# Angular distribution of cosmological parameters as a probe of inhomogeneities: a kinematic parametrisation

We use a kinematic parametrisation of the luminosity distance to measure the angular distribution on the sky of time derivatives of the scale factor, in particular the Hubble parameter the deceleration parameter and the jerk parameter We apply a recently published method to complement probing the inhomogeneity of the large–scale structure by means of the inhomogeneity in the cosmic expansion. This parametrisation is independent of the cosmological equation of state, which renders it adequate to test interpretations of the cosmic acceleration alternative to the cosmological constant. For the same analytical toy model of an inhomogeneous ensemble of homogenous pixels, we derive the backreaction term in due to the fluctuations of and measure it to be of order times the corresponding average over the pixels in the absence of backreaction. In agreement with that computed using a CDM parametrisation of the luminosity distance, the backreaction effect on remains below the detection threshold. Although the backreaction effect on is about ten times that on it is also below the detection threshold. Hence backreaction remains unobservable both in and in

## 1 Introduction

Supernova (SN) data have provided evidence that distant sources () appear dimmer than predicted in a universe with matter only, in comparison with nearby sources. This dimming led to the interpretation that the Universe is expanding in an accelerating fashion. This acceleration has been attributed to a dark energy component, whose simplest solution is a vacuum energy or equivalently a cosmological constant. However, this dimming could be caused by a variation of any other component that affects the luminosity distance, namely by inhomogeneities in the energy densities or in the cosmic expansion (Amendola et al., 2013).

In order to explain the underlying mechanism of the late–time accelerated expansion of the universe, one possibility is to give up of the cosmological principle and allow for an anisotropic expansion of the universe. The idea that we live in a locally underdense region, hence creating a ‘Hubble bubble’, can explain the cosmic acceleration at late times, subject to specific conditions (Zehavi et al., 1998; Caldwell & Stebbins, 2008). From the vast literature on the cosmological principle, it has been found that most cosmological observations can accommodate violations of the cosmological principle (see for example Zibin et al. (2008); Komita & Inoue (2009); Marra & Pääkkönen (2010); Marra & Notari (2011); Moss et al. (2011)). From the theoretical point of view, violations of the cosmological principle can be explained in the context of Lemaître–Tolman–Bondi (LTB) void models. These void models are spherically symmetric and radially inhomogeneous, and can mimic the cosmic expansion of the concordance CDM (Lan et al., 2010; Liu & Zhang, 2014).

In Carvalho & Marques (2015), we introduced a method to probe the inhomogeneity of the large–scale structure by measuring the angular distribution in the cosmological parameters that affect the luminosity distance, using SN data. Variation in the cosmological parameters across pixels in the sky implies inhomogeneity in the cosmic expansion. This inhomogeneity was then used to measure the extra component of cosmic acceleration predicted by backreaction, which derives from averaging over an inhomogeneous ensemble of homogeneous pixels, each pixel expanding at a different rate. However, this measurement presupposed an a priori dark energy component in each pixel. In order to investigate alternative interpretations of the cosmic acceleration, it is conceptually more consistent to use a parametrisation that does not assume a specific component as the cause of acceleration (Turner & Riess, 2002).

In this manuscript, we use the luminosity distance expressed in terms of time derivatives of the scale factor in particular the Hubble parameter the deceleration parameter and the jerk parameter Instead of assuming an equation of state and inferring the evolution of the scale factor via the Friedmann equation, the reasoning is to take the data on the scale factor and infer a cosmological equation of state via the Friedmann equation. This parametrisation records the cosmic expansion without regard to its cause, thus being independent of the cosmological equation of state and consequently adequate to test interpretations of the cosmic acceleration alternative to the cosmological constant. These parameters can be related to the Taylor expansion of the cosmological equation of state about the present values expressed up to linear order as

(1) |

hence yielding information about the present values of and defined as (Visser, 2004)

(2) |

Whereas (and hence ) contains information about the present value of (and hence ) contains information about how can evolve.

The purpose of this manuscript is to reapply the method first presented in Carvalho & Marques (2015) to estimate the parameters instead of by fitting the luminosity distance to SN data, and to compare the results from the two estimations in view of an interpretation of the cosmic acceleration independent of a particular energy content of the Universe. (Although the comparisons are made for the case that , for completion we also include the results for the case where ) Some studies have used the kinematic parametrization to measure inhomogeneities from SN data (see e.g. Schwarz & Weinhorst (2007); Kalus et al. (2013)). Most studies, however, have aimed at finding hemispherical anisotropies assuming a CDM energy content (e.g. Blomqvist et al. (2010); Mariano & Perivolaropoulos (2012); Heneka et al. (2014); Jiménez et al. (2015); Bengaly et al. (2015); Javanmardi et al. (2015); Migkas & Plionis (2016) and references therein).

This paper is organised as follows. In Sec. 2 we describe the data and estimate the observables by performing both a global and a local parameter estimation, obtaining fiducial values and maps respectively for the estimated parameters. We introduce an inhomogeneity test by rotating the supernova subsampling per pixel. In Sec. 3 we compute the power spectrum of the maps of the parameters using two methods for the noise bias removal. In Sec. 4, for the same toy model of backreaction used in Carvalho & Marques (2015), we compute the average values of and discuss possible cosmological implications. In Sec. 5 we draw conclusions.

## 2 Parameter estimation

We use the type Ia supernova sample compiled by the Joint Light–curve Analysis (JLA) collaborative effort (Betoule et al., 2014) from different supernova surveys, totalling supernovae (SNe) with redshift
and distributed on the sky according to Fig. 1 in Carvalho & Marques (2015).
^{1}^{1}1The sample was obtained from http://supernovae.in2p3.fr/sdss_snls_jla/ReadMe.html.

We use the luminosity distance with expressed as a Taylor expansion about the present value (Visser, 2004)

(3) | |||||

(4) |

to estimate the cosmological parameters that minimise the chi–square of the fit of the theoretical distance modulus

(6) |

( in units of Mpc) computed for the trial values of to the measured distance modulus

(7) |

computed for the light–curve parameters estimated from each SN’s observed magnitude, and for and estimated for all SNe (Betoule et al., 2014).

We recall that the expansion in Eq. (LABEL:eqn:dl) (i.e. up to the second term in the right–hand side) does not allow for an estimation of that is both accurate and precise (Neben & Turner, 2013). This is because for low redshifts where the Taylor expansion is most accurate, there is poor leverage for a precise estimation of conversely, for high redshifts where the leverage is larger and allows for a more precise estimation, the Taylor expansion is less accurate. Considering the expansion in Eq. (LABEL:eqn:dl) (i.e. all the terms in the right–hand side), then we expect that the uncertainty will be pushed to the estimation of An expansion in higher–order time derivatives of the expansion factor was considered in Aviles et al. (2012). An exhaustive comparison of luminosity distances can be found in Cattoën & Visser (2008).

Before proceeding further, we check the validity range of Eq. (LABEL:eqn:dl) using a spatially flat CDM model as reference model. In this case, we have and We compute the relative difference between the luminosity distance in Eq. (LABEL:eqn:dl) and the theoretical luminosity distance in the CDM model

(8) |

where for and (Ade et al., 2015). (See solid line in Fig. 1.) We find that, for the relative difference lies in the interval conversely, for the relative difference can reach about We also compute the relative difference between the luminosity distance with in Cattoën & Visser (2008) and the theoretical luminosity distance in the CDM model. (See dashed line in Fig. 1.) In this case we find that, for the relative difference lies in the interval conversely, for the relative difference is about . We also observe that, in the intermediate redshift range where most SNe were detected, Eq. (LABEL:eqn:dl) performs slightly better than conversely, clearly performs better for We also compute the corresponding relative differences in which is the quantity that we use in the subsequent estimation and which depends on logarithmically according to Eq. (6). (See inset plot of Fig. 1.) We observe that the relative differences in are less than which implies that the differences in the luminosity density translate in a negligible effect on our results.

We now proceed to estimate the parameters using the method described in Appendix A of Carvalho & Marques (2015). We assume that at the present epoch regardless of the value of which means that by setting the prior and estimating we are actually estimating (Neben & Turner, 2013). For a model consisting of an incoherent mixture of matter, where each component is described by an equation of state this assumption implies that We also assume that the measurements of the magnitude (and consequently of ) at different redshifts are independent, which is equivalent to assuming that the parameters’ distribution is Gaussian as a first approximation. We compute the error by assuming uncorrelated errors for the observables and by error propagating according to Eq. (7).

Parameter | Complete | Carvalho et al. | Carvalho et al. | Caldwell et al. | Riess et al. | Neben et al. |
---|---|---|---|---|---|---|

sample | ||||||

^{2}

^{2}2Column 1: Parameters estimated either directly or indirectly from the fit. Column 2: Values estimated from the complete SN sample. Columns 3–7: Values estimated by other collaborations from SN data.

### 2.1 Global parameter estimation

We first perform a parameter estimation using the complete SN sample, called the global estimation and corresponding to pixel, from which we estimate the maximum likelihood values for the parameters (Whenever unspecified, is measured in units of )

For the global estimation, we ran realizations of Markov chains of length The starting point of each realization is randomly generated. By removing the first 20% of entries in the chains to keep the burnt–in phase only, thinning down the remaining 80% to a half by removing one of each consecutive entry in order to remove correlations within the chain, and finally averaging over the various chains, we obtain the following results: (see Table 1). We will use these results as the fiducial values in the subsequent calculations. The errors contain the dispersion in each chain and the dispersion among the averages of the different chains added in quadrature.

The measurements of are consistent with the measurements of obtained in Carvalho & Marques (2015), which we include in Table 1 for convenience. The measurements of are equally precise, favouring at over The measurement of is comparatively less precise, as also observed in Neben & Turner (2013) in the context of the kinematic parametrisation, but nonetheless it favours at These results imply that, in the interval covered by the SN sample, the cosmic expansion accelerated and that previously it had decelerated, meaning that there was a time when the acceleration changed sign, which supports the evidence found in Riess et al. (2004).

For illustration, in the left panel of Fig. 2 we plot computed for the estimated values as the solid black line. In the right panel we plot the Markov chain of one realization.

We recall that Riess et al. (2004) found (using the gold sample) which implies and (using the combined gold+silver sample) which implies Moreover, Caldwell & Kamionkowski (2004) found and More recent results include Riess et al.’s for and using the Hubble Space Telescope set (Riess et al., 2011), and Neben & Turner’s and using the Constitution set (Neben & Turner, 2013). (See Table 1.)

### 2.2 Local parameter estimation

We then divide the SNe over a pixelated map of the sky with pixels of equal surface area according to the HEALPix pixelation (Gorski et al., 2005), and perform a parameter estimation using the SN subsample that falls into each pixel, called the local estimation. The number of pixels that guarantees non–empty SN subsamples in all pixels is pixels. The number of SNe in each pixel is indicated in Fig. 3 in Carvalho & Marques (2015). Each pixel is assumed to be described by a Friedmann–Lemaître–Roberston–Walker metric so that the full sky is an inhomogeneous ensemble of disjoint, locally homogeneous regions.

For the local estimation, we distinguish two cases as introduced in Carvalho & Marques (2015): the “Cosmic variance” estimation where in each subsample we use the original redshifts and positions, and the “Shuffle SNe” estimation where in each subsample we randomly shuffle the SNe in redshift while keeping the original positions in the sky. The “Shuffle SNe” estimation is hypothesised to be a measure of the noise bias due to the inhomogeneous coverage of the sky by the SN surveys, from which there results inhomogeneity in the SN subsampling. We model this noise bias by the local estimation obtained from randomizing the dependence of redshift with position, while keeping the original SN positions in the sky, and then averaging over the various randomizations.

In each pixel and for each case of local estimation, we ran realizations of Markov chains of length and repeated the procedure described above for the global estimation. From the local estimation, there result maps for each estimated parameter with the same pixelation as the SN subsamples, denoted by in the “Cosmic variance” estimation and by in the “Shuffle SNe” estimation, where the brackets denote averaging over the realizations. Subtracting the noise bias off we obtain unbiased maps For convenience, we also compare the local estimation with the fiducial values by defining the difference maps and the unbiased difference maps as

The difference maps are shown in Fig. 3 (top panel sets), before (left panel set) and after (right panel set) the noise bias subtraction. Comparing the difference maps with the fiducial values, we measure fluctuations of order 0.1–5% for , 1–187% for and 1–184% for before the noise bias subtraction; after the noise bias subtraction, we measure fluctuations of order 0.1–7% for , 1–136% for , and 1–221% for

We recall that Wiegand & Schwarz (2012) found using galaxy surveys, which is consistent with our results.

For comparison, in the bottom panels of Fig. 3, we reproduce the difference maps of from the estimation in Carvalho & Marques (2015). We recall that the estimation yielded fluctuations about the fiducial values of order 0.1–3% for , 0.1–63% for , and 0.001–34% for before the noise bias subtraction, and fluctuations about the fiducial values of order 0.1–5% for , 0.1–32% for , and 1–27% for after the noise bias subtraction. This amounts to an increase in the fluctuations by a factor of four and two orders of magnitude respectively before and after the noise bias subtraction, between the and the estimations. The increase in the fluctuations supports the reasoning that accuracy in the estimation of is compromised by the inclusion of as an estimated parameter. However, this is also a consequence of using a parametrisation that is independent of the cosmological equation of state, hence of assuming less about the cause of acceleration.

For further comparison, we compute the largest fluctuation in the maps, defined as which yields and before and after the noise bias subtraction respectively. We recall that the estimation yielded and before and after the noise bias subtraction respectively. In comparison with the results from other SN studies, namely Kalus et al.’s from the Union 2 data for fixed (Kalus et al., 2013), and Bengaly et al.’s and respectively from the Union 2.1 data and the JLA data (Bengaly et al., 2015), we observe that: a) our results for remain intermediate between the previous results, b) our results for are intermediate between those of Bengaly et al. (2015) for the two data sets, and c) our results for are to the best of our knowledge the first measurements of the kind.

A visual inspection of the pixels through which the Galactic plane crosses does not reveal consistently larger/smaller values of the fluctuations, as also noted in Carvalho & Marques (2015), thus suggesting negligible correlation with the Galactic plane in comparison with the measurement errors (Neben & Turner, 2013).

In order to verify the dependence of the results on the subsampling of the SN surveys per pixel, we perform a test of the effect of rotating the HEALPix pixelation. In particular, starting from the current pixelation and keeping the pixel size constant (i.e. keeping the same number of pixels on the sky), we consider divisions of each pixel and rotate the pixelation times in the same direction, the th rotation corresponding to the initial pixelation. At each such rotation by a fraction of of the pixel, we obtain a different pixelation of the sky and hence a different subsampling of the SN surveys per pixel, totalling different pixelations. We choose to guarantee a sufficient number of rotations and simultaneously little degeneracy among the rotations.

For each such pixelation we perform a local parameter estimation as described above, consisting of a “Cosmic covariance” estimation and a “Shuffle SNe” estimation, and hence obtaining an unbiased parameter estimation in each pixel We then compute the mean unbiased maps averaged over the different pixelations as weighted by the inverse of the variance in each pixel, over the different pixelations. From the average over the different pixelations there results a mean pixel derived by averaging the rotations of the same pixel in the initial pixelation. For convenience, we define the unbiased difference maps as

The resulting difference maps are shown in Fig. 3 centre panel sets, before (left panel set) and after (right panel set) the noise bias subtraction. Comparing the difference maps with the fiducial values, we measure fluctuations of order 0.1–7% for 0.1–180% for and 1–116% for before the noise bias subtraction; after the noise bias subtraction we measure fluctuations of order 0.1–7% for 0.1–112% for and 1–135% for The noise bias removal brings the pixel values closer to the corresponding values estimated from the complete sample, hence decreasing the fluctuations across the sky, albeit increasing slightly the fluctuations in Simultaneously, it increases the error by up to respectively. These results also seem to indicate the validity of as a measure of the noise bias due to the inhomogeneous SN sampling.

Similarly, the largest fluctuation in the maps yields and before and after the noise bias subtraction.

Hence by averaging over different pixelations of the sample, we obtain a decrease in the fluctuations of the parameters across the sky. However, the mean fluctuations are still larger (by a factor of four and two orders of magnitude respectively before and after the noise bias subtraction) than those obtained in the estimation. In the subsequent calculations, we will use the parameters’ maps obtained from all pixelations.

## 3 Power spectra

In order to probe the distribution of the fluctuations by scale, we compute the angular power spectrum of the difference maps normalised to the corresponding fiducial parameter value. The equations below follow closely those in Carvalho & Marques (2015) except for the extra degree of complexity introduced by the different pixelations. In particular, for each parameter and for each realization of the “Cosmic variance” local estimation, we define the map with value at each pixel for each pixelation An estimator of the power spectrum is

(9) |

where the harmonic coefficients for a pixelated map of pixels are given by

(10) |

We compute the mean power spectrum for the parameters by averaging the power spectra of the maps over the realizations of the “Cosmic variance” local estimation

(11) |

with variance

(12) |

where

(13) |

We then compute the mean power spectrum over the pixelations

(14) |

with variance given by

(15) |

In order to compute the unbiased power spectrum we devise two methods, as detailed below.

### 3.1 Difference of power spectra

In the first method, we define the unbiased power spectrum as the unbiased mean power spectrum. We compute the unbiased mean power spectrum by computing the power spectrum of the mean map averaged over the realizations of the “Shuffle SNe” local estimation and subtracting it from the mean power spectrum

(16) |

with total variance

(17) |

This was the method suggested in Carvalho & Marques (2015). We then compute the mean unbiased power spectrum by averaging over the different pixelations

(18) |

with total variance

(19) |

In the right panels of Fig. 4, we plot the resulting unbiased power spectra, for the different pixelations and for the average over the pixelations. The variability in and from the different pixelations is indicated as colour-shaded regions bordered by dashed and dotted lines respectively. For all parameters, the power spectrum has a maximum at the quadrupole (). However, given the size of the error, the results are also compatible with a flat spectrum.

For comparison, in the left panels of Fig. 4, using the same line types, we plot the power spectra for the parameters from the estimation in Carvalho & Marques (2015). In this estimation, the unbiased power spectra also follow the same behaviour as the power spectra before the noise bias removal, with the exception of whose subtle maximum at is erased with the noise bias removal and the power spectrum decreases always with the multipole. Conversely, for the power spectrum increases always with the multipole, and for the power spectrum has a maximum at Hence, between the and the estimation, we observe the creation of a peak at for and and the smoothing of the peak for We also observe that the power spectra in the estimation are up to times the power spectra in the estimation, hence yielding an increase of the amplitude.

The power spectrum that accounts for the noise bias is up to two orders of magnitude smaller that the mean power spectra the resulting unbiased mean power spectra following the same behaviour as Since for the estimation, this method might not remove entirely the noise bias contribution to the power spectrum. For this reason, we conceived another method.

### 3.2 Power spectrum of unbiased map

In the second method, we define the unbiased power spectrum as the power spectrum of the mean unbiased maps. We compute the unbiased power spectrum by computing the power spectrum of the unbiased mean maps with total variance

(20) |

We then average over the different pixelations, defining

(21) |

with variance

(22) |

In the right panels of Fig. 5, we plot the resulting unbiased power spectra, for the different pixelations and the average over the pixelations. The variability in (the power spectrum of the mean map averaged over the realizations of the “Cosmic variance” local estimation) and (the power spectrum of the mean map averaged over the realizations of the “Shuffle SNe” local estimation) from the different pixelations is indicated as colour–shaded regions bordered by dashed and dotted lines respectively and included for reference. For all parameters, the power spectrum has a maximum at Although this method has smaller errors than the previous method, the results are still compatible with a flat spectrum.

For comparison, in the left panels of Fig. 5, using the same line types, we plot the power spectra for the parameters from the estimation in Carvalho & Marques (2015). In this estimation, both and have a maximum at whereas decreases always with the multipole. Hence, between the and the estimation, we observe the creation of a peak at for and the smoothing of the peak for and We also observe that the power spectra in the estimation are up to times the power spectra in the estimation, hence yielding an increase of the amplitude.

The two methods return qualitatively equivalent results; quantitatively, however, the second method might be more efficient at removing the noise bias.

## 4 Average values

In order to compute the average values of the parameters from the corresponding mean maps we average the map of each parameter over the pixel subsamples. When averaging over homogeneous pixels, angular fluctuations in the expansion factor (and consequently in ) induce a backreaction term in the average deceleration parameter in the form of an extra positive acceleration (Räsänen, 2006). By the same reasoning, angular fluctuations in the expansion factor (and consequently in and ) will also induce a backreaction term in the average jerk parameter. In Carvalho & Marques (2015) we derived the analytical extra positive acceleration for a toy model of an arbitrary number of disjoint, homogeneous regions and computed the overall deceleration parameter assuming a) no backreaction and b) backreaction for the measured angular distribution of Here, for the same toy model, we derive the corresponding extra terms for the time variation in the acceleration and compute the overall jerk parameter assuming a) no backreaction and b) backreaction for the measured angular distribution of and

In the absence of backreaction, the averaging consists in taking the mean weighted by the variance’s inverse of parameter in each pixel

(23) |

For the estimated parameters, we find After the noise bias removal, we find These values are consistent with the fiducial values. After the noise bias removal, the pixel average values become closer to the corresponding values estimated using the complete sample.

In the presence of backreaction, the averaging consists in taking the mean weighted by the three–volume of each pixel

(24) |

Identifying a volume as a pixel and defining then for disjoint regions, the average of the Hubble parameter is given by

(25) |

### 4.1 Average value of the deceleration parameter

In order to derive the volume average of the acceleration, we take the time derivative of Eq. (25) and find that

(26) |

Equation (26) decomposes into a linear term in the pixel average of and a quadratic term in differences of between pairs of pixels. The quadratic (backreaction) term generates an acceleration due to the slower regions becoming less represented in the average. In the absence of the quadratic term, the volume average reduces to the pixel average above. Then the volume average of becomes

(27) | |||||

(28) |

Using the fluctuations in (measured in the “Cosmic variance” local estimation) we find and after the noise bias removal we find (see Table 2).

The quadratic term is of order times the linear term; the corresponding ratio measured in the estimation was of order (Carvalho & Marques, 2015). The difference due to the backreaction is below the standard deviation, hence unobservable. It follows that, for the angular fluctuations in measured with this SN sample, the contribution of the quadratic term in Eq. (28) is insignificant, which renders the volume averaging equivalent to the pixel averaging. These results confirm that, in the context of this toy model of an inhomogeneous space–time and for a kinetic parametrisation, backreaction is not a viable dynamical mechanism to emulate cosmic acceleration.

### 4.2 Average value of the jerk parameter

In order to derive the volume average of the time variation in the acceleration, we take the time derivative of Eq. (26) and find that

(29) | |||||

(30) | |||||

(32) | |||||

Equation (32) decomposes into a linear term in the pixel average of a quadratic term in differences of between pairs of pixels and a linear term in differences of between pairs of pixels. The quadratic term in differences of generates a contribution that is always positive similar to the quadratic term in Eq. (26). Conversely, the linear term in differences of generates a contribution that can have either sign; in particular, it will be positive in the pair of pixels where and vary in the same direction (i.e. both quantities increase or decrease between pixels) and it will be negative in the pair of pixels where and vary in opposite directions (i.e. one quantity increases while the other decreases). Since Eq. (32) measures the angular average of the time variation in the acceleration, the difference (backreaction) terms generate an extra jerk that is due to slower regions and/or more slowly varying regions becoming less represented in the average. Similarly to Eq. (26), in the absence of the difference terms, the volume average reduces to the pixel average above. Then the volume average of becomes

(33) | |||||

(34) | |||||

(36) | |||||

Using the fluctuations in (measured in the “Cosmic variance” local parameter estimation), we find and after the noise bias removal, we find (see Table 2).

The quadratic term in differences of is of order times the linear term, whereas the linear term in differences of is of order times the linear term; the corresponding ratios in the estimation in Carvalho & Marques (2015) were of order and Since is more poorly constrained than or in the global estimation, the total difference due to the backreaction is still below the standard deviation, hence unobservable. It follows that, for the angular fluctuations in measured with this SN sample, the contribution of the backreaction terms in Eq. (36) is insignificant, which renders the pixel averaging equivalent to the pixel averaging. These results imply that an inhomogeneous such that at different pixels the acceleration changed at different times, cannot be distinguished from a globally homogeneous such that the acceleration changed everywhere at the same time.

For comparison, we present the pixel average of from the estimation in Carvalho & Marques (2015), both in the absence and in the presence of backreaction, which we include in Table 2.

Parameter | Complete sample | Subsample into pixels | Averaging | ||
---|---|---|---|---|---|

Biased | Unbiased | ||||

: | |||||

^{3}

^{3}3Column 1: The parameters estimated either directly or indirectly from the fit. Column 2: The values estimated from the complete SN sample. Columns 3–4: The values estimated from the subsampling of SNe into pixels of equal surface area, before and after the noise bias subtraction. Column 5: The averaging method.

## 5 Conclusions

In this paper we used SN data to fit a kinematic parametrisation of the luminosity distance expressed in terms of time derivatives of the scale factor. This parametrisation records the cosmic expansion without regard to its cause, thus being independent of the cosmological equation of state and consequently adequate to test interpretations of the cosmic acceleration alternative to the cosmological constant. We followed the parameter estimation, first presented in Carvalho & Marques (2015), to fit the parameters by performing both a global and a local parameter estimation. From the global parameter estimation, using the complete SN sample, we obtained the fiducial values adopted in this manuscript. From the local parameter estimation, dividing the SNe into subsamples over a pixelated map, we obtained maps of the estimated parameters with the same pixelation as the SN subsamples. We then proceeded to the analysis of this paper’s results as well as to a comparative analysis with the results from Carvalho & Marques (2015) estimated by fitting instead.

The measurements of are consistent with the measurements of obtained in Carvalho & Marques (2015). However, whereas the error of is of the same order in both parametrisations (about a measurement), the error of is significantly larger in the kinematic parametrisation (from a to a measurement). This is a consequence of the kinematic parametrisation in part minimising the physical assumptions that enter in the model and in part truncating the Taylor expansion of the luminosity distance.

We measured fluctuations about the average values of order 0.1–5% for 1–150% for and 1–124% for Comparing with the fluctuations measured in Carvalho & Marques (2015), we observe an increase by a factor of two orders of magnitude. We also computed the power spectrum of the corresponding maps of the parameters up to as determined by the pixel size, finding that all power spectra have a maximum at regardless of the method used to subtract the noise bias. This observation can be partially ascribed to the absence of objects towards the galactic plane.

Finally, for an analytical toy model of an inhomogeneous ensemble of homogenous pixels, we measured the backreaction term in due to the fluctuations of to be of order the corresponding pixel average in the absence of backreaction, hence of smaller order than that measured in Carvalho & Marques (2015). We also derived the backreaction term in due to the fluctuations of