Photometric studies of some starburst galaxies

We present the results of a detailed morphological analysis of ten starburst galaxies selected from the Markarian catalogue of uv-excess objects. CCD surface photometry of these galaxies was carried out based on observations made in B, V (Johnson) and R, I (Kron-Cousins) band passes. We present the radial variations of surface brightness, ellipticity, position angle and the colour indices for each galaxy obtained using ellipse fitting isophotal analysis. The residual images constructed for extracting the fine structure are also presented. A variety of morphological types are found to host the starburst phenomenon. The star formation activity is not confined to the nuclear region alone, but it also occurs at various locations in the galaxy and is seen as clumpy regions. The colour index and the residual images are used for deriving information about the sites of enhanced star formation activity and the triggers of the starburst. The luminosity profiles show an exponential behaviour in the outer region. The disk scale lengths and the half-light radii are derived. The contribution of the burst component has been estimated and the colours of the burst component are presented. Strong isophotal twisting is detected in all the S0 and E galaxies: Mrk 1002, Mrk 1308 and Mrk 14, in the sample. This is accompanied by boxiness in some cases, suggesting that a merger is responsible for the starburst activity in these galaxies. In case of isolated spirals, a bar or a central oval distortion appear to be the likely trigger for the starburst.Comment: 12 pages of text and 28 figures. Uses aastex. To be published in A&A

Assessment of PET imaging devices: the case of a LSO/NaI PET-SPECT prototype

Hoekstra, O.S. [Promotor]Lingen, A. van [Copromotor

The effect of Pressure in Higher Dimensional Quasi-Spherical Gravitational Collapse

We study gravitational collapse in higher dimensional quasi-spherical Szekeres space-time for matter with anisotropic pressure. Both local and global visibility of central curvature singularity has been studied and it is found that with proper choice of initial data it is possible to show the validity of CCC for six and higher dimensions. Also the role of pressure in the collapsing process has been discussed.Comment: 11 pages, 6 figures, RevTeX styl

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines, a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. Synaptic sampling machines perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate & fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based synaptic sampling machines outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware