CWRU PAT Coffee Agenda

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

+2 Cosmological model discrimination with Deep Learning.

jbm120 +1 mro28 +1

+1 On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses.

sxk1031 +1

+1 A Scale-Up of Lambda_3

kjh92 +1

+1 Small-Scale Challenges to the $\Lambda$CDM Paradigm.

mro28 +1

+1 Towards a Lorentz Invariant UV Completion for Massive Gravity: dRGT Theory from Spontaneous Symmetry Breaking.

kjh92 +1

+1 Solar Extreme UV radiation and quark nugget dark matter model.

jxs1325 +1 jbm120 +1

0 A Scale-up of Lambda_3.

bump   lxj154 +1

0 Primordial black holes from scalar field evolution in the early universe.

bump   kxp265 +1 cxt282 +1

Showing votes from 2017-07-14 12:30 to 2017-07-18 11:30 | Next meeting is Tuesday May 12th, 10:30 am.

users

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astro-ph.CO

  • On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses.- [PDF] - [Article]

    Ana Diaz Rivero, Francis-Yan Cyr-Racine, Cora Dvorkin
     

    Studying the smallest self-bound dark matter structure in our Universe can yield important clues about the fundamental particle nature of dark matter. Galaxy-scale strong gravitational lensing provides a unique way to detect and characterize dark matter substructures at cosmological distances from the Milky Way. Within the cold dark matter (CDM) paradigm, the number of low-mass subhalos within lens galaxies is expected to be large, implying that their contribution to the lensing convergence field is approximately Gaussian and could thus be described by their power spectrum. We develop here a general formalism to compute from first principles the substructure convergence power spectrum for different populations of dark matter subhalos. As an example, we apply our framework to two distinct subhalo populations: a truncated Navarro-Frenk-White subhalo population motivated by standard CDM, and a truncated cored subhalo population motivated by self-interacting dark matter (SIDM). We study in detail how the subhalo abundance, mass function, internal density profile, and concentration affect the amplitude and shape of substructure power spectrum. We determine that the power spectrum is mostly sensitive to a specific combination of the subhalo abundance and moments of the mass function, as well as to the average tidal truncation scale of the largest subhalos included in the analysis. Interestingly, we show that the asymptotic slope of the substructure power spectrum at large wavenumber reflects the internal density profile of the subhalos. In particular, the SIDM power spectrum exhibits a characteristic steepening at large wavenumber absent in the CDM power spectrum, opening the possibility of using this observable, if at all measurable, to discern between these two scenarios.

  • Cosmological model discrimination with Deep Learning.- [PDF] - [Article]

    Jorit Schmelzle, Aurelien Lucchi, Tomasz Kacprzak, Adam Amara, Raphael Sgier, Alexandre Réfrégier, Thomas Hofmann
     

    We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our method to be able to distinguish between five models, which were chosen to lie along the $\sigma _8$ - $\Omega _m$ degeneracy, and have nearly the same two-point statistics. We design and implement a Deep Convolutional Neural Network (DCNN) which learns the relation between five cosmological models and the mass maps they generate. We develop a new training strategy which ensures the good performance of the network for high levels of noise. We compare the performance of this approach to commonly used non-Gaussian statistics, namely the skewness and kurtosis of the convergence maps. We find that our implementation of DCNN outperforms the skewness and kurtosis statistics, especially for high noise levels. The network maintains the mean discrimination efficiency greater than $85\%$ even for noise levels corresponding to ground based lensing observations, while the other statistics perform worse in this setting, achieving efficiency less than $70\%$. This demonstrates the ability of CNN-based methods to efficiently break the $\sigma _8$ - $\Omega _m$ degeneracy with weak lensing mass maps alone. We discuss the potential of this method to be applied to the analysis of real weak lensing data and other datasets.

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gr-qc

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