Joint JHU/STScI Colloquium: Kelsey Johnson (UVA)
STScI John Bahcall AuditoriumJoint JHU/STScI Colloquium: Kelsey Johnson (UVA) "The New View of Emerging Star Clusters"
Joint JHU/STScI Colloquium: Kelsey Johnson (UVA) "The New View of Emerging Star Clusters"
15-20 min long talks on astronomy-related papers, projects, ideas. Enjoy both Astronomy and Coffee (please bring your own mugs). If you have new results, ideas or want to discuss an interesting paper from arXiv, […]
Speaker 1: Alvin Modin - Crafting planar optics by 3D photoalignment of nematic liquid crystals Speaker 2: Nathan Prouse – Correlating Collective Self-Ordering in Polymers to Pair-Interactions Zoom link is https://JHUBlueJays.zoom.us/j/93026415427?pwd=ZGo1ditSNjh6V1JFY2dWNlhTOStLQT09 […]
15-20 min long talks on astronomy-related papers, projects, ideas. Enjoy both Astronomy and Coffee (please bring your own mugs). If you have new results, ideas or want to discuss an interesting paper from arXiv, […]
Informal science discussions and coffee, open to the entire department.
Title: de Sitter as an Axion Detector Abstract: Axions, scalar fields with compact field spaces, are some of the most well-motivated candidates for physics beyond the Standard Model. In this […]
Title: The Dawn of Multi-Messenger Collider Physics Abstract: The recent detection of neutrinos at the LHC has ushered in a new era of multi-messenger collider physics. Up to 2022, neutrinos […]
Jason Glenn (NASA Goddard) "Uncovering the Dust-Obscured Universe with the PRIMA Far-Infrared Probe: Growth of Galaxies, Supermassive Black Holes, and Planets"
15-20 min long talks on astronomy-related papers, projects, ideas. Enjoy both Astronomy and Coffee (please bring your own mugs). If you have new results, ideas or want to discuss an interesting paper from arXiv, […]
15-20 min long talks on astronomy-related papers, projects, ideas. Enjoy both Astronomy and Coffee (please bring your own mugs). If you have new results, ideas or want to discuss an interesting paper from arXiv, […]
Physics for ML and ML for Physics The recent success of machine learning suggests that neural networks may be capable of approximating some high-dimensional functions with controllably small errors, a […]
Informal science discussions and coffee, open to the entire department.