This is about the $$.
The board knows the next quarterly numbers already. They are scheduled to release them on Thursday 4/28.
So, the GS call about it being worth $30 might have been correct. Now they know Musk's offer is a premium. There will be a deal before Thursday's earnings announcement.
Guaranteed.
Supercomputer - BlueWaters - retired - now Delta
Dark Energy Survey and the Chile Telescope
Cerro Tololo Inter-American Observatory
https://www.darkenergysurvey.org/
Coupled with the South Pole Telescope https://pole.uchicago.edu/public/southpole.html
Uses NoirLab
Public news;
https://www.hpcwire.com/off-the-wire/dark-energy-survey-makes-public-catalog-of-nearly-700-million-astronomical-objects/
Dark Energy Survey Makes Public Catalog of Nearly 700 Million Astronomical Objects
January 15, 2021
Jan. 15, 2021 — The Dark Energy Survey, a global collaboration including the Department of Energy’s Fermi National Accelerator Laboratory, the National Center for Supercomputing Applications, and the National Science Foundation’s NOIRLab, has released DR2, the second data release in the survey’s seven-year history. DR2 is the topic of sessions this week at the 237th Meeting of the American Astronomical Society, which is being held virtually.
The second data release from the Dark Energy Survey, or DES, is the culmination of over a half-decade of astronomical data collection and analysis with the ultimate goal of understanding the accelerating expansion of the universe and the phenomenon of dark energy, which is thought to be responsible for this accelerated expansion. It is one of the largest astronomical catalogs released to date.
https://www.hpcwire.com/2019/07/08/scientists-leverage-hpc-and-ai-to-wrangle-the-galaxy-zoo/
Scientists Leverage HPC and AI to Wrangle the ‘Galaxy Zoo’
By Oliver Peckham
July 8, 2019
For the last 12 years, the “Galaxy Zoo” has been working hard to improve our understanding of the cosmos. Despite its name, the Galaxy Zoo doesn’t house any aliens; it is, instead, a crowdsourced astronomy project that asks its users to classify the shapes of massive numbers of galaxies. Now, researchers from the National Center for Supercomputing Applications (NCSA) and the Argonne Leadership Computing Facility (ALCF) are using AI and supercomputing to leverage that user-generated data and accelerate progress on the Galaxy Zoo.
Crossmatched Galaxy Zoo and DES image sets. Image courtesy of the researchers.
The research team developed a new approach to classifying these hundreds of millions of galaxies. Instead of relying on crowdsourced classification, the researchers used knowledge from the state-of-the-art Xception neural network, combined with the datasets generated by the Galaxy Zoo project, to train its deep learning models. They then applied the trained model to galactic images from the Dark Energy Survey (DES) – where it achieved a 99.6% accuracy in identifying spiral and elliptical galaxies.
“Using the millions of classifications carried out by the public in the Galaxy Zoo project to train a neural network is an inspiring use of the citizens science program,” said Elise Jennings, a computer scientist at ALCF. “This exciting research also sheds light on the inner workings of the neural network, which clearly learns two distinct feature clusters to identify spiral and elliptical galaxies.”
The researchers extracted the overlapping images from the two datasets using the NCSA’s Blue Waters supercomputer, then taught their deep learning model on the Pittsburgh Supercomputing Center’s Bridges supercomputer. The team also used the K80 Nvidia GPUs in the Cooley supercomputer at ALCF to reduce the training stage for the Xception model from five hours to eight minutes.
“We’re excited to work with the team at NCSA and Argonne as well as the researchers who drove the original Galaxy Zoo effort to pursue this important area of scientific discovery,” said Tom Gibbs, manager of developer relations at Nvidia. “Using these new methods, we’re taking an important step to understanding the mystery of dark energy.”