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Seminar TodayCarrollian hydrodynamics near the black hole horizon
December 8 (Thu) at 16:00 - 17:30, 2022
Dr. Puttarak Jai-akson (Postdoctoral Researcher, iTHEMS)
The membrane paradigm provides a fascinating bridge between gravitational dynamics near black hole horizons (null boundaries) and fluid dynamics. One question naturally follows: what type of fluids and hydrodynamics emerged at the horizon? Contrary to the longstanding belief, it turns out that the horizon fluid is Carrollian, rather than the Galilean (Navier-Stokes) fluid. The Carroll geometries and Carrollian physics, arising originally when the speed of light goes to zero (c to 0 limit), have recently gained increasing attention in the fields of black hole physics and flat holography. In this presentation, I will talk about the Carrollian limit and the resulting Carroll geometries and this unusual kind of hydrodynamics, the Carrollian hydrodynamics. I will then present the geometrical construction of the membrane (also known as the stretched horizon) in a way that a Carroll geometry manifest, therefore allowing us to spell out precisely the dictionary between gravitational degrees of freedom on the membrane and the Carrollian fluid quantities. I will also show that the Einstein’s equations projected onto the horizon are the Carrollian hydrodynamic conservation laws. Lastly, I will discuss the covariant phase space of the horizon, symmetries, and conservation laws. The talk is based on arXiv:2209.03328 and arXiv:2211.06415.
Venue: Hybrid Format (Common Room 246-248 and Zoom)
Event Official Language: English
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Seminar Tomorrow
Quantum kinetics of neutrinos in high-energy astrophysical phenomena
December 9 (Fri) at 14:00 - 15:00, 2022
Dr. Hiroki Nagakura (Specially Appointed Assistant Professor (NAOJ Fellow), Division of Science, NAOJ)
Neutrinos are the most mysterious and elusive particles in the standard model of particle physics. They play important roles in core-collapse supernovae and binary neutron star mergers as driving mass-ejection, synthesizing heavy elements including r-process nuclei, and neutrino signals from these sources. This exhibits the importance of accurate modeling of neutrino radiation field in these phenomena, which will be used to connect neutrino physics to multi-messenger astronomy. It has recently been suggested that neutrino-flavor conversion (or neutrino-oscillation) can ubiquitously occur in these astrophysical environments, exhibiting the requirement of quantum kinetic treatments in the modeling of neutrino transport. In this seminar, I will give an overview of the quantum kinetics neutrino transport and then introduce its recent progress, paying a special attention to the connection to astrophysics. I will also present the latest results of our numerical simulations of collective neutrino oscillations, which can be properly accounted for only by quantum kinetic framework.
Venue: via Zoom
Event Official Language: English
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Seminar
Chiral effects on lepton transport in core-collapse supernovae
December 13 (Tue) at 13:30 - 15:00, 2022
Dr. Di-Lun Yang (Assistant Research Fellow, Institute of Physics, Academia Sinica, Taiwan)
Dynamics of leptons such as electrons and neutrinos play an important role in the evolution of core-collapse supernovae (CCSN). Nevertheless, chirality as one of fundamental microscopic properties that could affect lepton transport, through e.g. weak interaction, has been mostly overlooked. In this talk, I will discuss how chiral effects such as the renowned chiral magnetic effect (CME), generating an electric charge current along magnetic fields with chirality imbalance, could result in the unstable modes of magnetic fields and inverse cascade, which potentially influence the matter evolution in CCSN and pulsar kicks. I will also show how an effective CME could be realized via the backreaction from neutrino radiation even in the absence of an axial charge characterizing an unequal number of right- and left-handed electrons.
Venue: Hybrid Format (Common Room 246-248 and Zoom)
Event Official Language: English
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ColloquiumThe Epidemiology and Economics of Physical Distancing during Infectious Disease Outbreaks
December 14 (Wed) at 11:00 - 12:30, 2022
Prof. Troy Day (Professor, Head of Department, Department of Mathematics and Statistics, Queen's University, Canada)
People's incentives during an infectious disease outbreak influence their behaviour, and this behaviour can impact how the outbreak unfolds. Early on during an outbreak, people are at little personal risk of infection and hence may be unwilling to change their lifestyle to slow the spread of disease. As the number of cases grows, however, people may then voluntarily take extreme measures to limit their exposure. Political leaders also respond to the welfare and changing desires of their constituents, through public health policies that themselves shape the course of the epidemic and its ultimate health and economic repercussions. In this talk I will use ideas from the study of differential games to model how individuals’ and politicians’ incentives change during an outbreak, and the epidemiological and economic consequences that ensue when these incentives are acted upon. Motivated by the COVID-19 pandemic, I focus on physical distancing behaviour and the imposition of stay-at-home orders by politicians. I show that there is a fundamental difference in the political, economic, and health consequences of an infectious disease outbreak depending on the degree of asymptomatic transmission. If transmission occurs primarily by asymptomatic carriers, then politicians will be incentivized to impose stay-at-home orders earlier and for longer than individuals would like. Despite such orders being unpopular, however, they ultimately benefit all individuals. On the other hand, if the disease is transmitted primarily by symptomatic infections, then individuals are incentivized to stay at home earlier and for longer than politicians would like. In this case, politicians will be incentivized to impose back-to-work orders that, despite being unpopular, will again ultimately be to the benefit of all individuals. This is joint work with David McAdams, Fuqua School of Business and Economics Department, Duke University.
Venue: via Zoom
Event Official Language: English
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Seminar
Lattice gauge theory in curved spacetimes
December 15 (Thu) at 14:00 - 15:30, 2022
Dr. Arata Yamamoto (Assistant Professor, Department of Physics, Graduate School of Science, The University of Tokyo)
Lattice gauge theory is a powerful computational approach in quantum field theory. It is also utilizable for investigating quantum phenomena in curved spacetimes, such as rotating frame, torsion, and gravitational backgrounds. In this talk, I would like to overview the formulation and results of lattice simulations in curved spacetimes.
Venue: Common Room #246-248 (Main Venue) / via Zoom
Event Official Language: English
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Seminar
Chemophoresis Engine: Theory of ATPase-driven Cargo Transport
December 15 (Thu) at 16:00 - 17:00, 2022
Dr. Takeshi Sugawara (Project Researcher, Universal Biology Institute, The University of Tokyo)
The formation of macromolecule patterns depending on chemical concentration under non-equilibrium conditions, first observed during morphogenesis, has recently been observed at the intracellular level, and its relevance as intracellular morphogen has been demonstrated in the case of bacterial cell division. These studies have discussed how cargos maintain positional information provided by chemical gradients. However, how cargo transports are directly mediated by chemical gradients remains unknown. Based on the previously proposed mechanism of chemotaxis-like behavior of cargos (referred to as chemophoresis), we introduce the chemophoresis engine as a physicochemical mechanism of cargo motion, which transforms chemical free energy to directed motion through the catalytic ATP hydrolysis [1]. We propose its possible role as a universal principle of hydrolysis-driven intracellular transport.
Venue: via Zoom
Event Official Language: English
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Seminar
Geometric decomposition of entropy production in stochastic and chemical systems
December 16 (Fri) at 13:30 - 15:00, 2022
Mr. Kohei Yoshimura (Ph.D. Student, Department of Physics, Graduate School of Science, The University of Tokyo)
Entropy production is central to understanding nonequilibrium phenomena. It is known that decomposing entropy production enables us to separately treat distinct two aspects of dynamics, nonstationarity and breaking of detailed balance. In this seminar, I talk about our recent progress on geometric decomposition of entropy production in discrete stochastic systems and deterministic chemical systems. For the audience who may not be familiar with nonequilibrium thermodynamics and linear algebraic graph theory, which the latter enables us to treat the two kinds of systems at once, I would like to start with a very basic introduction. Then I explain why and how we decompose entropy production. Specifically, I mainly focus on the "Onsager-projective decomposition" we study in arXiv:2205.15227 rather than the information geometric decomposition provided in the following paper arXiv:2206.14599. Further, several physical consequences will be discussed, including generalization of Schnakenberg's decomposition stemming from cycles in a steady system, and its relation to gradient flow expressions of a master equation and a rate equation.
Venue: via Zoom
Event Official Language: English
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Seminar
Understanding kilonova spectra and identification of r-process elements
January 20 (Fri) at 14:00 - 15:00, 2023
Dr. Nanae Domoto (Ph.D. Student, Department of Astronomy, Graduate School of Science, Tohoku University)
Binary neutron star (NS) merger is a promising site for the rapid neutron capture nucleosynthesis (r-process). The radioactive decay of newly synthesized elements powers electromagnetic radiation, as called kilonova. The detection of gravitational wave from a NS merger GW170817 and the observation of the associated kilonova AT2017gfo have provided with us the evidence that r-process happens in the NS merger. However, the abundance pattern synthesized in this event, which is important to understand the origin of the r-process elements, is not yet clear. In this talk, I will first introduce an overview and current understanding of kilonova. Then, I will discuss our recent findings of elemental features in photospheric spectra of kilonova toward identification of elements.
Venue: via Zoom
Event Official Language: English
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Seminar
Physics-informed deep learning approach for modeling crustal deformation
January 23 (Mon) at 10:30 - 12:00, 2023
Dr. Naonori Ueda (Deputy Director, RIKEN Center for Advanced Intelligence Project (AIP))
The movement and deformation of the Earth’s crust and upper mantle provide critical insights into the evolution of earthquake processes and future earthquake potentials. Crustal deformation can be modeled by dislocation models that represent earthquake faults in the crust as defects in a continuum medium. In this study, we propose a physics-informed deep learning approach to model crustal deformation due to earthquakes. Neural networks can represent continuous displacement fields in arbitrary geometrical structures and mechanical properties of rocks by incorporating governing equations and boundary conditions into a loss function. The polar coordinate system is introduced to accurately model the displacement discontinuity on a fault as a boundary condition. We illustrate the validity and usefulness of this approach through example problems with strike-slip faults. This approach has a potential advantage over conventional approaches in that it could be straightforwardly extended to high dimensional, anelastic, nonlinear, and inverse problems.
Venue: via Zoom
Event Official Language: English
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ColloquiumScaling Optimal Transport for High dimensional Learning
January 24 (Tue) at 17:00 - 18:30, 2023
Dr. Gabriel Peyré (Research Director, CNRS/École Normale Supérieure, France)
iTHEMS-AIP Joint Colloquium Optimal transport (OT) has recently gained a lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this talk, I will explain how to leverage entropic regularization methods to define computationally efficient loss functions, approximating OT with a better sample complexity. More information and references can be found on the website of our book "Computational Optimal Transport" (see related link below).
Venue: via Zoom
Event Official Language: English