Video Library

Since 2002 Perimeter Institute has been recording seminars, conference talks, and public outreach events using video cameras installed in our lecture theatres.  Perimeter now has 7 formal presentation spaces for its many scientific conferences, seminars, workshops and educational outreach activities, all with advanced audio-visual technical capabilities.  Recordings of events in these areas are all available On-Demand from this Video Library and on Perimeter Institute Recorded Seminar Archive (PIRSA)PIRSA is a permanent, free, searchable, and citable archive of recorded seminars from relevant bodies in physics. This resource has been partially modelled after Cornell University's arXiv.org. 

  

 

Tuesday Nov 21, 2017
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The Dark Energy Survey (DES) is a five-year, 5000 sq. deg. observing program using the Dark Energy Camera on the 4m Blanco telescope at CTIO. I will describe the cosmological analysis of large-scale structure in the Universe using 1321 sq. deg. of data taken in the first year of DES operations. The analysis combines unprecedented measurements of weak gravitational lensing and the clustering of galaxies over the redshift range 0.2 to 1.3 to derive the most precise such cosmological constraints to date.

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Monday Nov 20, 2017
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Talk is based on the joint work with Lev Rozansky. In my talk will outline a construction that provides complex $C_b$ of coherent sheaves on the Hilbert scheme of $n$ points on the plane for every $n$-stranded braid $b$. The space of global sections of $C_b$ is a categorification of the HOMFLYPT polynomial of the closure $L(b)$ of the braid. I will also present a physical interpretation of our construction as a particular case of Kapustin-Saulina-Rozansky 3D topological field theory.

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Monday Nov 20, 2017
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Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state.

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