Evergreen Publications India

Tuesday 1 October 2019

Scientists detect the ringing of a newborn black hole for the first time

If Albert Einstein's theory of general relativity holds true, then a black hole, born from the cosmically quaking collisions of two massive black holes, should itself "ring" in the aftermath, producing gravitational waves much like a struck bell reverbates sound waves. Einstein predicted that the particular pitch and decay of these gravitational waves should be a direct signature of the newly formed black hole's mass and spin.
Now, physicists from MIT and elsewhere have "heard" the ringing of an infant black hole for the first time, and found that the pattern of this ringing does, in fact, predict the black hole's mass and spin -- more evidence that Einstein was right all along.
The findings, published today in Physical Review Letters, also favor the idea that black holes lack any sort of "hair" -- a metaphor referring to the idea that black holes, according to Einstein's theory, should exhibit just three observable properties: mass, spin, and electric charge. All other characteristics, which the physicist John Wheeler termed "hair," should be swallowed up by the black hole itself, and would therefore be unobservable.
The team's findings today support the idea that black holes are, in fact, hairless. The researchers were able to identify the pattern of a black hole's ringing, and, using Einstein's equations, calculated the mass and spin that the black hole should have, given its ringing pattern. These calculations matched measurements of the black hole's mass and spin made previously by others.
If the team's calculations deviated significantly from the measurements, it would have suggested that the black hole's ringing encodes properties other than mass, spin, and electric charge -- tantalizing evidence of physics beyond what Einstein's theory can explain. But as it turns out, the black hole's ringing pattern is a direct signature of its mass and spin, giving support to the notion that black holes are bald-faced giants, lacking any extraneous, hair-like properties.
"We all expect general relativity to be correct, but this is the first time we have confirmed it in this way," says the study's lead author, Maximiliano Isi, a NASA Einstein Fellow in MIT's Kavli Institute for Astrophysics and Space Research. "This is the first experimental measurement that succeeds in directly testing the no-hair theorem. It doesn't mean black holes couldn't have hair. It means the picture of black holes with no hair lives for one more day."
A chirp, decoded
On Sept. 9, 2015, scientists made the first-ever detection of gravitational waves -- infinitesimal ripples in space-time, emanating from distant, violent cosmic phenomena. The detection, named GW150914, was made by LIGO, the Laser Interferometer Gravitational-wave Observatory. Once scientists cleared away the noise and zoomed in on the signal, they observed a waveform that quickly crescendoed before fading away. When they translated the signal into sound, they heard something resembling a "chirp."
Scientists determined that the gravitational waves were set off by the rapid inspiraling of two massive black holes. The peak of the signal -- the loudest part of the chirp -- linked to the very moment when the black holes collided, merging into a single, new black hole. While this infant black hole likely gave off gravitational waves of its own, its signature ringing, physicists assumed, would be too faint to decipher amid the clamor of the initial collision.
Isi and his colleagues, however, found a way to extract the black hole's reverberation from the moments immediately after the signal's peak. In previous work led by Isi's co-author, Matthew Giesler, the team showed through simulations that such a signal, and particularly the portion right after the peak, contains "overtones" -- a family of loud, short-lived tones. When they reanalyzed the signal, taking overtones into account, the researchers discovered that they could successfully isolate a ringing pattern that was specific to a newly formed black hole.
In the team's new paper, the researchers applied this technique to actual data from the GW150914 detection, concentrating on the last few milliseconds of the signal, immediately following the chirp's peak. Taking into account the signal's overtones, they were able to discern a ringing coming from the new, infant black hole. Specifically, they identified two distinct tones, each with a pitch and decay rate that they were able to measure.
"We detect an overall gravitational wave signal that's made up of multiple frequencies, which fade away at different rates, like the different pitches that make up a sound," Isi says. "Each frequency or tone corresponds to a vibrational frequency of the new black hole."
Listening beyond Einstein
Einstein's theory of general relativity predicts that the pitch and decay of a black hole's gravitational waves should be a direct product of its mass and spin. That is, a black hole of a given mass and spin can only produce tones of a certain pitch and decay. As a test of Einstein's theory, the team used the equations of general relativity to calculate the newly formed black hole's mass and spin, given the pitch and decay of the two tones they detected.
They found their calculations matched with measurements of the black hole's mass and spin previously made by others. Isi says the results demonstrate that researchers can, in fact, use the very loudest, most detectable parts of a gravitational wave signal to discern a new black hole's ringing, where before, scientists assumed that this ringing could only be detected within the much fainter end of the gravitational wave signal, and only with much more sensitive instruments than what currently exist.
"This is exciting for the community because it shows these kinds of studies are possible now, not in 20 years," Isi says.
As LIGO improves its resolution, and more sensitive instruments come online in the future, researchers will be able to use the group's methods to "hear" the ringing of other newly born black holes. And if they happen to pick up tones that don't quite match up with Einstein's predictions, that could be an even more exciting prospect.
"In the future, we'll have better detectors on Earth and in space, and will be able to see not just two, but tens of modes, and pin down their properties precisely," Isi says. "If these are not black holes as Einstein predicts, if they are more exotic objects like wormholes or boson stars, they may not ring in the same way, and we'll have a chance of seeing them."
This research was supported, in part, by NASA, the Sherman Fairchild Foundation, the Simons Foundation, and the National Science Foundation.
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Brain-inspired computing could tackle big problems in a small way

While computers have become smaller and more powerful and supercomputers and parallel computing have become the standard, we are about to hit a wall in energy and miniaturization. Now, Penn State researchers have designed a 2D device that can provide more than yes-or-no answers and could be more brainlike than current computing architectures.
Brain-inspired computing concept illustration (stock image). | Credit: © knssr / stock.adobe.com
"Complexity scaling is also in decline owing to the non-scalability of traditional von Neumann computing architecture and the impending 'Dark Silicon' era that presents a severe threat to multi-core processor technology," the researchers note in today's (Sept 13) online issue of Nature Communications.
The Dark Silicon era is already upon us to some extent and refers to the inability of all or most of the devices on a computer chip to be powered up at once. This happens because of too much heat generated from a single device. Von Neumann architecture is the standard structure of most modern computers and relies on a digital approach -- "yes" or "no" answers -- where program instruction and data are stored in the same memory and share the same communications channel.
"Because of this, data operations and instruction acquisition cannot be done at the same time," said Saptarshi Das, assistant professor of engineering science and mechanics. "For complex decision-making using neural networks, you might need a cluster of supercomputers trying to use parallel processors at the same time -- a million laptops in parallel -- that would take up a football field. Portable healthcare devices, for example, can't work that way."
The solution, according to Das, is to create brain-inspired, analog, statistical neural networks that do not rely on devices that are simply on or off, but provide a range of probabilistic responses that are then compared with the learned database in the machine. To do this, the researchers developed a Gaussian field-effect transistor that is made of 2D materials -- molybdenum disulfide and black phosphorus. These devices are more energy efficient and produce less heat, which makes them ideal for scaling up systems.
"The human brain operates seamlessly on 20 watts of power," said Das. "It is more energy efficient, containing 100 billion neurons, and it doesn't use von Neumann architecture."
The researchers note that it isn't just energy and heat that have become problems, but that it is becoming difficult to fit more in smaller spaces.
"Size scaling has stopped," said Das. "We can only fit approximately 1 billion transistors on a chip. We need more complexity like the brain."
The idea of probabilistic neural networks has been around since the 1980s, but it needed specific devices for implementation.
"Similar to the working of a human brain, key features are extracted from a set of training samples to help the neural network learn," said Amritanand Sebastian, graduate student in engineering science and mechanics.
The researchers tested their neural network on human electroencephalographs, graphical representation of brain waves. After feeding the network with many examples of EEGs, the network could then take a new EEG signal and analyze it and determine if the subject was sleeping.
"We don't need as extensive a training period or base of information for a probabilistic neural network as we need for an artificial neural network," said Das.
The researchers see statistical neural network computing having applications in medicine, because diagnostic decisions are not always 100% yes or no. They also realize that for the best impact, medical diagnostic devices need to be small, portable and use minimal energy.
Das and colleagues call their device a Gaussian synapse and it is based on a two-transistor setup where the molybdenum disulfide is an electron conductor, while the black phosphorus conducts through missing electrons, or holes. The device is essentially two variable resistors in series and the combination produces a graph with two tails, which matches a Gaussian function.
Others working on this project were Andrew Pannone, undergraduate in engineering science and mechanics; and Shiva Subbulakshmi, student in electrical engineering at Amrita Vishwa Vidyapeetham, India, and a summer intern in the Das laboratory.

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Thursday 26 September 2019

A developer has removed his Ruby library after discovering that it was used by US Immigration and Customs Enforcement (ICE)

A developer has removed his Ruby library after discovering that it was used by US Immigration and Customs Enforcement (ICE).
ICE has been called out by human rights groups and activists for inhumane practices. Most of us saw the headlines and images of children stripped from their parents and kept in cages in detainment centres.
Seth Vargo is an engineer that developed Chef Sugar, a Ruby library which aimed to simplify working with configuration management platform Chef.
Vargo has since taken down Chef Sugar after finding out a company using it recently signed a contract with ICE. Vargo cited ICE's "inhumane treatment, denial of basic human rights, and detaining children in cages," as the reason for removing his library.
Twitter user @Shanley first uncovered Chef’s $100k contract with ICE through a government contractor.
"I have a moral and ethical obligation to prevent my source from being used for evil," Vargo wrote on Chef Sugar’s now-former GitHub repo.
Vargo’s decision to remove the library caused downtime for some of Chef’s clients. In a blog post, Chef Software CEO Barry Crist noted the incident hit "production systems for a number of our customers."
Chef Software fixed the issue by going over some of Chef Sugar’s code and re-uploading it to their own GitHub repo.
Many developers praised and stood by Vargo’s decision, even if it hit their own projects. Some were especially vocal about Chef’s contract with ICE:
Some internal Chef employees also expressed dissatisfaction with their company’s ICE contract. The situation feels similar to Amazon, whose employees threatened to leave if the firm continued to supply facial recognition technology to ICE and other government agencies.
In a letter addressed to Amazon CEO Jeff Bezos, a group of Amazonians wrote:
"We don’t have to wait to find out how these technologies will be used. We already know that in the midst of historic militarization of police, renewed targeting of Black activists, and the growth of a federal deportation force currently engaged in human rights abuses — this will be another powerful tool for the surveillance state, and ultimately serve to harm the most marginalized.”
Microsoft employees also protested their company’s $20 million contract with ICE. CEO Satya Nadella was forced to reassure employees the contract was not tied to the Trump policy of separating children from their parents at the border.
In an email to employees that was also published on the company's blog, Chef CEO Barry Crist stood by his own decision to continue working with ICE:
“While I understand that many of you and many of our community members would prefer we had no business relationship with DHS-ICE, I have made a principled decision, with the support of the Chef executive team, to work with the institutions of our government, regardless of whether or not we personally agree with their various policies.
I want to be clear that this decision is not about contract value — it is about maintaining a consistent and fair business approach in these volatile times. I do not believe that it is appropriate, practical, or within our mission to examine specific government projects with the purpose of selecting which U.S. agencies we should or should not do business.
My goal is to continue growing Chef as a company that transcends numerous U.S. presidential administrations.”
Crist finishes his letter with a personal note that he does not agree with ICE’s practices of separating families and detaining children. Whether that’s a good enough excuse for Chef’s employees and its clients will have to be seen.

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Wednesday 25 September 2019

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Tuesday 17 September 2019

Water Detected on an Exoplanet Located in its Star's Habitable Zone

Water detected on an exoplanet located in its star's habitable zone


Exoplanet illustration (stock image; elements furnished by NASA). | Credit: © sdecoret / stock.adobe.com

Ever since the discovery of the first exoplanet in the 1990s, astronomers have made steady progress towards finding and probing planets located in the habitable zone of their stars, where conditions can lead to the formation of liquid water and the proliferation of life.
Results from the Kepler satellite mission, which discovered nearly 2/3 of all known exoplanets to date, indicate that 5 to 20% of Earths and super-Earths are located in the habitable zone of their stars. However, despite this abundance, probing the conditions and atmospheric properties on any of these habitable zone planets is extremely difficult and has remained elusive... until now.
A new study by Professor Björn Benneke of the Institute for Research on Exoplanets at the Université de Montréal, his doctoral student Caroline Piaulet and several of their collaborators reports the detection of water vapour and perhaps even liquid water clouds in the atmosphere of the planet K2-18b. This exoplanet is about nine times more massive than our Earth and is found in the habitable zone of the star it orbits. This M-type star is smaller and cooler than our Sun, but due to K2-18b's close proximity to its star, the planet receives almost the same total amount of energy from its star as our Earth receives from the Sun.
The similarities between the exoplanet K2-18b and the Earth suggest to astronomers that the exoplanet may potentially have a water cycle possibly allowing water to condense into clouds and liquid water rain to fall. This detection was made possible by combining eight transit observations -- the moment when an exoplanet passes in front of its star -- taken by the Hubble Space Telescope.
The Université de Montréal is no stranger to the K2-18 system located 111 light years away. The existence of K2-18b was first confirmed by Prof. Benneke and his team in a 2016 paper using data from the Spitzer Space Telescope. The mass and radius of the planet were then determined by former Université de Montréal and University of Toronto PhD student Ryan Cloutier. These promising initial results encouraged the iREx team to collect follow-up observations of the intriguing world."
Scientists currently believe that the thick gaseous envelope of K2-18b likely prevents life as we know it from existing on the planet's surface. However, the study shows that even these planets of relatively low mass which are therefore more difficult to study can be explored using astronomical instruments developed in recent years. By studying these planets which are in the habitable zone of their star and have the right conditions for liquid water, astronomers are one step closer to directly detecting signs of life beyond our Solar System.
"This represents the biggest step yet taken towards our ultimate goal of finding life on other planets, of proving that we are not alone. Thanks to our observations and our climate model of this planet, we have shown that its water vapour can condense into liquid water. This is a first," says Björn Benneke.

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Friday 13 September 2019

A smart artificial hand for amputees merges user and robotic control

EPFL scientists are developing new approaches for improved control of robotic hands -- in particular for amputees -- that combines individual finger control and automation for improved grasping and manipulation. This interdisciplinary proof-of-concept between neuroengineering and robotics was successfully tested on three amputees and seven healthy subjects. The results are published in today's issue of Nature Machine Intelligence.
The technology merges two concepts from two different fields. Implementing them both together had never been done before for robotic hand control, and contributes to the emerging field of shared control in neuroprosthetics.
One concept, from neuroengineering, involves deciphering intended finger movement from muscular activity on the amputee's stump for individual finger control of the prosthetic hand which has never before been done. The other, from robotics, allows the robotic hand to help take hold of objects and maintain contact with them for robust grasping.
"When you hold an object in your hand, and it starts to slip, you only have a couple of milliseconds to react," explains Aude Billard who leads EPFL's Learning Algorithms and Systems Laboratory. "The robotic hand has the ability to react within 400 milliseconds. Equipped with pressure sensors all along the fingers, it can react and stabilize the object before the brain can actually perceive that the object is slipping. "
How shared control works
The algorithm first learns how to decode user intention and translates this into finger movement of the prosthetic hand. The amputee must perform a series of hand movements in order to train the algorithm that uses machine learning. Sensors placed on the amputee's stump detect muscular activity, and the algorithm learns which hand movements correspond to which patterns of muscular activity. Once the user's intended finger movements are understood, this information can be used to control individual fingers of the prosthetic hand.
"Because muscle signals can be noisy, we need a machine learning algorithm that extracts meaningful activity from those muscles and interprets them into movements," says Katie Zhuang first author of the publication.
Next, the scientists engineered the algorithm so that robotic automation kicks in when the user tries to grasp an object. The algorithm tells the prosthetic hand to close its fingers when an object is in contact with sensors on the surface of the prosthetic hand. This automatic grasping is an adaptation from a previous study for robotic arms designed to deduce the shape of objects and grasp them based on tactile information alone, without the help of visual signals.
Many challenges remain to engineer the algorithm before it can be implemented in a commercially available prosthetic hand for amputees. For now, the algorithm is still being tested on a robot provided by an external party.
"Our shared approach to control robotic hands could be used in several neuroprosthetic applications such as bionic hand prostheses and brain-to-machine interfaces, increasing the clinical impact and usability of these devices," Silvestro Micera, EPFL's Bertarelli Foundation Chair in Translational Neuroengineering, and Professor of Bioelectronics at Scuola Superiore Sant'Anna.

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Tuesday 10 September 2019

Efforts On To Establish Contact With Chandrayaan 2 Lander Vikram: ISRO

Chandrayaan 2 lunar lander Vikram: ISRO has confirmed that the lander, one of three components of the spacecraft, which stopped transmitting just 2.1 kilometres from the surface, has been located.

Efforts On To Establish Contact With Chandrayaan 2 Lander Vikram: ISRO


Chandrayaan 2 lander Vikram has been located on the moon's surface by the lunar orbiter, ISRO confirmed this morning in a message posted on its official Twitter account, adding that "all possible efforts were being made to re-establish contact". The confirmation comes after ISRO chief K Sivan was quoted by news agency ANI on Monday as admitting that Vikram had made a "hard landing" and saying that the orbiter had managed to take thermal images of the lander.

On Monday Dr Sivan, while announcing that Vikram had been located, said "it must have been a hard landing". ISRO officials said images sent by the lunar orbiter showed that while the lander appeared to be undamaged, it was in "a tilted position". "The lander is there as a single piece, not broken into pieces. It's in a tilted position," another ISRO official said, while another cautioned that the chances of re-establishing contact were "very difficult".

"Unless and until everything is intact (lander), it's very difficult (to re-establish contact). Chances are less. Only if it had soft-landing, and if all systems functioned, then only communication can be restored. Things are bleak as of now," one official was quoted as saying. Lunar lander Vikram, one of three components of the Chandrayaan 2 spacecraft, had been scheduled to soft land on the moon at 1.55 am on Saturday but stopped transmitting 2.1 km from the surface.

India had expected to make space history with the Rs. 1,000-crore Chandrayaan 2 mission. A successful soft landing on the moon's surface would have made the country only the fourth - after the United States, Russia and China - to achieve the feat. It would also have made India the first country to complete a soft landing near the South Pole on its first attempt.

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In a statement to national broadcaster Doordarshan on Saturday, Dr Sivan had blamed faulty execution of the last stage of the operation for the loss of communication. The ISRO chief had earlier said the final minutes of the soft landing were the trickiest, calling them "15 minutes of terror". "This is a very complex process and it is new for us. It is a complex process even for those who have already done it. We are doing this for the first time, so it will be fifteen minutes of terror for us," he said.

Vikram and lunar rover Pragyan, which is housed inside the lander, were scheduled to operate for one lunar day (equal to 14 Earth days) and carry out a series of surface and sub-surface experiments. The lunar orbiter, which is in orbit around the moon, is now expected to be operational for seven years and help in the understanding of the moon's evolution, mapping of its minerals and water molecules in polar regions. Chandrayaan 2 was launched on July 22 from Sriharikota in Andhra Pradesh, on the back of a GSLV Mark III rocket - ISRO's largest and most powerful. The mission was originally scheduled to launch on July 15 but that was aborted, with less than an hour remaining, after a technical glitch was discovered.

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Scientists detect the ringing of a newborn black hole for the first time

If Albert Einstein's theory of general relativity holds true, then a black hole, born from the cosmically quaking collisions of two ma...