Lifeboat Foundation

Saturn ’s giant moon, Titan, is due to launch in 2027. When it arrives in the mid-2030s, it will begin a journey of discovery that could bring about a new understanding of the development of life in the universe. This mission, called Dragonfly, will carry an instrument called the Dragonfly Mass Spectrometer (DraMS), designed to help scientists hone in on the chemistry at work on Titan. It may also shed light on the kinds of chemical steps that occurred on Earth that ultimately led to the formation of life, called prebiotic chemistry.

Titan’s abundant complex carbon-rich chemistry, interior ocean, and past presence of liquid water on the surface make it an ideal destination to study prebiotic chemical processes and the potential habitability of an extraterrestrial environment.

Continue reading “NASA Dragonfly Bound for Saturn’s Giant Moon Titan Could Reveal Chemistry Leading to Life” »

It’s moving towards a “single network future.”

Scientists found large swaths of jagged landforms on Pluto’s surface.

In July 2015, the New Horizons spacecraft had its first close encounter with Pluto and its moons. It went on to explore the icy edge of the solar system, generating a wealth of data in the process.

The formation of Arrokoth.

Continue reading “New Horizons gives new insight about Pluto, finds bladed terrain” »

SpaceEngine is Google Earth meets No Man’s Sky in the best way.

The Axiom Extravehicular Mobility Unit (AxEMU) spacesuit is designed to enhance mobility and offer extra protection from hazards on the Moon. The prototype is dark gray in color, but NASA’s Johnson Space Center confirmed in a tweet (Opens in a new window) that the final design is probably going to be “all-white.”

Axiom Space, which last year snagged a $228.5 million contract to deliver a “moonwalking system,” will continue innovating the suit’s life support systems, pressure garments, and avionics ahead of NASA’s scheduled 2025 trip to the lunar South Pole.

A new methodology for analyzing the 3D distribution of galaxies borrows techniques from the study of colloids and other disordered materials.

No, it’s not hypermodern art. This image, generated by NASA

Established in 1958, the National Aeronautics and Space Administration (NASA) is an independent agency of the United States Federal Government that succeeded the National Advisory Committee for Aeronautics (NACA). It is responsible for the civilian space program, as well as aeronautics and aerospace research. Its vision is “To discover and expand knowledge for the benefit of humanity.” Its core values are “safety, integrity, teamwork, excellence, and inclusion.” NASA conducts research, develops technology and launches missions to explore and study Earth, the solar system, and the universe beyond. It also works to advance the state of knowledge in a wide range of scientific fields, including Earth and space science, planetary science, astrophysics, and heliophysics, and it collaborates with private companies and international partners to achieve its goals.

It’s a theoretical concept, but realistic enough that NASA’s Innovative Advanced Concepts program has given Davoyan’s group $175,000 to show that the technology is feasible. “There’s rich physics in there,” says Davoyan, a mechanical and aerospace engineer at UCLA. To create propulsion, he continues, “you either throw the fuel out of the rocket or you throw the fuel at the rocket.” From a physics perspective, they work the same: Both impart momentum to a moving object.

His team’s project could transform long-distance space exploration, dramatically expanding the astronomical neighborhood accessible to us. After all, we’ve only sent a few robotic visitors to scope out Uranus, Neptune, Pluto, and their moons. We know even less about objects lurking farther away. The even smaller handful of NASA craft en route to interstellar space include Pioneer 10 and 11, which blasted off in the early 1970s; Voyager 1 and 2, which were launched in 1977 and continue their mission to this day; and the more recent New Horizons, which took nine years to fly by Pluto in 2015, glimpsing the dwarf planet’s now famous heart-shaped plain. Over its 46-year journey, Voyager 1 has ventured farthest from home, but a pellet-beam-powered craft could overtake it in just five years, Davoyan says.

He takes inspiration from Breakthrough Starshot, a $100 million initiative announced in 2016 by Russian-born philanthropist Yuri Milner and British cosmologist Stephen Hawking to use a 100-gigawatt laser beam to blast a miniature probe toward Alpha Centauri. (The star nearest our solar system, it resides “only” 4 light-years away.) The Starshot team is exploring how they could hurl a 1-gram craft attached to a lightsail into interstellar space, using the laser to accelerate it to 20 percent of the speed of light, which is ludicrously fast and would reduce travel time from millennia to decades. “I’m increasingly optimistic that later this century, humanity’s going to be including nearby stars in our reach,” says Pete Worden, Breakthrough Starshot’s executive director.

Black box optimization methods are used in every domain, from Artificial Intelligence and Machine Learning to engineering and finance. These methods are used to optimize functions when an algebraic model is absent. Black box optimization looks into the design and analysis of algorithms for those problem statements where the structure of the objective function or the limitations defining the set is not known or explainable. Given a set of input parameters, black box optimization methods are designed to evaluate the optimal value of a function. This is done by iteratively assessing the function at multiple points in the input space so as to find the point that generates the optimal output.

Though gradient descent is the most used optimization approach for deep learning models, it is unsuitable for every problem. In cases where gradients cannot be calculated directly or where an objective function’s accurate analytical form is unknown, other approaches like Evolution Strategies (ES) are used. Evolution strategies come from evolutionary algorithms, which refer to a division of population-based optimization algorithms inspired by natural selection. Basically, Evolution Strategies (ES) is a type of Black Box Optimization method that operates by refining a sampling distribution based on the fitness of candidates and updating rules based on equations.

In a new AI paper, researchers from Deepmind, have introduced and developed a new way to use machine learning to learn the update rules from data, called meta-black-box optimization (MetaBBO), to make ES more flexible, adaptable, and scalable. MetaBBO works by meta-learning a neural network parametrization of a BBO update rule. The researchers have used MetaBBO to discover a new type of ES called learned evolution strategy (LES). The learned evolution strategy LES is a type of Set Transformer that updates its solutions based on the fitness of candidates and not depending upon the ordering of candidate solutions within the Black box evaluations. After meta-training, the LES can learn to choose the best-performing solution or update solutions based on a moving average.