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Showing votes from 2017-11-07 11:30 to 2017-11-10 12:30 | Next meeting is Tuesday Sep 9th, 10:30 am.
Calculations of the Cosmic Microwave Background lensing power implemented into the standard cosmological codes such as CAMB and CLASS usually treat the surface of last scatter as an infinitely thin screen. However, since the CMB anisotropies are smoothed out on scales smaller than the diffusion length due to the effect of Silk damping, the photons which carry information about the small-scale density distribution come from slightly earlier times than the standard recombination time. The dominant effect is the scale dependence of the mean redshift associated with the fluctuations during recombination. We find that fluctuations at $k = 0.01 {\rm \ Mpc^{-1}}$ come from a characteristic redshift of $z \approx 1090$, while fluctuations at $k = 1 {\rm \ Mpc^{-1}}$ come from a characteristic redshift of $z \approx 1200$. We then estimate the corrections to the lensing kernel due to the finite size of the thickness of the surface of last scatter. We conclude that neglecting it would result in a deviation from the true value of the lensing kernel at the half percent level at small scales. For future high signal-to-noise CMB experiments (e.g., CMB-S4), this corresponds to a $\sim 0.3 \sigma$ shift on scales $k \sim 1 {\rm \ Mpc^{-1}}$.
We show that GRB170817A and the subsequent radio and X-ray observations can be interpreted as due to an isotropic fireball loaded with a small amount ($M\sim 3\times 10^{-6}\,{\rm M_\odot}$) of neutron-rich ($Y_{\rm e}\sim 0.06$) material, which expands relativistically reaching a Lorentz factor $\Gamma\sim 5$. The physical picture resembles that of a giant flare from a magnetar, and could have been driven by an ultra-strong magnetic field $B\sim 3\times 10^{16}\,{\rm G}$ produced through amplification by magnetohydrodynamic turbulence at the beginning of the merger phase of the progenitor double neutron-star binary. Within such picture, the X-ray and radio data indicate a very tenuous ($n\sim 10^{-5}\,{\rm cm^{-3}}$) circum-binary medium, suggesting that the binary was outside the host galaxy in our direction, or that some process has blown a cavity around the binary before the merger. No relativistic jet is needed to explain the observations published in the literature so far, but we show that future radio and X-ray observations can be used to rule out the proposed picture. If our interpretation turns out to be correct, it indicates that not all double neutron-star mergers produce a jet, while most should feature this isotropic, hard X-ray component that can be a powerful guide to the discovery of additional kilonovae associated to relatively nearby gravitational wave events.
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.