Aurelia Butler

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    in Space & Astronomy

    Scientists mapped dark matter around galaxies in the early universe

    Scientists have mapped out the dark matter around some of the earliest, most distant galaxies yet.

    The 1.5 million galaxies appear as they were 12 billion years ago, or less than 2 billion years after the Big Bang. Those galaxies distort the cosmic microwave background — light emitted during an even earlier era of the universe — as seen from Earth. That distortion, called gravitational lensing, reveals the distribution of dark matter around those galaxies, scientists report in the Aug. 5 Physical Review Letters.

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    Understanding how dark matter collects around galaxies early in the universe’s history could tell scientists more about the mysterious substance. And in the future, this lensing technique could also help scientists unravel a mystery about how matter clumps together in the universe.

    Dark matter is an unknown, massive substance that surrounds galaxies. Scientists have never directly detected dark matter, but they can observe its gravitational effects on the cosmos (SN: 7/22/22). One of those effects is gravitational lensing: When light passes by a galaxy, its mass bends the light like a lens. How much the light bends reveals the mass of the galaxy, including its dark matter.

    It’s difficult to map dark matter around such distant galaxies, says cosmologist Hironao Miyatake of Nagoya University in Japan. That’s because scientists need a source of light that is farther away than the galaxy acting as the lens. Typically, scientists use even more distant galaxies as the source of that light. But when peering this deep into space, those galaxies are difficult to come by.

    So instead, Miyatake and colleagues turned to the cosmic microwave background, the oldest light in the universe. The team used measurements of lensing of the cosmic microwave background from the Planck satellite, combined with a multitude of distant galaxies observed by the Subaru Telescope in Hawaii (SN: 7/24/18). “The gravitational lensing effect is very small, so we need a lot of lens galaxies,” Miyatake says. The distribution of dark matter around the galaxies matched expectations, the researchers report.

    The researchers also estimated a quantity called sigma-8, a measure of how “clumpy” matter is in the cosmos. For years, scientists have found hints that different measurements of sigma-8 disagree with one another (SN: 8/10/20). That could be a hint that something is wrong with scientists’ theories of the universe. But the evidence isn’t conclusive.

    “One of the most interesting things in cosmology right now is whether that tension is real or not,” says cosmologist Risa Wechsler of Stanford University, who was not involved with the study. “This is a really nice example of one of the techniques that will help shed light on that.”

    Measuring sigma-8 using early, distant galaxies could help reveal what’s going on. “You want to measure this quantity, this sigma-8, from as many perspectives as possible,” says cosmologist Hendrik Hildebrandt of Ruhr University Bochum in Germany, who was not involved with the study.

    If estimates from different eras of the universe disagree with one another, that might help physicists craft a new theory that could better explain the cosmos. While the new measurement of sigma-8 isn’t precise enough to settle the debate, future projects, such as the Rubin Observatory in Chile, could improve the estimate (SN: 1/10/20). More

  • 100 Shares99 Views

    in Computers Math

    Researchers discover major roadblock in alleviating network congestion

    When users want to send data over the internet faster than the network can handle, congestion can occur — the same way traffic congestion snarls the morning commute into a big city.
    Computers and devices that transmit data over the internet break the data down into smaller packets and use a special algorithm to decide how fast to send those packets. These congestion control algorithms seek to fully discover and utilize available network capacity while sharing it fairly with other users who may be sharing the same network. These algorithms try to minimize delay caused by data waiting in queues in the network.
    Over the past decade, researchers in industry and academia have developed several algorithms that attempt to achieve high rates while controlling delays. Some of these, such as the BBR algorithm developed by Google, are now widely used by many websites and applications.
    But a team of MIT researchers has discovered that these algorithms can be deeply unfair. In a new study, they show there will always be a network scenario where at least one sender receives almost no bandwidth compared to other senders; that is, a problem known as starvation cannot be avoided.
    “What is really surprising about this paper and the results is that when you take into account the real-world complexity of network paths and all the things they can do to data packets, it is basically impossible for delay-controlling congestion control algorithms to avoid starvation using current methods,” says Mohammad Alizadeh, associate professor of electrical engineering and computer science (EECS).
    While Alizadeh and his co-authors weren’t able to find a traditional congestion control algorithm that could avoid starvation, there may be algorithms in a different class that could prevent this problem. Their analysis also suggests that changing how these algorithms work, so that they allow for larger variations in delay, could help prevent starvation in some network situations. More

  • 138 Shares169 Views

    in Computers Math

    Smart microrobots learn how to swim and navigate with artificial intelligence

    Researchers from Santa Clara University, New Jersey Institute of Technology and the University of Hong Kong have been able to successfully teach microrobots how to swim via deep reinforcement learning, marking a substantial leap in the progression of microswimming capability.
    There has been tremendous interest in developing artificial microswimmers that can navigate the world similarly to naturally-occuring swimming microorganisms, like bacteria. Such microswimmers provide promise for a vast array of future biomedical applications, such as targeted drug delivery and microsurgery. Yet, most artificial microswimmers to date can only perform relatively simple maneuvers with fixed locomotory gaits.
    In the researchers’ study published in Communications Physics, they reasoned microswimmers could learn — and adapt to changing conditions — through AI. Much like humans learning to swim require reinforcement learning and feedback to stay afloat and propel in various directions under changing conditions, so too must microswimmers, though with their unique set of challenges imposed by physics in the microscopic world.
    “Being able to swim at the micro-scale by itself is a challenging task,” said On Shun Pak, associate professor of mechanical engineering at Santa Clara University. “When you want a microswimmer to perform more sophisticated maneuvers, the design of their locomotory gaits can quickly become intractable.”
    By combining artificial neural networks with reinforcement learning, the team successfully taught a simple microswimmer to swim and navigate toward any arbitrary direction. When the swimmer moves in certain ways, it receives feedback on how good the particular action is. The swimmer then progressively learns how to swim based on its experiences interacting with the surrounding environment.
    “Similar to a human learning how to swim, the microswimmer learns how to move its ‘body parts’ — in this case three microparticles and extensible links — to self-propel and turn,” said Alan Tsang, assistant professor of mechanical engineering at the University of Hong Kong. “It does so without relying on human knowledge but only on a machine learning algorithm.”
    The AI-powered swimmer is able to switch between different locomotory gaits adaptively to navigate toward any target location on its own.
    As a demonstration of the powerful ability of the swimmer, the researchers showed that it could follow a complex path without being explicitly programmed. They also demonstrated the robust performance of the swimmer in navigating under the perturbations arising from external fluid flows.
    “This is our first step in tackling the challenge of developing microswimmers that can adapt like biological cells in navigating complex environments autonomously,” said Yuan-nan Young, professor of mathematical sciences at New Jersey Institute of Technology.
    Such adaptive behaviors are crucial for future biomedical applications of artificial microswimmers in complex media with uncontrolled and unpredictable environmental factors.
    “This work is a key example of how the rapid development of artificial intelligence may be exploited to tackle unresolved challenges in locomotion problems in fluid dynamics,” said Arnold Mathijssen, an expert on microrobots and biophysics at the University of Pennsylvania, who was not involved in the research. “The integration between machine learning and microswimmers in this work will spark further connections between these two highly active research areas.”
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  • 75 Shares179 Views

    in Computers Math

    Optimizing SWAP networks for quantum computing

    A research partnership at the Advanced Quantum Testbed (AQT) at Lawrence Berkeley National Laboratory (Berkeley Lab) and Chicago-based Super.tech (acquired by ColdQuanta in May 2022) demonstrated how to optimize the execution of the ZZ SWAP network protocol, important to quantum computing. The team also introduced a new technique for quantum error mitigation that will improve the network protocol’s implementation in quantum processors. The experimental data was published this July in Physical Review Research, adding more pathways in the near term to implement quantum algorithms using gate-based quantum computing.
    A Smart Compiler for Superconducting Quantum Hardware
    Quantum processors with two- or three-dimensional architectures have limited qubit connectivity where each qubit interacts with only a limited number of other qubits. Furthermore, each qubit’s information can only exist for so long before noise and errors cause decoherence, limiting the runtime and fidelity of quantum algorithms. Therefore, when designing and executing a quantum circuit, researchers must optimize the translation of the circuit made up of abstract (logical) gates to physical instructions based on the native hardware gates available in a given quantum processor. Efficient circuit decompositions minimize the operating time because they consider the number of gates and operations natively supported by the hardware to perform the desired logical operations.
    SWAP gates — which swap information between qubits — are often introduced in quantum circuits to facilitate interactions between information in non-adjacent qubits. If a quantum device only allows gates between adjacent qubits, swaps are used to move information from one qubit to another non-adjacent qubit.
    In noisy intermediate-scale quantum (NISQ) hardware, introducing swap gates can require a large experimental overhead. The swap gate must often be decomposed into native gates, such as controlled-NOT gates. Therefore, when designing quantum circuits with limited qubit connectivity, it is important to use a smart compiler that can search for, decompose, and cancel redundant quantum gates to improve the runtime of a quantum algorithm or application.
    The research partnership used Super.tech’s SuperstaQ software enabling scientists to finely tailor their applications and automate the compilations of circuits for AQT’s superconducting hardware, particularly for a native high-fidelity controlled-S gate, which is not available on most hardware systems. This smart compiling approach with four transmon qubits allows the SWAP networks to be decomposed more efficiently than standard decomposition methods. More

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    in Space & Astronomy

    The Windchime experiment could use gravity to hunt for dark matter ‘wind’

    The secret to directly detecting dark matter might be blowin’ in the wind.

    The mysterious substance continues to elude scientists even though it outweighs visible matter in the universe by about 8 to 1. All laboratory attempts to directly detect dark matter — seen only indirectly by the effect its gravity has on the motions of stars and galaxies — have gone unfulfilled.

    Those attempts have relied on the hope that dark matter has at least some other interaction with ordinary matter in addition to gravity (SN: 10/25/16). But a proposed experiment called Windchime, though decades from being realized, will try something new: It will search for dark matter using the only force it is guaranteed to feel — gravity.

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    “The core idea is extremely simple,” says theoretical physicist Daniel Carney, who described the scheme in May at a meeting of the American Physical Society’s Division of Atomic Molecular and Optical Physics in Orlando, Fla. Like a wind chime on a porch rattling in a breeze, the Windchime detector would try to sense a dark matter “wind” blowing past Earth as the solar system whips around the galaxy.  

    If the Milky Way is mostly a cloud of dark matter, as astronomical measurements suggest, then we should be sailing through it at about 200 kilometers per second. This creates a dark matter wind, for the same reason you feel a wind when you stick your hand out the window of a moving car.

    The Windchime detector is based on the notion that a collection of pendulums will swing in a breeze. In the case of backyard wind chimes, it might be metal rods or dangling bells that jingle in moving air. For the dark matter detector, the pendulums are arrays of minute, ultrasensitive detectors that will be jostled by the gravitational forces they feel from passing bits of dark matter. Instead of air molecules bouncing off metal chimes, the gravitational attraction of the particles that make up the dark matter wind would cause distinctive ripples as it blows through a billion or so sensors in a box measuring about a meter per side.

    Within the Windchime detector (illustrated as an array of small pendulums), a passing dark matter particle (red dot) would gravitationally tug on sensors (blue squares) and cause a detectable ripple, much like wind blowing through a backyard wind chime.D. Carney et al/Physical Review D 2020

    While it may seem logical to search for dark matter using gravity, no one has tried it in the nearly 40 years that scientists have been pursuing dark matter in the lab. That’s because gravity is, comparatively, a very weak force and difficult to isolate in experiments. 

    “You’re looking for dark matter to [cause] a gravitational signal in the sensor,” says Carney, of Lawrence Berkeley National Laboratory in California. “And you just ask . . . could I possibly see this gravitational signal? When you first make the estimate, the answer is no. It’s actually going to be infeasibly difficult.”

    That didn’t stop Carney and a small group of colleagues from exploring the idea anyway in 2020. “Thirty years ago, this would have been totally nuts to propose,” he says. “It’s still kind of nuts, but it’s like borderline insanity.”

    The Windchime Project collaboration has since grown to include 20 physicists. They have a prototype Windchime built of commercial accelerometers and are using it to develop the software and analysis that will lead to the final version of the detector, but it’s a far cry from the ultimate design. Carney estimates that it could take another few decades to develop sensors good enough to measure gravity even from heavy dark matter.

    Carney bases the timeline on the development of the Laser Interferometer Gravitational-Wave Observatory, or LIGO, which was designed to look for gravitational ripples coming from black holes colliding (SN: 2/11/16). When LIGO was first conceived, he says, it was clear that the technology would need to be improved by a hundred million times. Decades of development resulted in an observatory that views the sky in gravitational waves. With Windchime, “we’re in the exact same boat,” he says.

    Even in its final form, Windchime will be sensitive only to dark matter bits that are roughly the mass of a fine speck of dust. That’s enormous on the spectrum of known particles — more than a million trillion times the mass of a proton.

    “There is a variety of very interesting dark matter candidates at [that scale] that are definitely worth looking for … including primordial black holes from the early universe,” says Katherine Freese, a physicist at the University of Michigan in Ann Arbor who is not part of the Windchime collaboration. Black holes slowly evaporate, leaking mass back into space, she notes, which could leave many relics formed shortly after the Big Bang at the mass Windchime could detect.

    But if it never detects anything at all, the experiment still stands out from other dark matter detection schemes, says Dan Hooper, a physicist at Fermilab in Batavia, Ill., also not affiliated with the project. That’s because it would be the first experiment that could entirely rule out some types of dark matter.

    Even if the experiment turns up nothing, Hooper says, “the amazing thing about [Windchime] … is that, independent of anything else you know about dark matter particles, they aren’t in this mass range.” With existing experiments, a failure to detect anything could instead be due to flawed guesses about the forces that affect dark matter (SN: 7/7/22).  

    Windchime will be the only experiment yet imagined where seeing nothing would definitively tell researchers what dark matter isn’t. With a little luck, though, it could uncover a wind of tiny black holes, or even more exotic dark matter bits, blowing past as we careen around the Milky Way. More

  • 138 Shares199 Views

    in Computers Math

    New chip-based beam steering device lays groundwork for smaller, cheaper lidar

    Researchers have developed a new chip-based beam steering technology that provides a promising route to small, cost-effective and high-performance lidar (or light detection and ranging) systems. Lidar, which uses laser pulses to acquire 3D information about a scene or object, is used in a wide range of applications such as autonomous driving, free-space optical communications, 3D holography, biomedical sensing and virtual reality.
    “Optical beam steering is a key technology for lidar systems, but conventional mechanical-based beam steering systems are bulky, expensive, sensitive to vibration and limited in speed,” said research team leader Hao Hu from the Technical University of Denmark. “Although devices known as chip-based optical phased arrays (OPAs) can quickly and precisely steer light in a non-mechanical way, so far, these devices have had poor beam quality and a field of view typically below 100 degrees.”
    In Optica, Optica Publishing Group’s journal for high-impact research, Hu and co-author Yong Liu describe their new chip-based OPA that solves many of the problems that have plagued OPAs. They show that the device can eliminate a key optical artifact known as aliasing, achieving beam steering over a large field of view while maintaining high beam quality, a combination that could greatly improve lidar systems.
    “We believe our results are groundbreaking in the field of optical beam steering,” said Hu. “This development lays the groundwork for OPA-based lidar that is low cost and compact, which would allow lidar to be widely used for a variety of applications such as high-level advanced driver-assistance systems that can assist in driving and parking and increase safety.”
    A new OPA design
    OPAs perform beam steering by electronically controlling light’s phase profile to form specific light patterns. Most OPAs use an array of waveguides to emit many beams of light and then interference is applied in far field (away from the emitter) to form the pattern. However, the fact that these waveguide emitters are typically spaced far apart from each other and generate multiple beams in the far field creates an optical artifact known as aliasing. To avoid the aliasing error and achieve a 180° field of view, the emitters need to be close together, but this causes strong crosstalk between adjacent emitters and degrades the beam quality. Thus, until now, there has been a trade-off between OPA field of view and beam quality.
    To overcome this trade-off, the researchers designed a new type of OPA that replaces the multiple emitters of traditional OPAs with a slab grating to create a single emitter. This setup eliminates the aliasing error because the adjacent channels in the slab grating can be very close to each other. The coupling between the adjacent channels is not detrimental in the slab grating because it enables the interference and beam formation in the near field (close to the single emitter). The light can then be emitted to the far field with the desired angle. The researchers also applied additional optical techniques to lower the background noise and reduce other optical artifacts such as side lobes.
    High quality and wide field of view
    To test their new device, the researchers built a special imaging system to measure the average far-field optical power along the horizontal direction over a 180° field of view. They demonstrated aliasing-free beam steering in this direction, including steering beyond ±70°, although some beam degradation was seen.
    They then characterized beam steering in the vertical direction by tuning the wavelength from 1480 nm to 1580 nm, achieving a 13.5° tuning range. Finally, they showed the versatility of the OPA by using it to form 2D images of the letters “D,” “T” and “U” centered at the angles of -60°, 0° and 60° by tuning both the wavelength and the phase shifters. The experiments were performed with a beam width of 2.1°, which the researchers are now working to decrease to achieve beam steering with a higher resolution and a longer range.
    “Our new chip-based OPA shows an unprecedented performance and overcomes the long-standing issues of OPAs by simultaneously achieving aliasing-free 2D beam steering over the entire 180° field of view and high beam quality with a low side lobe level,” said Hu.
    This work is funded by VILLUM FONDEN and Innovationsfonden Denmark.
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  • 113 Shares139 Views

    in Computers Math

    Pairing imaging, AI may improve colon cancer screening, diagnosis

    A research team from the lab of Quing Zhu, the Edwin H. Murty Professor of Engineering in the Department of Biomedical Engineering at the McKelvey School of Engineering at Washington University in St. Louis, has combined optical coherence tomography (OCT) and machine learning to develop a colorectal cancer imaging tool that may one day improve the traditional endoscopy currently used by doctors.
    The results were published in the June issue of the Journal of Biophotonics.
    Screening for colon cancer now relies on human visual inspection of tissue during a colonoscopy procedure. This technique, however, does not detect and diagnose subsurface lesions.
    An endoscopy OCT essentially shines a light in the colon to help a clinician see deeper to visualize and diagnose abnormalities. By collaborating with physicians at Washington University School of Medicine and with Chao Zhou, associate professor of biomedical engineering, the team developed a small OCT catheter, which uses a longer wavelength of light, to penetrate 1-2 mm into the tissue samples.
    Hongbo Luo, a PhD student in Zhu’s lab, led the work.
    The technique provided more information about an abnormality than surface-level, white-light images currently used by physicians. Shuying Li, a biomedical engineering PhD student, used the imaging data to train a machine learning algorithm to differentiate between “normal” and “cancerous” tissue. The combined system allowed them to detect and classify cancerous tissue samples with a 93% diagnostic accuracy.
    Zhu also is a professor of radiology at the School of Medicine. Her team worked with Vladimir Kushnir and Vladimir Lamm at the School of Medicine, Zhu’s team of PhD students, including Tiger Nie, started a trial in patients in July 2022.
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  • 150 Shares199 Views

    in Computers Math

    Proteins and natural language: Artificial intelligence enables the design of novel proteins

    Artificial intelligence (AI) has created new possibilities for designing tailor-made proteins to solve everything from medical to ecological problems. A research team at the University of Bayreuth led by Prof. Dr. Birte Höcker has now successfully applied a computer-based natural language processing model to protein research. Completely independently, the ProtGPT2 model designs new proteins that are capable of stable folding and could take over defined functions in larger molecular contexts. The model and its potential are detailed scientifically in Nature Communications.
    Natural languages and proteins are actually similar in structure. Amino acids arrange themselves in a multitude of combinations to form structures that have specific functions in the living organism — similar to the way words form sentences in different combinations that express certain facts. In recent years, numerous approaches have therefore been developed to use principles and processes that control the computer-assisted processing of natural language in protein research. “Natural language processing has made extraordinary progress thanks to new AI technologies. Today, models of language processing enable machines not only to understand meaningful sentences but also to generate them themselves. Such a model was the starting point of our research. With detailed information concerning about 50 million sequences of natural proteins, my colleague Noelia Ferruz trained the model and enabled it to generate protein sequences on its own. It now understands the language of proteins and can use it creatively. We have found that these creative designs follow the basic principles of natural proteins,” says Prof. Dr. Birte Höcker, Head of the Protein Design Group at the University of Bayreuth.
    The language processing model transferred to protein evolution is called “ProtGPT2.” It can now be used to design proteins that adopt stable structures through folding and are permanently functional in this state. In addition, the Bayreuth biochemists have found out, through complex investigations, that the model can even create proteins that do not occur in nature and have possibly never existed in the history of evolution. These findings shed light on the immeasurable world of possible proteins and open a door to designing them in novel and unexplored ways. There is a further advantage: Most proteins that have been designed de novo so far have idealised structures. Before such structures can have a potential application, they usually must pass through an elaborate functionalization process — for example by inserting extensions and cavities — so that they can interact with their environment and take on precisely defined functions in larger system contexts. ProtGPT2, on the other hand, generates proteins that have such differentiated structures innately, and are thus already operational in their respective environments.
    “Our new model is another impressive demonstration of the systemic affinity of protein design and natural language processing. Artificial intelligence opens up highly interesting and promising possibilities to use methods of language processing for the production of customised proteins. At the University of Bayreuth, we hope to contribute in this way to developing innovative solutions for biomedical, pharmaceutical, and ecological problems,” says Prof. Dr. Birte Höcker.
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