CWRU PAT Coffee Agenda

Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30

+2 Is cosmic acceleration proven by local cosmological probes?.

jxs1325 +1 cjc5 +1

+1 Black holes in vector-tensor theories.

lxj154 +1

+1 Cosmology in the laboratory: an analogy between hyperbolic metamaterials and the Milne universe.

dmj15 +1 lxj154 +1

+1 Violation of lepton universality

cjc5 +1

+1 Statistical Gravitational Waveform Models: What to Simulate Next?.

jtd55 +1

Showing votes from 2017-06-16 12:30 to 2017-06-20 11:30 | Next meeting is Tuesday May 12th, 10:30 am.

users

  • No papers in this section today!

astro-ph.CO

  • No papers in this section today!

astro-ph.HE

  • Statistical Gravitational Waveform Models: What to Simulate Next?.- [PDF] - [Article]

    Zoheyr Doctor, Ben Farr, Daniel E. Holz, Michael Pürrer
     

    Models of gravitational waveforms play a critical role in detecting and characterizing the gravitational waves (GWs) from compact binary coalescences. Waveforms from numerical relativity (NR), while highly accurate, are too computationally expensive to produce to be directly used with Bayesian parameter estimation tools like Markov-chain-Monte-Carlo and nested sampling. We propose a Gaussian process regression (GPR) method to generate accurate reduced-order-model waveforms based only on existing accurate (e.g. NR) simulations. Using a training set of simulated waveforms, our GPR approach produces interpolated waveforms along with uncertainties across the parameter space. As a proof of concept, we use a training set of IMRPhenomD waveforms to build a GPR model in the 2-d parameter space of mass ratio $q$ and equal-and-aligned spin $\chi_1=\chi_2$. Using a regular, equally-spaced grid of 120 IMRPhenomD training waveforms in $q\in[1,3]$ and $\chi_1 \in [-0.5,0.5]$, the GPR mean approximates IMRPhenomD in this space to mismatches below $4.3\times 10^{-5}$. Our approach can alternatively use training waveforms directly from numerical relativity. Beyond interpolation of waveforms, we also present a greedy algorithm that utilizes the errors provided by our GPR model to optimize the placement of future simulations. In a fiducial test case we find that using the greedy algorithm to iteratively add simulations achieves GPR errors that are $\sim 1$ order of magnitude lower than the errors from using Latin-hypercube or square training grids.

astro-ph.GA

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astro-ph.IM

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gr-qc

  • Cosmology in the laboratory: an analogy between hyperbolic metamaterials and the Milne universe.- [PDF] - [Article]

    David Figueiredo, Fernando Moraes, Sébastien Fumeron, Bertrand Berche
     

    This article shows that the compactified Milne universe geometry, a toy model for the big crunch/big bang transition, can be realized in hyperbolic metamaterials, a new class of nanoengineered systems which have recently found its way as an experimental playground for cosmological ideas. On one side, Klein-Gordon particles, as well as tachyons, are used as probes of the Milne geometry. On the other side, the propagation of light in two versions of a liquid crystal-based metamaterial provides the analogy. It is shown that ray and wave optics in the metamaterial mimic, respectively, the classical trajectories and wave function propagation, of the Milne probes, leading to the exciting perspective of realizing experimental tests of particle tunneling through the cosmic singularity, for instance.

hep-ph

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hep-th

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hep-ex

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quant-ph

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other

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