CIGALEMC: Galaxy Parameter Estimation using a Markov Chain Monte Carlo Approach with Cigale

We introduce a fast Markov Chain Monte Carlo (MCMC) exploration of the astrophysical parameter space using a modified version of the publicly available code CIGALE (Code Investigating GALaxy emission). The original CIGALE builds a grid of theoretical Spectral Energy Distribution (SED) models and fits to photometric fluxes from Ultraviolet (UV) to Infrared (IR) to put contraints on parameters related to both formation and evolution of galaxies. Such a grid-based method can lead to a long and challenging parameter extraction since the computation time increases exponentially with the number of parameters considered and results can be dependent on the density of sampling points, which must be chosen in advance for each parameter. Markov Chain Monte Carlo methods, on the other hand, scale approximately linearly with the number of parameters, allowing a faster and more accurate exploration of the parameter space by using a smaller number of efficiently chosen samples. We test our MCMC version of the code CIGALE (called CIGALEMC) with simulated data. After checking the ability of the code to retrieve the input parameters used to build the mock sample, we fit theoretical SEDs to real data from the well known and studied SINGS sample. We discuss constraints on the parameters and show the advantages of our MCMC sampling method in terms of accuracy of the results and optimization of CPU time.Comment: 12 pages, 8 figures, 4 tables, updated to match the version accepted for publication in ApJ; code available at http://www.oamp.fr/cigale

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linear-nonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a random-weight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.Comment: 13 pages, 4 figure

Constraints On The Topology Of The Universe From The WMAP First-Year Sky Maps

We compute the covariance expected between the spherical harmonic coefficients $a_{\ell m}$ of the cosmic microwave temperature anisotropy if the universe had a compact topology. For fundamental cell size smaller than the distance to the decoupling surface, off-diagonal components carry more information than the diagonal components (the power spectrum). We use a maximum likelihood analysis to compare the Wilkinson Microwave Anisotropy Probe first-year data to models with a cubic topology. The data are compatible with finite flat topologies with fundamental domain $L > 1.2$ times the distance to the decoupling surface at 95% confidence. The WMAP data show reduced power at the quadrupole and octopole, but do not show the correlations expected for a compact topology and are indistinguishable from infinite models.Comment: 16 pages, 5 figure

Parameter estimation on gravitational waves from multiple coalescing binaries

Future ground-based and space-borne interferometric gravitational-wave detectors may capture between tens and thousands of binary coalescence events per year. There is a significant and growing body of work on the estimation of astrophysically relevant parameters, such as masses and spins, from the gravitational-wave signature of a single event. This paper introduces a robust Bayesian framework for combining the parameter estimates for multiple events into a parameter distribution of the underlying event population. The framework can be readily deployed as a rapid post-processing tool

Constraining the luminosity function parameters and population size of radio pulsars in globular clusters

Studies of the Galactic population of radio pulsars have shown that their luminosity distribution appears to be log-normal in form. We investigate some of the consequences that occur when one applies this functional form to populations of pulsars in globular clusters. We use Bayesian methods to explore constraints on the mean and standard deviation of the luminosity function, as well as the total number of pulsars, given an observed sample of pulsars down to some limiting flux density, accounting for measurements of flux densities of individual pulsars as well as diffuse emission from the direction of the cluster. We apply our analysis to Terzan 5, 47 Tucanae and M 28, and demonstrate, under reasonable assumptions, that the number of potentially observable pulsars should be within 95% credible intervals of $147^{+112}_{-65}$, $83^{+54}_{-35}$ and $100^{+91}_{-52}$, respectively. Beaming considerations would increase the true population size by approximately a factor of two. Using non-informative priors, however, the constraints are not tight due to the paucity and quality of flux density measurements. Future cluster pulsar discoveries and improved flux density measurements would allow this method to be used to more accurately constrain the luminosity function, and to compare the luminosity function between different clusters.Comment: 9 pages, 4 figures, Accepted for publication in MNRA

Reconstructing the massive black hole cosmic history through gravitational waves

The massive black holes we observe in galaxies today are the natural end-product of a complex evolutionary path, in which black holes seeded in proto-galaxies at high redshift grow through cosmic history via a sequence of mergers and accretion episodes. Electromagnetic observations probe a small subset of the population of massive black holes (namely, those that are active or those that are very close to us), but planned space-based gravitational-wave observatories such as the Laser Interferometer Space Antenna (LISA) can measure the parameters of ``electromagnetically invisible'' massive black holes out to high redshift. In this paper we introduce a Bayesian framework to analyze the information that can be gathered from a set of such measurements. Our goal is to connect a set of massive black hole binary merger observations to the underlying model of massive black hole formation. In other words, given a set of observed massive black hole coalescences, we assess what information can be extracted about the underlying massive black hole population model. For concreteness we consider ten specific models of massive black hole formation, chosen to probe four important (and largely unconstrained) aspects of the input physics used in structure formation simulations: seed formation, metallicity ``feedback'', accretion efficiency and accretion geometry. For the first time we allow for the possibility of ``model mixing'', by drawing the observed population from some combination of the ``pure'' models that have been simulated. A Bayesian analysis allows us to recover a posterior probability distribution for the ``mixing parameters'' that characterize the fractions of each model represented in the observed distribution. Our work shows that LISA has enormous potential to probe the underlying physics of structure formation.Comment: 24 pages, 16 figures, submitted to Phys. Rev.

Basic Parameter Estimation of Binary Neutron Star Systems by the Advanced LIGO/Virgo Network

Within the next five years, it is expected that the Advanced LIGO/Virgo network will have reached a sensitivity sufficient to enable the routine detection of gravitational waves. Beyond the initial detection, the scientific promise of these instruments relies on the effectiveness of our physical parameter estimation capabilities. The majority of this effort has been towards the detection and characterization of gravitational waves from compact binary coalescence, e.g. the coalescence of binary neutron stars. While several previous studies have investigated the accuracy of parameter estimation with advanced detectors, the majority have relied on approximation techniques such as the Fisher Matrix. Here we report the statistical uncertainties that will be achievable for optimal detection candidates (SNR = 20) using the full parameter estimation machinery developed by the LIGO/Virgo Collaboration via Markov-Chain Monte Carlo methods. We find the recovery of the individual masses to be fractionally within 9% (15%) at the 68% (95%) credible intervals for equal-mass systems, and within 1.9% (3.7%) for unequal-mass systems. We also find that the Advanced LIGO/Virgo network will constrain the locations of binary neutron star mergers to a median uncertainty of 5.1 deg^2 (13.5 deg^2) on the sky. This region is improved to 2.3 deg^2 (6 deg^2) with the addition of the proposed LIGO India detector to the network. We also report the average uncertainties on the luminosity distances and orbital inclinations of ideal detection candidates that can be achieved by different network configurations.Comment: Second version: 15 pages, 9 figures, accepted in Ap