Events on Monday, October 7th, 2024
- Climate & Diversity
- Climate and Diversity Committee Open Hours
- Time: 12:00 pm - 2:00 pm
- Place: Chamberlin 5310
- Speaker: Rachel Zizmann, UW-Madison Physics
- Abstract: Open Hours are welcome for everyone in the department! During these sessions, we have the option to discuss the topic listed, that is not required or necessary for attending
- Host: Rachel Zizmann
- Preliminary Exam
- Search for TeV Halos Using HAWC Data
- Time: 3:00 pm - 5:00 pm
- Place: 5280 Chamberlin Hall
- Speaker: Hongyi Wu, Physics PhD Graduate Student
- Abstract: Extended gamma-ray emission at TeV energies, known as TeV halos, has been observed around a few middle-aged isolated pulsars. It has been suggested that they may also be powered by millisecond pulsars (MSPs). Using data from the High Altitude Water Cherenkov (HAWC) Observatory, we searched for extended gamma-ray emission around 36 isolated middle-aged pulsars and 57 MSPs. Through a stacking analysis comparing TeV flux models against a background-only hypothesis, we identified TeV halo-like emission from isolated middle-aged pulsars at a significance level of 5.10σ, but found no significant emission from MSPs. The results imply that TeV halos may commonly exist around middle-aged pulsars, while MSPs are not as efficient in producing them. These findings provide a method to identify pulsars that are invisible to radio, X-ray, and GeV gamma-ray observations, and have significant implications for the physics interpretation of the Galactic center GeV excess and high-latitude Galactic diffuse emission. Future works including HAWC and Fermi-LAT data analysis around TeV halo sources will also be discussed.
- Host: Ke Fang
- Theory Seminar (High Energy/Cosmology)
- Machine Learning Symmetries in Physics from First Principles
- Time: 4:30 pm - 5:30 pm
- Place: 5310 Chamberlin Hall
- Speaker: Konstantin Matchev, University of Florida
- Abstract: Symmetries are the cornerstones of modern theoretical physics, as they imply fundamental conservation laws. The recent boom in AI algorithms and their successful application to high-dimensional large datasets from all aspects of life motivates us to approach the problem of discovery and identification of symmetries in physics as a machine-learning task. In a series of papers, we have developed and tested a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural network architectures to model the symmetry transformations and the corresponding generators. Our proposed loss functions ensure that the applied transformations are symmetries and that the corresponding set of generators is orthonormal and forms a closed algebra. One variant of our method is designed to discover symmetries in a reduced-dimensionality latent space, while another variant is capable of obtaining the generators in the canonical sparse representation. Our procedure is completely agnostic and has been validated with several examples illustrating the discovery of the symmetries behind the orthogonal, unitary, Lorentz, and exceptional Lie groups.
- Host: Lisa Everett
