More Like this

1. The multiwavelength picture of GRB 221009A’s afterglow

   https://doi.org/10.1093/mnrasl/slad185  

   Klinger, Marc; Taylor, Andrew M.; Parsotan, Tyler; Beardmore, Andrew; Heinz, Sebastian; Zhu, Sylvia J. (December 2023, Monthly Notices of the Royal Astronomical Society: Letters)

   ABSTRACT We present counts-level fits to the multi-instrument (keV–GeV) data of the early afterglow (4 ks, 22 ks) of the brightest gamma-ray burst detected to date, GRB 221009A. The complexity of the data reduction, due to the unprecedented brightness and the location in the Galactic plane, is critically addressed. The energy spectrum is found to be well described by a smoothly broken power law with a break energy at a few keV. Three interpretations (slow/fast cooling or the transition between these) within the framework of forward shock synchrotron emission, from accelerated and subsequently cooled electrons, are found. The physical implications for each of these scenarios are discussed. 

   more » « less
2. Atomic picture of structural relaxation in silicate glasses

   https://doi.org/10.1063/1.5095529  

   Song, Weiying; Li, Xin; Wang, Bu; Anoop Krishnan, N. M.; Goyal, Sushmit; Smedskjaer, Morten M.; Mauro, John C.; Hoover, Christian G.; Bauchy, Mathieu (June 2019, Applied Physics Letters)
3. A Unified Picture of Radical Anion Photoredox Chemistry

   https://doi.org/10.1021/jacs.5c09029  

   Johnston, Brandon; Reynolds, Kristopher G; Campbell, Brandon M; Li, Alexander; Nocera, Daniel G (August 2025, Journal of the American Chemical Society)
4. A Picture of the Space of Typical Learnable Tasks

   Ramesh, Rahul; Mao, Jialin; Griniasty, Itay; Yan, Rubing; Teoh, Han Kheng; Transtrum, Mark K; Sethna, James P; Chaudhari, Pratik (May 2023, Proceedings of the 40th International Conference on Machine Learning)

   We develop information geometric techniques to understand the representations learned by deep networks when they are trained on different tasks using supervised, meta-, semi-supervised and con- trastive learning. We shed light on the following phenomena that relate to the structure of the space of tasks: (1) the manifold of probabilistic models trained on different tasks using different represen- tation learning methods is effectively low-dimen- sional; (2) supervised learning on one task results in a surprising amount of progress even on seem- ingly dissimilar tasks; progress on other tasks is larger if the training task has diverse classes; (3) the structure of the space of tasks indicated by our analysis is consistent with parts of the Word- net phylogenetic tree; (4) episodic meta-learning algorithms and supervised learning traverse differ- ent trajectories during training but they fit similar models eventually; (5) contrastive and semi-su- pervised learning methods traverse trajectories similar to those of supervised learning. We use classification tasks constructed from the CIFAR- 10 and Imagenet datasets to study these phenom- ena. Code is available at https://github.com/grasp- lyrl/picture of space of tasks. 

   more » « less
5. A Picture of the Space of Typical Learnable Tasks

   Ramesh, Rahul; Mao, Jialin; Griniasty, Itay; Yang, Rubing; Teoh, Han Kheng; Transtrum, Mark K; Sethna, James P; Chaudhari, Pratik (May 2023, Proceedings of the 40 th International Conference on Machine Learning)