March 12, 2021
Of the more than 4,300 planets discovered outside our solar system, super-Earths — rocky planets up to twice as large and up to five times as massive as Earth — are among the most common. What they’re made of, how they form and what their interior structure and dynamics look like are still relatively unclear.
To get a grasp on the inner workings of super-Earths, recent experiments put iron oxide under the pressures expected within the mantles of these rocky exoplanets. The experiments showed that this common planetary material likely takes a different shape in those planets’ mantles than it does in Earth’s.
Working with one of the most powerful lasers in the world allowed researchers to conduct “laboratory experiments that tell you something about the interior structure of planets so far away and which we can’t even look at directly,” said Federica Coppari, a planetary materials scientist at Lawrence Livermore National Laboratory.
Coppari said many planetary scientists begin studying super-Earths with simplified models of Earth’s interior and proceed to scale them up to approximate super-Earth sizes, pressures and temperatures. That approach is a good starting point, she said, but it doesn’t account for how properties of mantle materials might change. In recent years, experimentalists have begun to explore how common planetary materials behave at the pressures and temperatures inside super-Earths to build a picture of the structure and dynamics inside those planets.
"If you could imagine a 10-year-old boy holding on to rabbit ear antennas of a black and white vacuum-tube technology TV watching the Apollo 17 mission, that was me."
That is José Hernández, the Mexican American NASA astronaut and former Lawrence Livermore engineer whose life story will be featured in the new Netflix film "A Million Miles Away," highlighting the inspiring journey that led him to space.
Hernández still remembers listening to Walter Cronkite talking about Gene Cernan walking on the surface of the moon in 1972. It was Cernan's historic walk on the moon that would inspire a young boy, working alongside his family as a migrant farmworker, to dream of becoming an astronaut.
"A Million Miles Away" will chronicle the life of Hernández as he persevered through rejection to become a part of the space shuttle mission STS-128 in 2009.
There are a lot of new technologies available now or are going to be available shortly that have the potential to radically change the compute, memory and storage hierarchies in systems. It is a great time to be a system architect, given all of the component and interconnect choices.
Machines like the future “El Capitan” exascale-class system at Lawrence Livermore National Laboratory have the potential to change the memory system field. The Lab, like several others around the world, helps define and develop and deploy the future that the rest of us will eventually live. And the labs actually talk about what they are doing and why they are doing it, thereby enabling us all to learn something.
El Capitan will have in excess of 2 exaflops of raw computing power spread across nodes based on stock (meaning not custom) AMD “Genoa” Epyc CPUs and a quad of stock AMD Instinct GPUs. The super computer is expected to be installed in late 2022 and go into production around the middle of 2023. This integrated storage system, which is called “Rabbit” presumably because it is small and fast, is a key aspect of the El Capitan system. It is what will make this exascale system useful across traditional HPC simulation and modeling, AI training and inference, data analytics workloads as well as workflows that mesh together these techniques and need to chew on the same data as it enters and exits various compute elements of the system.
New work by computer scientists at Lawrence Livermore and IBM Research on deep learning models to accurately diagnose diseases from X-ray images with less labeled data recently won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference.
The technique, which includes novel regularization and "self-training" strategies, addresses some well-known challenges in the adoption of artificial intelligence (AI) for disease diagnosis, namely the difficulty in obtaining abundant labeled data due to cost, effort or privacy issues and the inherent sampling biases in the collected data, researchers said. AI algorithms also are not currently able to effectively diagnose conditions that are not sufficiently represented in the training data.
LLNL computer scientist Jay Thiagarajan said the team's approach demonstrates that accurate models can be created with limited labeled data and perform as well or even better than neural networks trained on much larger labeled datasets.
Lawrence Livermore National Laboratory scientists have achieved a near 100 percent increase in the amount of antimatter created in the laboratory.
Using targets with microstructures on the laser interface, the team shot a high-intensity laser through them and saw a 100 percent increase in the amount of antimatter (also known as positrons).
Previous research using a tiny gold sample created about 100 billion particles of antimatter. The new experiments doubled that.
"These successful experimental results are important for the Livermore positron project, whose grand goal is to make enough electron-positron antimatter to study the physics of gamma-ray bursts,” said Hui Chen, the project lead. “But we found that the experiments also created a high energy X-ray backlight that can penetrate very dense objects, which is important for many aspects of high energy density science.”