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Category: mapping

“I give you God’s view,” said Toby Cubitt, a physicist turned computer scientist at University College London and part of the vanguard of the current charge into the unknowable, and “you still can’t predict what it’s going to do.”

Eva Miranda, a mathematician at the Polytechnic University of Catalonia (UPC) in Spain, calls undecidability a “next-level chaotic thing.”

Undecidability means that certain questions simply cannot be answered. It’s an unfamiliar message for physicists, but it’s one that mathematicians and computer scientists know well. More than a century ago, they rigorously established that there are mathematical questions that can never be answered, true statements that can never be proved. Now physicists are connecting those unknowable mathematical systems with an increasing number of physical ones and thereby beginning to map out the hard boundary of knowability in their field as well.

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The frequency regime lying in the shortwave infrared (SWIR) has very unique properties that make it ideal for several applications, such as being less affected by atmospheric scattering as well as being “eye-safe.” These include Light Detection and Ranging (LIDAR), a method for determining ranges and distances using lasers, space localization and mapping, adverse weather imaging for surveillance and automotive safety, environmental monitoring, and many others.

However, SWIR light is currently confined to niche areas, like scientific instrumentation and military use, mainly because SWIR photodetectors rely on expensive and difficult-to-manufacture materials. In the past few years, —solution-processed semiconducting nanocrystals—have emerged as an alternative for mainstream consumer electronics.

While toxic heavy-metals (like lead or mercury) have typically been used, quantum dots can also be made with environmentally friendly materials such as silver telluride (Ag2Te). In fact, silver telluride colloidal quantum dots show device performance comparable to their toxic counterparts. But they are still in their infancy, and several challenges must be addressed before they can be used in practical applications.

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A team of physicists have discovered a new approach that redefines the conception of a black hole by mapping out their detailed structure, as shown in a research study recently published in Journal of High Energy Physics.

The study details new theoretical structures called “supermazes” that offer a more universal picture of to the field of theoretical physics. Based in , supermazes are pivotal to understanding the structure of black holes on a microscopic level.

“General relativity is a powerful theory for describing the large-scale structure of black holes, but it is a very, very blunt instrument for describing black-hole microstructure,” said Nicholas Warner, co-author of the study and professor of physics, astronomy and mathematics at the USC Dornsife College of Letters, Arts and Sciences. In a framework of theories extending beyond Einstein’s equations, supermazes provide a detailed portrait of the microscopic structure of brane black holes.

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Network models provide a flexible way of representing objects and their multifaceted relationships. Deriving a network entails mapping hidden structures in inevitably noisy data—a critical task known as reconstruction. Now Gang Yan and Jia-Jie Qin of Tongji University in China have provided a mathematical proof showing what makes some networks easier to reconstruct than others [1].

Complex systems in biology, physics, and social sciences tend to involve a vast number of interacting entities. In a network model, these entities are represented by nodes, linked by connections weighted to describe the strength of each interaction. Yan and Qin took an empirical dataset and used a statistical inference method to calculate the likelihood that any pair of nodes is directly linked. Then, based on the true positive and false positive rates of these inferred connections, they analyzed the fidelity of the reconstructed networks. They found that the most faithful reconstructions are obtained with systems for which the number of connections per node varies most widely across the network. Yan and Qin saw the same tendency when they tested their model on synthetic and real networks, including metabolic networks, plant-pollinator webs, and power grids.

With the rapid increase in available data across research areas, network reconstruction has become an important tool for studying complex systems. Yan and Qin say their new result both solves the problem of what complex systems can be easily mapped into a network and provides a solid foundation for developing methods of doing so.

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Researchers at the University of Virginia have created the first comprehensive protein-level atlas of brain development, providing unprecedented insight into how the brain forms and potential implications for understanding neurological disorders. The study, published in Nature Neuroscience, analyzed over 24 million individual cells from mouse brains, revealing detailed molecular pathways that guide brain development from early embryonic stages through early postnatal development.

The research team, led by Professors Christopher Deppmann and Eli Zunder, used an innovative technique called mass cytometry to track 40 different proteins across various brain regions and developmental stages. The approach provided a more detailed view of cellular function than previous studies that primarily examined RNA.

“While RNA studies have given us important insights, proteins are the actual workforce of cells,” explained Deppmann, a professor in the College and Graduate School of Arts & Sciences’ Department of Biology. “By studying proteins directly, we can better understand how cells are functioning and communicating during brain development.”

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Physicists have made a major leap in our understanding of quantum entanglement by fully mapping out the statistics it can produce – essentially decoding the language of the quantum world.

This breakthrough reveals how the bizarre but powerful correlations in quantum systems can be used to test, secure, and certify the behavior of quantum devices, all without knowing their inner workings. The ability to self-test even partially entangled systems now opens doors to more robust quantum communication, encryption, and computing methods. It’s a game-changer for both fundamental physics and real-world quantum tech.

Cracking the code of quantum entanglement.

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Well done Gaia & crew. [ https://www.spacedaily.com/reports/Star-mapping_space_telesc…t_999.html](https://www.spacedaily.com/reports/Star-mapping_space_telesc…t_999.html)

After more than a decade mapping out our home galaxy, the Gaia space telescope was powered down and sent into “retirement orbit” around the Sun on Thursday, the European Space Agency said.

Since launching in 2013, the telescope has been charting the positions, motion and properties of nearly two billion stars to create a vast map of the Milky Way, revealing many secrets of the cosmos along the way, the ESA said in a statement.

Gaia uncovered evidence of massive galaxies slamming into each other, identified vast clusters of stars, helped discover new exoplanets and mapped millions of galaxies and blazing galactic monsters called quasars.

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This week, ALMA researchers reported the discovery of oxygen in the most distant known galaxy. Geologists believe unusual structures in rock in the desert regions of Namibia, Oman and Saudia Arabia may be evidence of an unknown microorganism. And a group of physicists may have generated a tiny charge of electricity using the Earth’s rotational energy. But the biggest story by far is the second release of data from the DESI survey of the universe, which could upend the standard model:

An emerging generation of cosmological surveys launched this week with the second release of data from the Dark Energy Spectroscopic Instrument at Kitt Peak National Observatory in Arizona, which is mapping an unprecedentedly huge number of galaxies spanning 11 billion years of cosmic history in order to better understand dark energy.

Astronomers have known for many decades that the universe is expanding; in the 1990s, the first image of the cosmic microwave background—the echo of the big bang—revealed that this expansion is accelerating for unknown reasons. Astronomers call this expansion “dark energy,” which translates to “we don’t understand what this energy is.”

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The Dark Energy Spectroscopic Instrument (DESI) is mapping millions of celestial objects to better understand dark energy—the mysterious driver of our universe’s accelerating expansion. Today, the DESI collaboration released a new collection of data for anyone in the world to investigate.

The dataset is the largest of its kind, with information on 18.7 million objects: roughly 4 million stars, 13.1 million galaxies, and 1.6 million quasars (extremely bright but distant objects powered by supermassive black holes at their cores).

While the experiment’s main mission is illuminating , DESI’s Data Release 1 (DR1) could yield discoveries in other areas of astrophysics, such as the evolution of galaxies and black holes, the nature of dark matter, and the structure of the Milky Way.

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