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Register a new userQuantifying parameter errors due to the peculiar velocities of type Ia supernovae
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R. Ali Vanderveld
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The fitting of the observed redshifts and magnitudes of type Ia supernovae to what we would see in homogeneous cosmological models has led to constraints on cosmological parameters. However, in doing such fits it is assumed that the sampled supernovae are moving with the Hubble flow, i.e. that their peculiar velocities are zero. In reality, peculiar velocities will modify supernova data in a way that can impact best-fit cosmological parameters. We theoretically quantify this effect in the nonlinear regime with a Monte-Carlo analysis, using data from semi-analytic galaxy catalogs that are built from the Millennium N-body simulation. We find scaling relations for the errors in best-fit parameters resulting solely from peculiar velocities, as a function of the total number of sources in a supernova survey N and its maximum redshift z_max. For low redshift surveys, we find that these errors can be of the same order of magnitude as the errors due to an intrinsic magnitude scatter of 0.1 mag. For a survey with N=2000 and z_max=1.7, we estimate that the expected peculiar velocity-induced errors in the best-fit cosmological constant density and equation of state can be sigma_Lambda~0.009 and sigma_w~0.01, respectively, which are subdominant to the errors due to the intrinsic scatter. We further find that throwing away supernova data below a redshift z~0.01-0.02 can reduce the combined error, due to peculiar velocities and the intrinsic scatter, but by only about 10%.
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Type Ia Supernovae (SNe Ia) are widely used to measure the expansion of the Universe. To perform such measurements the luminosity and cosmological redshift ($z$) of the SNe Ia have to be determined. The uncertainty on $z$ includes an unknown peculiar velocity, which can be very large for SNe Ia in the virialized cores of massive clusters. We determine which SNe Ia exploded in galaxy clusters. We then study how the correction for peculiar velocities of host galaxies inside the clusters improves the Hubble residuals. Using 145 SNe Ia from the Nearby Supernova Factory we found 11 candidates for membership in clusters. To estimate the redshift of a cluster we applied the bi-weight technique. Then, we use the galaxy cluster redshift instead of the host galaxy redshift to construct the Hubble diagram. For SNe Ia inside galaxy clusters the dispersion around the Hubble diagram when peculiar velocities are taken into account is smaller in comparison with a case without peculiar velocity correction, with a $wRMS=0.130pm0.038$ mag instead of $wRMS=0.137pm0.036$ mag. The significance of this improvement is 3.58 $sigma$. If we remove the very nearby Virgo cluster member SN2006X ($z<0.01$) from the analysis, the significance decreases to 1.34 $sigma$. The peculiar velocity correction is found to be highest for the SNe Ia hosted by blue spiral galaxies, with high local specific star formation rate and smaller stellar mass, seemingly counter to what might be expected given the heavy concentration of old, massive elliptical galaxies in clusters. As expected, the Hubble residuals of SNe Ia associated with massive galaxy clusters improve when the cluster redshift is taken as the cosmological redshift of the SN. This fact has to be taken into account in future cosmological analyses in order to achieve higher accuracy for cosmological redshift measurements. Here we provide an approach to do so.
We quantify the effect of supernova Type Ia peculiar velocities on the derivation of cosmological parameters. The published distant and local Ia SNe used for the Supernova Legacy Survey first-year cosmology report form the sample for this study. While previous work has assumed that the local SNe are at rest in the CMB frame (the No Flow assumption), we test this assumption by applying peculiar velocity corrections to the local SNe using three different flow models. The models are based on the IRAS PSCz galaxy redshift survey, have varying beta = Omega_m^0.6/b, and reproduce the Local Group motion in the CMB frame. These datasets are then fit for w, Omega_m, and Omega_Lambda using flatness or LambdaCDM and a BAO prior. The chi^2 statistic is used to examine the effect of the velocity corrections on the quality of the fits. The most favored model is the beta=0.5 model, which produces a fit significantly better than the No Flow assumption, consistent with previous peculiar velocity studies. By comparing the No Flow assumption with the favored models we derive the largest potential systematic error in w caused by ignoring peculiar velocities to be Delta w = +0.04. For Omega_Lambda, the potential error is Delta Omega_Lambda = -0.04 and for Omega_m, the potential error is Delta Omega_m < +0.01. The favored flow model (beta=0.5) produces the following cosmological parameters: w = -1.08 (+0.09,-0.08), Omega_m = 0.27 (+0.02,-0.02) assuming a flat cosmology, and Omega_Lambda = 0.80 (+0.08,-0.07) and Omega_m = 0.27 (+0.02,-0.02) for a w = -1 (LambdaCDM) cosmology.
Peculiar velocities of type Ia supernova (SNIa) host galaxies affect the dark-energy parameter constraints in a small but very specific way: the parameters are biased in a single direction in parameter space that is a-priori knowable for a given SNIa dataset. We demonstrate the latter fact with a combination of inference from a cosmological N-body simulation with overwhelming statistics applied to the Pantheon SNIa data set, then confirm it by simple quantitative arguments. We quantify small modifications to the current analyses that would ensure that the effect of cosmological parameters is essentially guaranteed to be negligible.
Type Ia Supernovae have yet again the opportunity to revolutionize the field of cosmology as the new generation of surveys are acquiring thousands of nearby SNeIa opening a new era in cosmology: the direct measurement of the growth of structure parametrized by $fD$. This method is based on the SNeIa peculiar velocities derived from the residual to the Hubble law as direct tracers of the full gravitational potential caused by large scale structure. With this technique, we could probe not only the properties of dark energy, but also the laws of gravity. In this paper we present the analytical framework and forecasts. We show that ZTF and LSST will be able to reach 5% precision on $fD$ by 2027. Our analysis is not significantly sensitive to photo-typing, but known selection functions and spectroscopic redshifts are mandatory. We finally introduce an idea of a dedicated spectrograph that would get all the required information in addition to boost the efficiency to each SNeIa so that we could reach the 5% precision within the first two years of LSST operation and the few percent level by the end of the survey.
We show that peculiar velocities of Type Ia supernovae can be used to derive constraints on the sum of neutrino masses, $Sigma m_{ u}$, and dark energy equation of state, $w = w_0+w_a(1-a)$, from measurements of the magnitude-redshift relation, complementary to galaxy redshift and weak lensing surveys. Light from a supernova propagates through a perturbed Universe so the luminosity distance is modified from its homogeneous prediction. This modification is proportional to the matter density fluctuation and its time derivative due to gravitational lensing and peculiar velocity respectively. At low redshifts, the peculiar velocity signal dominates while at high redshifts lensing does. We show that using lensing and peculiar velocity of supernovae from the upcoming surveys WFIRST and ZTF, without other observations, we can constrain $Sigma m_{ u} lesssim 0.31$ eV, $sigma(w_0) lesssim 0.02$, and ${sigma(w_a)} lesssim 0.18$ ($1-sigma$ CL) in the $Sigma m_{ u}$-$w_0$-$w_a$ parameter space, where all the other cosmological parameters are fixed. We find that adding peculiar velocity information from low redshifts shrinks the volume of the parameter ellipsoid in this space by $sim 33$%. We also allow $Omega_{text{CDM}}$ to vary as well as $Sigma m_{ u}$, $w_0$ and $w_a$, and demonstrate how these constraints degrade as a consequence.
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