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Searching for codes credited to 'Sharma, Sanjib'

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[ascl:1101.007] Galaxia: A Code to Generate a Synthetic Survey of the Milky Way

We present here a fast code for creating a synthetic survey of the Milky Way. Given one or more color-magnitude bounds, a survey size and geometry, the code returns a catalog of stars in accordance with a given model of the Milky Way. The model can be specified by a set of density distributions or as an N-body realization. We provide fast and efficient algorithms for sampling both types of models. As compared to earlier sampling schemes which generate stars at specified locations along a line of sight, our scheme can generate a continuous and smooth distribution of stars over any given volume. The code is quite general and flexible and can accept input in the form of a star formation rate, age metallicity relation, age velocity dispersion relation and analytic density distribution functions. Theoretical isochrones are then used to generate a catalog of stars and support is available for a wide range of photometric bands. As a concrete example we implement the Besancon Milky Way model for the disc. For the stellar halo we employ the simulated stellar halo N-body models of Bullock & Johnston (2005). In order to sample N-body models, we present a scheme that disperses the stars spawned by an N-body particle, in such a way that the phase space density of the spawned stars is consistent with that of the N-body particles. The code is ideally suited to generating synthetic data sets that mimic near future wide area surveys such as GAIA, LSST and HERMES. As an application we study the prospect of identifying structures in the stellar halo with a simulated GAIA survey.

[ascl:1109.012] EnBiD: Fast Multi-dimensional Density Estimation

We present a method to numerically estimate the densities of a discretely sampled data based on a binary space partitioning tree. We start with a root node containing all the particles and then recursively divide each node into two nodes each containing roughly equal number of particles, until each of the nodes contains only one particle. The volume of such a leaf node provides an estimate of the local density and its shape provides an estimate of the variance. We implement an entropy-based node splitting criterion that results in a significant improvement in the estimation of densities compared to earlier work. The method is completely metric free and can be applied to arbitrary number of dimensions. We use this method to determine the appropriate metric at each point in space and then use kernel-based methods for calculating the density. The kernel-smoothed estimates were found to be more accurate and have lower dispersion. We apply this method to determine the phase-space densities of dark matter haloes obtained from cosmological N-body simulations. We find that contrary to earlier studies, the volume distribution function v(f) of phase-space density f does not have a constant slope but rather a small hump at high phase-space densities. We demonstrate that a model in which a halo is made up by a superposition of Hernquist spheres is not capable in explaining the shape of v(f) versus f relation, whereas a model which takes into account the contribution of the main halo separately roughly reproduces the behaviour as seen in simulations. The use of the presented method is not limited to calculation of phase-space densities, but can be used as a general purpose data-mining tool and due to its speed and accuracy it is ideally suited for analysis of large multidimensional data sets.

[ascl:1603.009] Asfgrid: Asteroseismic parameters for a star

asfgrid computes asteroseismic parameters for a star with given stellar parameters and vice versa. Written in Python, it determines delta_nu, nu_max or masses via interpolation over a grid.

[ascl:1709.009] bmcmc: MCMC package for Bayesian data analysis

bmcmc is a general purpose Markov Chain Monte Carlo package for Bayesian data analysis. It uses an adaptive scheme for automatic tuning of proposal distributions. It can also handle Bayesian hierarchical models by making use of the Metropolis-Within-Gibbs scheme.