Bloch State Tomography Using Wilson Lines

May 27, 2016

Fig. 1b: Wilson lines and effectively degenerate Bloch bands.* [Reprinted with permission from AAAS ©2016.]

Topology and geometry are essential to our understanding of modern physics, underlying many foundational concepts from high-energy theories, quantum information, and condensed-matter physics. In condensed-matter systems, a wide range of phenomena stem from the geometry of the band eigenstates, which is encoded in the matrix-valued Wilson line for general multiband systems.


Congratulations to the 2015-16 Physics Graduates!

May 18, 2016
Physics PhD Graduates

Physics Concentrators: Tara Aida, Mark Arildsen, Steven Barroqueiro, Roman Berens, Amir Bitran, Jimmy Castano, Nicky Charles, Aftab Chitalwala, Alex Coeytaux, Keno Fischer, Tudor Giurgica-Tiron, Connor Harris, Achim Harzheim, Roger Huang, Grace Huckins, Natalie Janzow, Patrick Komiske, Michael Landry, Dennis Lee, Peter Lu, Aaron Markowitz, Kyle Matsuda, Nat Mayer, Eric Metodiev, Whitney Nimitpattana, Matt Pasquini, Ray Qian, David Roberts, Ellen Robo, Sadik Shahidain, Austin Shin, Olivier Simon, Zack Soule, Erik Tamre, Will Tobias, Joy Wang, Lucian Wang, Annie Wei, Daniel Windham, Cyndia Yu, Daniel Yue, Alan Zhou, Ting Zhou


Scientists Extend Laser 'Combing' Method to Identify Large, Complex Molecules

May 5, 2016

Figure 2: A schematic of the combined CE-DFCS and buffer gas cooling apparatus*
[Reprinted with permission from AAAS]

JILA physicists have extended the capability of their powerful laser "combing" technique to identify the structures of large, complex molecules of the sort found in explosives, pharmaceuticals, fuels and the gases around stars.


Non-Hermitian Localization in Biological Networks

April 27, 2016

[reprinted by permission from APS]

In a recent Phys. Rev. E article, David Nelson, together with Ariel Amir (SEAS; formerly Harvard Physics Junior Fellow) and Naomichi Hatano (UTokyo), analyzed the eigenvalues and eigenvectors of certain asymmetric tridiagonal matrices. The authors found a rich and complex behavior in the distribution of eigenvalues and the localization properties of eigenvectors. This has implications that can help understand features of the dynamics of neural networks, as well as neural development in organisms.