اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMaximum Likelihood Random Galaxy Catalogues and Luminosity Function Estimation
101
0
0.0
(
0
)
تأليف
Shaun Cole
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present a new algorithm to generate a random (unclustered) version of an magnitude limited observational galaxy redshift catalogue. It takes into account both galaxy evolution and the perturbing effects of large scale structure. The key to the algorithm is a maximum likelihood (ML) method for jointly estimating both the luminosity function (LF) and the overdensity as a function of redshift. The random catalogue algorithm then works by cloning each galaxy in the original catalogue, with the number of clones determined by the ML solution. Each of these cloned galaxies is then assigned a random redshift uniformly distributed over the accessible survey volume, taking account of the survey magnitude limit(s) and, optionally, both luminosity and number density evolution. The resulting random catalogues, which can be employed in traditional estimates of galaxy clustering, make fuller use of the information available in the original catalogue and hence are superior to simply fitting a functional form to the observed redshift distribution. They are particularly well suited to studies of the dependence of galaxy clustering on galaxy properties as each galaxy in the random catalogue has the same list of attributes as measured for the galaxies in the genuine catalogue. The derivation of the joint overdensity and LF estimator reveals the limit in which the ML estimate reduces to the standard 1/Vmax LF estimate, namely when one makes the prior assumption that the are no fluctuations in the radial overdensity. The new ML estimator can be viewed as a generalization of the 1/Vmax estimate in which Vmax is replaced by a density corrected Vdc,max.
قيم البحث
اقرأ أيضاً
The random coefficients model $Y_i={beta_0}_i+{beta_1}_i {X_1}_i+{beta_2}_i {X_2}_i+ldots+{beta_d}_i {X_d}_i$, with $mathbf{X}_i$, $Y_i$, $mathbf{beta}_i$ i.i.d, and $mathbf{beta}_i$ independent of $X_i$ is often used to capture unobserved heterogene
ity in a population. We propose a quasi-maximum likelihood method to estimate the joint density distribution of the random coefficient model. This method implicitly involves the inversion of the Radon transformation in order to reconstruct the joint distribution, and hence is an inverse problem. Nonparametric estimation for the joint density of $mathbf{beta}_i=({beta_0}_i,ldots, {beta_d}_i)$ based on kernel methods or Fourier inversion have been proposed in recent years. Most of these methods assume a heavy tailed design density $f_mathbf{X}$. To add stability to the solution, we apply regularization methods. We analyze the convergence of the method without assuming heavy tails for $f_mathbf{X}$ and illustrate performance by applying the method on simulated and real data. To add stability to the solution, we apply a Tikhonov-type regularization method.
We revisit the problem of exact CMB likelihood and power spectrum estimation with the goal of minimizing computational cost through linear compression. This idea was originally proposed for CMB purposes by Tegmark et al. (1997), and here we develop i
t into a fully working computational framework for large-scale polarization analysis, adopting WMAP as a worked example. We compare five different linear bases (pixel space, harmonic space, noise covariance eigenvectors, signal-to-noise covariance eigenvectors and signal-plus-noise covariance eigenvectors) in terms of compression efficiency, and find that the computationally most efficient basis is the signal-to-noise eigenvector basis, which is closely related to the Karhunen-Loeve and Principal Component transforms, in agreement with previous suggestions. For this basis, the information in 6836 unmasked WMAP sky map pixels can be compressed into a smaller set of 3102 modes, with a maximum error increase of any single multipole of 3.8% at $ellle32$, and a maximum shift in the mean values of a joint distribution of an amplitude--tilt model of 0.006$sigma$. This compression reduces the computational cost of a single likelihood evaluation by a factor of 5, from 38 to 7.5 CPU seconds, and it also results in a more robust likelihood by implicitly regularizing nearly degenerate modes. Finally, we use the same compression framework to formulate a numerically stable and computationally efficient variation of the Quadratic Maximum Likelihood implementation that requires less than 3 GB of memory and 2 CPU minutes per iteration for $ell le 32$, rendering low-$ell$ QML CMB power spectrum analysis fully tractable on a standard laptop.
Whitbourn & Shanks (2014) have reported evidence for a local void underdense by ~15% extending to 150-300h-1Mpc around our position in the Southern Galactic Cap (SGC). Assuming a local luminosity function they modelled K- and r-limited number counts
and redshift distributions in the 6dFGS/2MASS and SDSS redshift surveys and derived normalised n(z) ratios relative to the standard homogeneous cosmological model. Here we test further these results using maximum likelihood techniques that solve for the galaxy density distributions and the galaxy luminosity function simultaneously. We confirm the results from the previous analysis in terms of the number density distributions, indicating that our detection of the Local Hole in the SGC is robust to the assumption of either our previous, or newly estimated, luminosity functions. However, there are discrepancies with previously published K and r band luminosity functions. In particular the r-band luminosity function has a steeper faint end slope than the r0.1 results of Blanton et al. (2003) but is consistent with the r0.1 results of Montero-Dorta & Prada (2009); Loveday et al. (2012).
We present an analysis of star formation and nuclear activity of about 28000 galaxies in a volume-limited sample taken from SDSS DR4 low-redshift catalogue (LRC) taken from the New York University Value Added Galaxy Catalogue (NYU-VAGC) of Blanton et
al. 2005, with 0.005<z<0.037, ~90% complete to M_r=-18.0. We find that in high-density regions ~70 per cent of galaxies are passively evolving independent of luminosity. In the rarefied field, however, the fraction of passively evolving galaxies is a strong function of luminosity, dropping from 50 per cent for Mr <~ -21 to zero by Mr ~ -18. Moreover the few passively evolving dwarf galaxies in field regions appear as satellites to bright (>~ L*) galaxies. Moreover the fraction of galaxies with the optical signatures of an active galactic nucleus (AGN) decreases steadily from ~50% at Mr~-21 to ~0 per cent by Mr~-18 closely mirroring the luminosity dependence of the passive galaxy fraction in low-density environments (see fig. 1 continuous lines). This result reflects the increasing importance of AGN feedback with galaxy mass for their evolution, such that the star formation histories of massive galaxies are primarily determined by their past merger history.
We present and describe im3shape, a new publicly available galaxy shape measurement code for weak gravitational lensing shear. im3shape performs a maximum likelihood fit of a bulge-plus-disc galaxy model to noisy images, incorporating an applied poin
t spread function. We detail challenges faced and choices made in its design and implementation, and then discuss various limitations that affect this and other maximum likelihood methods. We assess the bias arising from fitting an incorrect galaxy model using simple noise-free images and find that it should not be a concern for current cosmic shear surveys. We test im3shape on the GREAT08 Challenge image simulations, and meet the requirements for upcoming cosmic shear surveys in the case that the simulations are encompassed by the fitted model, using a simple correction for image noise bias. For the fiducial branch of GREAT08 we obtain a negligible additive shear bias and sub-two percent level multiplicative bias, which is suitable for analysis of current surveys. We fall short of the sub-percent level requirement for upcoming surveys, which we attribute to a combination of noise bias and the mis-match between our galaxy model and the model used in the GREAT08 simulations. We meet the requirements for current surveys across all branches of GREAT08, except those with small or high noise galaxies, which we would cut from our analysis. Using the GREAT08 metric we we obtain a score of Q=717 for the usable branches, relative to the goal of Q=1000 for future experiments. The code is freely available from https://bitbucket.org/joezuntz/im3shape
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
