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Akash Garg

Publications and source records attributed to Akash Garg.

20 records · Page 2Linked to original sources

Identifying the radiative components responsible for Quasi-Periodic Oscillations of black hole systems

While the dynamical origin of the variability observed in Galactic Black hole systems, such as quasi-periodic oscillations (QPOs), are still a matter of debate, insight into the radiative components responsible for such behaviour can be obtained by studying their energy dependent temporal behaviour. In particular, one needs to ascertain which variations of the parameters of the best fit time-averaged spectral components reproduce the observed energy dependent fractional r.m.s and time-lags. However, to obtain meaningful interpretation, the standard spectral component parameters have to be recast to physically relevant ones. Then, the energy dependent temporal variations that their fluctuations will cause, needs to be predicted and compared with observations. In this work, we describe a generic method to do this and apply the technique to the ~ 3-4 Hz QPOs observed in the black hole system GRS 1915+105 as observed by AstroSat where the time-averaged spectra can be represented by emission from a truncated disk and a hot thermal Comptonizing coronae in the inner regions. We find that the QPOs and their harmonic can be explained in terms of correlated local accretion rate variations in the disk, the truncated disk radius, the optical depth and the heating rate of the coronae with time-delays between them. We highlight the potential of such techniques to unravel the radiative process responsible for variability using high quality spectral and temporal data from AstroSat and NICER.

astro-ph.HE↗

Chance Constraints Integrated MPC Navigation in Uncertainty amongst Dynamic Obstacles: An overlap of Gaussians approach

In this paper, we formulate a novel trajectory optimization scheme that takes into consideration the state uncertainty of the robot and obstacle into its collision avoidance routine. The collision avoidance under uncertainty is modeled here as an overlap between two distributions that represent the state of the robot and obstacle respectively. We adopt the minmax procedure to characterize the area of overlap between two Gaussian distributions, and compare it with the method of Bhattacharyya distance. We provide closed form expressions that can characterize the overlap as a function of control. Our proposed algorithm can avoid overlapping uncertainty distributions in two possible ways. Firstly when a prescribed overlapping area that needs to be avoided is posed as a confidence contour lower bound, control commands are accordingly realized through a MPC framework such that these bounds are respected. Secondly in tight spaces control commands are computed such that the overlapping distribution respects a prescribed range of overlap characterized by lower and upper bounds of the confidence contours. We test our proposal with extensive set of simulations carried out under various constrained environmental configurations. We show usefulness of proposal under tight spaces where finding control maneuvers with minimal risk behavior becomes an inevitable task.

math.OC↗