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  • richardmitnick 4:45 pm on January 21, 2022 Permalink | Reply
    Tags: "Any Single Galaxy Reveals the Composition of an Entire Universe", A group of scientists may have stumbled upon a radical new way to do cosmology., , Cosmic density of matter, , , , , Neural networks, , , The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project, Theoretical Astrophysics   

    From Quanta Magazine (US): “Any Single Galaxy Reveals the Composition of an Entire Universe” 

    From Quanta Magazine (US)

    January 20, 2022
    Charlie Wood

    Credit: Kaze Wong / CAMELS collaboration.

    In the CAMELS project, coders simulated thousands of universes with diverse compositions, arrayed at the end of this video as cubes.

    A group of scientists may have stumbled upon a radical new way to do cosmology.

    Cosmologists usually determine the composition of the universe by observing as much of it as possible. But these researchers have found that a machine learning algorithm can scrutinize a single simulated galaxy and predict the overall makeup of the digital universe in which it exists — a feat analogous to analyzing a random grain of sand under a microscope and working out the mass of Eurasia. The machines appear to have found a pattern that might someday allow astronomers to draw sweeping conclusions about the real cosmos merely by studying its elemental building blocks.

    “This is a completely different idea,” said Francisco Villaescusa-Navarro, a theoretical astrophysicist at The Flatiron Institute Center for Computational Astrophysics (US) and lead author of the work. “Instead of measuring these millions of galaxies, you can just take one. It’s really amazing that this works.”

    It wasn’t supposed to. The improbable find grew out of an exercise Villaescusa-Navarro gave to Jupiter Ding, a Princeton University(US) undergraduate: Build a neural network that, knowing a galaxy’s properties, can estimate a couple of cosmological attributes. The assignment was meant merely to familiarize Ding with machine learning. Then they noticed that the computer was nailing the overall density of matter.

    “I thought the student made a mistake,” Villaescusa-Navarro said. “It was a little bit hard for me to believe, to be honest.”

    The results of the investigation that followed appeared on January 6 submitted for publication. The researchers analyzed 2,000 digital universes generated by The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project [The Astrophysical Journal]. These universes had a range of compositions, containing between 10% and 50% matter with the rest made up of Dark Energy, which drives the universe to expand faster and faster. (Our actual cosmos consists of roughly one-third Dark Matter and visible matter and two-thirds Dark Energy.) As the simulations ran, Dark Matter and visible matter swirled together into galaxies. The simulations also included rough treatments of complicated events like supernovas and jets that erupt from supermassive black holes.

    Ding’s neural network studied nearly 1 million simulated galaxies within these diverse digital universes. From its godlike perspective, it knew each galaxy’s size, composition, mass, and more than a dozen other characteristics. It sought to relate this list of numbers to the density of matter in the parent universe.

    It succeeded. When tested on thousands of fresh galaxies from dozens of universes it hadn’t previously examined, the neural network was able to predict the cosmic density of matter to within 10%. “It doesn’t matter which galaxy you are considering,” Villaescusa-Navarro said. “No one imagined this would be possible.”

    “That one galaxy can get [the density to] 10% or so, that was very surprising to me,” said Volker Springel, an expert in simulating galaxy formation at The MPG Institute for Astrophysics [MPG Institut für Astrophysik](DE) who was not involved in the research.

    The algorithm’s performance astonished researchers because galaxies are inherently chaotic objects. Some form all in one go, and others grow by eating their neighbors. Giant galaxies tend to hold onto their matter, while supernovas and black holes in dwarf galaxies might eject most of their visible matter. Still, every galaxy had somehow managed to keep close tabs on the overall density of matter in its universe.

    One interpretation is “that the universe and/or galaxies are in some ways much simpler than we had imagined,” said Pauline Barmby, an astronomer at The Western University (CA). Another is that the simulations have unrecognized flaws.

    The team spent half a year trying to understand how the neural network had gotten so wise. They checked to make sure the algorithm hadn’t just found some way to infer the density from the coding of the simulation rather than the galaxies themselves. “Neural networks are very powerful, but they are super lazy,” Villaescusa-Navarro said.

    Through a series of experiments, the researchers got a sense of how the algorithm was divining the cosmic density. By repeatedly retraining the network while systematically obscuring different galactic properties, they zeroed in on the attributes that mattered most.

    Near the top of the list was a property related to a galaxy’s rotation speed, which corresponds to how much matter (dark and otherwise) sits in the galaxy’s central zone. The finding matches physical intuition, according to Springel. In a universe overflowing with Dark Matter, you’d expect galaxies to grow heavier and spin faster. So you might guess that rotation speed would correlate with the cosmic matter density, although that relationship alone is too rough to have much predictive power.

    The neural network found a much more precise and complicated relationship between 17 or so galactic properties and the matter density. This relationship persists despite galactic mergers, stellar explosions and black hole eruptions. “Once you get to more than [two properties], you can’t plot it and squint at it by eye and see the trend, but a neural network can,” said Shaun Hotchkiss, a cosmologist at The University of Auckland (NZ).

    While the algorithm’s success raises the question of how many of the universe’s traits might be extracted from a thorough study of just one galaxy, cosmologists suspect that real-world applications will be limited. When Villaescusa-Navarro’s group tested their neural network on a different property — cosmic clumpiness — it found no pattern. And Springel expects that other cosmological attributes, such as the accelerating expansion of the universe due to Dark Energy, have little effect on individual galaxies.

    The research does suggest that, in theory, an exhaustive study of the Milky Way and perhaps a few other nearby galaxies could enable an exquisitely precise measurement of our universe’s matter. Such an experiment, Villaescusa-Navarro said, could give clues to other numbers of cosmic import such as the sum of the unknown masses of the universe’s three types of neutrinos.

    Neutrinos- Universe Today

    But in practice, the technique would have to first overcome a major weakness. The CAMELS collaboration cooks up its universes using two different recipes. A neural network trained on one of the recipes makes bad density guesses when given galaxies that were baked according to the other. The cross-prediction failure indicates that the neural network is finding solutions unique to the rules of each recipe. It certainly wouldn’t know what to do with the Milky Way, a galaxy shaped by the real laws of physics. Before applying the technique to the real world, researchers will need to either make the simulations more realistic or adopt more general machine learning techniques — a tall order.

    “I’m very impressed by the possibilities, but one needs to avoid being too carried away,” Springel said.

    But Villaescusa-Navarro takes heart that the neural network was able to find patterns in the messy galaxies of two independent simulations. The digital discovery raises the odds that the real cosmos may be hiding a similar link between the large and the small.

    “It’s a very beautiful thing,” he said. “It establishes a connection between the whole universe and a single galaxy.”

    The Dark Energy Survey

    Dark Energy Camera [DECam] built at DOE’s Fermi National Accelerator Laboratory(US).

    NOIRLab National Optical Astronomy Observatory(US) Cerro Tololo Inter-American Observatory(CL) Victor M Blanco 4m Telescope which houses the Dark-Energy-Camera – DECam at Cerro Tololo, Chile at an altitude of 7200 feet.

    NOIRLab(US)NSF NOIRLab NOAO (US) Cerro Tololo Inter-American Observatory(CL) approximately 80 km to the East of La Serena, Chile, at an altitude of 2200 meters.

    Timeline of the Inflationary Universe WMAP.

    The The Dark Energy Survey is an international, collaborative effort to map hundreds of millions of galaxies, detect thousands of supernovae, and find patterns of cosmic structure that will reveal the nature of the mysterious dark energy that is accelerating the expansion of our Universe. The Dark Energy Survey began searching the Southern skies on August 31, 2013.

    According to Albert Einstein’s Theory of General Relativity, gravity should lead to a slowing of the cosmic expansion. Yet, in 1998, two teams of astronomers studying distant supernovae made the remarkable discovery that the expansion of the universe is speeding up.

    Saul Perlmutter (center) [The Supernova Cosmology Project] shared the 2006 Shaw Prize in Astronomy, the 2011 Nobel Prize in Physics, and the 2015 Breakthrough Prize in Fundamental Physics with Brian P. Schmidt (right) and Adam Riess (left) [The High-z Supernova Search Team] for providing evidence that the expansion of the universe is accelerating.

    To explain cosmic acceleration, cosmologists are faced with two possibilities: either 70% of the universe exists in an exotic form, now called Dark Energy, that exhibits a gravitational force opposite to the attractive gravity of ordinary matter, or General Relativity must be replaced by a new theory of gravity on cosmic scales.

    The Dark Energy Survey is designed to probe the origin of the accelerating universe and help uncover the nature of Dark Energy by measuring the 14-billion-year history of cosmic expansion with high precision. More than 400 scientists from over 25 institutions in the United States, Spain, the United Kingdom, Brazil, Germany, Switzerland, and Australia are working on the project. The collaboration built and is using an extremely sensitive 570-Megapixel digital camera, DECam, mounted on the Blanco 4-meter telescope at Cerro Tololo Inter-American Observatory, high in the Chilean Andes, to carry out the project.

    Over six years (2013-2019), the Dark Energy Survey collaboration used 758 nights of observation to carry out a deep, wide-area survey to record information from 300 million galaxies that are billions of light-years from Earth. The survey imaged 5000 square degrees of the southern sky in five optical filters to obtain detailed information about each galaxy. A fraction of the survey time is used to observe smaller patches of sky roughly once a week to discover and study thousands of supernovae and other astrophysical transients.

    Fritz Zwicky discovered Dark Matter in the 1930s when observing the movement of the Coma Cluster., Vera Rubin a Woman in STEM, denied the Nobel, some 30 years later, did most of the work on Dark Matter.

    Fritz Zwicky.
    Coma cluster via NASA/ESA Hubble, the original example of Dark Matter discovered during observations by Fritz Zwicky and confirmed 30 years later by Vera Rubin.
    In modern times, it was astronomer Fritz Zwicky, in the 1930s, who made the first observations of what we now call dark matter. His 1933 observations of the Coma Cluster of galaxies seemed to indicated it has a mass 500 times more than that previously calculated by Edwin Hubble. Furthermore, this extra mass seemed to be completely invisible. Although Zwicky’s observations were initially met with much skepticism, they were later confirmed by other groups of astronomers.

    Thirty years later, astronomer Vera Rubin provided a huge piece of evidence for the existence of dark matter. She discovered that the centers of galaxies rotate at the same speed as their extremities, whereas, of course, they should rotate faster. Think of a vinyl LP on a record deck: its center rotates faster than its edge. That’s what logic dictates we should see in galaxies too. But we do not. The only way to explain this is if the whole galaxy is only the center of some much larger structure, as if it is only the label on the LP so to speak, causing the galaxy to have a consistent rotation speed from center to edge.

    Vera Rubin, following Zwicky, postulated that the missing structure in galaxies is dark matter. Her ideas were met with much resistance from the astronomical community, but her observations have been confirmed and are seen today as pivotal proof of the existence of dark matter.
    Astronomer Vera Rubin at the Lowell Observatory in 1965, worked on Dark Matter (The Carnegie Institution for Science).

    Vera Rubin, with Department of Terrestrial Magnetism (DTM) image tube spectrograph attached to the Kitt Peak 84-inch telescope, 1970.

    Vera Rubin measuring spectra, worked on Dark Matter(Emilio Segre Visual Archives AIP SPL).
    Dark Matter Research

    LBNL LZ Dark Matter Experiment (US) xenon detector at Sanford Underground Research Facility(US) Credit: Matt Kapust.

    Lamda Cold Dark Matter Accerated Expansion of The universe http scinotions.com the-cosmic-inflation-suggests-the-existence-of-parallel-universes. Credit: Alex Mittelmann.

    DAMA at Gran Sasso uses sodium iodide housed in copper to hunt for dark matter LNGS-INFN.

    Yale HAYSTAC axion dark matter experiment at Yale’s Wright Lab.

    DEAP Dark Matter detector, The DEAP-3600, suspended in the SNOLAB (CA) deep in Sudbury’s Creighton Mine.

    The LBNL LZ Dark Matter Experiment (US) Dark Matter project at SURF, Lead, SD, USA.

    DAMA-LIBRA Dark Matter experiment at the Italian National Institute for Nuclear Physics’ (INFN’s) Gran Sasso National Laboratories (LNGS) located in the Abruzzo region of central Italy.

    DARWIN Dark Matter experiment. A design study for a next-generation, multi-ton dark matter detector in Europe at The University of Zurich [Universität Zürich](CH).

    PandaX II Dark Matter experiment at Jin-ping Underground Laboratory (CJPL) in Sichuan, China.

    Inside the Axion Dark Matter eXperiment U Washington (US) Credit : Mark Stone U. of Washington. Axion Dark Matter Experiment.

    See the full article here .


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    Formerly known as Simons Science News, Quanta Magazine (US) is an editorially independent online publication launched by the Simons Foundation to enhance public understanding of science. Why Quanta? Albert Einstein called photons “quanta of light.” Our goal is to “illuminate science.” At Quanta Magazine, scientific accuracy is every bit as important as telling a good story. All of our articles are meticulously researched, reported, edited, copy-edited and fact-checked.

  • richardmitnick 7:31 am on July 1, 2017 Permalink | Reply
    Tags: , , Neural networks, Peering into neural networks   

    From MIT: “Peering into neural networks” 

    MIT News

    MIT Widget

    MIT News

    June 29, 2017
    Larry Hardesty

    Neural networks learn to perform computational tasks by analyzing large sets of training data. But once they’ve been trained, even their designers rarely have any idea what data elements they’re processing. Image: Christine Daniloff/MIT

    Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for today’s best-performing artificial intelligence systems, from speech recognition systems, to automatic translators, to self-driving cars.

    But neural nets are black boxes. Once they’ve been trained, even their designers rarely have any idea what they’re doing — what data elements they’re processing and how.

    Two years ago, a team of computer-vision researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) described a method for peering into the black box of a neural net trained to identify visual scenes. The method provided some interesting insights, but it required data to be sent to human reviewers recruited through Amazon’s Mechanical Turk crowdsourcing service.

    At this year’s Computer Vision and Pattern Recognition conference, CSAIL researchers will present a fully automated version of the same system. Where the previous paper reported the analysis of one type of neural network trained to perform one task, the new paper reports the analysis of four types of neural networks trained to perform more than 20 tasks, including recognizing scenes and objects, colorizing grey images, and solving puzzles. Some of the new networks are so large that analyzing any one of them would have been cost-prohibitive under the old method.

    The researchers also conducted several sets of experiments on their networks that not only shed light on the nature of several computer-vision and computational-photography algorithms, but could also provide some evidence about the organization of the human brain.

    Neural networks are so called because they loosely resemble the human nervous system, with large numbers of fairly simple but densely connected information-processing “nodes.” Like neurons, a neural net’s nodes receive information signals from their neighbors and then either “fire” — emitting their own signals — or don’t. And as with neurons, the strength of a node’s firing response can vary.

    In both the new paper and the earlier one, the MIT researchers doctored neural networks trained to perform computer vision tasks so that they disclosed the strength with which individual nodes fired in response to different input images. Then they selected the 10 input images that provoked the strongest response from each node.

    In the earlier paper, the researchers sent the images to workers recruited through Mechanical Turk, who were asked to identify what the images had in common. In the new paper, they use a computer system instead.

    “We catalogued 1,100 visual concepts — things like the color green, or a swirly texture, or wood material, or a human face, or a bicycle wheel, or a snowy mountaintop,” says David Bau, an MIT graduate student in electrical engineering and computer science and one of the paper’s two first authors. “We drew on several data sets that other people had developed, and merged them into a broadly and densely labeled data set of visual concepts. It’s got many, many labels, and for each label we know which pixels in which image correspond to that label.”

    The paper’s other authors are Bolei Zhou, co-first author and fellow graduate student; Antonio Torralba, MIT professor of electrical engineering and computer science; Aude Oliva, CSAIL principal research scientist; and Aditya Khosla, who earned his PhD as a member of Torralba’s group and is now the chief technology officer of the medical-computing company PathAI.

    The researchers also knew which pixels of which images corresponded to a given network node’s strongest responses. Today’s neural nets are organized into layers. Data are fed into the lowest layer, which processes them and passes them to the next layer, and so on. With visual data, the input images are broken into small chunks, and each chunk is fed to a separate input node.

    For every strong response from a high-level node in one of their networks, the researchers could trace back the firing patterns that led to it, and thus identify the specific image pixels it was responding to. Because their system could frequently identify labels that corresponded to the precise pixel clusters that provoked a strong response from a given node, it could characterize the node’s behavior with great specificity.

    The researchers organized the visual concepts in their database into a hierarchy. Each level of the hierarchy incorporates concepts from the level below, beginning with colors and working upward through textures, materials, parts, objects, and scenes. Typically, lower layers of a neural network would fire in response to simpler visual properties — such as colors and textures — and higher layers would fire in response to more complex properties.

    But the hierarchy also allowed the researchers to quantify the emphasis that networks trained to perform different tasks placed on different visual properties. For instance, a network trained to colorize black-and-white images devoted a large majority of its nodes to recognizing textures. Another network, when trained to track objects across several frames of video, devoted a higher percentage of its nodes to scene recognition than it did when trained to recognize scenes; in that case, many of its nodes were in fact dedicated to object detection.

    One of the researchers’ experiments could conceivably shed light on a vexed question in neuroscience. Research involving human subjects with electrodes implanted in their brains to control severe neurological disorders has seemed to suggest that individual neurons in the brain fire in response to specific visual stimuli. This hypothesis, originally called the grandmother-neuron hypothesis, is more familiar to a recent generation of neuroscientists as the Jennifer-Aniston-neuron hypothesis, after the discovery that several neurological patients had neurons that appeared to respond only to depictions of particular Hollywood celebrities.

    Many neuroscientists dispute this interpretation. They argue that shifting constellations of neurons, rather than individual neurons, anchor sensory discriminations in the brain. Thus, the so-called Jennifer Aniston neuron is merely one of many neurons that collectively fire in response to images of Jennifer Aniston. And it’s probably part of many other constellations that fire in response to stimuli that haven’t been tested yet.

    Because their new analytic technique is fully automated, the MIT researchers were able to test whether something similar takes place in a neural network trained to recognize visual scenes. In addition to identifying individual network nodes that were tuned to particular visual concepts, they also considered randomly selected combinations of nodes. Combinations of nodes, however, picked out far fewer visual concepts than individual nodes did — roughly 80 percent fewer.

    “To my eye, this is suggesting that neural networks are actually trying to approximate getting a grandmother neuron,” Bau says. “They’re not trying to just smear the idea of grandmother all over the place. They’re trying to assign it to a neuron. It’s this interesting hint of this structure that most people don’t believe is that simple.”

    See the full article here .

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