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Michael Ross

Publications and source records attributed to Michael Ross.

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From neutron skins and neutron matter to the neutron star crust

We present the first Bayesian inference of neutron star crust properties to incorporate neutron skin data, including the recent PREX measurement of the neutron skin of $^{208}$Pb, combined with recent chiral effective field theory predictions of pure neutron matter with statistical errors. Using a compressible liquid drop model with an extended Skyrme energy-density functional, we obtain the most stringent constraints to date on the transition pressure $P_{\rm cc}=0.33^{+0.07}_{-0.07}$ MeV fm$^{-3}$ and chemical potential $\mu_{\rm cc}=12.6^{+1.8}_{-1.9}$ MeV (which control the mass, moment of inertia and thickness of a neutron star crust), the proton fractions that bracket the pasta phases $y_{\rm p}=0.115^{+0.016}_{-0.017}$ and $y_{\rm cc}=0.041^{+0.007}_{-0.006}$, as well as the relative mass and moment of inertia $\Delta M_{\rm p} / \Delta M_{\rm c}\approx \Delta I_{\rm p} / \Delta I_{\rm c} = 0.54^{+0.05}_{-0.09}$ and thickness $\Delta R_{\rm p} / \Delta R_{\rm c}=0.129^{+0.019}_{-0.030}$ of the layers of non-spherical nuclei (nuclear pasta) in the crust.

nucl-th

Observation of a potential future sensitivity limitation from ground motion at LIGO Hanford

A first detection of terrestrial gravity noise in gravitational-wave detectors is a formidable challenge. With the help of environmental sensors, it can in principle be achieved before the noise becomes dominant by estimating correlations between environmental sensors and the detector. The main complication is to disentangle different coupling mechanisms between the environment and the detector. In this paper, we analyze the relations between physical couplings and correlations that involve ground motion and LIGO strain data h(t) recorded during its second science run in 2016 and 2017. We find that all noise correlated with ground motion was more than an order of magnitude lower than dominant low-frequency instrument noise, and the dominant coupling over part of the spectrum between ground and h(t) was residual coupling through the seismic-isolation system. We also present the most accurate gravitational coupling model so far based on a detailed analysis of data from a seismic array. Despite our best efforts, we were not able to unambiguously identify gravitational coupling in the data, but our improved models confirm previous predictions that gravitational coupling might already dominate linear ground-to-h(t) coupling over parts of the low-frequency, gravitational-wave observation band.

gr-qc

Nonparametric Curve Alignment

Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering.

cs.LG