BAYESIAN ANALYSIS OF COSMIC RAY PROPAGATION: EVIDENCE AGAINST HOMOGENEOUS DIFFUSION.

We present the results of the most complete scan of the parameter space for cosmic ray (CR) injection and propagation. We perform a Bayesian search of the main GALPROP parameters, using the MultiNest nested sampling algorithm, augmented by the BAMBI neural network machine-learning package. This is the first study to separate out low-mass isotopes (p, p ¯ , and He) from the usual light elements (Be, B, C, N, and O). We find that the propagation parameters that best-fit p, p ¯ , and He data are significantly different from those that fit light elements, including the B/C and 10Be/9Be secondary-to-primary ratios normally used to calibrate propagation parameters. This suggests that each set of species is probing a very different interstellar medium, and that the standard approach of calibrating propagation parameters using B/C can lead to incorrect results. We present posterior distributions and best-fit parameters for propagation of both sets of nuclei, as well as for the injection abundances of elements from H to Si. The input GALDEF files with these new parameters will be included in an upcoming public GALPROP update.

Simultaneous widefield single molecule orientation and FRET microscopy in cells.

We combine single molecule fluorescence orientation imaging with single-pair fluorescence resonance energy transfer microscopy, using a total internal reflection microscope. We show how angles and FRET efficiencies can be determined for membrane proteins at the single molecule level and provide data from the epidermal growth factor receptor system in cells.