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

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Li Tang, Hai-Nan Lin and Xin Li. Inferring redshift and energy distributions of fast radio bursts from the first CHIME/FRB catalog[J]. Chinese Physics C. doi: 10.1088/1674-1137/acda1c
Li Tang, Hai-Nan Lin and Xin Li. Inferring redshift and energy distributions of fast radio bursts from the first CHIME/FRB catalog[J]. Chinese Physics C.  doi: 10.1088/1674-1137/acda1c shu
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Inferring redshift and energy distributions of fast radio bursts from the first CHIME/FRB catalog

  • 1. Department of Math and Physics, Mianyang Teachers’ College, Mianyang 621000, China
  • 2. Department of Physics, Chongqing University, Chongqing 401331, China
  • 3. Chongqing Key Laboratory for Strongly Coupled Physics, Chongqing University, Chongqing 401331, China

Abstract: We reconstruct the extragalactic dispersion measure – redshift (DMEz) relation from well-localized fast radio bursts (FRBs) using Bayesian inference. Then, the DMEz relation is used to infer the redshift and energy of the first CHIME/FRB catalog. We find that the distributions of the extragalactic dispersion measure and inferred redshift of the non-repeating CHIME/FRBs follow a cut-off power law but with a significant excess at the low-redshift range. We apply a set of criteria to exclude events that are susceptible to the selection effect, but the excess at low redshifts still exists in the remaining FRBs (which we call the 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 require further investigation.

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    I.   INTRODUCTION
    • Fast radio bursts (FRBs) are energetic radio pulses with durations on the order of milliseconds happening in the Universe; see, e.g., [14] for recent reviews. The discovery of the first FRB dates back to 2007, when Lorimer et al. [5] reanalyzed the 2001 archive data of the Parkes 64-m telescope and found an anomalous radio pulse, which is now named FRB010724. Later, Thornton et al. [6] discovered several other similar radio pulses, and FRBs received great attention within the astronomy community. The origin of FRBs was still a mystery at that time, but the large dispersion measure (DM) implies that they are unlikely to originate from the Milky Way. The identification of the host galaxy and the direct measurement of redshift further confirmed that they have extragalactic origin [79]. To date, several hundreds of FRBs have been discovered [10, 11], among which only one is confirmed to originate from our galaxy [12]. FRBs can be divided into two phenomenological types: repeaters and non-repeaters, according to whether they are one-off events or not. The majority of FRBs are apparently non-repeating, but it remains unclear if they will be repeating in the future. Most repeating FRBs are not very active and repeat only two to three times [13]. However, more than one thousand bursts have been observed from two extremely active sources: FRB20121102A [14] and FRB20201124A [15].

      The physical origin of FRBs continues to be under extensive debate. Several theoretical models have been proposed to explain repeating and non-repeating FRBs, such as giant pulses from young rapidly rotating pulsars [16], the black hole battery model [17], the "Cosmic Comb" model [18], the inspiral and merger of binary neutron stars [19, 20], neutron star-white dwarf binary model [21], collision between neutron stars and asteroids [22], highly magnetized pulsars travelling through asteroid belts [23, 24], young magnetars with fracturing crusts [25], and axion stars moving through pulsar magnetospheres [26]. Although a standard model has not yet been established, it is widely accepted that the progenitor of an FRB should at least involve one neutron star or magnetar. The recently discovered magnetar-associated burst in our Milky Way strongly supports the magnetar origin of some, if not all FRBs [12, 27]. The statistical similarity between repeating FRBs and soft gamma repeaters further implies that they may have similar origin [28, 29].

      FRBs are energetic enough to be detectable up to high redshift; therefore, they can be used as probes to investigate the cosmology [3037], as well as to test the fundamental physics [3842]. Unfortunately, up to now, most FRBs have no direct measurement of redshift. Although hundreds of FRBs have been measured, only a dozen of them are well localized. With such a small sample, we even do not clearly know the redshift distribution of FRBs. One way to solve this problem is to use the observed DM, which is an indicator of distance, to infer the redshift [4346]. Accordingly, the DM contribution from the host galaxy should be reasonably modeled and subtracted from the total observed DM. This is not an easy task because too many factors may affect the host DM, such as the galaxy type, inclination angle, mass of the host galaxy, and offset of FRB site from galaxy center, among others. A simple but approximate assumption is that the host DM is a universal constant for all FRBs [31, 35, 46]. Alternatively, Luo et al. [47] assumed that the host DM follows the star-formation rate (SFR) of the host galaxy. However, Lin et al. [48] found no strong correlation between host DM and SFR from the limited sample of localized FRBs. A more reasonable way to deal with the host DM is to model it using a proper probability distribution and marginalize over the free parameters [36, 49, 50]. For example, Macquart et al. [49] assumed that the host DM follows log-normal distribution, and reconstructed the DM-redshift relation from five well-localized FRBs. However, due to the small data sample, the DM-redshift relation has large uncertainty. With the discovery of more and more FRBs in recent years, it is interesting to recheck the DM-redshift relation and use it to infer the redshift of FRBs, as there is currently no direct measurement of spectroscopic or photometric redshift.

      In this paper, we assume that the host DM of FRBs follows log-normal distribution, and reconstruct the DM-redshift relation from well localized FRBs using Bayesian inference. Then, the DM-redshift relation is used to infer the redshift of the first CHIME/FRB catalog [11]. We further consider the inferred redshift to calculate the isotropic energy of the CHIME/FRBs. The rest of this paper is arranged as follows: In section II, we reconstruct the DM-redshift relation from well-localized FRBs. In section III, we investigate the redshift and energy distributions of CHIME/FRBs. Finally, discussion and conclusions are provided in section IV.

    II.   DM-REDSHIFT RELATION FROMLOCALIZED FRBs
    • The interaction of electromagnetic waves with plasma leads to the frequency-dependent light speed. This plasma effect, although small, may cause detectable time delay between electromagnetic waves of different frequencies, if it accumulates at cosmological distance. This phenomenon is more obvious for low-frequency electromagnetic waves, such as the radiowave, as is observed in FRBs, for instance. The time delay between low- and high-frequency electromagnetic waves propagating from a distant source to earth is proportional to the integral of electron number density along the line-of-sight, i.e., the DM. The observed DM of an extragalactic FRB can generally be decomposed into four main parts: Milky Way interstellar medium (DMMW), galactic halo (DMhalo), intergalactic medium (DMIGM), and host galaxy (DMhost) [49, 51, 52],

      DMobs=DMMW+DMhalo+DMIGM+DMhost1+z,

      (1)

      where DMhost is the DM of the host galaxy in the FRB source frame, and the factor 1+z arises from the cosmic expansion. Occasionally, the DMhalo term is ignored, but this term is comparable to, or even larger than the DMMW term for FRBs at high Galactic latitude.

      The Milky Way ISM term (DMMW) can be well modeled from pulsar observations, such as the NE2001 model [53] and the YMW16 model [54]. For FRBs at high galactic latitude, both models produce consistent results. However, the YMW16 model may overestimate DMMW at low Galactic latitude [55]. Therefore, we adopt the NE2001 model to estimate DMMW . The galactic halo term (DMhalo) is not well constrained yet, and Prochaska & Zheng [56] estimated that it is approximately 5080pccm3. Herein, we follow Macquart et al. [49] and assume a conservative estimation, i.e. DMhalo=50pccm3. The concrete value of DMhalo should not strongly affect our results, as its uncertainty is much smaller than the uncertainties of the DMIGM and DMhost terms described bellow. Therefore, the first two terms on the right-hand-side of equation (1) can be subtracted from the observed DMobs. For convenience, we define the extragalactic DM as

      DMEDMobsDMMWDMhalo=DMIGM+DMhost1+z.

      (2)

      Given a specific cosmological model, the DMIGM term can be calculated theoretically. Assuming that both hydrogen and helium are fully ionized [57, 58], the DMIGM term can be written in the standard ΛCDM model as [43, 51]

      DMIGM(z)=21cH0ΩbfIGM64πGmpz01+zΩm(1+z)3+ΩΛdz,

      (3)

      where fIGM is the fraction of baryon mass in IGM, mp is the proton mass, H0 is the Hubble constant, G is the Newtonian gravitational constant, Ωb is the normalized baryon matter density, Ωm and ΩΛ are the normalized densities of matter (including baryon matter and dark matter) and dark energy, respectively. In this paper, we work in the standard ΛCDM model with the Planck 2018 parameters, i.e., H0=67.4kms1Mpc1, Ωm=0.315, ΩΛ= 0.685, and Ωb=0.0493 [59]. The fraction of baryon mass in IGM can be tightly constrained by directly observing the budget of baryons in different states [60], or observing the radio dispersion on gamma-ray bursts [61]. All the observations show that fIGM is approximately 0.84. Using five well-localized FRBs, Li et al. [33] also obtained the similar result. Therefore, we fix fIGM=0.84 to reduce the freedom. The uncertainty of these parameters should not significantly affect our results as they are much smaller than the variation of DMIGM described below.

      Note that equation (3) should be interpreted as the mean contribution from IGM. Due to the large-scale matter density fluctuation, the actual value would vary around the mean. Theoretical analysis and hydrodynamic simulations show that the probability distribution for DMIGM has a flat tail at large values, which can be fitted with the following function [49, 62]

      pIGM(Δ)=AΔβexp[(ΔαC0)22α2σ2IGM],Δ>0,

      (4)

      where ΔDMIGM/DMIGM, σIGM is the effective standard deviation, α and β are related to the inner density profile of gas in haloes, A is a normalization constant, and C0 is chosen such that the mean of this distribution is unity. Hydrodynamic simulations indicate that α=β=3 provides the best match to the model [49, 62]; thus, we fix these two parameters. Simulations also show that standard deviation σIGM approximately scales with redshift as z1/2 in the redshift range z1 [63, 64]. The redshift-dependence of σIGM is still unclear at z>1, so we simply extrapolate this relation to high-redshift region. Therefore, following Macquart et al. [49], we parameterize it as σIGM=Fz1/2, where F is a free parameter.

      Due to the lack of detailed observation on the local environment of FRB source, host term DMhost is poorly known and may range from several tens to several hundreds pccm3. For example, Xu et al. [15] estimated that DMhost of repeating burst FRB20201124A is in the range 10<DMhost<310pccm3; Niu et al. [65] inferred DMhost900pccm3 for FRB20190520B. Numerical simulations show that the probability of DMhost follows the log-normal distribution [49, 50],

      phost(DMhost|μ,σhost)=12πDMhostσhost×exp[(lnDMhostμ)22σ2host],

      (5)

      where μ and σhost are the mean and standard deviation of lnDMhost, respectively. This distribution has a median value of eμ and variance eμ+σ2host/2(eσ2host1)1/2. Theoretically, the log-normal distribution allows for the appearance of a large value of DMhost, as shown by simulations; DMhost may be as large as 1000pccm3 [44]. Generally, the two parameters (μ,σhost) may be redshift-dependent, but for non-repeating bursts, they do not vary significantly with redshift [50]. For simplicity, we first follow Macquart et al. [49] and treat them as two constant parameters. The possible redshift-dependence will be investigated later.

      Given the distributions pIGM and phost, the probability distribution of DME at redshift z can be calculated as [49]

      pE(DME|z)=(1+z)DME0phost(DMhost|μ,σhost)×pIGM(DMEDMhost1+z|F,z)dDMhost.

      (6)

      The likelihood that we observe a sample of FRBs with DME,i at redshift zi (i=1,2,3,...,N) is given by

      L(FRBs|F,μ,σhost)=Ni=1pE(DME,i|zi),

      (7)

      where N is the total number of FRBs. Considering the FRB data (zi,DME,i), the posterior probability distribution of the parameters (F,μ,σhost) is obtained according to Bayes theorem by

      P(F,μ,σhost|FRBs)L(FRBs|F,μ,σhost)P0(F,μ,σhost),

      (8)

      where P0 is the prior of the parameters.

      Thus far, there are 19 well-localized extragalactic FRBs that have direct identification of the host galaxy and well measured redshift 1. Among them, we ignore FRB20200120E and FRB20190614D: the former is very close to our galaxy (3.6 Mpc) and has a negative redshift ofz=0.0001 because the peculiar velocity dominates over the Hubble flow [66, 67]. Meanwhile, there is no direct measurement of spectroscopic redshift for the latter, but photometric redshift of z0.6 has been determined [68]. The remaining 17 FRBs have well measured spectroscopic redshifts; their main properties are listed inTable 1, which are regarded to reconstruct the DME-redshift relation.

      FRBs RA Dec DMobs DMMW DME zsp repeat? reference
      /() /() /(pccm3) /(pccm3) /(pccm3)
      20121102A 82.99 33.15 557.00 157.60 349.40 0.1927 Yes Chatterjee et al. [8]
      20180301A 93.23 4.67 536.00 136.53 349.47 0.3305 Yes Bhandari et al. [69]
      20180916B 29.50 65.72 348.80 168.73 130.07 0.0337 Yes Marcote et al. [70]
      20180924B 326.11 40.90 362.16 41.45 270.71 0.3214 No Bannister et al. [71]
      20181030A 158.60 73.76 103.50 40.16 13.34 0.0039 Yes Bhardwaj et al. [72]
      20181112A 327.35 52.97 589.00 41.98 497.02 0.4755 No Prochaska et al. [73]
      20190102C 322.42 79.48 364.55 56.22 258.33 0.2913 No Macquart et al. [49]
      20190523A 207.06 72.47 760.80 36.74 674.06 0.6600 No Ravi et al. [74]
      20190608B 334.02 7.90 340.05 37.81 252.24 0.1178 No Macquart et al. [49]
      20190611B 320.74 79.40 332.63 56.60 226.03 0.3778 No Macquart et al. [49]
      20190711A 329.42 80.36 592.60 55.37 487.23 0.5217 Yes Macquart et al. [49]
      20190714A 183.98 13.02 504.13 38.00 416.13 0.2365 No Heintz et al. [75]
      20191001A 323.35 54.75 507.90 44.22 413.68 0.2340 No Heintz et al. [75]
      20191228A 344.43 29.59 297.50 33.75 213.75 0.2432 No Bhandari et al. [69]
      20200430A 229.71 12.38 380.25 27.35 302.90 0.1608 No Bhandari et al. [69]
      20200906A 53.50 14.08 577.80 36.19 491.61 0.3688 No Bhandari et al. [69]
      20201124A 77.01 26.06 413.52 126.49 237.03 0.0979 Yes Fong et al. [76]

      Table 1.  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, respectively; 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 DMMW and DMhalo from the observed DMobs, assuming DMhalo=50pccm3 for the Milky Way halo; Column 7: the spectroscopic redshift; Column 8: indication on whether the FRB is repeating or non-repeating; Column 9: references.

      We first consider the full 17 FRBs to constrain the free parameters (F,eμ,σhost). We use eμrather than μ as a free parameter, similar to that used by Macquart et al. [49], because the former directly represents the median value of DMhost. The posterior probability density functions of the free parameters are calculated using the publicly available python package emcee [77], while the other cosmological parameters are fixed to the Planck 2018 values [59]. The same flat priors as those used by Macquart et al. [49] are considered for the free parameters: FU(0.01,0.5), eμU(20,200)pccm3, and σhostU(0.2,2.0). The posterior probability density functions and the confidence contours of the free parameters are plotted in the left panel of Fig. 1. The median values and 1σ uncertainties of the free parameters are F=0.32+0.110.10, eμ=102.02+37.6531.06pccm3, and σhost=1.10+0.310.23.

      Figure 1.  Constraints on the free parameters (F, eμ,σhost) using the full sample (left panel) and the non-repeaters (right panel). The contours from the inner to outer ones represent 1σ, 2σ, and 3σ confidence regions, respectively.

      With the parameters (F, eμ,σhost) constrained, we calculate the probability distribution of DME at any redshift in the range 0<z<4 according to equation (6). The reconstructed DMEz relation is plotted in the left panel of Fig. 2. The dark blue line is the median value, and the light blue region is the 1σ uncertainty. For comparison, we also plot the best-fitting curve, obtained by directly fitting equation (2) to the FRB data using the least-χ2 method (the red-dashed line), where DMIGM is replaced by its mean given in equation (3). The least-χ2 method is equivalent to assuming that both DMIGM and DMhost follow a Gaussian distribution around the mean. The least-χ2 curve gradually deviates from the median value of the reconstructed DMEzrelation at high redshift, but due to the large uncertainty, it remains consistent within1σ uncertainty. We find that 15 out of the 17 FRBs fall well into the 1σ range of the reconstructed DMEz relation. Two outliers, FRB20181030A and FRB20190611B (the red dots in Fig. 2), fall bellow the 1σ range of the DMEz relation, implying that the DME values of these two FRBs are smaller than expected. We note that the outlier FRB20181030A has a much smaller redshift (z=0.0039) and a very low extragalactic DM (DME=13.34pccm3); therefore, the peculiar velocity of its host galaxy cannot be ignored. The redshift of the other outlier FRB20190611B is z=0.3778, and the observed DM of this burst is DMobs=332.63pccm3. The normal burst FRB20200906A has a redshift (z=0.3688) similar to that of FRB20190611B but with a much larger DM (DMobs=577.8pccm3). Note that both FRB20200906A and FRB20190611B are non-repeating, and their positions differ significantly. The large difference in DMobsbetween these two bursts may be caused by, e.g., the fluctuation of matter density in the IGM, variation of the host DM, or difference in local environment of the FRB source [65, 78].

      Figure 2.  (color online) DMEz relation obtained from full sample (left panel) and non-repeaters (right panel). The dark blue line is the median value, and the light blue region is 1σ uncertainty. The dots are the FRB data points, and the outliers are highlighted in red. The red-dashed line is the best-fitting result obtained using the least-χ2 method. The inset is the zoom-in view of the low-redshift range.

      The full FRB sample includes 11 non-repeating FRBs and 6 repeating FRBs, which may have different DMhostvalues. To check this, we re-constrain the parameters (F,eμ,σhost) using the 11 non-repeating FRBs. The confidence contours and the posterior probability distributions of the parameter space are plotted in the right panel of Fig. 1. The median values and 1σ uncertainties of the free parameters are F=0.38+0.090.11, eμ=126.86+39.7741.07pccm3, and σhost=0.88+0.420.28. We obtain a slightly larger eμ value but a smaller σhostvalue than that constrained from the full FRBs. Nevertheless, these values are still consistent with 1σ uncertainty. The reconstructed DMEz relation using the non-repeating sample is shown in the right panel of Fig. 2. FRB20190611B is still an outlier (the other outlier FRB20181030A is a repeater). The DMEzrelations of the full sample and the non-repeaters are well consistent with each other, but the latter has a slightly larger uncertainty, particularly at the low-redshift range.

      In general, eμ and σhost may evolve with redshift. Numerical simulations show that the median value of DMhost has a power-law dependence on redshift, but σhost does not change significantly [50]. To check this, we parameterize eμ in the power-law form,

      eμ=eμ0(1+z)α,

      (9)

      and use the full FRB sample to constrain the parameters (F,eμ0,σhost,α). A flat prior is adopted for α in the range αU(2,2). The posterior probability density functions and the confidence contours of the free parameters are plotted in the left panel of Fig. 3. The best-fitting parameters are F=0.32+0.110.10, eμ0=98.71+45.7533.06pccm3, σhost=1.08+0.320.22, and α=0.15+1.211.33. As can be seen, parameter α couldn't be tightly constrained, while the constraints on the other three parameters are almost unchanged compared with the case when α=0 was fixed. This implies that there is no evidence for the redshift-dependence of eμ with the present data. Regarding the non-repeating FRBs, we arrive at the same conclusion (see the right panel of Fig. 3). Therefore, it is safe to assume that eμ is redshift-independent, at least in the low-redshift range z<1. However, note that the universality of eμ has not been proven at high redshift. Hence, the uncertainty on the DMEz relation in the z>1 range may be underestimated.

      Figure 3.  Constraints on the free parameters (F,eμ0,σhost,α) using the full sample (left panel) and the non-repeaters (right panel). The contours from the inner to outer ones represent 1σ, 2σ, and 3σ confidence regions, respectively.

    III.   REDSHIFT AND ENERGY DISTRIBUTION OF CHIME/FRBs
    • The first CHIME/FRB catalog comprises 536 bursts, including 474 apparently non-repeating bursts and 62 repeating bursts from 18 FRB sources [11]. In this paper, we focus on the 474 apparently non-repeating bursts, whose properties are listed in a long table in the online material. All the bursts have well measured DMobs, but there is no direct measurement of their redshift. We calculate the extragalactic DME by subtracting DMMW and DMhalo from the observed DMobs, where DMMW is calculated using the NE2001 model [53], and DMhalo is assumed to be 50pccm3 [49]. The DME values of the 474 apparently non-repeating bursts fall into the range of 203000 pc cm3. Among them, 444 bursts have DME>100 pc cm3, while the remaining 30 bursts have DME<100 pc cm3. The mean and median values of DME are 557 and 456 pc cm3, respectively. We divide DME of the full non-repeating bursts into 30 uniform bins, with bin width ΔDME=100 pc cm3, and plot the histogram in the left panel of Fig. 4. The distribution of DMEcan be well fitted by the cut-off power law (CPL),

      Figure 4.  (color online) Histogram of DME (left panel) and inferred redshift (right panel) of the first non-repeating CHIME/FRB catalog. The left-most gray bar represents the 30 FRBs with DME<100pccm3, which are expected to have z<0.1. The blue and red lines are the best-fitting CPL models for the full sample and gold sample, respectively.

      CPL:N(x)xαexp(xxc),x>0,

      (10)

      with the best-fitting parameters α=0.86±0.07 and xc=289.49±17.90 pc cm3. This distribution exhibits a peak at xp=αxc250 pc cm3, which is much smaller than the median and mean values of DME.

      Next, we use the DMEz relation reconstructed using the full sample (using the non-repeating sample does not significantly affect our results) to infer the redshift of the non-repeating CHIME/FRBs. For FRBs with DME<100 pc cm3, the DMhost term may dominate over the DMIGM term, hence a smaller uncertainty on DMhost may cause large bias on the estimation of redshift. Therefore, when inferring the redshift using the DMEz relation, we only consider the FRBs with DME>100 pc cm3. From the DMEz relation, DME(z=0.1)=169.9+196.973.4 pc cm3 (1σ uncertainty). Therefore, FRBs with DME<100 pc cm3 are expected to have redshift z<0.1, while the lower limit cannot be determined. The inferred redshifts for FRBs with DME>100 pc cm3 are provided in the online material, spanning the range zinf(0.023,3.935). Three bursts have inferred redshifts larger than 3, i.e., FRB20180906B with zinf=3.935+0.4630.705, FRB20181203C with zinf=3.003+0.4430.657, and FRB20190430B with zinf=3.278+0.4490.650.

      We divide the redshift range 0<z<3 into 30 uniform bins, with bin width Δz=0.1, and plot the histogram of the inferred redshift in the right panel of Fig. 4. The distribution of the inferred redshift can be fitted via the CPL model given in equation (10). The best-fitting parameters are α=0.39±0.09 and xc=0.48±0.06. The distribution displays a peak at zp=αxc0.19. The mean and median values of this distribution are 0.67 and 0.52, respectively. Considering the FRBs with DME<100 pccm3 (30 FRBs in total), which are expected to have z<0.1, there is a large excess compared with the CPL model in the redshft range z<0.1 (see the left-most gray bar in Fig. 4). This may be caused by the selection effect, as the detector is more sensitive to nearer FRBs.

      Amiri et al. [11] provided a set of criteria to exclude events that are unsuitable for use in population analyses: (1) events with S/N<12; (2) events having DMobs<1.5max(DMNE2001,DMYMW16); (3) events detected in far sidelobes; (4) events detected during non-nominal telescope operations; and (5) highly scattered events (τscat>10 ms). We call the remaining FRBs the gold sample, constituting 253 non-repeating FRBs. We plot the distributions of DME and redshifts of the gold sample, together with those of the full sample, in Fig. 4. Similar to the full sample, the distributions of DME and redshifts of the gold sample can also be fitted by the CPL model. The best-fitting CPL model parameters are summarized in Table 2. It is clear that the parameters are not significantly changed compared with those of the full sample. Note that the redshift distribution of the gold sample shown in the right panel of Fig. 4 only contains the FRBs with DME>100pccm3 (236 FRBs). The gold sample still contains 17 FRBs with DME<100pccm3, whose redshifts are expected to be z<0.1. Thus, the low-redshift excess still exists in the gold sample.

      DME (Full) α=0.86±0.07 xc=289.49±17.90pccm3
      DME (Gold) α=0.77±0.09 xc=302.82±23.92pccm3
      redshift (Full) α=0.39±0.09 xc=0.48±0.06
      redshift (Gold) α=0.31±0.11 xc=0.52±0.08

      Table 2.  Best-fitting CPL model parameters for the distributions of DME and redshift.

      Given the redshift, the isotropic energy of a burst can be calculated as [79]

      E=4πd2LFΔν(1+z)2+α,

      (11)

      where dL is the luminosity distance, F is the average fluence, α is the spectral index (Fννα), and Δν is the waveband in which the fluence is observed. The fluence listed in the first CHIME/FRB catalog is averaged over the 400800 MHz waveband, hence Δν=400 MHz. The spectral indices of some bursts are not clear. Macquart et al. [80] showed that, for a sample of ASKAP/FRBs, α=1.5 provides a reasonable fit. Hence, we fix α=1.5 for all the bursts. Note that the fluence given in the CHIME/FRB catalog is lower limit, as the fluence is measured assuming each FRB is detected at the location of maximum sensitivity. Therefore, the energy calculated using equation (11) is the lower limit. With the inferred redshift, we calculate the isotropic energy in the standard ΛCDM cosmology with the Planck 2018 parameters [59]. The uncertainty of energy propagates from the uncertainties of fluence and redshift. The results are presented in the online material. The isotropic energy spans approximately five orders of magnitude, from 1037 erg to 1042 erg, with the median value of 1040 erg. Three bursts have energy above 1042 erg, see Table 3. The isotropic energy of the furthest burst, FRB20180906B, is approximately 4×1041 erg.

      FRBs RA Dec DMobs DMMW DME Fluence zinf log(E/erg) flag
      /() /() /(pc/cm3) /(pc/cm3) /(pc/cm3) /(Jy ms)
      20181219B 180.79 71.55 1950.7 35.8 1864.9 27.00±22.00 2.300+0.3570.511 42.405+0.3880.962 1
      20190228B 50.01 81.94 1125.8 81.9 993.9 66.00±32.00 1.175+0.2050.355 42.170+0.3240.633 0
      20190319A 113.43 5.72 2041.3 109.0 1882.3 19.40±4.20 2.325+0.3590.516 42.271+0.2140.335 1

      Table 3.  Most energetic bursts with E>1042 erg. Column 1: FRB name; Columns 2 and 3: the right ascension and declination of the FRB source on the sky, respectively; 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 DMMW and DMhalo from the observed DMobs, assuming DMhalo=50pccm3 for the Milky Way halo; Column 7: the observed fluence; Column 8: the inferred redshift; Column 9: the isotropic energy; Column 10: the flag indicating whether the sample is gold (flag=1) or not (flag=0). Note that the uncertainty of energy may be underestimated due to the lack of well-localized FRBs at z>1.

      Several works have shown that the distributions of fluence and energy of repeating FRBs follow a simple power law (SPL) [81, 82]. To check if the fluence and energy of the apparently non-repeating FRBs follow the same distribution, we calculate the cumulative distributions of fluence and energy of the non-repeating CHIME/FRBs (for both the full and gold samples), and plot the results in Fig. 5. We try to fit the cumulative distributions of fluence and energy using the SPL model, where xc is the cut-off value above which the FRB count is zero. The uncertainty of N is given by σN=N [82]. The best-fitting parameters are detailed in Table 4, and the best-fitting lines are shown in Fig. 5 as dashed lines. As can be seen, for both the full sample and the gold sample, the SPL model fails to fit the distributions of fluence and energy. In particular, at the left end, the model prediction considerably exceeds the data points.

      Figure 5.  (color online) Cumulative distribution of fluence (left panel) and isotropic energy (right panel) of the non-repeating CHIME/FRBs with DME>100pccm3. The solid and dashed lines are the best-fitting BPL model and SPL model, respectively.

      Fluence (full) SPL β=0.54±0.02 xc=66.30±3.52 Jy ms χ2/dof=7.48
      BPL γ=1.55±0.01 xb=3.36±0.04 Jy ms χ2/dof=0.23
      Fluence (gold) SPL β=0.48±0.03 xc=58.59±4.02 Jy ms χ2/dof=5.79
      BPL γ=1.65±0.02 xb=3.96±0.07 Jy ms χ2/dof=0.29
      Energy (full) SPL β=0.09±0.01 xc=(1.17±0.06)×1042erg χ2/dof=11.10
      BPL γ=0.90±0.01 xb=(1.55±0.02)×1040erg χ2/dof=0.50
      Energy (gold) SPL β=0.08±0.01 xc=(1.13±0.09)×1042erg χ2/dof=7.12
      BPL γ=0.95±0.01 xb=(1.82±0.04)×1040erg χ2/dof=0.29

      Table 4.  Best-fitting parameters of the cumulative distributions of fluence and energy for the full sample and the gold sample.

      SPL:N(>x)(xβxβc),x<xc,

      (12)

      Lin & Sang [83] showed that the bent power law (BPL) model fits the distributions of fluence and energy of repeating burst FRB121102 much better than the SPL model. The BPL model takes the form

      BPL:N(>x)[1+(xxb)γ]1,x>0,

      (13)

      where xb is the median value of x, i.e. N(x>xb)=N(x<xb). The BPL model has a flat tail at xxb and behaves like the SPL model at xxb. The BPL model was initially employed to fit the power density spectra of gamma-ray bursts [84]. Then, it was shown that the BPL model can well fit the distribution of fluence and energy of soft-gamma repeaters [29, 85]. The choice of the BPL model is inspired by the fact that the cumulative distributions of fluence and energy have a flat tail at the left end, as can be seen from Fig. 5. We therefore try to fit the cumulative distributions of fluence and energy of CHIME/FRBs using the BPL model. The best-fitting parameters are summarized in Table 4, and the best-fitting lines are shown in Fig. 5 (solid lines). It is apparent that the BPL model fits the data of both the full and gold samples much better than the SPL model. The BPL model fits the distribution of fluence very well in the full range. For the distribution of energy, the BPL model also fits the data well, except at the very high energy end.

    IV.   DISCUSSION AND CONCLUSIONS
    • In this study, we reconstructed the DMEz relation from 17 well-localized FRBs at z<1 using the Bayesian inference method. The host DM was assumed to follow log-normal distribution with mean exp(μ) and variance σhost, and the variance of the DM of the IGM was assumed to be redshift-dependent (σIGM=Fz1/2). The free parameters were tightly constrained by 17 well-localized FRBs: F=0.32+0.110.10, exp(μ)=102.02+37.6531.06pccm3, and σhost=1.10+0.310.23. These parameters are well consistent with those of Macquart et al. [49], who obtained F=0.31+0.130.16, exp(μ)=68.2+59.635.0pccm3, and σhost=0.88+0.650.45 from five well-localized FRBs. With a larger FRB sample and one less free parameter (Ωb), our constraint is more stringent than that of Macquart et al. [49]. We directly extrapolated these parameters to high redshift and reconstructed the DMEz relation up to z=4.

      We further adopted the DMEz relation to infer the redshift of the first CHIME/FRB catalog. We found that the extragalactic DM of the non-repeating CHIME/FRBs follows a CPL distribution, with a peak at 250 pc cm3. The inferred redshift of the non-repeating CHIME/FRBs can also be fitted by the CPL distribution but with a significant excess at the low redshift range 0<z<0.1, which may be caused by selection effect. Thus, we applied a set of criteria to exclude events that are susceptible to selection effect, as described by Amiri et al. [11]. We found that the extragalactic DM and the redshift of the remaining FRBs (i.e., the gold sample) follow a CPL distribution, and the excess at low redshifts still exists. We further used the inferred redshift to calculate the isotropic energy of the non-repeating CHIME/FRBs. As a result, the distributions of energy and fluence can be well fitted by the BPL model, with power indexes of γ=0.90±0.01 and γ=1.55±0.01 for energy and fluence, respectively. However, the SPL model fails to fit both the distributions of fluence and energy, even for the gold sample. The statistical properties of the non-repeating CHIME/FRBs are similar to those of the bursts from the repeating FRB source, FRB121102 [83]. As the BPL model has a flat tail at the low-energy (low-fluence) end, it detects considerably fewer dim bursts than the SPL model. The flatness at the low-energy (low-fluence) end can be explained by the observational incompleteness, as some dim bursts may be missing from detection. Note that the BPL model reduces to the SPL model at the high energy end, N(>E)Eγ. The power-law index of the energy accumulative distribution is γ0.9, corresponding to ˆγ1.90for the differential distribution. Interestingly, the power-law index of the non-repeating CHIME/FRBs is similar to that of repeating bursts from the single source FRB 121102, with ˆγ1.61.8 [82].

      We emphasize that the CPL distribution of redshift is not intrinsic. The intrinsic redshift distribution should consider the selection effect of the detector. Due to the lack of well-localized FRBs, the intrinsic redshift distribution remains poorly known. Several possibilities have been discussed in literature, such as distributions similar to those of gamma-ray bursts [31], a constant comoving number density with a Gaussian cutoff [86], the SFR history model [43], the modified SFR history model [87], and the compact star merger model with various time delays [43]. In a recent work, Qiang et al. [46] considered several modified SFR history models and found good overall consistency with the observed data of the first CHIME/FRB catalog, as long as the model parameters were chosen properly, but the simple SFR history model was fully ejected by the data. Hackstein et al. [44] investigated three different intrinsic redshift distribution models: the constant comoving density model, SFR history model, and stellar mass density model. After considering the selection effects of the CHIME telescope, they showed that the distribution of the observed redshift should have a CPL shape. The model that fits the CHIME/FRB best remains to be determined in future work. In addition, Shin et al. [88] studied the FRB population assuming a Schechter luminosity function; after calibrating the selection effects, they found that the distribution of redshift exhibits a CPL shape.

      When reconstructing the DMEz relation, it is important to reasonably deal with the DMhost term. The simplest way is to assume that DMhost is a constant [31, 35, 46]. As expected, this is inappropriate because the actual value can vary significantly from burst to burst. Luo et al. [47] parameterized DMhost as a function of SFR. However, statistical analysis of the well-localized FRBs showed that there is no strong correlation between DMhost and the host galaxy properties, including SFR [48]. Because there is a lack of direct observation on DMhost, at present, the most reasonable approach is to model it using a probability distribution. Theoretical analysis and numerical simulations indicate that the probability of DMhost can be modeled by a log-normal distribution with mean value μ and deviation σhost [49, 50]. Based on the IllustrisTNG simulation, Zhang et al. [50] showed that exp(μ) has a power-law dependence on redshift, and the power-law indices for repeating and non-repeating FRBs slightly differ. However, we found no evidence for the redshift evolution of exp(μ) here. The median value of DMhost for the well localized FRBs obtained herein is approximately exp(μ)100pccm3. This is consistent with DMhost of FRB20190608B (137±43pccm3) obtained from optical/UV observations [89].

      Due to the lack of high-redshift FRBs, the uncertainty of the DMEz relation is large at high redshift. The uncertainty mainly comes from those regarding DMIGM and DMhost. The uncertainty on DMIGM at redshift z=1 is approximately δDMIGM0.3DMIGM270pccm3. From the lognormal distribution, the uncertainty of DMhost is estimated to be δDMhost=exp(μ+σhost/2)×(exp(σ2host)1)1/2200pccm3, where exp(μ)100pccm3 and σhost1. The uncertainties of DMMW and DMhalo are expected to be much smaller than those of DMIGM and DMhost and were thus ignored herein. We also ignored the DM of the FRB source, which is difficult to model due to the lack of knowledge on the local environment of FRBs. With the present knowledge, the probability distribution of DMsource remains unclear. In some models involving the merger of compact binary, this term is expected to be small [90, 91]. Therefore, in most studies, this term is directly neglected. If DMsource does not strongly vary from burst to burst (such that it can be treated approximately as a constant), it can be absorbed into the DMhost term, while the probability distribution phost does not change except for an overall shift. In this case, parameter exp(μ) should be explained as the median value of the sum of DMhost and DMsource. Therefore, if DMsource does not vary significantly, its inclusion should not affect our results. Another uncertainty comes from parameter fIGM. In general, fIGM should be treated as a free parameter, together with F, exp(μ), and σhost. However, due to the small FRB sample, free fIGM will lead to an unreasonable result. Therefore, we fixed fIGM= 0.84 based on other independent observations. This will lead to underestimation of the uncertainty of the DMEz relation.

      The conclusions of our paper are based on the assumption that the DMEz relation obtained from low-redshift data can be extrapolated to a high redshift region. As demonstrated in section II, there is no strong evidence for the redshift dependence of the host DM, at least in the low-redshift region z1. However, we cannot prove this assumption at the high redshift region because there is a lack of data points at z>1. Therefore, we simply extrapolated the DMEz relation to the high redshift region without proving it. Recent works [79, 87] have shown that the DMEz relation may be nonmonotonic, with a turn point at a certain redshift. This is because an FRB at a low redshift is easier to detect than one at a high redshift, for a given intrinsic luminosity. Therefore, a highly dispersed FRB is mainly caused by a large DM of the host galaxy, rather than by a high redshift. For example, the large DM of FRB20190520B (DMobs1200pccm3, z0.241) is mainly attributed to the large value of DMhost (900pccm3) [65]. Therefore, the uncertainty of the DMEz relation obtained in this study may be significantly underestimated. We hope that the uncertainty can be reduced if more high-redshift FRBs are detected in the future.

    ONLINE MATERIAL
    • The parameters of the first (non-repeating) CHIME/ FRB catalog are listed in a long table in the online material.

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