Optical Transmission Spectra of Hot-Jupiters: Effects of Scattering

We present new grids of transmission spectra for hot-Jupiters by solving the multiple scattering radiative transfer equations with non-zero scattering albedo instead of using the Beer-Bouguer-Lambert law for the change in the transmitted stellar intensity. The diffused reflection and transmission due to scattering increases the transmitted stellar flux resulting into a decrease in the transmission depth. Thus we demonstrate that scattering plays a double role in determining the optical transmission spectra -- increasing the total optical depth of the medium and adding the diffused radiation due to scattering to the transmitted stellar radiation. The resulting effects yield into an increase in the transmitted flux and hence reduction in the transmission depth. For a cloudless planetary atmosphere, Rayleigh scattering albedo alters the transmission depth up to about 0.6 micron but the change in the transmission depth due to forward scattering by cloud or haze is significant throughout the optical and near-infrared regions. However, at wavelength longer than about 1.2 $\mu$m, the scattering albedo becomes negligible and hence the transmission spectra match with that calculated without solving the radiative transfer equations. We compare our model spectra with existing theoretical models and find significant difference at wavelength shorter than one micron. We also compare our models with observational data for a few hot-Jupiters which may help constructing better retrieval models in future.Comment: 20 pages (AASTEX6.2) including 14 eps colour figures. Accepted for publication in The Astrophysical Journa

Effect of multiple scattering on the Transmission spectra and the Polarization phase curves for Earth-like Exoplanets

It is the most appropriate time to characterize the Earth-like exoplanets in order to detect biosignature beyond the Earth because such exoplanets will be the prime targets of big-budget missions like JWST, Roman Space Telescope, HabEx, LUVOIR, TMT, ELT, etc. We provide models for the transmission spectra of the Earth-like exoplanets by incorporating effects of multiple scattering. For this purpose we numerically solve the full multiple-scattering radiative transfer equations instead of using Beer-Bouguer-Lambert's law that doesn't include the diffuse radiation due to scattering. Our models demonstrate that the effect of this diffuse transmission radiation can be observationally significant, especially in the presence of clouds. We also calculate the reflection spectra and polarization phase curves of Earth-like exoplanets by considering both cloud-free and cloudy atmospheres. We solve the 3D vector radiative transfer equations numerically and calculate the phase curves of albedo and disk-integrated polarization by using appropriate scattering phase matrices and integrating the local Stokes vectors over the illuminated part of the disks along the line of sight. We present the effects of the globally averaged surface albedo on the reflection spectra and phase curves as the surface features of such planets are known to significantly dictate the nature of these observational quantities. Synergic observations of the spectra and phase curves will certainly prove to be useful in extracting more information and reducing the degeneracy among the estimated parameters of terrestrial exoplanets. Thus, our models will play a pivotal role in driving future observations.Comment: Accepted for publication in The Astrophysical Journal, 12 pages, 6 figure

Drinking from Both Glasses: Combining Pessimistic and Optimistic Tracking of Cross-Thread Dependences *

Abstract It is notoriously challenging to develop parallel software systems that are both scalable and correct. Runtime support for parallelism-such as multithreaded record & replay, data race detectors, transactional memory, and enforcement of stronger memory models-helps achieve these goals, but existing commodity solutions slow programs substantially in order to track (i.e., detect or control) an execution's cross-thread dependences accurately. Prior work tracks cross-thread dependences either "pessimistically," slowing every program access, or "optimistically," allowing for lightweight instrumentation of most accesses but dramatically slowing accesses involved in cross-thread dependences. This paper seeks to hybridize pessimistic and optimistic tracking, which is challenging because there exists a fundamental mismatch between pessimistic and optimistic tracking. We address this challenge based on insights about how dependence tracking and program synchronization interact, and introduce a novel approach called hybrid tracking. Hybrid tracking is suitable for building efficient runtime support, which we demonstrate by building hybridtracking-based versions of a dependence recorder and a region serializability enforcer. An adaptive, profile-based policy makes runtime decisions about switching between pessimistic and optimistic tracking. Our evaluation shows that hybrid tracking enables runtime support to overcome the performance limitations of both pessimistic and optimistic tracking alone

Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions

Big spatial datasets are very common in scientific problems, such as those involving remote sensing of the earth by satellites, climate-model output, small-area samples from national surveys, and so forth. In this article, our interest lies primarily in very large, non-Gaussian datasets. We consider a hierarchical statistical model consisting of a conditional exponential family model for the data and an underlying (hidden) geostatistical process for some transformation of the (conditional) mean of the data model. Within this hierarchical model, dimension reduction is achieved by modeling the geostatistical process as a linear combination of a fixed number of spatial basis functions, which results in substantial computational speedups. These models do not rely on specifying a spatial-weights matrix, and no assumptions of homogeneity, stationarity, or isotropy are made. Our approach to inference using these models is empirical-Bayesian in nature. We develop maximum likelihood (ML) estimates of the unknown parameters using Laplace approximations in an expectation-maximization (EM) algorithm. We illustrate the performance of the resulting empirical hierarchical model using a simulation study. We also apply our methodology to analyze a remote sensing dataset of aerosol optical depth

Intracellular drug delivery using laser activated carbon nanoparticles

We demonstrate intracellular delivery of various molecules by inducing controlled and reversible cell damage through pulsed laser irradiation of carbon black (CB) nanoparticles. We then characterized and optimized the system for maximal uptake and minimal loss of viability. At our optimal condition 88% of cells exhibited uptake with almost no loss of viability. In other more intense cases it was shown that cell death could be prevented through addition of poloxamer. The underlying mechanism of action is also studied and our hypothesis is that the laser heats the CB leading to thermal expansion, vapor formation and/or chemical reaction leading to generation of acoustic waves and then there is energy transduction to the cell causing poration of the cell membrane. We also delivered anti-EGFR siRNA to ovarian cancer cells. Cells exposed to a laser at 18.75 mJ/cm2 for 7 minutes resulted in a 49% knockdown of EGFR compared to negative control. We established an alternative way to deliver siRNA to knockdown proteins, for the first time using laser CB interaction.Ph.D

Statistical modeling of MODIS cloud data using the spatial random effects model

Remote sensing of the earth by satellites yields datasets that can be massive in size. To overcome computational challenges, we make use of the reduced-rank Spatial Random Effects (SRE) model in our statistical analysis of cloud mask data from NASA’s Moderate Resolution Imaging Spectrora-diometer (MODIS) instrument on board NASA’s Terra satellite, launched in December 1999. A set of retrieval algorithms has been developed by members of the MODIS atmospheric team for detecting clouds. Clouds play an important role in climate studies, and hence an accurate quantification of the spatial distribution of clouds is necessary. In this paper, we build a statistical model for the underlying clear-sky-probability (or conversely, the cloud-probability) process, and we quantify the uncertainty in our predictions. We consider a hierarchical statistical model for analyzing the cloud data, where we postulate a hidden process for the probability of clear sky that makes use of the SRE model. Its advantages are considerable: It can represent many types of spatial behavior, it permits fast computations when datasets are very large, and it has attractive change-of-support properties

Empirical hierarchical modelling for count data using the Spatial Random Effects model

Count data over spatial lattices are the building blocks of spatial econometric data (e.g. unemployment rates in small areas). We consider a hierarchical statistical model made up of a Poisson model for the counts and an underlying Spatial Random Effects process for the logarithm of the mean of the Poisson distribution. The resulting dimension reduction leads to substantial computational speed-ups. These models make no assumptions of homogeneity, stationarity, or isotropy. We develop maximum-likelihood estimates (MLEs) for the parameters of the underlying process using an EM algorithm, and we predict unknown mean counts over the entire spatial lattice

Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions

Big spatial datasets are very common in scientific problems, such as those involving remote sensing of the earth by satellites, climate-model output, small-area samples from national surveys, and so forth. In this article, our interest lies primarily in very large, non-Gaussian datasets. We consider a hierarchical statistical model consisting of a conditional exponential-family model for the data and an underlying (hidden) geostatistical process for some transformation of the (conditional) mean of the data model. Within this hierarchical model, dimension reduction is achieved by modeling the geostatistical process as a linear combination of a fixed number of spatial basis functions, which results in substantial computational speed-ups. These models do not rely on specifying a spatial-weights matrix, and no assumptions of homogeneity, stationarity, or isotropy are made. Our approach to inference using these models is empirical-Bayesian in nature. We develop maximum likelihood (ML) estimates of the unknown parameters using Laplace approximations in an expectation-maximization (EM) algorithm. We illustrate the performance of the resulting empirical hierarchical model using a simulation study. We also apply our methodology to analyze a remote sensing dataset of aerosol optical depth