اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدAnatomy of Cosmic Tidal Reconstruction
89
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
21-cm intensity surveys aim to map neutral hydrogen atoms in the universe through hyper-fine emission. Unfortunately, long-wavelength (low-wavenumber) radial modes are highly contaminated by smooth astrophysical foregrounds that are six orders of magnitude brighter than the cosmological signal. This contamination also leaks into higher radial and angular wavenumber modes and forms a foreground wedge. Cosmic tidal reconstruction aims to extract the large-scale signal from anisotropic features in the local small-scale power spectrum through non-linear tidal interactions; losing small-scale modes to foreground wedge will impair its performance. In this paper, we review tidal interaction theory and estimator construction, and derive the theoretical expressions for the reconstructed spectra. We show the reconstruction is robust against peculiar velocities. Removing low line-of-sight $k$ modes, we demonstrate cross-correlation coefficient $r$ is greater than 0.7 on large scales ($k <0.1$ $h/$Mpc) even with a cutoff value $k^c_{|}=0.1$ $h/$Mpc. Discarding wedge modes yields $0.3< r < 0.5$ and completely removes the dependency on $k^c_{|}$. Our theoretical predictions agree with these numerical simulations.
قيم البحث
اقرأ أيضاً
A nearby friable cloud in Ursa Majoris contains 270 galaxies with radial velocities 500 < VLG < 1500 km s^-1 inside the area of RA= [11h; 13h] and DEC= [+40deg; +60deg]. At present, 97 galaxies of them have individual distance estimates. We use these
data to clarify the structure and kinematics of the UMa complex. According to Makarov & Karachentsev (2011), most of the UMa galaxies belong to seven bound groups, which have the following median parameters: velocity dispersion of 58 km s^-1, harmonic projected radius of 300 kpc, virial mass of 2.10^12 Msol, and virial- mass-to-K-band-luminosity of 27Msol/Lsol. Almost a half of the UMa cloud population are gas-rich dwarfs (Ir, Im, BCD) with active star formation seen in the GALEX UV-survey. The UMa groups reside within 15-19 Mpc from us, being just at the same distance as Virgo cluster. The total virial mass of the UMa groups is 4.10^13 Msol, yielding the average density of dark matter in the UMa cloud to be Omega_m = 0.08, i.e. a factor three lower than the cosmic average. This is despite the fact that the UMa cloud resides in a region of the Universe that is an apparent overdensity. A possible explanation for this is that most mass in the Universe lies in the empty space between clusters. Herewith, the mean distances and velocities of the UMa groups follow nearly undisturbed Hubble flow without a sign of the Z-wave effect caused by infall toward a massive attractor. This constrains the total amount of dark matter between the UMa groups within the cloud volume.
We describe the Bayesian BARCODE formalism that has been designed towards the reconstruction of the Cosmic Web in a given volume on the basis of the sampled galaxy cluster distribution. Based on the realization that the massive compact clusters are r
esponsible for the major share of the large scale tidal force field shaping the anisotropic and in particular filamentary features in the Cosmic Web. Given the nonlinearity of the constraints imposed by the cluster configurations, we resort to a state-of-the-art constrained reconstruction technique to find a proper statistically sampled realization of the original initial density and velocity field in the same cosmic region. Ultimately, the subsequent gravitational evolution of these initial conditions towards the implied Cosmic Web configuration can be followed on the basis of a proper analytical model or an N-body computer simulation. The BARCODE formalism includes an implicit treatment for redshift space distortions. This enables a direct reconstruction on the basis of observational data, without the need for a correction of redshift space artifacts. In this contribution we provide a general overview of the the Cosmic Web connection with clusters and a description of the Bayesian BARCODE formalism. We conclude with a presentation of its successful workings with respect to test runs based on a simulated large scale matter distribution, in physical space as well as in redshift space.
We investigate the possibilities of reconstructing the cosmic equation of state (EoS) for high redshift. In order to obtain general results, we use two model-independent approaches. The first reconstructs the EoS using comoving distance and the secon
d makes use of the Hubble parameter data. To implement the first method, we use a recent set of Gamma-Ray Bursts (GRBs) measures. To implement the second method, we generate simulated data using the Sandage-Loeb ($SL$) effect; for the fiducial model, we use the $Lambda CDM$ model. In both cases, the statistical analysis is conducted through the Gaussian processes (non-parametric). In general, we demonstrate that this methodology for reconstructing the EoS using a non-parametric method plus a model-independent approach works appropriately due to the feasibility of calculation and the ease of introducing a priori information ($H_ {0}$ and $Omega_{m0}$). In the near future, following this methodology with a higher number of high quality data will help obtain strong restrictions for the EoS.
Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio
r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.
In this work we present a nonparametric approach, which works on minimal assumptions, to reconstruct the cosmic expansion of the Universe. We propose to combine a locally weighted scatterplot smoothing method and a simulation-extrapolation method. Th
e first one (Loess) is a nonparametric approach that allows to obtain smoothed curves with no prior knowledge of the functional relationship between variables nor of the cosmological quantities. The second one (Simex) takes into account the effect of measurement errors on a variable via a simulation process. For the reconstructions we use as raw data the Union2.1 Type Ia Supernovae compilation, as well as recent Hubble parameter measurements. This work aims to illustrate the approach, which turns out to be a self-sufficient technique in the sense we do not have to choose anything by hand. We examine the details of the method, among them the amount of observational data needed to perform the locally weighted fit which will define the robustness of our reconstruction. In view of our results, we believe that our proposal offers a promising alternative for reconstructing global trends of cosmological data when there is little intuition on the relationship between the variables and we also think it even presents good prospects to generate reliable mock data points where the original sample is poor.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
