اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدThe Statistical Significance of the Dark Flow
32
0
0.0
(
0
)
تأليف
Ryan Keisler
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We revisit the statistical significance of the dark flow presented in Kashlinsky et al. (2009). We do not find a statistically significant detection of a bulk flow. Instead we find that CMB correlations between the 8 WMAP channels used in this analysis decrease the inferred significance of the detection to 0.7sigma.
قيم البحث
اقرأ أيضاً
We present the results of the Cosmogrid cosmological N-body simulation suites based on the concordance LCDM model. The Cosmogrid simulation was performed in a 30Mpc box with 2048^3 particles. The mass of each particle is 1.28x10^5 Msun, which is suff
icient to resolve ultra-faint dwarfs. We found that the halo mass function shows good agreement with the Sheth & Tormen fitting function down to ~10^7 Msun. We have analyzed the spherically averaged density profiles of the three most massive halos which are of galaxy group size and contain at least 170 million particles. The slopes of these density profiles become shallower than -1 at the inner most radius. We also find a clear correlation of halo concentration with mass. The mass dependence of the concentration parameter cannot be expressed by a single power law, however a simple model based on the Press-Schechter theory proposed by Navarro et al. gives reasonable agreement with this dependence. The spin parameter does not show a correlation with the halo mass. The probability distribution functions for both concentration and spin are well fitted by the log-normal distribution for halos with the masses larger than ~10^8 Msun. The subhalo abundance depends on the halo mass. Galaxy-sized halos have 50% more subhalos than ~10^{11} Msun halos have.
The recent detection of a 3.5 keV X-ray line from the centres of galaxies and clusters by Bulbul et al. (2014a) and Boyarsky et al. (2014a) has been interpreted as emission from the decay of 7 keV sterile neutrinos which could make up the (warm) dark
matter (WDM). As part of the COpernicus COmplexio (COCO) programme, we investigate the properties of dark matter haloes formed in a high-resolution cosmological $N$-body simulation from initial conditions similar to those expected in a universe in which the dark matter consists of 7 keV sterile neutrinos. This simulation and its cold dark matter (CDM) counterpart have $sim13.4$bn particles, each of mass $sim 10^5, h^{-1} M_odot$, providing detailed information about halo structure and evolution down to dwarf galaxy mass scales. Non-linear structure formation on small scales ($M_{200}, leq, 2 times 10^9,h^{-1},M_odot$) begins slightly later in COCO-Warm than in COCO-Cold. The halo mass function at the present day in the WDM model begins to drop below its CDM counterpart at a mass $sim 2 times 10^{9},h^{-1},M_odot$ and declines very rapidly towards lower masses so that there are five times fewer haloes of mass $M_{200}= 10^{8},h^{-1},M_odot$ in COCO-Warm than in COCO-Cold. Halo concentrations on dwarf galaxy scales are correspondingly smaller in COCO-Warm, and we provide a simple functional form that describes its evolution with redshift. The shapes of haloes are similar in the two cases, but the smallest haloes in COCO-Warm rotate slightly more slowly than their CDM counterparts.
We summarize three recent efforts to constrain the first few moments of cosmic creation before and during the epoch of inflation. We consider two means to explain a slight dip in the power spectrum of the cosmic microwave background for multipoles in
the range of $ell= 10-30$ from both the {it Planck} and {it WMAP} data. We show that such a dip could be the result of resonant creation of a massive particle that couples to the inflaton field. For best-fit models, the epoch of resonant particle creation reenters the horizon at wave numbers of $k_* sim 0.00011 pm 0.0004 $ ($h$ Mpc$^{-1}$). The amplitude and location of these features correspond to the creation of a number of degenerate fermion species of mass $sim 15/lambda^{3/2} $ $m_{pl}$ during inflation where $lambda$ is the coupling constant between the inflaton field and the created fermion species. Alternatively, one can explain the existence of such a dip as due to a jump in the inflation generating potential. We show that such a jump can also resolve the excessively large dark flow predicted from the M-theory landscape. Finally, we summarize our efforts to quantify constraints on the cosmic dark flow from a new analysis of the Type Ia supernova distance-redshift relation.
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as opposed to spurious sampling artifacts. This chal
lenge is especially serious, and very few methods are available when the data are very high in dimension. Statistical Significance of Clustering (SigClust) is a recently developed cluster evaluation tool for high dimensional low sample size data. An important component of the SigClust approach is the very definition of a single cluster as a subset of data sampled from a multivariate Gaussian distribution. The implementation of SigClust requires the estimation of the eigenvalues of the covariance matrix for the null multivariate Gaussian distribution. We show that the original eigenvalue estimation can lead to a test that suffers from severe inflation of type-I error, in the important case where there are huge single spikes in the eigenvalues. This paper addresses this critical challenge using a novel likelihood based soft thresholding approach to estimate these eigenvalues which leads to a much improved SigClust. These major improvements in SigClust performance are shown by both theoretical work and an extensive simulation study. Applications to some cancer genomic data further demonstrate the usefulness of these improvements.
In experiments where one searches a large parameter space for an anomaly, one often finds many spurious noise-induced peaks in the likelihood. This is known as the look-elsewhere effect, and must be corrected for when performing statistical analysis.
This paper introduces a method to calibrate the false alarm probability (FAP), or $p$-value, for a given dataset by considering the heights of the highest peaks in the likelihood. In the simplest form of self-calibration, the look-elsewhere-corrected $chi^2$ of a physical peak is approximated by the $chi^2$ of the peak minus the $chi^2$ of the highest noise-induced peak. Generalizing this concept to consider lower peaks provides a fast method to quantify the statistical significance with improved accuracy. In contrast to alternative methods, this approach has negligible computational cost as peaks in the likelihood are a byproduct of every peak-search analysis. We apply to examples from astronomy, including planet detection, periodograms, and cosmology.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
