-
When FRB signal travels from the host galaxy to the earth, it experiences a time delay due to the interaction of electromagnetic waves with free electrons. The time delay depends on the frequency of electromagnetic waves. Measuring the time delay between photons of different frequencies allows us to determine the number density of electrons integrated along the wave path, referred to as the dispersion measure (DM). The total dispersion measure of an extragalactic FRB can be generally separated into four components [18],
$ {\rm{DM_{obs}} = DM_{MW} + DM_{halo} + DM_{IGM}} + \frac{\rm{DM_{host}}}{1+z}, $
(1) where
$ \rm DM_{MW} $ comes from the contribution of Galactic interstellar medium, which can be estimated from the Galactic electron density models such as the NE2001 model [34]. The$ \rm DM_{halo} $ term is the contribution from Galactic halo, which is not fully constrained yet. Prochaska et al. [35] provided an estimation that it is about$ \rm 50- 100\ pc\ cm^{-3} $ . Here we follow Macquart et al. [18] and assume$ \rm DM_{halo}=50\ pc\ cm^{-3} $ . The$ \rm DM_{host} $ term is the contribution from the host galaxy, and the factor$ 1+z $ arises from the cosmic expansion. The$ \rm DM_{IGM} $ term is the contribution from intergalactic medium, and it carries the information of the universe. Several other studies also mentioned the$ \rm DM_{source} $ term [3, 4, 23], which represents the DM contribution from the immediate environment of the source.$ \rm DM_{source} $ term is highly dependent on the source environment of each FRB, which is not fully understood yet. The value of$ \rm DM_{source} $ is usually smaller than the error of$ \rm DM_{host} $ and$ \rm DM_{IGM} $ [36]. Therefore, we ignored the$ \rm DM_{source} $ term here. The$ \rm DM_{MW} $ and$ \rm DM_{halo} $ terms can be subtracted from the total observed$ {\rm{DM}} $ , leaving behind the extragalactic$ \rm DM $ , which is defined by$ {\rm{DM_E\equiv DM_{obs}} - DM_{MW} - DM_{halo} = \rm DM_{IGM} } + \frac{\rm{DM_{host}}}{1+z}. $
(2) In the flat
$ \rm \Lambda CDM $ model, the average value of the$ \rm DM_{IGM} $ term can be written as [37, 38]$ \langle {\rm{DM_{IGM}}}(z)\rangle=\frac{3cH_0 {\rm{\Omega_b}} f_{\rm{IGM}} f_e}{8 \pi G m_p}\int_0^z \frac{1+z}{\sqrt{ {\rm{\Omega_m}}(1+z)^3+{\rm{\Omega_{\Lambda}}} }}\ dz, $
(3) where
$ H_0 $ is the Hubble constant,$ \rm \Omega_b $ is the cosmic baryon mass density,$ \Omega_m $ is the matter density, and$ \Omega_{\Lambda} $ is the vacuum energy density of the Universe. The c, G, and$ m_p $ are three constants, which represent the speed of light in vacuum, the Newtonian gravitational constant, and the mass of proton, respectively.$ f_e= Y_{\rm{H}} X_{e,{\rm{H}}}+\dfrac{1}{2}Y_{\rm{He}}X_{e,{\rm{He}}} $ denotes the extent of ionization progress of hydrogen and helium, where$ Y_{\rm{H}}=0.75 $ and$ Y_{\rm{He}}=0.25 $ are the hydrogen mass fraction and helium mass fraction respectively.$ X_{e,{\rm{H}}} $ and$ X_{e,{\rm{He}}} $ are the corresponding ionization fractions. Since both hydrogen and helium are expected to be fully ionized at$ z \leq 3 $ [39, 40], we take$ X_{e,{\rm{H}}}=X_{e,{\rm{He}}}=1 $ .$ f_{\rm{IGM}} $ is the fraction of baryon mass in IGM.However, Eq. (3) only describes the mean value of
$ \rm DM_{IGM} $ . The actual value is associated with large-scale matter density fluctuations and may vary around the mean value. Usually, the Gaussian distribution is used to describe$ \rm DM_{IGM} $ [41–43]. However, theoretical motivation and numerical simulations show that the probability distribution of$ \rm DM_{IGM} $ can be modeled using the quasi-Gaussian distribution [18, 44],$ p_{\rm{IGM}}(\Delta)=A\Delta^{-\beta}{\exp } \left[ -\frac{(\Delta^{-\alpha}-C_0)^2}{2\alpha^2\sigma_{\rm{IGM}}^2}\right],\quad \Delta>0, $
(4) where
$ \rm \Delta \equiv DM_{IGM}/\langle DM_{IGM} \rangle $ ,$ \rm \sigma_{IGM} $ is the effective standard deviation, α and β are related to the inner density profile of gas in haloes. Hydrodynamic simulations show that$ \alpha=\beta=3 $ provides the best fit to the model [18, 44], hence we fix these two parameters. We also followed Macquart et al. [18] to parameterize the effective standard deviation as$ {\rm{\sigma_{IGM}}} = Fz^{-1/2} $ , where F is a free parameter and z is the redshift of FRB source. Here, A is a normalization constant and$ C_0 $ is chosen to make sure that the mean of this distribution is unity. Note that the distribution of$ \rm DM_{IGM} $ is a function of z, implying that A and$ C_0 $ vary with z.For the
$ \rm DM_{host} $ term, it may range from tens to hundreds of$ \rm pc\ cm^{-3} $ . For example, Xu et al. [45] estimated that the$ \rm DM_{host} $ of FRB20201124A can range from 10 to 310$ \rm pc\ cm^{-3} $ , while Niu et al. [46] estimated that the$ \rm DM_{host} $ of FRB20190520B can be up to 900$ \rm pc\ cm^{-3} $ . To account for the large variation of$ \rm DM_{host} $ , it is often modeled with the log-normal distribution [18, 33],$\begin{aligned}[b]& p_{\rm{host}}({\rm{DM_{host}} }|\mu,\sigma_{\rm{host}})\\=\;&\frac{1}{\sqrt{2\pi}{\rm{DM_{host}}}\sigma_{\rm{host}}}{\exp} \left[-\frac{(\ln\rm DM_{host}-\mu)^2}{2\sigma_{\rm{host}}^2}\right],\end{aligned} $
(5) where μ and
$ \sigma_{\rm{host}} $ are the mean and standard deviation of$ \rm \ln{DM_{host}} $ , respectively. This log-normal distribution allows for the appearance of large values of$ \rm DM_{host} $ . Generally, both μ and$ \sigma_{\rm{host}} $ might be redshift-dependent. However, Zhang et al. [33] showed that for non-repeating bursts they do not vary significantly with redshift. Lin et al. [32] also proved that there is no strong evidence for the redshift evolution of$ \rm DM_{host} $ within the present data. Hence, we follow Macquart et al. [18] and treat them as two constants.For that
$ \rm DM_{IGM} $ term and$ \rm DM_{host} $ term are challenging to separate, we introduce the probability distribution of the extragalactic$ \rm DM $ as [18]$\begin{aligned}\\[-8pt] p_{E}({\rm{DM_{E}}}|z) = \int_0^{(1+z){\rm{DM_E}}} p_{\rm{IGM}}({\rm{DM_E}}-\frac{\rm{DM_{host}}}{1+z}|H_0, F) \, p_{\rm{host}}({\rm{DM_{host}}}|\mu,\sigma_{\rm{host}}) \, d{\rm{DM_{host}}}. \end{aligned}$ (6) If a large sample of well-localized FRBs are observed, the joint likelihood function can be written as
$ \begin{array}{*{20}{l}} \mathcal{L}({\rm{FRBs}}|H_0, \mu,\sigma_{\rm{host}},F)=\prod\limits_{i=1}^N p_{E}({\rm{DM_{E,\it i}}}|z_i), \end{array} $
(7) where N is the number of FRBs. According to Bayesian theorem, the posterior probability density function of the free parameters is given by
$ \begin{aligned}[b]& P(H_0, \mu, \sigma_{\rm{host}},F|{\rm{FRBs}}) \\\propto\;& \mathcal{L}({\rm{FRBs}}|H_0, \mu, \sigma_{\rm{host}}, F) \, P_{0}(H_0, \mu, \sigma_{\rm{host}}, F), \end{aligned} $
(8) where
$ P_0 $ is the prior probability function of the parameters. -
Till now, there are over 30 published well-localized extragalactic FRBs that have identified host galaxies and well-measured redshifts
1 . Among them, FRB20200120E, FRB20190614D, FRB20190520B and FRB 20220319D are excluded. FRB20200120E is so near to the Milky Way that the peculiar velocity dominates the Hubble flow, resulting in a negative spectroscopic redshift$ z=-0.001 $ [47, 48]. FRB20190614D only has a photometric redshift$ z\approx 0.6 $ [49], with no available spectroscopic redshift. The$ {\rm{DM_{host}}} $ of FRB20190520B is estimated to be as large as$ 900\ \rm pc\ cm^{-3} $ [46], which is much larger than the normal FRBs. FRB20220319D is also excluded due to its total DM being lower than$ \rm DM_{MW} $ [50]. The remaining 35 FRBs all have well-measured spectroscopic redshifts, and their main properties are listed in Table 1. These FRBs will be used to constrain the cosmological parameters.FRBs RA Dec $ {\rm DM_{obs}} $ [$ ^{\circ} $ ]$ {\rm DM_{MW}} $ [$ {\rm pc cm^{-3}} $ ]$ {\rm DM_E} $ [$ {\rm pc cm^{-3}} $ ]$ z_{\rm sp} $ [$ {\rm pc cm^{-3}} $ ]reference 20121102A 82.99 33.15 557.00 157.60 349.40 0.1927 Chatterjee et al. [12] 20171020A 22.15 −19.40 114.10 38.00 26.10 0.0087 Li et al. [51] 20180301A 93.23 4.67 536.00 136.53 349.47 0.3305 Bhandari et al. [52] 20180916B 29.50 65.72 348.80 168.73 130.07 0.0337 Marcote et al. [53] 20180924B 326.11 −40.90 362.16 41.45 270.71 0.3214 Bannister et al. [54] 20181030A 158.60 73.76 103.50 40.16 13.34 0.0039 Bhardwaj et al. [55] 20181112A 327.35 −52.97 589.00 41.98 497.02 0.4755 Prochaska et al. [56] 20190102C 322.42 −79.48 364.55 56.22 258.33 0.2913 Macquart et al. [18] 20190523A 207.06 72.47 760.80 36.74 674.06 0.6600 Ravi et al. [57] 20190608B 334.02 −7.90 340.05 37.81 252..24 0.1178 Macquart et al. [18] 20190611B 320.74 −79.40 332.63 56.60 226.03 0.3778 Macquart et al. [18] 20190711A 329.42 −80.36 592.60 55.37 487.23 0.5217 Macquart et al. [18] 20190714A 183.98 −13.02 504.13 38.00 416.13 0.2365 Heintz et al. [58] 20191001A 323.35 −54.75 507.90 44.22 413.68 0.2340 Heintz et al. [58] 20191228A 344.43 −29.59 297.50 33.75 213.75 0.2432 Bhandari et al. [52] 20200430A 229.71 12.38 380.25 27.35 302.90 0.1608 Bhandari et al. [52] 20200906A 53.50 −14.08 577.80 36.19 491.61 0.3688 Bhandari et al. [52] 20201124A 77.01 26.06 413.52 126.49 237.03 0.0979 Fong et al. [59] 20210405I 255.34 −48.48 565.17 468.18 46.99 0.066 Driessen et al. [60] 20210410D 326.09 −78.68 572.62 56.20 466.45 0.1415 Caleb et al. [61] 20210603A 10.27 21.23 500.15 40.00 410.15 0.1772 Cassanelli et al. [62] 20211127A 199.81 −18.84 234.83 41.75 143.08 0.0496 Khrykin et al. [63] 20211212A 157.35 1.36 206.00 38.29 117.71 0.0713 Khrykin et al. [63] 20220207C 310.20 72.88 262.38 74.99 137.39 0.0430 Law et al. [64] 20220307B 350.87 72.19 499.27 119.82 329.45 0.2481 Law et al. [64] 20220310F 134.72 73.49 462.24 45.07 367.17 0.4780 Law et al. [64] 20220418A 219.10 70.10 623.25 36.49 536.76 0.6220 Law et al. [64] 20220506D 318.04 72.83 396.97 82.88 264.09 0.3004 Law et al. [64] 20220509G 282.67 70.24 269.53 55.36 164.17 0.0894 Law et al. [64] 20220825A 311.98 72.58 651.24 77.26 523.98 0.2414 Law et al. [64] 20220912A 347.27 48.71 220.7 120.44 50.26 0.0771 Zhang et al. [65] 20220914A 282.06 73.34 631.28 54.38 526.9 0.1139 Law et al. [64] 20220920A 240.26 70.92 314.99 39.62 225.37 0.1582 Law et al. [64] 20221012A 280.80 70.52 441.08 53.69 337.39 0.2847 Law et al. [64] 20221022A 48.63 86.87 116.84 60.12 6.72 0.0149 Mckinven et al. [66] Table 1. The properties of the Host/FRB catalog. Column 1: FRB name; Columns 2 and 3: the right ascension and declination of FRB source on the sky; Column 4: the observed DM; Column 5: the DM of the Milky Way ISM calculated using the NE2001 model; Column 6: the extragalactic DM calculated by subtracting
$ {\rm{DM_{\rm MW}}} $ and$ {\rm{DM_{\rm halo}}} $ from the observed$ {\rm{DM_{\rm obs}}} $ , assuming$ {\rm{DM_{\rm halo}}}=50\; {\rm{pc\; cm^{-3}}} $ for the Milky Way halo; Column 7: the spectroscopic redshift; Column 8: the references.We employ Monte Carlo Markov Chain (MCMC) analysis to constrain
$ H_0 $ and three free parameters ($ e^{\mu},\ \sigma_{\rm{host}}, F $ ) from the probability functions of$ \rm DM_{host} $ and$ \rm DM_{IGM} $ . We use$ e^{\mu} $ instead of μ as a free parameter because$ e^{\mu} $ directly represents the median value of$ \rm DM_{host} $ . For$ f_{\rm{IGM}} $ , there is no precise constraint yet. It may slowly increase with redshift [67], and Li et al. [31] parameterized it as$ f_{\rm{IGM}}=f_{{\rm{IGM}},0}(1+\alpha z/(1+z)) $ . However, there is no strong evidence to show that$ f_{\rm{IGM}} $ increases with redshift in the current FRB data [68]. To be conservative, we assume that$ f_{\rm{IGM}} $ follows a uniform distribution$ U(0.747,0.913) $ and marginalized over it [26]. Additionally, We adopt$ \Omega_b h^2 = 0.0224 $ and$ \Omega_m = 0.315 $ from Planck 2018 results [69]. The posterior probability density functions of the free parameters are calculated using the publicly available Python code emcee [70]. Based on previous results [18, 71], flat priors are applied to all four parameters:$ H_0 \in U(0,100)\; {\rm{km\; s^{-1}}\; Mpc^{-1}} $ ,$ e^{\mu} \in U(20,200) {\rm{\ pc\ cm^{-3}}} $ ,$ \sigma_{\rm{host}} \in U(0.2,2) $ , and$ F \in U(0.01,0.5) $ .The 2D marginalized posterior distributions and the
$ 1-3\ \sigma $ confidence contours of the four parameters are plotted in the left panel of Figure 1. It is evident that only the parameters$ H_0 $ and$ \sigma_{\rm{host}} $ are tightly constrained, while the other two parameters,$ e^{\mu} $ and F, are not well-constrained. One possible reason is that the FRB sample is not large enough to simultaneously constrain a model with too many free parameters. A notable feature is that the best-fitting value of the Hubble constant$ H_0=49.34^{+4.39}_{-3.83}\; {\rm{km\; s^{-1}}\; Mpc^{-1}} $ is much lower than the Planck 2018 value. This discrepancy may be caused by the correlation between parameters. As can be seen from the contour plot,$ H_0 $ seems to be positively correlated with$ e^{\mu}_{\rm{host}} $ , while is negatively correlated with F.Figure 1. Constraints on the four free parameters (
$ H_0,\ e^{\mu},\ \sigma_{\rm{host}} $ , F) and three free parameters ($ H_0,\ e^{\mu},\ \sigma_{\rm{host}} $ ) using the 35 FRBs samples. The black-dashed lines from left to right in each subfigure represents the 16%, 50% and 84% quantiles of the distribution, respectively. The contours from the inner to outer represent$ 1\sigma $ ,$ 2\sigma $ and$ 3\sigma $ confidence regions, respectively. For simplicity, units are omitted in this figure.Several works suggest that the parameter F prefers a value around
$ F=0.2 $ or even smaller at$ z\leq 1 $ [72, 73]. However, our four-parameter fit from the FRB sample indicates a preference for a larger value of F, although it can not be tightly constrained. This discrepancy might introduce bias to the other parameters. To address this, we try to fix$ F = 0.2 $ while keeping the other three parameters$ (H_0,\ e^{\mu},\ \sigma_{\rm{host}}) $ free. The corresponding 2D marginalized posterior distributions and the$ 1-3\ \sigma $ confidence contours of the three parameters are presented in the right panel of Figure 1. One can see that both$ H_0 $ and$ \sigma_{\rm{host}} $ are well-constrained, the parameter$ e^{\mu}_{\rm{host}} $ is also constrained but with a relatively small value. The best-fitting Hubble constant,$ H_0=60.99^{+4.57}_{-4.90}\; {\rm{km\; s^{-1}}\; Mpc^{-1}} $ , is notably larger than that from the four-parametric fit. However, the median value of the Hubble constant is still relatively lower than the Planck 2018 result. One possible explanation is that the FRB sample is not large enough to provide a robust result. The results of the two parameters from the probability functions of$ \rm DM_{host} $ are given by$ e^{\mu}_{\rm{host}}=48.35^{+20.81}_{-15.00} {\rm{\ pc\ cm^{-3}}} $ and$ \sigma_{\rm{host}}=1.63^{+0.23}_{-0.20} $ , both of which have relatively large uncertainties. -
With the progress of observational technique, more and more FRBs are expected to be detected, and a fraction of them can be well-localized. Therefore, it is interesting to assess the constraining capability of a large sample of FRBs across a wide redshift range. To this end, we conduct Monte Carlo simulations to investigate the efficiency of our method.
The intrinsic redshift distribution of FRBs is still unclear due to the small well-localized sample. Li et al. [31] assumed that FRBs have a constant comoving number density but with a Gaussian cutoff. Zhang et al. [38] argued that the redshift distribution of FRBs is expected to be related with cosmic star formation rate (SFR), or influenced by the compact star merger but with an additional time delay. In this paper, we adopt the SFR-related model, in which the probability density function takes the form [38]
$ P(z) \propto \frac{4\pi D_c^2(z){\rm{SFR}}(z)}{(1+z)H(z)}, $
(9) where
$ D_c(z)= \int_0^z \dfrac{cdz}{H(z)} $ represents the comoving distance, with c the speed of light and$ H(z) $ the Hubble expansion rate, and the SFR takes the form [74]$ {\rm{SFR}}(z)=0.02\left[(1+z)^{a\eta}+\left(\frac{1+z}{B}\right)^{b\eta}+\left(\frac{1+z}{C}\right)^{c\eta}\right]^{1/\eta} , $
(10) where
$ a=3.4,\ b=-0.3,\ c=-3.5,\ B=5000,\ C=9 $ and$ \eta = -10 $ .We simulate a set of mock FRB samples to constrain the parameters. The simulations are performed based on the flat
$ \rm \Lambda CDM $ model with Planck 2018 results [69]. The fiducial parameters include$ F=0.2,\ f_{\rm{IGM}}=0.83, \ e^{\mu}=100{\rm\ pc\ cm^{-3}} $ and$ \sigma_{\rm{host}}=1.0 $ . The simulation procedures are outlined as follows. First, a certain number of redshifts are randomly drawn from Eq. (9), with$ z_{\rm{max}} $ set to$ 3.0 $ . Subsequently, the same number of Δ values are randomly drawn from Eq. (4). The average$ \rm \langle DM_{IGM}(z)\rangle $ is calculated using Eq. (3), and$ \rm DM_{IGM} $ for each redshift is obtained as$ \Delta \times \langle {\rm{DM_{IGM}}}(z)\rangle $ . Next, the same number of$ \rm DM_{host} $ values are randomly drawn from Eq. (5), and$ \rm DM_{E} $ is calculated according to Eq. (2). Finally there is a sample of mock FRBs$ (z_i, {\rm{DM_{E,\it i}}}) $ .We use the mock FRBs to constrain the four parameters (
$ H_0 $ ,$ e^\mu $ ,$ \sigma_{\rm{host}} $ , F) using the same method described above. The corresponding contour plots constrained from$ N=100 $ mock FRBs are shown in the left panel of Figure 2. One can see that except for the parameter$ H_0 $ , the other parameters can not be tightly constrained. Additionally, the parameter$ H_0 $ fails to recover the fiducial value within 1σ uncertainty.Figure 2. Constraints on the four free parameters (
$ H_0 $ ,$ e^\mu $ ,$ \sigma_{\rm{host}} $ , F) using 100 mock FRBs with quasi-Gaussian distribution (left panel) and Gaussian distribution (right panel) of$ \rm DM_{IGM} $ . The blue lines represent the fiducial values. The black-dashed lines from left to right in each subfigure represents the 16%, 50% and 84% quantiles of the distribution, respectively. The contours from the inner to outer represent$ 1\sigma $ ,$ 2\sigma $ and$ 3\sigma $ confidence regions, respectively. For simplicity, units are omitted in this figure.For comparison, we also conducted MCMC analysis using a Gaussian distribution of
$ \rm DM_{IGM} $ . The method is similar to the quasi-Gaussian distribution case, with the primary difference being the replacement of the distribution function of$ \rm DM_{IGM} $ (i.e., Eq. (4)) with the Gaussian distribution$ \mathcal{G}(\langle {\rm{DM_{IGM}}} \rangle, \sigma_{\rm{IGM}}) $ in generating mock FRBs samples, as well as in the joint likelihood function. In the simulation, the fiducial value of$ \sigma_{\rm{IGM}} $ is set to$ 100{\rm{\ pc\ cm^{-3}}} $ [41, 42]. In the MCMC analysis,$ \sigma_{\rm{IGM}} $ is treated as a free parameter, replacing the parameter F of the quasi-Gaussian case. And the prior of$ \sigma_{\rm{IGM}} $ is$ U(0,200)\ {\rm{pc\ cm^{-3}}} $ . We employ mock FRBs generated with a Gaussian distribution of$ \rm DM_{IGM} $ to constrain the four parameters ($ H_0 $ ,$ e^\mu $ ,$ \sigma_{\rm{host}} $ ,$ \sigma_{\rm{IGM}} $ ). The corresponding contour plots constrained from$ N=100 $ mock FRBs are shown in the right panel of Figure 2. We can see that with the Gaussian distribution of$ \rm DM_{IGM} $ , the parameters$ H_0 $ and$ \sigma_{\rm{host}} $ can well recover their fiducial values. However, the other two parameters ($ e^{\mu} $ ,$ \sigma_{\rm{IGM}} $ ), especially$ \sigma_{\rm{IGM}} $ , can not be tightly constrained, showing a strong bias with respect to the fiducial value.Since the best-fitting value of F is much larger than expected, we then fix
$ F = 0.2 $ to see if the results can be improved or not. The corresponding contour plots constrained from$ N=100 $ mock FRBs with quasi-Gaussian distribution of$ \rm DM_{IGM} $ are shown in the left panel of Figure 3. With 100 mock FRBs, we obtain a well-constrained Hubble constant, providing a more precise result compared to the situation with 35 real FRBs. The other two parameters,$ e^\mu $ and$ \sigma_{\rm{host}} $ , are also well-constrained but exhibit relatively large uncertainty. Regardless of the large uncertainty, all three parameters can well recover the fiducial values within$ 1\sigma $ uncertainty. However, with a thousand repetitions, each involving a sample of 100 mock FRBs, nearly half of the estimates for$ e^{\mu} $ and$ \sigma_{\rm{host}} $ could not be well constrained. Therefore, caution is advised when discussing the results of$ e^{\mu} $ and$ \sigma_{\rm{host}} $ in real-world situations.Figure 3. Constraints on the three free parameters (
$ H_0 $ ,$ e^\mu $ ,$ \sigma_{\rm{host}} $ ) using 100 mock FRBs with quasi-Gaussian distribution (left panel) and Gaussian distribution (right panel) of$ \rm DM_{IGM} $ . The blue lines represent the fiducial values. The black-dashed lines from left to right in each subfigure represents the 16%, 50% and 84% quantiles of the distribution, respectively. The contours from the inner to outer represent$ 1\sigma $ ,$ 2\sigma $ and$ 3\sigma $ confidence regions, respectively. For simplicity, units are omitted in this figure.For comparison, the contour plots corresponding to a Gaussian distribution of
$ \rm DM_{IGM} $ constrained from$ N=100 $ mock FRBs are also presented in the right panel of Figure 3. We can see that the quasi-Gaussian distribution of$ \rm DM_{IGM} $ may influence the results of$ e^{\mu} $ and$ \sigma_{\rm{host}} $ . However, for the Hubble constant, the quasi-Gaussian distribution of$ \rm DM_{IGM} $ does not affect its reliability, it yields consistent results. We also do this comparison with$ N=500 $ mock FRBs, and the results are presented in Figure 4. For the Gaussian distribution of$ \rm DM_{IGM} $ , all three parameters are well-constrained and can recover the fiducial values. In the case of the quasi-Gaussian distribution, the parameter$ e^{\mu} $ is not well-constrained, as mentioned before, due to the influence fo the$ \rm DM_{IGM} $ distribution. The Hubble constant$ H_0 $ can still be constrained and recover the fiducial value within$ 1\sigma $ uncertainty, albeit with a relatively small value. Furthermore, the results indicate that further enlarging the FRB sample size does not significantly improve the precision of constraint on$ H_0 $ , primarily due to the uncertainty associated with$ f_{\rm{IGM}} $ .Figure 4. Constraints on the three free parameters (
$ H_0 $ ,$ e^\mu $ ,$ \sigma_{\rm{host}} $ ) using 500 mock FRBs with quasi-Gaussian distribution (left panel) and Gaussian distribution (right panel) of$ \rm DM_{IGM} $ . The blue lines represent the fiducial values. The black-dashed lines from left to right in each subfigure represents the 16%, 50% and 84% quantiles of the distribution, respectively. The contours from the inner to outer represent$ 1\sigma $ ,$ 2\sigma $ and$ 3\sigma $ confidence regions, respectively. For simplicity, units are omitted in this figure.To mitigate simulation fluctuations, we perform the simulation 100 times. Specifically, we randomly generate 100 FRB samples, with each sample containing 100 mock FRBs. The 100 mock samples are then used to constrain the three free parameters (
$ H_0 $ ,$ e^\mu $ ,$ \sigma_{\rm{host}} $ ) using the method described above, resulting in 100 sets of best-fitting parameters. The distributions of the best-fitting parameter$ H_0 $ in the 100 simulations are shown in the left panel of Figure 5. The fiducial values are also shown as the red-solid lines. We can see that the Hubble constant can be tightly constrained in all cases. For comparison, we also present the results with a Gaussian distribution of$ \rm DM_{IGM} $ in the right panel of Figure 5. It is obvious that with the quasi-Gaussian distribution of$ \rm DM_{IGM} $ , the best-fitting value of$ H_0 $ is systematically lower than the fiducial value, although it is still consistent with the fiducial value within$ 1\sigma $ uncertainty. On the contrary, if$ \rm DM_{IGM} $ is modelled with Gaussian distribution, the best-fitting value of$ H_0 $ well recovers the fiducial value. This implies that the choice of the distribution of$ \rm DM_{IGM} $ may cause bias on the estimation of Hubble constant.Figure 5. (color online) The distribution of the best-fitting value for the Hubble constant is shown with quasi-Gaussian distribution (left panel) and Gaussian distribution (right panel) of
$ \rm DM_{IGM} $ in 100 simulations, with$ N=100 $ FRBs in each simulation. The black dots and blue lines represent the median values of Hubble constant and their corresponding 1σ uncertainties, respectively. The red lines represent the fiducial values.
The quasi-Gaussian distribution of DMIGM in fast radio bursts may bias the constraints on the Hubble constant
- Received Date: 2024-01-15
- Available Online: 2024-07-01
Abstract: Fast radio bursts (FRBs) are useful cosmological probes with numerous applications in cosmology. The distribution of the dispersion measure contribution from the intergalactic medium is a key issue. A quasi-Gaussian distribution has been used to replace the traditional Gaussian distribution, which yields promising results. However, our work suggests that there may be additional challenges in its application. Here we use 35 well-localized FRBs to constrain the Hubble constant