Self-adjointness is a fundamental property of a linear operator in quantum mechanics. In physics, a self-adjoint operator is usually defined to be an operator which is own adjoint. However, this definition is in fact not satisfactory since a self-adjoint operator in this definition does not always have nice properties such as the spectral decomposition. Hence, in mathematics, a kind of completeness is also assumed in the definition of a self-adjoint operator. Here a natural question is how to judge whether an operator is self-adjoint. It has been believed that self-adjointness is closely related to completeness of the classical dynamics for a long time although a complete description of such relations has not been given so far. I am planning to talk about how self-adjointness is important in mathematical physics. Moreover, I will explain relations between self-adjointness and classical dynamics by introducing some examples.

*Please contact Keita Mikami or Hiroyasu Miyazaki's mailing address to get access to the Zoom meeting room.

In many scientific fields, ranging from astrophysics to particle physics and neuroscience, simulators for dynamical systems generate a massive amount of data. One of the crucial tasks scientists are spending their precious time on is comparing observational data to the aforementioned simulations in order to infer physically relevant parameters and their uncertainties, based on the model embedded in the simulator. This poses a problem because the likelihood function for realistic simulations of complex physical systems is intractable. Simulation-based inference techniques attack this problem using machine learning tools and probabilistic programming. I will start with an overview of the problem and explain the general application of simulation-based inference methods. Then I will describe an application of the methods to a model of neurons in the visual cortex of mice."

Bootstrap resampling is quite useful for computing “confidence values” or “p-values” of trees and edges. However, they are biased and may lead to false positives (too many wrong discoveries) or false negatives (too few correct discoveries) depending on the “curvature” of the boundary surface of a hypothesis region in the data space. In addition, we face the issue of selection bias because we tend to use the dataset twice for hypothesis selection and its evaluation. I will explain these two types of bias and show methods to adjust the confidence values.

FASER, the ForwArd Search ExpeRiment, is an experiment dedicated to searching for light, extremely weakly-interacting particles at the LHC. Such particles may be produced in the LHC’s high-energy collisions and then decay to visible particles in FASER, which is placed 480 m downstream of the ATLAS interaction point. FASER, also includes a sub-detector, FASER$\nu$, designed to detect neutrino’s produced in the LHC collisions and to study their properties. This seminar will describe the physics motivations, detector design, expected performance of FASER, and current status, as well as the physics prospects.