersion measure and inferred redshift of the non-repeating CHIME/FRBs follow cut-off power law, but with a significant excess at the low-redshift range. We apply a set of criteria to exclude events which are susceptible to selection effect, but find that the excess at low redshift still exists in the remaining FRBs (which we call Gold sample). The cumulative distributions of fluence and energy for both the full sample and the Gold sample do not follow the simple power law, but they can be well fitted by the bent power law. The underlying physical implications remain to be further investigated.

astro-ph.HE↗

arXiv2023

Probing the baryon mass fraction in IGM and its redshift evolution with fast radio bursts using Bayesian inference method

We investigate the fraction of baryon mass in intergalactic medium ($f_\mathrm{IGM}$), using 18 well-localized FRBs in the redshift range $z\in (0.0039,0.66)$. We construct a five-parameter Bayesian inference model, with the probability distributions of dispersion measures (DM) of IGM and host galaxy properly taken into account. To check the possible redshift evolution, we parameterize $f_\mathrm{IGM}$ as a mildly evolving function of redshift, $f_\mathrm{IGM}=f_\mathrm{IGM,0}[1+αz/(1+z)]$. By simultaneously constraining five parameters, we get $f_\mathrm{IGM,0} = 0.92^{+0.06}_{-0.12}$ and $α= 0.49^{+0.59}_{-0.47}$, and the median value of DM of host galaxy is $\exp(μ)=72.49^{+33.31}_{-25.62}~{\rm pc ~ cm ^ {-3}}$. By fixing two parameters which can be constrained independently with other observations, we obtain $α=0.11^{+0.24}_{-0.27}$ in the three-parameter fit, which is consistent with zero within $1σ$ uncertainty. Monte Carlo simulations show that even 300 FRBs are not enough to tightly constrain five parameters simultaneously. This is mainly caused by the correlation between parameters. Only if two parameters are fixed, 100 FRBs are necessary to achieve unbiased constraints on the remaining parameters.

astro-ph.CO↗

arXiv2023

Revised constraints on the photon mass from well-localized fast radio bursts

We constrain the photon mass from well-localized fast radio bursts (FRBs) using Bayes inference method. The probability distributions of dispersion measures (DM) of host galaxy and intergalactic medium are properly taken into account. The photon mass is tightly constrained from 17 well-localized FRBs in the redshift range $0<z<0.66$. Assuming that there is no redshift evolution of host DM, the $1σ$ and $2σ$ upper limits of photon mass are constrained to be $m_γ<4.8\times 10^{-51}$ kg and $m_γ<7.1\times 10^{-51}$ kg, respectively. Monte Carlo simulations show that, even enlarging the FRB sample to 200 and extending the redshift range to $0<z<3$ couldn't significantly improve the constraining ability on photon mass. This is because of the large uncertainty on the DM of intergalactic medium.

gr-qc↗

arXiv2022

Deep learning method in testing the cosmic distance duality relation

The cosmic distance duality relation (DDR) is constrained from the combination of type-Ia supernovae (SNe Ia) and strong gravitational lensing (SGL) systems using deep learning method. To make use of the full SGL data, we reconstruct the luminosity distance from SNe Ia up to the highest redshift of SGL using deep learning, then it is compared with the angular diameter distance obtained from SGL. Considering the influence of lens mass profile, we constrain the possible violation of DDR in three lens mass models. Results show that in the SIS model and EPL model, DDR is violated at high confidence level, with the violation parameter $η_0=-0.193^{+0.021}_{-0.019}$ and $η_0=-0.247^{+0.014}_{-0.013}$, respectively. In the PL model, however, DDR is verified within 1$σ$ confidence level, with the violation parameter $η_0=-0.014^{+0.053}_{-0.045}$. Our results demonstrate that the constraints on DDR strongly depend on the lens mass models. Given a specific lens mass model, DDR can be constrained at a precision of $\textit{O}(10^{-2})$ using deep learning.

astro-ph.CO↗

arXiv2022

Search for the correlations between host properties and ${\rm DM_{host}}$ of fast radio bursts: constraints on the baryon mass fraction in IGM

The application of fast radio bursts (FRBs) as probes to investigate astrophysics and cosmology requires the proper modelling of the dispersion measures of Milky Way (${\rm DM_{MW}}$) and host galaxy (${\rm DM_{host}}$). ${\rm DM_{MW}}$ can be estimated using the Milky Way electron models, such as NE2001 model and YMW16 model. However, ${\rm DM_{host}}$ is hard to model due to limited information on the local environment of FRBs. In this paper, using 17 well-localized FRBs, we search for the possible correlations between ${\rm DM_{host}}$ and the properties of host galaxies, such as the redshift, the stellar mass, the star-formation rate, the age of galaxy, the offset of FRB site from galactic center, and the half-light radius. We find no strong correlation between ${\rm DM_{host}}$ and any of the host property. Assuming that ${\rm DM_{host}}$ is a constant for all host galaxies, we constrain the fraction of baryon mass in the intergalactic medium today to be $f_{\rm IGM,0}=0.78_{-0.19}^{+0.15}$. If we model ${\rm DM_{host}}$ as a log-normal distribution, however, we obtain a larger value, $f_{\rm IGM,0}=0.83_{-0.17}^{+0.12}$. Based on the limited number of FRBs, no strong evidence for the redshift evolution of $f_{\rm IGM}$ is found.

astro-ph.CO↗

arXiv2021

Statistical similarity between soft gamma repeaters and the repeating fast radio bursts

We study the statistical properties of the soft gamma repeater SGR 1935+2154. We find that the cumulative distributions of duration, waiting time, fluence and flux can be well fitted by bent power law. In addition, the probability density functions of fluctuations of duration, waiting time, fluence and flux well follow the Tsallis $q$-Gaussian distribution. The $q$ values keep steady for different temporal scale intervals, indicating a scale-invariant structure of the bursts. Those features are very similar to the property of the repeating fast radio burst FRB 121102, indicating the underlying association between the origins of soft gamma repeaters and repeating fast radio bursts.

astro-ph.HE↗

arXiv2021

Probing the anisotropic distribution of baryon matter in the Universe using fast radio bursts

We propose that fast radio bursts (FRBs) can be used as the probes to constrain the possible anisotropic distribution of baryon matter in the Universe. Monte Carlo simulations show that, 400 (800) FRBs are enough to detect the anisotropy at 95\% (99\%) confidence level, if the dipole amplitude is at the order of magnitude 0.01. However, much more FRBs are required to tightly constrain the dipole direction. Even 1000 FRBs are far from enough to constrain the dipole direction within angular uncertainty $Δθ<40^{\circ}$ at 95\% confidence level. The uncertainty on the dispersion measure of host galaxy does not significantly affect the results. If the dipole amplitude is in the level of 0.001, however, 1000 FRBs are not enough to correctly detect the anisotropic signal.

astro-ph.CO↗

arXiv2021

Reconstructing the Hubble diagram of gamma-ray bursts using deep learning

We calibrate the distance and reconstruct the Hubble diagram of gamma-ray bursts (GRBs) using deep learning. We construct an artificial neural network, which combines the recurrent neural network and Bayesian neural network, and train the network using the Pantheon compilation of type-Ia supernovae. The trained network is used to calibrate the distance of 174 GRBs based on the Combo-relation. We verify that there is no evident redshift evolution of Combo-relation, and obtain the slope and intercept parameters, $γ=0.856^{+0.083}_{-0.078}$ and $\log A=49.661^{+0.199}_{-0.217}$, with an intrinsic scatter $σ_{\rm int}=0.228^{+0.041}_{-0.040}$. Our calibrating method is independent of cosmological model, thus the calibrated GRBs can be directly used to constrain cosmological parameters. It is shown that GRBs alone can tightly constrain the $Λ$CDM model, with $Ω_{\rm M}=0.280^{+0.049}_{-0.057}$. However, the constraint on the $ω$CDM model is relatively looser, with $Ω_{\rm M}=0.345^{+0.059}_{-0.060}$ and $ω<-1.414$. The combination of GRBs and Pantheon can tightly constrain the $ω$CDM model, with $Ω_{\rm M}=0.336^{+0.055}_{-0.050}$ and $ω=-1.141^{+0.156}_{-0.135}$.

gr-qc↗

arXiv2021

Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events

We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.

astro-ph.CO↗