اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA Cosmic Variance Cookbook
124
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Deep pencil beam surveys (<1 deg^2) are of fundamental importance for studying the high-redshift universe. However, inferences about galaxy population properties are in practice limited by cosmic variance. This is the uncertainty in observational estimates of the number density of galaxies arising from the underlying large-scale density fluctuations. This source of uncertainty can be significant, especially for surveys which cover only small areas and for massive high-redshift galaxies. Cosmic variance for a given galaxy population can be determined using predictions from cold dark matter theory and the galaxy bias. In this paper we provide tools for experiment design and interpretation. For a given survey geometry we present the cosmic variance of dark matter as a function of mean redshift z and redshift bin size Dz. Using a halo occupation model to predict galaxy clustering, we derive the galaxy bias as a function of mean redshift for galaxy samples of a given stellar mass range. In the linear regime, the cosmic variance of these galaxy samples is the product of the galaxy bias and the dark matter cosmic variance. We present a simple recipe using a fitting function to compute cosmic variance as a function of the angular dimensions of the field, z, Dz and stellar mass m*. We also provide tabulated values and a software tool. We find that for GOODS at z=2 and with Dz=0.5 the relative cosmic variance of galaxies with m*>10^11 Msun is ~38%, while it is ~27% for GEMS and ~12% for COSMOS. For galaxies of m*~10^10 Msun the relative cosmic variance is ~19% for GOODS, ~13% for GEMS and ~6% for COSMOS. This implies that cosmic variance is a significant source of uncertainty at z=2 for small fields and massive galaxies, while for larger fields and intermediate mass galaxies cosmic variance is less serious.
قيم البحث
اقرأ أيضاً
Current and future generations of intensity mapping surveys promise dramatic improvements in our understanding of galaxy evolution and large-scale structure. An intensity map provides a census of the cumulative emission from all galaxies in a given r
egion and redshift, including faint objects that are undetectable individually. Furthermore, cross-correlations between line intensity maps and galaxy redshift surveys are sensitive to the line intensity and clustering bias without the limitation of cosmic variance. Using the Fisher information matrix, we derive simple expressions describing sensitivities to the intensity and bias obtainable for cross-correlation surveys, focusing on cosmic variance evasion. Based on these expressions, we conclude that the optimal sensitivity is obtained by matching the survey depth, defined by the ratio of the clustering power spectrum to noise in a given mode, between the two surveys. We find that mid- to far-infrared space telescopes could benefit from this technique by cross-correlating with coming galaxy redshift surveys such as those planned for the Nancy Grace Roman Space Telescope, allowing for sensitivities beyond the cosmic variance limit. Our techniques can therefore be applied to survey design and requirements development to maximize the sensitivities of future intensity mapping experiments to tracers of galaxy evolution and large-scale structure cosmology.
Galaxy surveys that map multiple species of tracers of large-scale structure can improve the constraints on some cosmological parameters far beyond the limits imposed by a simplistic interpretation of cosmic variance. This enhancement derives from co
mparing the relative clustering between different tracers of large-scale structure. We present a simple but fully generic expression for the Fisher information matrix of surveys with any (discrete) number of tracers, and show that the enhancement of the constraints on bias-sensitive parameters are a straightforward consequence of this multi-tracer Fisher matrix. In fact, the relative clustering amplitudes between tracers are eigenvectors of this multi-tracer Fisher matrix. The diagonalized multi-tracer Fisher matrix clearly shows that while the effective volume is bounded by the physical volume of the survey, the relational information between species is unbounded. As an application, we study the expected enhancements in the constraints of realistic surveys that aim at mapping several different types of tracers of large-scale structure. The gain obtained by combining multiple tracers is highest at low redshifts, and in one particular scenario we analyzed, the enhancement can be as large as a factor of ~3 for the accuracy in the determination of the redshift distortion parameter, and a factor ~5 for the local non-Gaussianity parameter. Radial and angular distance determinations from the baryonic features in the power spectrum may also benefit from the multi-tracer approach.
The next generation of Cosmic Microwave Background experiments will produce cosmic variance limited observations over a large fraction of sky and for a large range of multipoles. In this work we discuss different consistency tests that can be perform
ed with the upcoming data from the Simons Observatory and the Planck data. We quantify the level of expected cosmological parameter shifts probed by these tests. We discuss the effect of difference in frequency of observation and present forecasts on a direct measurement of the Planck T-to-E leakage beam. We find that instrumental systematics in either of the experiments will be assessed with an exquisite precision, well beyond the intrinsic uncertainties due to the CMB cosmic variance.
AMS-02 on the International Space Station has been releasing data of unprecedented accuracy. This poses new challenges for their interpretation. We refine the methodology to get a statistically sound determination of the cosmic-ray propagation parame
ters. We inspect the numerical precision of the model calculation, nuclear cross-section uncertainties, and energy correlations in data systematic errors. We used the 1D diffusion model in USINE. Our $chi^2$ analysis includes a covariance matrix of errors for AMS-02 systematics and nuisance parameters to account for cross-section uncertainties. Mock data were used to validate some of our choices. We show that any mis-modelling of nuclear cross-section values or the energy correlation length of the covariance matrix of errors biases the analysis. It also makes good models ($chi^2_{rm min}/{rm dof}approx1$) appear as excluded ($chi^2_{rm min}/{rm dof}gg1$). We provide a framework to mitigate these effects (AMS-02 data are interpreted in a companion paper). New production cross-section data and the publication by the AMS-02 collaboration of a covariance matrix of errors for each data set would be an important step towards an unbiased view of cosmic-ray propagation in the Galaxy.
Intrinsic variations of the projected density profiles of clusters of galaxies at fixed mass are a source of uncertainty for cluster weak lensing. We present a semi-analytical model to account for this effect, based on a combination of variations in
halo concentration, ellipticity and orientation, and the presence of correlated haloes. We calibrate the parameters of our model at the 10 per cent level to match the empirical cosmic variance of cluster profiles at M_200m=10^14...10^15 h^-1 M_sol, z=0.25...0.5 in a cosmological simulation. We show that weak lensing measurements of clusters significantly underestimate mass uncertainties if intrinsic profile variations are ignored, and that our model can be used to provide correct mass likelihoods. Effects on the achievable accuracy of weak lensing cluster mass measurements are particularly strong for the most massive clusters and deep observations (with ~20 per cent uncertainty from cosmic variance alone at M_200m=10^15 h^-1 M_sol and z=0.25), but significant also under typical ground-based conditions. We show that neglecting intrinsic profile variations leads to biases in the mass-observable relation constrained with weak lensing, both for intrinsic scatter and overall scale (the latter at the 15 per cent level). These biases are in excess of the statistical errors of upcoming surveys and can be avoided if the cosmic variance of cluster profiles is accounted for.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
