Issue 
A&A
Volume 585, January 2016



Article Number  A68  
Number of page(s)  9  
Section  Cosmology (including clusters of galaxies)  
DOI  https://doi.org/10.1051/00046361/201526485  
Published online  18 December 2015 
Measuring dark energy with the E_{iso} – E_{p} correlation of gammaray bursts using modelindependent methods
^{1}
School of Astronomy and Space Science, Nanjing University,
210093
Nanjing,
PR China
email:
fayinwang@nju.edu.cn
^{2}
Department of Physics, University of Hong Kong,
Pokfulam Road, Hong Kong, PR China
^{3}
Key Laboratory of Modern Astronomy and Astrophysics (Nanjing
University), Ministry of Education, 210093
Nanjing, PR
China
Received: 7 May 2015
Accepted: 28 September 2015
We use two modelindependent methods to standardize long gammaray bursts (GRBs) using the E_{iso} − E_{p} correlation (log E_{iso} = a + blog E_{p}), where E_{iso} is the isotropicequivalent gammaray energy and E_{p} is the spectral peak energy. We update 42 long GRBs and attempt to constrain the cosmological parameters. The full sample contains 151 long GRBs with redshifts from 0.0331 to 8.2. The first method is the simultaneous fitting method. We take the extrinsic scatter σ_{ext} into account and assign it to the parameter E_{iso}. The bestfitting values are a = 49.15 ± 0.26, b = 1.42 ± 0.11, σ_{ext} = 0.34 ± 0.03 and Ω_{m} = 0.79 in the flat ΛCDM model. The constraint on Ω_{m} is 0.55 < Ω_{m}< 1 at the 1σ confidence level. If reduced χ^{2} method is used, the bestfit results are a = 48.96 ± 0.18, b = 1.52 ± 0.08, and Ω_{m} = 0.50 ± 0.12. The second method uses type Ia supernovae (SNe Ia) to calibrate the E_{iso} − E_{p} correlation. We calibrate 90 highredshift GRBs in the redshift range from 1.44 to 8.1. The cosmological constraints from these 90 GRBs are Ω_{m} = 0.23^{+0.06}_{0.04} for flat ΛCDM and Ω_{m} = 0.18 ± 0.11 and Ω_{Λ} = 0.46 ± 0.51 for nonflat ΛCDM. For the combination of GRB and SNe Ia sample, we obtain Ω_{m} = 0.271 ± 0.019 and h = 0.701 ± 0.002 for the flat ΛCDM and the nonflat ΛCDM, and the results are Ω_{m} = 0.225 ± 0.044, Ω_{Λ} = 0.640 ± 0.082, and h = 0.698 ± 0.004. These results from calibrated GRBs are consistent with that of SNe Ia. Meanwhile, the combined data can improve cosmological constraints significantly, compared to SNe Ia alone. Our results show that the E_{iso} − E_{p} correlation is promising to probe the highredshift Universe.
Key words: cosmological parameters / dark energy / gamma rays: general
© ESO, 2015
1. Introduction
Gammaray bursts (GRBs) are the most violent explosions in the Universe, with the highest isotropic energy up to 10^{54} erg (for reviews, see Mészáros 2006; Zhang 2007; Gehrels et al. 2009). Thus, they can be detected to the edge of the visible Universe (Ciardi & Loeb 2000; Lamb & Reichart 2000; Wang et al. 2012). For instance, the spectroscopically confirmed redshift of GRB090423 is about 8.2 (Tanvir et al. 2009; Salvaterra et al. 2009). Therefore, they are promising probes for the highredshift Universe (for a recent review, see Wang et al. 2015). Many studies have been carried out to use GRBs for cosmological purposes, such as the star formation rate (Totani 1997; Wijers et al. 1998; Porciani & Madau 2001; Wang & Dai 2009, 2011a), the intergalactic medium metal enrichment (Barkana & Loeb 2004; Wang et al. 2012), dark energy (Dai et al. 2004; Friedman & Bloom 2005; Schaefer 2007; Basilakos & Perivolaropoulos 2008; Wang et al. 2011b), reionization (Totani et al. 2006; Gallerani et al. 2008; Wang 2013), possible anisotropic acceleration (Wang & Wang 2014a), and the twopoint correlation (Li & Lin 2015).
To constrain the cosmological parameters, standard rulers or candles such as baryon acoustic oscillations (BAO; Cole et al. 2005; Eisenstein et al. 2005; Anderson et al. 2014), cosmic microwave background (CMB; Komatsu et al. 2011; Planck Collaboration XVI 2013; Planck Collaboration XIII 2015) and SNe Ia (Riess et al. 1998; Perlmutter et al. 1999; Suzuki et al. 2012) are required. The redshifts of BAO and SNe Ia are low, however, and the CMB is only a snapshot of cosmic expansion. Some parameters, such as the density and EOS parameter of dark energy (Wang 2012; Wang & Dai 2014; Wang & Wang 2014b), might evolve with redshift. GRBs can probe the evolution of these parameters at high redshifts and serve as complementary tools for SNe Ia. The study of these evolutions can differentiate dark energy models. Some luminosity correlations have been proposed to standardize GRBs (Amati et al. 2002; Ghirlanda et al. 2004a; Liang & Zhang 2005). Ghirlanda et al. (2004a) found a tight correlation between collimated energy E_{γ} and the peak energy E_{p} of νF_{ν} spectrum. Dai et al. (2004) used this correlation to constrain cosmological parameters with 12 GRBs. Liang & Zhang (2005) found the E_{iso} − E_{p} − t_{b} correlation and used this correlation to constrain cosmological parameters. Recently, Wang et al. (2011b) constrained cosmological parameters with 109 GRBs using six GRB empirical correlations, and found in the flat Λ cold dark matter (CDM) model. Other attempts have also been made to standardize GRBs (Ghirlanda et al. 2004b; Friedman & Bloom 2005; Schaefer 2007; Wang et al. 2007; Liang et al. 2008; Kodama et al. 2008; Qi et al. 2009; Cardone et al. 2010; Wang & Dai 2011c). These methods of standardizing the long GRBs are mainly based on some empirical correlations, such as the E_{iso} − E_{p} (Amati et al. 2002), E_{p} − L_{p} (Schaefer 2003; Wei & Gao 2003), and E_{p} − E_{γ} (Ghirlanda et al. 2004a), where L_{p} is the peak luminosity, E_{p} is the peak energy in cosmological rest frame, E_{iso} is the isotropicequivalent energy, and E_{γ} is the collimationcorrected energy. Correlations within Xray afterglow light curves have also been studied (Dainotti et al. 2008, 2010; Qi & Lu 2010).
In this paper, we focus on the usage of the E_{iso} − E_{p} correlation. Amati et al. (2002) discovered this correlation with a small sample of BeppoSAX GRBs. Since many more GRBs are detected, attempts have been made to use this correlation for the purpose of cosmology. Amati et al. (2008) used a simultaneous fitting method to constrain the E_{iso} − E_{p} correlation coefficients and cosmological parameters together with 70 long GRBs. The extrinsic scatter σ_{ext} was taken into consideration in this method (D’Agostini 2005). Amati et al. (2008) assigned σ_{ext} to E_{p} and found 0.04 < Ω_{m}< 0.40 and σ_{ext} = 0.17 ± 0.02 at 1σ confidence level in the flat ΛCDM Universe. For nonflat ΛCDM model, the results are Ω_{m} ∈ [ 0.04,0.40 ] and Ω_{Λ}< 1.05 (Amati et al. 2008). However, Ghirlanda (2009) doubted this result. He claimed that the extrinsic scatter term should be assigned to E_{iso}. This is consistent with D’Agostini (2005), who described that the extrinsic scatter σ_{ext} should be assigned to the parameter that also depends on hidden variables (cosmological parameters in our study). We discuss this point in detail in Sect. 3.1. However, this would lead to no constraint on cosmological parameters with the same 70 GRBs from Amati et al. (2008). We test it again with a larger sample in this paper.
The calibration method is also helpful to standardize GRBs. Imitating the example of standardizing the standard candle of SNe Ia with Cepheid variables, GRBs can also be calibrated with SNe Ia (Liang et al. 2008; Kodama et al. 2008; Wei 2010; Lin et al. 2016). This method is also cosmological model independent. Liang et al. (2008) calibrated 42 high redshift GRBs with SNe Ia. Five interpolation methods were used and the results were consistent with each other. Wei (2010) standardized 59 highredshift GRBs with SNe Ia, using the E_{iso} − E_{p} correlation, and found that GRBs can improve the constraint on cosmological parameters. Wang & Dai (2011c) calibrated 116 GRBs with Union 2 SNe Ia with cosmographic parameters.
We use 151 GRBs, 109 of which are taken from Amati et al. (2008) and Amati et al. (2009). The remaining 42 GRBs are the updated long GRBs, which were detected by Fermi GBM, KonusWind, SwiftBAT, and SuzakuWAM. The energy band, fluence, low (α), high (β) energy photon indices, spectral peak energy, and redshift are taken from the refined analysis of the corresponding GRB team. We test whether this larger GRB sample can help to constrain cosmological models better. First, we constrain the cosmological parameters and coefficients of the E_{iso} − E_{p} correlation simultaneously. Then, we calibrate these GRBs with SNe Ia using the E_{iso} − E_{p} correlation. At last, we compare these two methods and discuss them.
This paper is organized as follows. In the next section, we introduce the GRBs data and perform the Kcorrection. In Sect. 3, we test whether the redshift evolution of the E_{iso} − E_{p} correlation is significant, and use a simultaneous fitting method to constrain cosmological parameters and coefficients of the E_{iso} − E_{p} correlation. In Sect. 4, we use SNe Ia to calibrate the E_{iso} − E_{p} correlation, then we use these calibrated GRBs to constrain cosmological parameters. Summary and discussions are given in Sect. 5.
42 updated long GRBs.
2. Updated GRB sample
We collect all GRBs with information of redshift, fluence, peak energy, and photon indices from GCN Circulars Archive^{1}Cucchiara et al. (2011) and Gendre et al. (2013) until February 13, 2014. The updated sample contains 42 updated long GRBs. We list these GRBs in Table 1. The spectra of these GRBs are obtained from the refined analysis of Fermi GBM team, KonusWind team, SwiftBAT team, and SuzakuWAM team. The redshifts extend from 0.34 to 5.91. The spectrum is modeled by a broken power law (Band et al. 1993), (1)where E_{p,obs} is the observed peak energy, α and β are the low and high energy photon indices, respectively. We take the typical spectral index values for those GRB whose indices are not given out in the references, i.e., α = −1.0 and β = −2.2 (Salvaterra et al. 2009).
With these spectra parameters, we can obtain the peak energy in the cosmological rest frame by E_{p} = E_{p,obs} × (1 + z) and the bolometric fluence in the band of 1−10^{4} keV by (Bloom et al. 2001) (2)where S is the observed fluence, E_{min} and E_{max} are the detection limits of the instrument, and z is the redshift.
In the E_{iso} − E_{p} plane, E_{p} is an observed value, which is not dependent on the cosmological model. However, E_{iso} depends on the cosmological model from (3)where d_{L} is the luminosity distance. Assuming a flat ΛCDM model, the d_{L} can be expressed with Hubble expansion rate (4)where Ω_{m} is the matter density at present, and H_{0} is the Hubble constant. Since the Hubble constant is precisely measured, we take H_{0} = 67.8 km s^{1} Mpc^{1} (Planck Collaboration XVI 2013; Planck Collaboration XIII 2015), except when we use the combination data of SNe and GRB to constrain cosmological models.
We list 42 updated GRBs in Table 1. The isotropic energy E_{iso} is calculated with benchmark parameters with Ω_{m} = 0.308 for the flat ΛCDM Universe (Planck Collaboration XVI 2013; Planck Collaboration XIII 2015). During the calculation, we only take the errors propagating from the spectrum parameters, namely observed fluence S and peak energy E_{p,obs}. The uncertainties from other parameters are attributed into the extrinsic scatter σ_{ext}.
3. The E_{iso} – E_{p} correlation and constraints on cosmological parameters
3.1. The E_{iso} – E_{p} correlation
To constrain cosmological models more precisely, we combine our updated 42 GRBs with 109 GRBs from Amati et al. (2008) and Amati et al. (2009). The full sample contains 151 GRBs and covers the redshift range from 0.0331 to 8.2. We parameterize the E_{iso} − E_{p} correlation as follows: (5)where a and b are the intercept and slope. Here E_{p} has been corrected into the cosmological rest frame.
E_{iso} − E_{p} correlation fitting results of full data and four redshift bins.
Before constraining cosmological models, we test the possible redshift evolution of the E_{iso} − E_{p} correlation using the maximum likelihood method. The full data is divided into four redshift bins: [ 0.0331,0.958 ], [ 0.966,1.613 ], [ 1.619,2.671 ] , and [ 2.69,8.2 ]. Each bin almost includes the same number of GRBs. The results are shown in Table 2. We give out the bestfit values and 1σ uncertainties in the coefficients a, b, and the extrinsic scatter σ_{ext}. The σ_{ext} is almost constant in different bins. Its value is about 0.34, which implies that the extrinsic scatter dominates the error size. The results show no statistically significant evidence for the redshift evolution of the E_{iso} − E_{p} correlation. This result is consistent with those of Basilakos & Perivolaropoulos (2008) and Wang et al. (2011b). The full data result are also shown in Figure 1. This result illustrates that the E_{iso} − E_{p} correlation fits the data well.
As discussed by D’Agostini (2005), we use the following likelihood to fit the linear relation y = a + bx, (6)Following the description of D’Agostini (2005), the parameter y should not only depend on x, but also depend on some hidden variables (Ω_{m} here). Thus, the expression of the E_{iso} − E_{p} plane should be written as and . However, Amati et al. (2008) set , thus the extrinsic scatter σ_{ext} does not contain the error from the cosmological models.
Fig. 1
E_{iso} − E_{p} correlation. The solid black, dotted, and dashed lines represent the bestfit line, 1σ_{ext} region, and 2σ_{ext} region, respectively. 
3.2. Simultaneous fitting
Since the E_{iso} − E_{p} correlation does not evolve with redshift, it can be used to constrain parameters directly. We emphasize that there is no circularity problem in the simultaneous fitting method because we do not assume any cosmological model. In this section, we focus on the constraint on the flat ΛCDM model. The luminosity distance is expressed as Eq. (4).
Fig. 2
Evolution of log (ℒ)/log (ℒ)_{min} as a function of Ω_{m} in the flat ΛCDM Universe is shown with solid line from maximum likelihood method. The dashed line is the plot from reduced χ^{2} method. The dotted line is obtained with the 90 GRBs calibrated on the SNe Ia (see Sect. 4). 
Constraints of cosmological parameters by GRBs.
Using the likelihood expressed in equation (6), we can constrain the current matter density Ω_{m}, the extrinsic scatter parameter σ_{ext}, and the coefficients of the E_{iso} − E_{p} correlation simultaneously. In our calculations, the bestfit values are a = 49.15 ± 0.26, b = 1.42 ± 0.12, σ_{ext} = 0.34 ± 0.03, and Ω_{m} = 0.76. We show the constraint on Ω_{m} in Fig. 2 with a solid line. The 1σ uncertainty is Ω_{m} ∈ [ 0.55,1 ]. We also use the reduced χ^{2} method to constrain the matter density. This method also includes the effect of extrinsic scatter (7)The hidden variables (cosmological parameters) are included in E_{iso}. The extrinsic scatter is used to set the reduced χ^{2} to unity, which is also used in SNe Ia cosmology (Suzuki et al. 2012). The value of σ_{ext} is 0.34 when the reduced χ^{2} is unity. The bestfit results are a = 48.96 ± 0.18, b = 1.52 ± 0.08, and Ω_{m} = 0.50 ± 0.12. The constraint from reduced χ^{2} method is roughly consistent with the likelihood method. The evolution with Ω_{m} are shown in Fig. 2 with a dashed line. If the extrinsic scatter is not considered, the results are a = 48.50 ± 0.05, b = 1.81 ± 0.02, and Ω_{m} = 0.19 ± 0.05.
There is a mild tension between the results from the likelihood method and the reduced χ^{2} method. The extrinsic scatter is large, which loosely constrains the cosmological parameters. When we calculate the parameter E_{iso}, a cosmological model and a spectrum model are used, while the uncertainties from them are not well established, thus we take these uncertainties into a scatter parameter σ_{ext}. This scatter should be assigned to the parameter E_{iso}. In the future, this scatter can be reduced, since precise observation and data analysis will be performed by the team of SinoFrench spacebased multiband astronomical variable objects monitor (SVOM; Basa et al. 2008; Götz et al. 2009; Paul et al. 2011).
We also compare our results to the current precise measurements, such as the results from Planck+WMAP (Planck Collaboration XVI 2013), BAO (Beutler et al. 2011; Anderson et al. 2014; Kazin et al. 2014; Ross et al. 2015), and SNe Ia (Conley et al. 2011; Suzuki et al. 2012). We show them in Table 3. The bestfit Ω_{m} by GRBs, using χ^{2} method, conflicts with the observation of CMB and BAO. For the results from SNe Ia, however, if both statistical and systematic errors are included, the constraints on cosmological parameters are loose(Kowalski et al. 2008; Amanullah et al. 2010; Suzuki et al. 2012). In this case, the bestfit Ω_{m} with GRBs, using χ^{2} method, is consistent with those from SNe Ia at 1σ confidence level; see Fig. 12 of Kowalski et al. (2008), Fig. 10 of Amanullah et al. (2010) and Fig. 5 of Suzuki et al. (2012).
90 calibrated GRBs with redshift, bolometric fluence, peak energy in cosmological rest frame, and distance moduli.
4. Calibration of the E_{iso} – E_{p} correlation
4.1. Standardizing GRBs with SNe Ia
Just as using Cepheid variables to standardize SNe Ia, the GRBs can be calibrated with SNe Ia. We can use the calibrating method to standardize the GRBs with the E_{iso} − E_{p} correlation. With this approach, the parameters a and b are obtained and only cosmological parameters remain free. We use the latest Union 2.1 data from Suzuki et al. (2012). This method is also cosmological model independent (Liang et al. 2008; Kodama et al. 2008; Wei 2010). The extrinsic scatter is also be taken into account when calculating the error propagation of E_{iso}. The full GRB data is separated into two groups. The dividing line is the highest redshift in SNe Ia Union 2.1 data, namely, z = 1.414. The lowredshift group (z< 1.414) includes 61 GRBs and the highredshift group (z> 1.414) contains 90 GRBs.
Firstly, the linear interpolation method is used to calibrate the distance moduli μ of 61 lowredshift GRBs. Liang et al. (2008) have shown that there are no differences on the final result between the linear interpolation and the cubic interpolation. The 1σ error of the distance moduli σ_{μ,i} can be obtained as follows:(8)where z_{i + 1} and z_{i} are the redshift of the two nearest SNe Ia and ϵ_{μ,i + 1} and ϵ_{μ,i} are the errors of these two SNe Ia. The redshift of interpolated GRB lies between z_{i} and z_{i + 1}.
After the distance moduli of 61 lowredshift GRBs are obtained, the luminosity distance can be derived from (9)Then the isotropicequivalent energy E_{iso} can be calculated from Eq. (3). Following Schaefer (2007) and Liang et al. (2008), we use the bisector of the two ordinary least squares method (Isobe et al. 1990) to fit the E_{iso} − E_{p} correlation. The bestfit values are a = 48.46 ± 0.033 and b = 1.766 ± 0.007. The result is shown in Fig. 3. The errors of distance moduli are not taken into consideration because the extrinsic scatter σ_{ext} dominates the error size in the regression analysis (Schaefer 2007). Thus, we take σ_{ext} directly into account during the calculations of the uncertainties of highredshift GRBs (σ_{log Eiso}). From the previous section, the value of σ_{ext} is nearly constant, so we typically set σ_{ext} = 0.34.
Fig. 3
Lowredshift GRM sample E_{iso} − E_{p} correlation. Black line is the bestfit result obtained by using the bisector of the two ordinary least squares method. The dotted line represents the 1 σ_{ext} region and dashed line the 2 σ_{ext} region. 
We have shown that the E_{iso} − E_{p} correlation does not evolve with redshift in the previous section. Thus, the calibrated E_{iso} − E_{p} correlation can be extrapolated to the highredshift sample, namely, z> 1.414 group. Using Eq. (5), we can derive E_{iso} of highredshift GRBs. The propagated uncertainties of E_{iso} can be calculated from (10)where the value of σ_{ext} is 0.34. The values of σ_{a} and σ_{b} are derived from the bisector of the two ordinary least squares method.
Then, we use Eqs. (3) and (9) to derive the distance moduli. The propagated uncertainty is given by the following equation: (11)The calibrated 90 highredshift GRBs are listed in Table 4. This sample can be used to constrain cosmological models directly. Compared with Wei (2010), the error bars of distance moduli of our results are smaller. The main reason is that we use a larger sample, which leads to a smaller σ_{ext}. The extrinsic scatter parameter has been taken into consideration during the calculation of the error size of E_{iso}.
4.2. Constraining cosmological models
These GRBs carry the information of highredshift Universe, and can be taken as good complements to the Union 2.1 data set. We test if these highredshift GRBs alone can constrain the ΛCDM model. Using the distance modulus in Eq. (4) and Eq. (9), the χ^{2} is (12)where μ_{cal} is the calibrated GRB distance modulus listed in Table 4. The bestfit result is with 1σ uncertainty. The χ^{2} evolution with Ω_{m} is shown in Fig. 2. This result is consistent with the constraints from SNe Ia (Conley et al. 2011; Suzuki et al. 2012), CMB (Planck Collaboration XVI 2013; Planck Collaboration XIII 2015), and BAO (Beutler et al. 2011; Anderson et al. 2014; Kazin et al. 2014; Ross et al. 2015) at 1σ confidence level, as shown in Table 3.
Since this GRB sample can constrain cosmological parameters successfully, we also combine the calibrated GRB data with SNe Ia from Union 2.1 sample to constrain cosmological models. For the flat ΛCDM, we obtain Ω_{m} = 0.271 ± 0.019 and h = 0.701 ± 0.002, where h is the Hubble constant in units of 100 km s^{1} Mpc^{1}. This is very consistent with the Union 2.1 SNe Ia data. For the nonflat ΛCDM, the luminosity distance is different and can be expressed as follows: (13)where (14)and (15)The χ^{2} of SNe Ia is constructed as follows: (16)Then the total χ^{2} is (17)The bestfit values with 1σ uncertainties are Ω_{m} = 0.225 ± 0.044, Ω_{Λ} = 0.640 ± 0.082, and h = 0.698 ± 0.004 for the combined sample (SNe+GRB). For the GRB sample, we obtain Ω_{m} = 0.18 ± 0.11 and Ω_{Λ} = 0.46 ± 0.51, which is consistent with the SNe Ia results at 1σ confidence level. The combined sample can help to constrain cosmological parameters much tighter because not only is the sample enlarged, but also the redshift covers a much wider. The flatness of the Universe depends on the curvature parameter, that is to say, Ω_{k} = 1 − Ω_{Λ} − Ω_{m}. In Fig. 4, we use three samples, GRB, SNe, and combination of GRB+SNe to constrain the cosmological model. Both results prefer a flat Universe at the 1σ confidence level. The constraint from the GRB is almost perpendicular to that from SNe Ia in the Ω_{m} − Ω_{Λ} plane. Thus GRBs can significantly help to constrain Ω_{m} because, in this redshift domain, the dark matter dominates the evolution of the Universe. We also show constraints on Ω_{m} − h in Fig. 5, and Ω_{Λ} − h in Fig. 6.
Fig. 4
1σ and 2σ constraints on Ω_{m} and Ω_{Λ}. We use three samples and plot them into different colors. The solid line shows the Ω_{k} = 0 case. 
Fig. 5
1σ and 2σ constraints on Ω_{m} and h from SNe Ia and GRB data. 
Fig. 6
1σ and 2σ constraints on the Ω_{Λ} and h from SNe Ia and GRB data. 
5. Discussions and summary
In this paper, we update 42 long GRBs for the E_{iso} − E_{p} correlation and combine them with 109 long GRBs from Amati et al. (2008) and Amati et al. (2009). This sample contains GRBs detected by different detectors with different sensitivities. Thus, the sample might be biased, but this bias should only have a weak effect on our results. We also use the complete sample to perform our analysis. We use the same criteria as Salvaterra et al. (2012) and Pescalli et al. (2015) to collect GRBs. The results are a = 49.45 ± 0.61, b = 1.24 ± 0.22 and σ_{ext} = 0.38 ± 0.06, while no constraint on Ω_{m} is found. These results are in tension with that of our updated full sample with a larger extrinsic scatter. No statistical evidence for the redshift evolution of the E_{iso} − E_{p} is found in the full sample.
For cosmological purposes, we fit the E_{iso} − E_{p} plane and the cosmological parameters simultaneously. Using a likelihood function we obtain a = 49.15 ± 0.26, b = 1.42 ± 0.11, σ_{ext} = 0.34 ± 0.03, and Ω_{m} ∈ [ 0.55,1 ]. Using the reduced χ^{2}, we obtain a = 48.96 ± 0.18, b = 1.52 ± 0.08, and Ω_{m} = 0.50 ± 0.12. The results from these two fitting methods are in mild tension. The main reason is that the extrinsic scatter of this correlation is too large. Thus, Ghirlanda (2009) finds no constraint with a smaller sample using the likelihood method. We also use a calibrating method. Based on the SNe Ia data, we obtain 90 calibrated GRBs. From these calibrated GRBs, we acquire for flat ΛCDM and for the nonflat ΛCDM, we obtain Ω_{m} = 0.18 ± 0.11 and Ω_{Λ} = 0.46 ± 0.51. We also combine the GRB sample with SNe Ia Union 2.1 data and obtain Ω_{m} = 0.271 ± 0.019 and h = 0.701 ± 0.002 for the flat ΛCDM. For the nonflat ΛCDM, the results are Ω_{m} = 0.225 ± 0.044, Ω_{Λ} = 0.640 ± 0.082, and h = 0.698 ± 0.004. We list our results in Table 3, and compare them with the results from other current measurements. The results from GRBs are consistent with results from SNe Ia in 1σ confidence level (Conley et al. 2011; Suzuki et al. 2012), while they conflict with CMB (Planck Collaboration XVI 2013; Planck Collaboration XIII 2015) and BAO (Beutler et al. 2011; Anderson et al. 2014; Kazin et al. 2014; Ross et al. 2015). We also found that the GRBs can help to constrain dark matter better. The constraint from GRB are almost perpendicular to that from SNe Ia in the Ω_{m} − Ω_{Λ} plane. The main reason might be that at high redshift, the dark matter dominates the Universe.
The extrinsic scatter is taken into account in both the simultaneous fitting method and the calibrating method. Our results shows that tighter constraints on cosmological model can be obtained with the calibrating method. For the simultaneous fitting method, the reduced χ^{2} method gives a more stringent constraint on cosmological parameters than the likelihood method, but the constraint is still loose because of the large extrinsic scatter. This scatter is introduced by both cosmological models and the GRB spectrum parameters, such as E_{p}, fluence, and photon index. The spectrum parameters can be precisely measured by SVOM (Basa et al. 2008; Götz et al. 2009; Paul et al. 2011), which can reduce the extrinsic scatter. The GRBs from SVOM would better help shed light on the properties of early Universe.
Acknowledgments
We thank an anonymous referee for useful suggestions and comments. This work is supported by the National Basic Research Program of China (973 Program, grant No. 2014CB845800), the National Natural Science Foundation of China (grants 11422325, 11373022, 11103007, and 11033002), the Excellent Youth Foundation of Jiangsu Province (BK20140016), and the Program for New Century Excellent Talents in University (grant No. NCET130279). K.S.C. is supported by the CRF Grants of the Government of the Hong Kong SAR under HUKST4/CRF/13G.
References
 Amanullah, R., Lidman, C., Rubin, D., et al. 2010, ApJ, 716, 712 [NASA ADS] [CrossRef] [Google Scholar]
 Amati, L., Frontera, F., Tavani, M., et al. 2002, A&A, 390, 81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Amati, L., Guidorzi, C., Frontera, F., et al. 2008, MNRAS, 391, 577 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 Amati, L., Frontera, F., & Guidorzi, C. 2009, A&A, 508, 173 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Anderson, L., Aubourg, É., Bailey, S., et al. 2014, MNRAS, 441, 24 [NASA ADS] [CrossRef] [Google Scholar]
 Band, D., Matteson, J., Ford, L., et al. 1993, ApJ, 413, 281 [NASA ADS] [CrossRef] [Google Scholar]
 Barkana, R., & Loeb, A. 2004, ApJ, 601, 64 [NASA ADS] [CrossRef] [Google Scholar]
 Basa, S., Wei, J., Paul, J., Zhang, S. N., & Svom Collaboration 2008, SF2A2008, 161 [Google Scholar]
 Basilakos, S., & Perivolaropoulos, L. 2008, MNRAS, 391, 411 [NASA ADS] [CrossRef] [Google Scholar]
 Beutler, F., Blake, C., Colless, M., et al. 2011, MNRAS, 416, 3017 [NASA ADS] [CrossRef] [Google Scholar]
 Bloom, J. S., Frail, D. A., & Sari, R. 2001, AJ, 121, 2879 [NASA ADS] [CrossRef] [Google Scholar]
 Cardone, V. F., Dainotti, M. G., Capozziello, S., & Willingale, R. 2010, MNRAS, 408, 1181 [NASA ADS] [CrossRef] [Google Scholar]
 Ciardi, B., & Loeb, A. 2000, ApJ, 540, 687 [NASA ADS] [CrossRef] [Google Scholar]
 Cole, S., Percival, W. J., Peacock, J. A., et al. 2005, MNRAS, 362, 505 [NASA ADS] [CrossRef] [Google Scholar]
 Collazzi, A. C. 2012, GRB Coordinates Network Circular, 13145, 1 [Google Scholar]
 Conley, A., Guy, J., Sullivan, M., et al. 2011, ApJS, 192, 1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Cucchiara, A., Cenko, S. B., Bloom, J. S., et al. 2011, ApJ, 743, 154 [NASA ADS] [CrossRef] [Google Scholar]
 D’Agostini, G. 2005, ArXiv eprints [arXiv:physics/0511182] [Google Scholar]
 Dai, Z. G., Liang, E. W., & Xu, D. 2004, ApJ, 612, L101 [NASA ADS] [CrossRef] [Google Scholar]
 Dainotti, M. G., Cardone, V. F., & Capozziello, S. 2008, MNRAS, 391, L79 [NASA ADS] [CrossRef] [Google Scholar]
 Dainotti, M. G., Willingale, R., Capozziello, S., Fabrizio Cardone, V., & Ostrowski, M. 2010, ApJ, 722, L215 [NASA ADS] [CrossRef] [Google Scholar]
 Eisenstein, D. J., Zehavi, I., Hogg, D. W., et al. 2005, ApJ, 633, 560 [NASA ADS] [CrossRef] [Google Scholar]
 Fitzpatrick, G. 2013, GRB Coordinates Network Circular, 14896, 1 [Google Scholar]
 Fitzpatrick, G., & Jenke, P. 2013, GRB Coordinates Network Circular, 15455, 1 [Google Scholar]
 Fitzpatrick, G., & Pelassa, V. 2013, GRB Coordinates Network Circular, 14858, 1 [Google Scholar]
 Friedman, A. S., & Bloom, J. S. 2005, ApJ, 627, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Gallerani, S., Salvaterra, R., Ferrara, A., & Choudhury, T. R. 2008, MNRAS, 388, L84 [NASA ADS] [Google Scholar]
 Gehrels, N., RamirezRuiz, E., & Fox, D. B. 2009, ARA&A, 47, 567 [NASA ADS] [CrossRef] [Google Scholar]
 Gendre, B., Stratta, G., Atteia, J. L., et al. 2013, ApJ, 766, 30 [NASA ADS] [CrossRef] [Google Scholar]
 Ghirlanda, G. 2009, AIP Conf. Proc., 1111, 579 [NASA ADS] [CrossRef] [Google Scholar]
 Ghirlanda, G., Ghisellini, G., & Lazzati, D. 2004a, ApJ, 616, 331 [NASA ADS] [CrossRef] [Google Scholar]
 Ghirlanda, G., Ghisellini, G., Lazzati, D., & Firmani, C. 2004b, ApJ, 613, L13 [NASA ADS] [CrossRef] [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2010a, GRB Coordinates Network Circular, 10882, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2010b, GRB Coordinates Network Circular, 10937, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Mazets, E., et al. 2010c, GRB Coordinates Network Circular, 11119, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2010d, GRB Coordinates Network Circular, 11251, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2011a, GRB Coordinates Network Circular, 11723, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Mazets, E., et al., 2011b, GRB Coordinates Network Circular, 11971, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2011c, GRB Coordinates Network Circular, 12008, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2011d, GRB Coordinates Network Circular, 12166, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Mazets, E., et al. 2011e, GRB Coordinates Network Circular, 12223, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2011f, GRB Coordinates Network Circular, 12433, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Mazets, E., et al. 2012a, GRB Coordinates Network Circular, 13736, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2012b, GRB Coordinates Network Circular, 14010, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2012c, GRB Coordinates Network Circular, 12872, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2013a, GRB Coordinates Network Circular, 14368, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2013b, GRB Coordinates Network Circular, 14487, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2013c, GRB Coordinates Network Circular, 14575, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Mazets, E., et al. 2013d, GRB Coordinates Network Circular, 14808, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2013e, GRB Coordinates Network Circular, 14958, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2013f, GRB Coordinates Network Circular, 15145, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2013g, GRB Coordinates Network Circular, 15203, 1 [Google Scholar]
 Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2013h, GRB Coordinates Network Circular, 15413, 1 [Google Scholar]
 Götz, D., Paul, J., Basa, S., et al. 2009, AIP Conf. Ser., 1133, 25 [NASA ADS] [CrossRef] [Google Scholar]
 Isobe, T., Feigelson, E. D., Akritas, M. G., & Babu, G. J. 1990, ApJ, 364, 104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Kazin, E. A., Koda, J., Blake, C., et al. 2014, MNRAS, 441, 3524 [NASA ADS] [CrossRef] [Google Scholar]
 Kodama, Y., Yonetoku, D., Murakami, T., et al. 2008, MNRAS, 391, L1 [NASA ADS] [CrossRef] [Google Scholar]
 Komatsu, E., Smith, K. M., Dunkley, J., et al. 2011, ApJS, 192, 18 [NASA ADS] [CrossRef] [Google Scholar]
 Kowalski, M., Rubin, D., Aldering, G., et al. 2008, ApJ, 686, 749 [NASA ADS] [CrossRef] [Google Scholar]
 Krimm, H. A., Barthelmy, S. D., Baumgartner, W. H., et al. 2012a, GRB Coordinates Network Circular, 13517, 1 [Google Scholar]
 Krimm, H. A., Barlow, B. N., Barthelmy, S. D., et al. 2012b, GRB Coordinates Network Circular, 13634, 1 [Google Scholar]
 Krimm, H. A., Barthelmy, S. D., Baumgartner, W. H., et al. 2012c, GRB Coordinates Network Circular, 13806, 1 [Google Scholar]
 Krimm, H. A., Barthelmy, S. D., Baumgartner, W. H., et al. 2013, GRB Coordinates Network Circular, 15499, 1 [Google Scholar]
 Lamb, D. Q., & Reichart, D. E. 2000, ApJ, 536, 1 [Google Scholar]
 Li, M.H., & Lin, H.N. 2015, ApJ, 807, 76 [NASA ADS] [CrossRef] [Google Scholar]
 Liang, E., & Zhang, B. 2005, ApJ, 633, 611 [NASA ADS] [CrossRef] [Google Scholar]
 Liang, N., Xiao, W. K., Liu, Y., & Zhang, S. N. 2008, ApJ, 685, 354 [NASA ADS] [CrossRef] [Google Scholar]
 Lin, H.N., Li, X., & Change, Z. 2016, MNRAS, 455, 2131 [NASA ADS] [CrossRef] [Google Scholar]
 Mészáros, P. 2006, Rep. Prog. Phys., 69, 2259 [NASA ADS] [CrossRef] [Google Scholar]
 Palshin, V., et al. 2013, GRB Coordinates Network Circular, 14702, 1 [Google Scholar]
 Paul, J., Wei, J., Basa, S., & Zhang, S.N. 2011, Comptes Rendus Physique, 12, 298 [NASA ADS] [CrossRef] [Google Scholar]
 Pelassa, V. 2011, GRB Coordinates Network Circular, 12545, 1 [Google Scholar]
 Perlmutter, S., Aldering, G., Goldhaber, G., et al. 1999, ApJ, 517, 565 [NASA ADS] [CrossRef] [Google Scholar]
 Pescalli, A., Ghirlanda, G., Salvaterra, R., et al. 2015, A&A, submitted [arXiv:1506.05463v1] [Google Scholar]
 Planck Collaboration XVI. 2014, A&A, 571, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XIII. 2015, A&A, submitted [arXiv:1502.01589] [Google Scholar]
 Porciani, C., & Madau, P. 2001, ApJ, 548, 522 [NASA ADS] [CrossRef] [Google Scholar]
 Qi, S., & Lu, T. 2010, ApJ, 717, 1274 [NASA ADS] [CrossRef] [Google Scholar]
 Qi, S., Lu, T., & Wang, F.Y. 2009, MNRAS, 398, L78 [NASA ADS] [CrossRef] [Google Scholar]
 Riess, A. G., Filippenko, A. V., Challis, P., et al. 1998, AJ, 116, 1009 [NASA ADS] [CrossRef] [Google Scholar]
 Ross, A. J., Samushia, L., Howlett, C., et al. 2015, MNRAS, 449, 835 [NASA ADS] [CrossRef] [Google Scholar]
 Salvaterra, R., Della Valle, M., Campana, S., et al. 2009, Nature, 461, 1258 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Salvaterra, R., Campana, S., Vergani, S. D., et al. 2012, ApJ, 749, 68 [NASA ADS] [CrossRef] [Google Scholar]
 Schaefer, B. E. 2003, ApJ, 583, L67 [NASA ADS] [CrossRef] [Google Scholar]
 Schaefer, B. E. 2007, ApJ, 660, 16 [NASA ADS] [CrossRef] [Google Scholar]
 Stamatikos, M., Barthelmy, S. D., Baumgartner, W. H., et al. 2012, GRB Coordinates Network Circular, 13559, 1 [Google Scholar]
 Sugita, S., Yamaoka, K., Ohno, M., et al. 2010, GRB Coordinates Network Circular, 10604, 1 [Google Scholar]
 Suzuki, N., Rubin, D., Lidman, C., et al. 2012, ApJ, 746, 85 [NASA ADS] [CrossRef] [Google Scholar]
 Tanvir, N. R., Fox, D. B., Levan, A. J., et al. 2009, Natur, 461, 1254 [Google Scholar]
 Totani, T. 1997, ApJ, 486, L71 [NASA ADS] [CrossRef] [Google Scholar]
 Totani, T., Kawai, N., Kosugi, G., et al. 2006, PASJ, 58, 485 [NASA ADS] [Google Scholar]
 von Kienlin, A. 2010, GRB Coordinates Network Circular, 11015, 1 [Google Scholar]
 von Kienlin, A. 2013, GRB Coordinates Network Circular, 14473, 1 [Google Scholar]
 von Kienlin, A., & Bhat, P. N. 2014, GRB Coordinates Network Circular, 15796, 1 [Google Scholar]
 Wang, F. Y. 2012, A&A, 543, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Wang, F. Y. 2013, A&A, 556, A90 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Wang, F. Y., & Dai, Z. G. 2009, MNRAS, 400, L10 [NASA ADS] [Google Scholar]
 Wang, F. Y., & Dai, Z. G. 2011a, ApJ, 727, L34 [NASA ADS] [CrossRef] [Google Scholar]
 Wang, F. Y., & Dai, Z. G. 2011c, A&A, 536, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Wang, F. Y., & Dai, Z. G. 2014, Phys. Rev. D, 89, 023004 [NASA ADS] [CrossRef] [Google Scholar]
 Wang, F. Y., Dai, Z. G., & Zhu, Z.H. 2007, ApJ, 667, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Wang, F.Y., Qi, S., & Dai, Z.G. 2011, MNRAS, 415, 3423 [NASA ADS] [CrossRef] [Google Scholar]
 Wang, F. Y., Bromm, V., Greif, T. H., et al. 2012, ApJ, 760, 27 [NASA ADS] [CrossRef] [Google Scholar]
 Wang, F. Y., Dai, Z. G., & Liang, E. W. 2015, New Astron. Rev., 67, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Wang, J. S., & Wang, F. Y. 2014a, MNRAS, 443, 1680 [NASA ADS] [CrossRef] [Google Scholar]
 Wang, J. S., & Wang, F. Y. 2014b, A&A, 564, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Wei, D. M., & Gao, W. H. 2003, MNRAS, 345, 743 [NASA ADS] [CrossRef] [Google Scholar]
 Wei, H. 2010, JCAP, 8, 20 [NASA ADS] [CrossRef] [Google Scholar]
 Wijers, R. A. M. J., Bloom, J. S., Bagla, J. S., & Natarajan, P. 1998, MNRAS, 294, L13 [NASA ADS] [CrossRef] [Google Scholar]
 Xiong, S. 2011, GRB Coordinates Network Circular, 12287, 1 [Google Scholar]
 Xiong, S. 2013, GRB Coordinates Network Circular, 14674, 1 [Google Scholar]
 Xiong, S., & Rau, A. 2013, GRB Coordinates Network Circular, 14429, 1 [Google Scholar]
 Younes, G., & Bhat, P. N. 2013, GRB Coordinates Network Circular, 14219, 1 [Google Scholar]
 Zhang, B. 2007, Chin. J. Astron. Astrophys., 7, 1 [Google Scholar]
 Zhang, B. B. 2014, GRB Coordinates Network Circular, 15833, 1 [Google Scholar]
All Tables
E_{iso} − E_{p} correlation fitting results of full data and four redshift bins.
90 calibrated GRBs with redshift, bolometric fluence, peak energy in cosmological rest frame, and distance moduli.
All Figures
Fig. 1
E_{iso} − E_{p} correlation. The solid black, dotted, and dashed lines represent the bestfit line, 1σ_{ext} region, and 2σ_{ext} region, respectively. 

In the text 
Fig. 2
Evolution of log (ℒ)/log (ℒ)_{min} as a function of Ω_{m} in the flat ΛCDM Universe is shown with solid line from maximum likelihood method. The dashed line is the plot from reduced χ^{2} method. The dotted line is obtained with the 90 GRBs calibrated on the SNe Ia (see Sect. 4). 

In the text 
Fig. 3
Lowredshift GRM sample E_{iso} − E_{p} correlation. Black line is the bestfit result obtained by using the bisector of the two ordinary least squares method. The dotted line represents the 1 σ_{ext} region and dashed line the 2 σ_{ext} region. 

In the text 
Fig. 4
1σ and 2σ constraints on Ω_{m} and Ω_{Λ}. We use three samples and plot them into different colors. The solid line shows the Ω_{k} = 0 case. 

In the text 
Fig. 5
1σ and 2σ constraints on Ω_{m} and h from SNe Ia and GRB data. 

In the text 
Fig. 6
1σ and 2σ constraints on the Ω_{Λ} and h from SNe Ia and GRB data. 

In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.