Self-consistent computation of gamma-ray spectra due to proton-proton interactions in black hole systems

In the inner regions of an accretion disk around a black hole, relativistic protons can interact with ambient matter to produce electrons, positrons and $\gamma$-rays. The resultant steady state electron and positron particle distributions are self-consistently computed taking into account Coulomb and Compton cooling, $e^-e^+$ pair production (due to $\gamma-\gamma$ annihilation) and pair annihilation. While earlier works used the diffusion approximation to obtain the particle distributions, here we solve a more general integro-differential equation that correctly takes into account the large change in particle energy that occur when the leptons Compton scatter off hard X-rays. Thus this formalism can also be applied to the hard state of black hole systems, where the dominant ambient photons are hard X-rays. The corresponding photon energy spectrum is calculated and compared with broadband data of black hole binaries in different spectral states. The results indicate that the $\gamma$-ray spectra ($E > 0.8$ MeV) of both the soft and hard spectral states and the entire hard X-ray/$\gamma$-ray spectrum of the ultra-soft state, could be due to $p-p$ interactions. These results are consistent with the hypothesis that there always exists in these systems a $\gamma$-ray spectral component due to $p-p$ interactions which can contribute between 0.5 to 10% of the total bolometric luminosty. The model predicts that {\it GLAST} would be able to detect black hole binaries and provide evidence for the presence of non-thermal protons which in turn would give insight into the energy dissipation process and jet formation in these systems.Comment: Accepted for publication in MNRA

Shallow-Depth Variational Quantum Hypothesis Testing

We present a variational quantum algorithm for differentiating several hypotheses encoded as quantum channels. Both state preparation and measurement are simultaneously optimized using success probability of single-shot discrimination as an objective function which can be calculated using localized measurements. Under constrained signal mode photon number quantum illumination we match the performance of known optimal 2-mode probes by simulating a bosonic circuit. Our results show that variational algorithms can prepare optimal states for binary hypothesis testing with resource constraints. Going beyond the binary hypothesis testing scenario, we also demonstrate that our variational algorithm can learn and discriminate between multiple hypotheses.Comment: Version 2, 12 pages, 8 figures, comments welcom

Hybrid viscosity and the magnetoviscous instability in hot, collisionless accretion disks

We aim to illustrate the role of hot protons in enhancing the magnetorotational instability (MRI) via the ``hybrid'' viscosity, which is due to the redirection of protons interacting with static magnetic field perturbations, and to establish that it is the only relevant mechanism in this situation. It has recently been shown by Balbus \cite{PBM1} and Islam & Balbus \cite{PBM11} using a fluid approach that viscous momentum transport is key to the development of the MRI in accretion disks for a wide range of parameters. However, their results do not apply in hot, advection-dominated disks, which are collisionless. We develop a fluid picture using the hybrid viscosity mechanism, that applies in the collisionless limit. We demonstrate that viscous effects arising from this mechanism can significantly enhance the growth of the MRI as long as the plasma \beta \gapprox 80. Our results facilitate for the first time a direct comparison between the MHD and quasi-kinetic treatments of the magnetoviscous instability in hot, collisionless disks.Comment: To appear in the proceedings of the first Kodai-Trieste workshop on Plasma Astrophysics (Aug 27-Sept 07 2007), Springer Astrophysics and Space Science Proceedings serie

Sleep duration in school-age children with epilepsy: A cross-sectional study

Background: Normal sleep is required for the optimal growth and development of the children. Ineffective or inadequate sleep is common in children with epilepsy. Objectives: The objectives of this study were to study the sleep duration and describe the factors affecting it in school-aged children with epilepsy attending the seizure clinic of a pediatric tertiary care hospital. Materials and Methods: 6–12-year-old children with epilepsy, attending the seizure clinic formed the study subjects. They were assessed for inclusion in the study using INCLEN diagnostic tool for epilepsy (INDT-Epi) to achieve a sample size of 139. Informed written consent was obtained from parents. Background sociodemographic information, seizure type and treatment details, and duration of sleep of the child were collected from the parents. The proportion of children with epilepsy who had sleep problems were expressed as percentage. Results: The mean age of study population was 9.07±2.09 years. The average sleep duration of the study population was 9.41±1.41 h. The mean nap time of the study population was 68.51±33.88 min. No significant association was seen among the factors that determine sleep duration. Conclusion: Children with epilepsy tend to sleep for lesser hours when compared to historic controls of normal school-age children reported in literature

Measuring Market Liquidity Risk - Which Model Works Best?

Market liquidity risk, the difficulty or cost of trading assets in crises, has been recognized as an important factor in risk management. Literature has already proposed several models to include liquidity risk in the standard Value-at-Risk framework. While theoretical comparisons between those models have been conducted, their empirical performance has never been benchmarked. This paper performs comparative back-tests of daily risk forecasts for a large selection of traceable liquidity risk models. In a 5.5 year stock sample we show which model provides most accurate results and provide detailed recommendations which model is most suitable in a specific situation

Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models

Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism’s metabolic network. Principal Findings: For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not. Conclusions: Inferences about a microorganism’s nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism’s alteration of gene expression is adaptive to its nutrient environment.National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 2722008000059C)National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 26620040000IC)Bill & Melinda Gates Foundation (grant 18651010-37352-A

Macronumerical Summing Code

There are many methods to minimise or shrink binary codes or data compression. Making data shrinkage is better for faster communications and effective storage. Macronumerical summing code is a relatively new technique to shrink binary code structures and you can easily decode it to the original size for without data loss.</jats:p

DC POLARIZATION IN A NONLINEAR DIELECTRIC MEDIUM AT OPTICAL FREQUENCIES FREQUENCIES

Abstract not availabl