Nucleosynthesis in the accretion disks of type II collapsars

We investigate nucleosynthesis inside the gamma-ray burst (GRB) accretion disks formed by the Type II collapsars. In these collapsars, the core collapse of massive stars first leads to the formation of a proto-neutron star. After that, an outward moving shock triggers a successful supernova. However, the supernova ejecta lacks momentum and within a few seconds the newly formed neutron star gets transformed to a stellar mass black hole via massive fallback. The hydrodynamics of such an accretion disk formed from the fallback material of the supernova ejecta has been studied extensively in the past. We use these well-established hydrodynamic models for our accretion disk in order to understand nucleosynthesis, which is mainly advection dominated in the outer regions. Neutrino cooling becomes important in the inner disk where the temperature and density are higher. The higher the accretion rate (M) over dot is, the higher the density and temperature are in the disks. We deal with accretion disks with relatively low accretion rates: 0.001 M-circle dot s(-1) less than or similar to (M) over dot less than or similar to 0.01 M-circle dot s(-1) and hence these disks are predominantly advection dominated. We use He-rich and Si-rich abundances as the initial condition of nucleosynthesis at the outer disk, and being equipped with the disk hydrodynamics and the nuclear network code, we study the abundance evolution as matter inflows and falls into the central object. We investigate the variation in the nucleosynthesis products in the disk with the change in the initial abundance at the outer disk and also with the change in the mass accretion rate. We report the synthesis of several unusual nuclei like P-31, K-39, Sc-43, Cl-35 and various isotopes of titanium, vanadium, chromium, manganese and copper. We also confirm that isotopes of iron, cobalt, nickel, argon, calcium, sulphur and silicon get synthesized in the disk, as shown by previous authors. Much of these heavy elements thus synthesized are ejected from the disk via outflows and hence they should leave their signature in observed data.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.