Lifeboat Foundation

American astrophysicists have used the Decadal Survey (DS)—also called Astro 2020 and produced by the National Academies of Science—to recommend a space telescope capable of photographing potentially habitable worlds.

The report recommends that a flagship space observatory will need a six-meter mirror to “provide an appropriate balance between scale and feasibility.”

An eight-meter aperture telescope of the scale of LUVOIR-B would be unlikely to launch before the late 2040… See more.

DeepMind is mostly known for its work in deep reinforcement learning, especially in mastering complicated games and predicting protein structures. Now, it is taking its next step in robotics research.

According to a blog post on DeepMind’s website, the company has acquired the rigid-body physics simulator MuJoCo and has made it freely available to the research community. MuJoCo is now one of several open-source platforms for training artificial intelligence agents used in robotics applications. Its free availability will have a positive impact on the work of scientists who are struggling with the costs of robotics research. It can also be an important factor for DeepMind’s future, both as a science lab seeking artificial general intelligence and as a business unit of one of the largest tech companies in the world.

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Something is killing-off galaxies by preventing the birth of stars—and astronomers now think they know why.

While studying 51 galaxies in a “galaxy-cluster” called the Virgo Cluster an international team of scientists have found that molecular gas—the fuel for new stars—is being “swept away by a huge cosmic broom.”

Exactly what is preventing nearby galaxies from birthing new stars has been a long-standing mystery in astrophysics. The new paper, now available online, blames the extreme environment of the Virgo Cluster. It’s been accepted by the journal Astrophysical Journal Supplement Series.

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A clearer understanding of how a type of brain cell known as astrocytes function and can be emulated in the physics of hardware devices, may result in artificial intelligence (AI) and machine learning that autonomously self-repairs and consumes much less energy than the technologies currently do, according to a team of Penn State researchers.

Astrocytes are named for their star shape and are a type of glial cell, which are support cells for neurons in the brain. They play a crucial role in brain functions such as memory, learning, self-repair and synchronization.

“This project stemmed from recent observations in computational neuroscience, as there has been a lot of effort and understanding of how the brain works and people are trying to revise the model of simplistic neuron-synapse connections,” said Abhronil Sengupta, assistant professor of electrical engineering and computer science. “It turns out there is a third component in the brain, the astrocytes, which constitutes a significant section of the cells in the brain, but its role in machine learning and neuroscience has kind of been overlooked.”

An international team of astrophysicists from South Africa, the UK, France and the US have found large variations in the brightness of light seen from around one of the closest black holes in our Galaxy, 9,600 light-years from Earth, which they conclude is caused by a huge warp in its accretion disc.

This object, MAXI J1820+070, erupted as a new X-ray transient in March 2018 and was discovered by a Japanese X-ray telescope onboard the International Space Station. These transients, systems that exhibit violent outbursts, are binary stars, consisting of a low-mass star, similar to our Sun and a much more compact object, which can be a white dwarf 0 neutron star 0 or black hole. In this case, MAXI J1820+070 contains a black hole that is at least 8 times the mass of our Sun.

The first findings have now been published in the international highly ranked journal, Monthly Notices of the Royal Astronomical Society, whose lead author is Dr. Jessymol Thomas, a Postdoctoral Research Fellow at the South African Astronomical Observatory (SAAO).

The authors present a high-resolution palaeomagnetic record for a Late Cretaceous limestone in Italy. They claim that their record robustly shows a ~12° true polar wander oscillation between 86 and 78 Ma, with the greatest excursion at 84–82 Ma.

The authors propose a new framework, deep evolutionary reinforcement learning, evolves agents with diverse morphologies to learn hard locomotion and manipulation tasks in complex environments, and reveals insights into relations between environmental physics, embodied intelligence, and the evolution of rapid learning.

As a scientist at both NASA and JAXA, James O’Donoghue has studied the planets. In his free time, he makes award-winning animations of them.

The ATLAS collaboration is breathing new life into its LHC Run 2 dataset, recorded from 2015 to 2018. Physicists will be reprocessing the entire dataset – nearly 18 PB of collision data – using an updated version of the ATLAS offline analysis software (Athena). Not only will this improve ATLAS physics measurements and searches, it will also position the collaboration well for the upcoming challenges of Run 3 and beyond.

Athena converts raw signals recorded by the ATLAS experiment into more simplified datasets for physicists to study. Its new-and-improved version has been in development for several years and includes multi-threading capabilities, more complex physics-analysis functions and improved memory consumption.

“Our aim was to significantly reduce the amount of memory needed to run the software, widen the types of physics analyses it could do and – most critically – allow current and future ATLAS datasets to be analysed together,” says Zach Marshall, ATLAS Computing Coordinator. “These improvements are a key part of our preparations for future high-intensity operations of the LHC – in particular the High-Luminosity LHC (HL-LHC) run beginning around 2,028 which will see ATLAS’s computing resources in extremely high demand.”