Kathryn Hartling
Canadian Nuclear Laboratories(CNL)

Abstract

An overview will be presented of several on-going research projects at Canadian Nuclear Laboratories (CNL) involving the application of muon, gamma, and liquid argon detectors for nuclear security and forensics, as well as contributions to Ac-225 medical isotope production.

Muon tomography leverages naturally-occurring cosmic-ray muon radiation to image target structures. CNL is currently home to four types of muon detectors based on plastic scintillators or Micromegas, including variants that are field deployable or integrate momentum measurement. In a nuclear security context, muon tomography can be used to identify and characterize special nuclear materials or associated shielding structures to support border security as well as nuclear waste management, disarmament, and infrastructure monitoring.

Nuclear forensics examines nuclear or radioactive materials, or other materials contaminated with radionuclides, in the context of legal proceedings. In this field, gamma-ray spectroscopy is a valuable method of characterizing the isotopic composition of materials in a non-destructive way. Coincident-gamma systems can help to isolate isotopic signatures in complex mixtures or high-background measurements. CNL is currently engaged in the development of a field-deployable coincident-gamma detector based on an array of cadmium zinc telluride crystals.

Liquid argon detectors are commonly used in dark matter and neutrino detection experiments, where it has been shown that they can provide excellent discrimination of nuclear and electron recoils. Consequently, LAr detectors are capable of detecting and differentiating neutron and gamma/beta radiation, and it is expected that their efficiencies and gamma energy resolution can exceed current standards for radiation portal monitors deployed for border security. CNL has developed a bench-top LAr detector prototype, and is currently planning for a future field-deployable design.

Ac-225 is a popular medical isotope candidate for targeted alpha therapy, a developmental method of cancer treatment. However, clinical trials are challenging due to low global production of this isotope. CNL is one of only a few locations globally with the ability to produce this material in research scale quantities. Ac-225 is produced by a thorium generator at CNL's Chalk River site, and by cyclotron irradiation of Ra-226 in collaboration with the Sylvia Fedoruk Canadian Center for Nuclear Innovation and Isotope Technologies Munich.

Timbits, coffee, tea will be served in STI A before the colloquium.

Stewart Prager
Princeton University

Abstract

The danger from nuclear weapons is increasing, with a new nuclear competition race underway, a deterioration of the multi-decade arms control regime, and new destabilizing technologies. This talk will overview nuclear weapons and their effects, the current critical situation, feasible steps to reduce the nuclear threat, and a new project initiated to engage physical scientists in advocacy for nuclear threat reduction.

Timbits, coffee, tea will be served in STI A before the colloquium.

Dr. Spencer Pitcher
Stellarex Group Ltd, CEO

Abstract

Fusion is the process that powers the Sun and the stars and is the primary source of energy in the Universe. It is an inexhaustible, CO2 free source of energy, which generates no long-lived radioactive waste and is intrinsically safe. Fusion energy is on the brink of commercialization worldwide. This lecture will discuss the historical development of fusion energy, give a status of worldwide activities, and describe plans for fusion energy development in Canada and the associated opportunities for universities, research institutes and industry.

Timbits, coffee, tea will be served in STI A before the colloquium.

Dr. Spencer Pitcher
Stellarex Group Ltd. - CEO

Fusion energy is the process that powers the Sun and stars. Scientists have been working on harnessing this power for the benefit of mankind since shortly after the Second World War. Incredible progress has been made over the intervening decades. These advances are to the point now that this technology is on the verge of commercialization.

The recent technical advances have given rise to what is effectively a gold-rush towards fusion energy. Advances include breakthroughs in supercomputers, computer simulations using Artificial Intelligence, advanced materials, and particularly the advent of high-temperature superconductors that are used to make the powerful magnets that contain the fusion fuel that is hotter than the centre of the Sun!

Fusion energy uses fuel that is virtually unlimited on Earth, does not produce greenhouse gas emissions, is intrinsically safe and produces no long-lived or high-level radioactive waste. Fusion is the ultimate energy source, and the original source of all energy in the Universe. Nothing comes after fusion energy.

n this talk, the global status of fusion energy research will be discussed, and the roadmap for Canada’s fusion energy development will be presented.

General admission is free.

Register here

Geoffrey Hinton
University of Toronto

Abstract

To train a neural net efficiently we need to compute the gradient of some measure of the performance of the net with respect to each of the connection weights. The standard way to do this is to use the chain rule to backpropagate gradients through layers of neurons. I shall  describe a very different way of getting the gradients that, for a while, seemed a lot more plausible as a model of how the brain gets gradients.

Consider a system composed of binary neurons that can be active or inactive with weighted pairwise couplings between pairs of neurons, including long range couplings. If the neurons represent pixels in a binary image, we can store a set of binary training images by adjusting the coupling weights so that the images are local minima of a Hopfield energy function which is minus the sum over all pairs of active neurons of their coupling weights. But this energy function can only capture pairwise correlations. It cannot represent the kinds of complicated higher-order correlations that occur in images. Now suppose that in addition to the "visible" neurons that represent the pixel intensities, we also have a large set of hidden neurons that have weighted couplings with each other and with the visible neurons. Suppose also that all of the neurons are asynchronous and stochastic: They adopt the active state with a log odds that is equal to the difference in the energy function when the neuron is inactive versus active. Given a set of training images, is there a simple way to set the weights on all of the couplings so that the training images are local minima of the free energy function obtained by integrating out the states of the hidden neurons?  The Boltzmann machine learning algorithm solved this problem in an elegant way. It  was proof of principle that learning in neural networks with hidden neurons was possible using only locally available information, contrary to what was generally believed at the time.

Timbits, coffee, tea will be served in STI A before the colloquium.

_{Chi-Kwan "CK" Chan,
Associate Astronomer, Steward Observatory}

Abstract

Black holes are the most powerful engines in the Universe, converting gravitational energy into radiation and, in some systems, relativistic jets that propagate across their host galaxies. By resolving these engines on event-horizon scales, the Event Horizon Telescope (EHT) has turned black holes into laboratories for testing gravity and extreme astrophysical models. In this talk, I will discuss my work on the Galactic Center black hole, Sgr A*, whose low luminosity and rapid variability make it a uniquely probe of spacetime and accretion physics. I will also discuss how observing and understanding black hole engines can drive innovation and advances in scientific computing, data science, machine learning, and even space technology

Timbits, coffee, tea will be served in STI A before the colloquium.

Mike McDonald
MIT

Abstract

In the past several decades it has become clear that mechanical (radio-mode) feedback from supermassive blackholes is necessary to moderate the growth of the most massive galaxies in which they reside. In particular, in the cores of galaxy clusters, the evolution of giant elliptical galaxies appears to be primarily governed by black hole feedback. Despite its apparent importance, our understanding of how feedback works is quite incomplete, particularly when it comes to mechanical or radio-mode feedback. In this talk I will discuss two directions that we are pursuing to understand the balance between cooling and feedback in galaxy cluster cores: (i) identifying systems for which feedback appears to not work, in an effort to understand the limitations and failure modes of the feedback/cooling cycle, and (ii) searching for both short- and long-term trends in the importance of AGN feedback, by considering large samples of clusters spanning 10 Gyr in cosmic time. These efforts are made possible by combining data from a variety of X-ray, optical, mm-wave, and radio telescopes, including Chandra, Hubble, James Webb, and the South Pole Telescope. I will conclude with a look towards the future of this field, and highlight some outstanding questions.

Timbits, coffee, tea will be served in STI A before the colloquium.