Brownian Motion of Black Holes in Dense Nuclei

We evaluate the Brownian motion of a massive particle ( black hole ) at the center of a galaxy using N-body simulations. Our galaxy models have power-law central density cusps like those observed at the centers of elliptical galaxies. The simulations show that the black hole achieves a steady-state kinetic energy that is substantially different than would be predicted based on the properties of the galaxy model in the absence of the black hole. The reason appears to be that the black hole responds to stars whose velocities have themselves been raised by the presence of the black hole. Over a wide range of density slopes and black hole masses, the black hole’s mean kinetic energy is equal to what would be predicted under the assumption that it is in energy equipartition with stars lying within a distance ∼ rh/2 from it, where rh is the black hole’s influence radius. The dependence of the Brownian velocity on black hole mass is approximately hV2i µ M−1/(3−g) BH with g the power-law index of the stellar density profile, r µ r−g. This is less steep than the M−1 BH dependence predicted in a model where the effect of the black hole on the stellar velocities is ignored. The influence of a stellar mass spectrum on the black hole’s Brownian motion is also evaluated and found to be consistent with predictions from Chandrasekhar’s theory. We use these results to derive a probability function for the mass of the Milky Way black hole based on a measurement of its proper motion velocity. Interesting constraints on MBH will require a velocity resolution exceeding 0.5 km s−1 (Refer to PDF file for exact formulas)

Determination of the Defining Boundary in Nuclear Magnetic Resonance Diffusion Experiments

While nuclear magnetic resonance diffusion experiments are widely used to resolve structures confining the diffusion process, it has been elusive whether they can exactly reveal these structures. This question is closely related to X-ray scattering and to Kac's "hear the drum" problem. Although the shape of the drum is not "hearable", we show that the confining boundary of closed pores can indeed be detected using modified Stejskal-Tanner magnetic field gradients that preserve the phase information and enable imaging of the average pore in a porous medium with a largely increased signal-to-noise ratio.Comment: 13 pages, 2 figure

Brownian Motion of Black Holes in Dense Nuclei

We evaluate the Brownian motion of a massive particle ("black hole") at the center of a galaxy using N-body simulations. Our galaxy models have power-law central density cusps like those observed at the centers of elliptical galaxies. The simulations show that the black hole achieves a steady-state kinetic energy that is substantially different than would be predicted based on the properties of the galaxy model in the absence of the black hole. The reason appears to be that the black hole responds to stars whose velocities have themselves been raised by the presence of the black hole. Over a wide range of density slopes and black hole masses, the black hole's mean kinetic energy is equal to what would be predicted under the assumption that it is in energy equipartition with stars lying within a distance ~r_h/2 from it, where r_h is the black hole's influence radius. The dependence of the Brownian velocity on black hole mass is approximately ~ 1/M^{1/(3-gamma)} with gamma the power-law index of the stellar density profile, rho~1/r^gamma. This is less steep than the 1/M dependence predicted in a model where the effect of the black hole on the stellar velocities is ignored. The influence of a stellar mass spectrum on the black hole's Brownian motion is also evaluated and found to be consistent with predictions from Chandrasekhar's theory. We use these results to derive a probability function for the mass of the Milky Way black hole based on a measurement of its proper motion velocity. Interesting constraints on M will require a velocity resolution exceeding 0.5 km/s.Comment: 11 pages, uses emulateapj.st

NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data

Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast

Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.Comment: Accepted conference paper at MICCAI 201