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Prasenjit Saha

Publications and source records attributed to Prasenjit Saha.

96 records · Page 6Linked to original sources

A method for comparing discrete kinematic data and N-body simulations

This paper describes a method for quantitatively comparing an N-body model with a sample of discrete kinematic data. The comparison has two stages: (i) finding the optimum scaling and orientation of the model relative to the data; and (ii) calculating a goodness of fit, and hence assessing the plausibility of the model in vew of the data. The method derives from considering the data and model both as samples from some underlying binned distribution function, and applying probability theory arguments. As an example, I consider a published N-body model for the Galactic bulge and disc, and fictitious l,b,v measurements, and recover (with error estimates) the spatial and velocity scales of the model and the orientation of the bar. The fictitious data are actually derived from the model by assuming the mass scale and the solar position, but their size and extent mimics a recent survey of OH/IR stars. The results indicate that mass of the bulge and our viewing angle of the bar are usefully estimable from current surveys.

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Cluster Mass Profile from Lensing

We propose a new technique to reconstruct non-parametrically the projected mass distribution of galaxy clusters from their gravitational lens effect on background galaxies. The beauty of our technique, is that it combines information from multiple imaging (strong lensing) and shear (weak lensing) as linear constraints on the projected mass distribution. Moreover, our technique overcomes all the drawbacks of non-linear methods. The method is applied to the first cluster-lens A370 and to the cluster-lens A2218 which is exceptionally rich in multiple images and arc(let)s. The reconstructed maps for both cases revealed unprecedented levels of detail.

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Non-parametric reconstruction of the galaxy-lens in PG1115+080

We describe a new, non-parametric, method for reconstructing lensing mass distributions in multiple-image systems, and apply it to PG1115, for which time delays have recently been measured. It turns out that the image positions and the ratio of time delays between different pairs of images constrain the mass distribution in a linear fashion. Since observational errors on image positions and time delay ratios are constantly improving, we use these data as a rigid constraint in our modelling. In addition, we require the projected mass distributions to be inversion-symmetric and to have inward-pointing density gradients. With these realistic yet non-restrictive conditions it is very easy to produce mass distributions that fit the data precisely. We then present models, for $H_0=42$, 63 and 84 \kmsmpc, that in each case minimize mass-to-light variations while strictly obeying the lensing constraints. (Only a very rough light distribution is available at present.) All three values of $H_0$ are consistent with the lensing data, but require quite different morphologies for the lensing galaxy. If $H_0$ is low, the main lensing galaxy could be an early type. If $H_0$ is high, the galaxy is reconstructed as a late elliptical or nearly edge-on disc; a binary or merging system is also plausible.

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A parallel integration method for solar system dynamics

We describe how long-term solar system orbit integration could be implemented on a parallel computer. The interesting feature of our algorithm is that each processor is assigned not to a planet or a pair of planets but to a time-interval. Thus, the 1st week, 2nd week,..., 1000th week of an orbit are computed concurrently. The problem of matching the input to the (n+1)-st processor with the output of the n-th processor can be solved efficiently by an iterative procedure. Our work is related to the so-called waveform relaxation methods in the computational mathematics literature, but is specialized to the Hamiltonian and nearly integrable nature of solar system orbits. Simulations on serial machines suggest that, for the reasonable accuracy requirement of 1" per century, our preliminary parallel algorithm running on a 1000-processor machine would be about 50 times faster than the fastest available serial algorithm, and we have suggestions for further improvements in speed.

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A Varying Mass-To-Light Ratio in the Galactic Center Cluster?

We reanalyze published kinematic and photometric data for the cool star population in the central 10 pc (240 arcsec) of the Galaxy, while (a) isolating the photometric data appropriate to this population, and (b) properly allowing for projection effects. Under the assumptions that the system is spherical and isotropic, we find that M/L_K varies from <=1 outside a radius of 0.8 pc to >2 at 0.35 pc. We suggest that such a varying M/L_K could be due to an increasing concentration of stellar remnants towards the Galactic center. Our derived mass-radius curve confirms the existence of ~3x10^6 M_sol within 0.35 pc of the Galactic Center, and ~1.5x10^6 M_sol with 0.2 pc. However, the latter estimate is subject to the uncertain distribution of cool stars in the region. We also consider the dynamics of the hot star population close to the Galactic center and show that the velocity dispersion of He I stars and the surface brightness distribution of the hot stars are consistent with the mass distribution inferred from cool stars.

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Long-term planetary integration with individual time steps

We describe an algorithm for long-term planetary orbit integrations, including the dominant post-Newtonian effects, that employs individual timesteps for each planet. The algorithm is symplectic and exhibits short-term errors that are $O(εΩ^2τ^2)$ where $τ$ is the timestep, $Ω$ is a typical orbital frequency, and $ε\ll1$ is a typical planetary mass in solar units. By a special starting procedure long-term errors over an integration interval $T$ can be reduced to $O(ε^2Ω^3τ^2T)$. A sample 0.8 Myr integration of the nine planets illustrates that Pluto can have a timestep more than 100 times Mercury's, without dominating the positional error. Our algorithm is applicable to other $N$-body systems.

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