اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدStrong lensing probability in TeVeS theory
66
0
0.0
(
0
)
تأليف
Da-Ming Chen
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We recalculate the strong lensing probability as a function of the image separation in TeVeS (tensor-vector-scalar) cosmology, which is a relativistic version of MOND (MOdified Newtonian Dynamics). The lens is modeled by the Hernquist profile. We assume an open cosmology with $Omega_b=0.04$ and $Omega_Lambda=0.5$ and three different kinds of interpolating functions. Two different galaxy stellar mass functions (GSMF) are adopted: PHJ (Panter-Heavens-Jimenez, 2004) determined from SDSS data release one and Fontana (Fontana et al., 2006) from GOODS-MUSIC catalog. We compare our results with both the predicted probabilities for lenses by Singular Isothermal Sphere (SIS) galaxy halos in LCDM (lambda cold dark matter) with Schechter-fit velocity function, and the observational results of the well defined combined sample of Cosmic Lens All-Sky Survey (CLASS) and Jodrell Bank/Very Large Array Astrometric Survey (JVAS). It turns out that the interpolating function $mu(x)=x/(1+x)$ combined with Fontana GSMF matches the results from CLASS/JVAS quite well.
قيم البحث
اقرأ أيضاً
We calculate the strong lensing probability as a function of the image-separation $Deltatheta$ in TeVeS (tensor-vector-scalar) cosmology, which is a relativistic version of MOND (MOdified Newtonian Dynamics). The lens, often an elliptical galaxy, is
modeled by the Hernquist profile. We assume a flat cosmology with $Omega_b=1-Omega_Lambda=0.04$ and the simplest interpolating function $mu(x)={rm min}(1,x)$. For comparison, we recalculated the probabilities for lenses by Singular Isothermal Sphere (SIS) galaxy halos in LCDM with Schechter-fit velocity function. The amplification bias is calculated based on the magnification of the second bright image rather than the total of the two brighter images. Our calculations show that the Hernquist model predicts insufficient but acceptable probabilities in flat TeVeS cosmology compared with the results of the well defined combined sample of Cosmic Lens All-Sky Survey (CLASS) and Jodrell Bank/Very Large Array Astrometric Survey (JVAS); at the same time, it predicts higher probabilities than SIS model in LCDM at small image separations.
In this work, we discuss the polarization contents of Einstein-ae ther theory and the generalized tensor-vector-scalar (TeVeS) theory, as both theories have a normalized timelike vector field. We derive the linearized equations of motion around the f
lat spacetime background using the gauge-invariant variables to easily separate physical degrees of freedom. We find the plane wave solutions are then found, and identify the polarizations by examining the geodesic deviation equations. We find that there are five polarizations in Einstein-ae ther theory and six polarizations in the generalized TeVeS theory. In particular, the transverse breathing mode is mixed with the pure longitudinal mode. We also discuss the experimental tests of the extra polarizations in Einstein-ae ther theory using pulsar timing arrays combined with the gravitational-wave speed bound derived from the observations on GW 170817 and GRB 170817A. It turns out that it might be difficult to use pulsar timing arrays to distinguish different polarizations in Einstein-ae ther theory. The same speed bound also forces one of the propagating modes in the generalized TeVeS theory to travel much faster than the speed of light. Since the strong coupling problem does not exist in some parameter subspaces, the generalized TeVeS theory is excluded in these parameter subspaces.
We define a natural operation of conditioning of tropical diagrams of probability spaces and show that it is Lipschitz continuous with respect to the asymptotic entropy distance.
We survey the published work of Harry Kesten in probability theory, with emphasis on his contributions to random walks, branching processes, percolation, and related topics. A complete bibliography is included of his publications.
These are lecture notes written at the University of Zurich during spring 2014 and spring 2015. The first part of the notes gives an introduction to probability theory. It explains the notion of random events and random variables, probability measure
s, expectation, distributions, characteristic function, independence of random variables, types of convergence and limit theorems. The first part is separated into two different chapters. The first chapter is about combinatorial aspects of probability theory and the second chapter is the actual introduction to probability theory, which contains the modern probability language. The second part covers conditional expectations, martingales and Markov chains, which are easily accessible after reading the first part. The chapters are exactly covered in this order and go into some more details of the respective topic.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
