A new estimator of the local density of dark energy is suggested which comes from the virial theorem for non-relativistic gravitating systems embedded in the uniform dark energy background.
[Abridged] Broad MgII 2800 and Hbeta lines have emerged as the most reliable virial estimators of black hole mass in quasars. Which is more reliable? Comparison of MgII 2800 and Hbeta profile measures in the same sources and especially FWHM measures
that provide the virial broadening estimator. Identification of 680 bright Sloan Digital Sky Survey DataRelease 7 quasars with spectra showing both MgII 2800 and Hbeta lines, at redshift 0.4 < z < 0.75. The s/n of these spectra are high enough to allow binning in the four-dimensional (4D) eigenvector 1 optical plane and construction of high s/n composite spectra. We confirm that MgII 2800 shows a profile that is ~ 20% narrower as suggested in some previous studies. FWHM measures for Population B sources (i.e., with FWHM of Hbeta larger than 4000 km/s) are uncertain because they show complex profiles with at least two broad-line components involving a nearly unshifted broad and redshifted very-broad component. Only the broad component is likely to be a valid virial estimator. If Hbeta and MgII 2800 are not corrected for the very broad component then black hole mass values for Population B sources will be systematically overestimated by up to logM ~ 0.3-0.4 dex. We suggest a simple correction that can be applied to the majority of sources. MgII 2800 is the safer virial estimator for Population B sources because the centroid shifts with respect to rest frame are lower than for Hbeta. In the broad and very broad component profile interpretation this is a consequence of the lower very broad to broad component intensity ratio for MgII 2800. Effective discrimination of black hole mass and Eddington ratio at fixed redshift is not achieved via luminosity binning but rather by binning in a 4D eigenvector 1 context that reflects different broad line region geometry/kinematics likely driven by Eddington ratio.
We show that the impact of energy injection by dark matter annihilation on the cosmic microwave background power spectra can be apprehended via a residual likelihood map. By resorting to convolutional neural networks that can fully discover the under
lying pattern of the map, we propose a novel way of constraining dark matter annihilation based on the Planck 2018 data. We demonstrate that the trained neural network can efficiently predict the likelihood and accurately place bounds on the annihilation cross-section in a $textit{model-independent}$ fashion. The machinery will be made public in the near future.
$Om$ diagnostic can differentiate between different models of dark energy without the accurate current value of matter density. We apply this geometric diagnostic to dilaton dark energy(DDE) model and differentiate DDE model from LCDM. We also invest
igate the influence of coupled parameter $alpha$ on the evolutive behavior of $Om$ with respect to redshift $z$. According to the numerical result of $Om$, we get the current value of equation of state $omega_{sigma0}$=-0.952 which fits the WMAP5+BAO+SN very well.
In this work we discuss a general approach for the dark energy thermodynamics considering a varying equation of state (EoS) parameter of the type $omega(a)=omega_0+F(a)$ and taking into account the role of a non-zero chemical potential $mu$. We deriv
e generalized expressions for the entropy density, chemical potential and dark energy temperature $T$ and use the positiveness of the entropy to impose thermodynamic bounds on the EoS parameter $omega(a)$. In particular, we find that a phantom-like behavior $omega(a)< -1$ is allowed only when the chemical potential assumes negative values ($mu<0$).
We consider a cosmological model where dark matter and dark energy feature a coupling that only affects their momentum transfer in the corresponding Euler equations. We perform a fit to cosmological observables and confirm previous findings within th
ese scenarios that favour the presence of a coupling at more than $3sigma$. This improvement is driven by the Sunyaev-Zeldovich data. We subsequently perform a forecast for future J-PAS data and find that clustering measurements will permit to clearly discern the presence of an interaction within a few percent level with the uncoupled case at more than $10sigma$ when the complete survey, covering $8500$ sq. deg., is considered. We found that the inclusion of weak lensing measurements will not help to further constrain the coupling parameter. For completeness, we compare to forecasts for DESI and Euclid, which provide similar discriminating power.