A polarimetric view of black hole accretion flows and jets
Monika Moscibrodzka Radboud Universiteit Nijmegen
5593 - 5604 of 20071 Results
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Efficient Data Compression and Causal Order Discovery for Multipartite Quantum Systems
Ge Bai University of Hong Kong (HKU)
Testing the standard cosmological model with the Dark Energy Survey
Jessica Muir University of Cincinnati
2D Holography beyond JT
Hamid Reza Afshar Institute for Research in Fundamental Sciences (IPM)
New Pathways to the Relic Abundance of Vector-Portal Dark Matter
Patrick Fitzpatrick Massachusetts Institute of Technology (MIT) - Department of Nuclear Science and Engineering
Measurement phase transitions and statistical mechanics
Adam Nahum University of Oxford
Offline Reinforcement Learning and Model-Based Optimization
Sergey Levine (UC Berkeley)Stable Reinforcement Learning with Unbounded State Space
Qiaomin Xie (Cornell)Special Topics in Astrophysics - Numerical Hydrodynamics - Lecture 23
Daniel Siegel University of Greifswald
A polarimetric view of black hole accretion flows and jets
Monika Moscibrodzka Radboud Universiteit Nijmegen
The Event Horizon Telescope is a global effort to construct an
Earth-sized virtual radio telescope array, with the goal to make pictures and
movies of two nearby supermassive black holes. A detailed theoretical
understanding of black hole accretion is now crucial to interpret these
observations. I will review our current efforts to model polarimetric
properties of light produced in synchrotron processes in plasma falling
towards the event horizon. The numerical models are based on general
relativistic magnetohydrodynamics simulations so they are capable of capturing
the complex dynamics of magnetic fields and their interactions with plasma. It is now
important to understand the polarized radiative transfer in these simulations
to correctly predict the observational signatures of the events at the event
horizon scales where the accretion disk and jet are connected.Efficient Data Compression and Causal Order Discovery for Multipartite Quantum Systems
Ge Bai University of Hong Kong (HKU)
In this talk, I will discuss two problems: quantum data compression
and quantum causal order discovery, both for multipartite quantum
systems. For data compression, we model finitely correlated states as
tensor networks, and design quantum compression algorithms. We first
establish an upper bound on the amount of memory needed to store an
arbitrary state from a given state family. The bound is determined by
the minimum cut of a suitable flow network, and is related to the flow
of information from the manifold of parameters that specify the states
to the physical systems in which the states are embodied. We then
provide a compression algorithm for general state families, and show
that the algorithm runs in polynomial time for matrix product states.
For quantum causal order discovery, we develop the first efficient
quantum causal order discovery algorithm with polynomial black-box
queries with respect to the number of systems. We model the causal
order with quantum combs, and our algorithm outputs the order of
inputs and outputs that the given process is compatible with. Our
method guarantees a polynomial running time for quantum combs with a
low Kraus rank, namely processes with low noise and little information
loss. For special cases where the causal order can be inferred from
local observations, we also propose algorithms that have lower query
complexity and only require local state preparation and local
measurements. Our results will provide efficient ways to detect and
optimize available transmission paths in quantum communication
networks, as well as methods to verify quantum circuits and to
discover the latent structure of multipartite quantum systems.Testing the standard cosmological model with the Dark Energy Survey
Jessica Muir University of Cincinnati
The Dark Energy Survey (DES) is a photometric galaxy survey which, using measurements of distortions to galaxy shapes from weak gravitational lensing and other observables, we can use to test the validity of our standard cosmological model, LambdaCDM. As an example of this, I will motivate and discuss a recent analysis of the DES Year 1 data (described in https://arxiv.org/abs/2010.05924) in which we use a "growth-geometry split" parameterization to check the consistency of constraints from structure growth and expansion history. I will also highlight some of the ongoing work on the DES Year 3 analysis, as well as challenges we will face as we subject LambdaCDM to increasingly precise tests with future cosmological experiments.
2D Holography beyond JT
Hamid Reza Afshar Institute for Research in Fundamental Sciences (IPM)
Dilaton-gravity models are integrable in two dimensions and admit a holographic description. In this talk, the holographic description of the Dilaton-gravity in flat spacetime is discussed. Using the gauge theory formulation of the model we obtain the boundary action which under certain boundary conditions is of the Warped-Schwarzian type. We calculate the 1-loop partition function of the model as the coadjoint orbit of the warped Virasoro group.
New Pathways to the Relic Abundance of Vector-Portal Dark Matter
Patrick Fitzpatrick Massachusetts Institute of Technology (MIT) - Department of Nuclear Science and Engineering
In the conventional weakly-interacting massive particle (WIMP) paradigm the late-time density of dark matter (DM) is set by the rate of two-body annihilations, but there has been considerable recent interest in exploring alternative DM scenarios where other interactions control the final abundance. I will show that by fully exploring the parameter space of a simple, weakly-coupled dark sector, we can find a rich set of novel pathways which lead to the observed relic density of DM. In particular, we can identify and characterize a general class of mechanisms in which the DM relic abundance is determined by processes controlling the thermal coupling of the DM and Standard Model (dubbed the KINetically DEcoupling Relic -- KINDER), generalizing previously-studied special cases of this behavior.
Torsion cosmology and beyond
William Barker University of Cambridge
Torsion is a popular ingredient in gravity, yet fraught with quantum and classical pathologies. I develop a novel torsion theory, consistent with power-counting and unitarity. The Friedmann equations emerge (with dark energy and radiation), as do pp waves and the Schwarzschild vacuum, all without an Einstein-Hilbert term. I show that cosmology sees torsion as a non-canonical scalar, revealing a rich phenomenology of conformal or waterfall inflatons, and cuscutons. I finally argue that future work will be driven less by toy-models, and more by computer surveys. I advocate Hamiltonian and effective field theory approaches to non-Riemannian geometry in general, relevant to ultraviolet completion and modified gravity alike. Such methods should be oriented towards the ultimate test of gravity: observed cosmological structure.
Measurement phase transitions and statistical mechanics
Adam Nahum University of Oxford
A many-body quantum system that is continually monitored by an external observer may be in distinct dynamical phases, depending on whether or not the observer’s repeated local measurements prevent the buildup of long-range entanglement. The universal properties of the “measurement phase transitions” between these phases remain a challenge. In this talk I will describe new theoretical approaches to measurement phase transitions, making connections with problems in statistical mechanics such as disordered magnets and travelling waves. I will show that exact results are possible in some regimes, including for quantum circuits with all-to-all interactions. (Based on arxiv:2009.11311)
Offline Reinforcement Learning and Model-Based Optimization
Sergey Levine (UC Berkeley)Modern supervised machine learning algorithms are at their best when provided with large datasets and large, high-capacity models. This kind of data-driven paradigm has driven remarkable progress in fields ranging from computer vision to natural language processing and speech recognition. However, reinforcement learning algorithms have proven difficult to scale to such large data regimes without the use of simulation. Online reinforcement learning algorithms require recollecting data in each experiment -- when the dataset is on the scale of ImageNet and MS-COCO, this becomes infeasible to do in the real world. Offline reinforcement learning algorithms have so far been difficult to integrate with deep neural networks. In this talk, I will discuss some recent advances in offline reinforcement learning that I think represent a step toward bridging this gap, and in addition carry a number of appealing theoretical properties. I will discuss the theoretical reasons why offline reinforcement learning is challenging, discuss the solutions that have been proposed in the literature, and describe our recent advances in developing conservative Q-learning methods that provide theoretical guarantees in the face of distributional shift, providing not only a practical way of deploying offline deep RL, but also a degree of confidence that the resulting solution will avoid common pitfalls of overestimation. I will also describe how the ideas in offline RL can be applied more broadly to data-driven optimization problems that do not necessarily involve sequential decision making, such as designing protein sequences or robot morphologies from prior experimental data. This emerging field shares many of the theoretical foundations with offline RL, but represents the possibility to broaden the impact of data-driven decision making to a wider range of applications.Convergence and Sample Complexity of Gradient Methods for the Model-Free Linear Quadratic Regulator Problem
Mihailo Jovanovic (USC)Model-free reinforcement learning attempts to find an optimal control action for an unknown dynamical system by directly searching over the parameter space of controllers. The convergence behavior and statistical properties of these approaches are often poorly understood because of the nonconvex nature of the underlying optimization problems and the lack of exact gradient computation. In this talk, we discuss performance and efficiency of such methods by focusing on the standard infinite-horizon linear quadratic regulator problem for continuous-time systems with unknown state-space parameters. We establish exponential stability for the ordinary differential equation (ODE) that governs the gradient-flow dynamics over the set of stabilizing feedback gains and show that a similar result holds for the gradient descent method that arises from the forward Euler discretization of the corresponding ODE. We also provide theoretical bounds on the convergence rate and sample complexity of the random search method with two-point gradient estimates. We prove that the required simulation time for achieving $\epsilon$-accuracy in the model-free setup and the total number of function evaluations both scale as $\log (1/\epsilon)$.Policy Evaluation under Interference
Stefan Wager (Stanford)Most literature on policy evaluation, bandit methods, etc., is focused on settings where actions taken on one unit do not affect other units. Such lack of interference, however, fails to hold in many applications of interest. For example, in a vaccine study, one person getting vaccinated also protects others; in a microcredit study, loans given to one person may stimulate the economy and indirectly benefit others; or, in a jobs-training study, training more people to perform a given task may create over-supply of qualified workers, thus reducing the market value of the training. In this talk, I'll survey various approaches to modeling cross-unit interference, and discuss associated methods for policy evaluation.Stable Reinforcement Learning with Unbounded State Space
Qiaomin Xie (Cornell)We consider the problem of reinforcement learning (RL) with unbounded state space, motivated by the classical problem of scheduling in a queueing network. Traditional policies as well as error metric that are designed for finite, bounded or compact state space, require infinite samples for providing any meaningful performance guarantee (e.g. ℓ_∞ error) for unbounded state space. We need a new notion of performance metric. Inspired by the literature in queuing systems and control theory, we propose stability as the notion of “goodness”: the state dynamics under the policy should remain in a bounded region with high probability. As a proof of concept, we propose an RL policy using Sparse-Sampling-based Monte Carlo Oracle and argue that it satisfies the stability property as long as the system dynamics under the optimal policy respects a Lyapunov function. The assumption of existence of a Lyapunov function is not restrictive as it is equivalent to the positive recurrence or stability property of any Markov chain. And, our policy does not utilize the knowledge of the specific Lyapunov function. To make our method sample efficient, we provide an improved, sample efficient Monte Carlo Oracle with Lipschitz value. We also design an adaptive version of the algorithm, based on carefully constructed statistical tests, which finds the correct tuning parameter automatically. This work is joint with Devavrat Shah and Zhi Xu.Special Topics in Astrophysics - Numerical Hydrodynamics - Lecture 23
Daniel Siegel University of Greifswald