Science in telegram

Частые вопросы

Сколько подписчиков?

Подписчиков — 120,1 тыс. Медиана просмотров поста — 5,4 тыс. Просмотров на подписчика — 4,5%. Данные на 1 октября 2026 г.

Как часто выходят посты?

За 30 дней вышло постов: 62 — это несколько раз в день. Данные на 1 октября 2026 г.

Есть ли у канала галочка Telegram?

Галочки Telegram нет.

Кто ведёт страницу канала в каталоге?

Пока никто. Если это ваш канал — заберите страницу: сможете отвечать на отзывы и видеть её статистику.

О чём писал канал

По постам 9–13 сентября · составлено автоматически

Физика элементарных частиц и биология. Канал разбирал попытку обнаружить тёмную материю в подземной лаборатории, механизм взаимодействия молекул ДНК через ионные мосты и открытие останков денисовцев в пещере в южной части Китая.

Последние посты

Посты — на английском: это то, что публикует канал.

• сегодня419Открыть в Telegram

  Медиа

• вчера2,1 тыс.17 пересылок13 реакцийОткрыть в Telegram
  🌌 The Ingredients for Planets Were Spreading Through Space Just 500 Million Years After the Big Bang The newborn universe started simple. After the Big Bang, almost everything was hydrogen and helium. Carbon, oxygen, silicon and nearly every other element needed to build planets — and eventually us — had to be manufactured later inside stars. Astronomers expected that process to take time. JWST has now shown that it happened remarkably fast. Researchers analyzed nearly 30 hours of Webb observations of three galaxies seen as they existed roughly 500–700 million years after the Big Bang. They found unmistakable chemical fingerprints of carbon, oxygen and silicon in gas associated with the galaxies. But the really interesting part was where that gas was going. The absorption signatures were blueshifted by roughly 50–250 km/s, indicating that metal-enriched material was moving outward from the galaxies — consistent with powerful galactic winds carrying newly forged elements into surrounding space. That means an entire cosmic recycling system was already operating while the universe was only about 3% of its present age. Stars formed. They forged heavier elements. Stellar winds and explosions returned those elements to their galaxies. And galaxies began spraying them outward, chemically transforming the surrounding universe. Remarkably, the chemical fingerprints look similar to those seen around galaxies billions of years later. The result may also help solve another mystery. Astronomers have spent decades searching for Population III stars — the hypothetical first generation of stars, made almost entirely from pristine hydrogen and helium. None has ever been conclusively found. If early galaxies contaminated their surroundings with heavier elements this quickly, the window in which truly pristine stars could form may simply have been much shorter than expected. Important caveat: the result comes from only three unusually bright early galaxies. We don’t yet know whether such rapid enrichment was universal across the young cosmos. Still, the implication is striking. Only half a billion years after the Big Bang, the universe had already started distributing the carbon in our bodies, the oxygen in our water and the silicon beneath our feet. Cosmic chemistry apparently wasted very little time. #JWST #Space #Astronomy #Cosmology #BigBang #EarlyUniverse #Science

  nature.com/…

  https://www.nature.com/articles/s41550-026-02988-2

  Ещё
• вчера2,1 тыс.7 пересылок11 реакцийОткрыть в Telegram

  Медиа

• вчера2,5 тыс.22 пересылки12 реакцийОткрыть в Telegram

  Медиа

• 2 дня назад3 тыс.14 пересылок19 реакцийОткрыть в Telegram

  Медиа

• 3 дня назад3,4 тыс.32 пересылки36 реакцийОткрыть в Telegram
  ⚛️ Scientists Watched Matter “Appear” Inside a Quantum Computer Pull two quarks apart and something deeply strange happens. You never actually get two isolated quarks. Instead, the energy binding them grows — almost as if an invisible string were being stretched between them. Eventually, storing more energy in that string becomes so expensive that nature takes another option: it creates a new particle–antiparticle pair. The string breaks. This process, called string breaking, is fundamental to the strong nuclear force and probably played an important role in how matter evolved in the extremely hot early universe. But calculating its real-time quantum dynamics is extraordinarily difficult. So researchers from Duke University, the University of Maryland, Oxford, Caltech, Cornell and KU Leuven built a miniature analogue of the problem inside a quantum machine. They programmed a chain of 13 trapped ytterbium ions to behave according to a simplified lattice gauge theory. The ions were not literally turned into quarks. Instead, their quantum states encoded the particles, fields and “string” connecting them — allowing researchers to watch the simulated system evolve with both spatial and temporal resolution. And then the string broke. New effective particle pairs appeared and propagated through the simulated system, reproducing the essential quantum dynamics physicists wanted to study. But the experiment also produced a surprise. The conventional expectation was that particle pairs would spontaneously appear throughout the string through a process related to the Schwinger mechanism. Instead, the researchers observed pairs forming preferentially near the two ends of the string, then spreading inward. Their calculations indicate this is a distinct, previously unobserved mechanism for dynamical string breaking. This is not a simulation of the full Standard Model, and no real matter was created inside the computer. The experiment used a simplified 1+1-dimensional Z₂ gauge theory, and today’s classical computers can still reproduce a system this small. The real prize comes later. As quantum simulators grow, they could attack versions of these problems that conventional supercomputers cannot efficiently calculate — potentially letting physicists experimentally explore the quantum dynamics of particle collisions and conditions resembling the universe shortly after the Big Bang. We built computers out of quantum mechanics. Now we’re beginning to use them to ask quantum mechanics how the universe built matter. #QuantumComputing #QuantumPhysics #ParticlePhysics #BigBang #Physics #Science

  doi.org/…

  https://doi.org/10.1038/s41567-026-03422-0

  Ещё
• 3 дня назад3,3 тыс.12 пересылок38 реакцийОткрыть в Telegram

  Медиа

• 4 дня назад3,7 тыс.38 пересылок43 реакцииОткрыть в Telegram

  Медиа

• 4 дня назад3,9 тыс.39 пересылок39 реакцийОткрыть в Telegram
  🩻 AI that reads a CT scan in 3D—and explains its findings NVIDIA, the NIH’s National Cancer Institute, and the University of Zurich have released NV-Reason-CT, an open model that analyzes full 3D CT volumes and generates reports with explanations. ⚙️ How it works The model pairs the Qwen3.5-4B language model with Primus, a 3D visual encoder. Each scan becomes 13,824 visual tokens, passed to the language model without further compression. Three-dimensional positional encoding preserves spatial information, helping it distinguish, for example, a finding in the right kidney from one in the left. 📚 How it was trained Training used 550,000 examples from 70,111 CT volumes. Supervised fine-tuning on radiologists’ analyses was followed by reinforcement learning, with rewards for correctly identifying abnormalities and following the required report structure. 📊 What the results show On CT-RATE, NV-Reason-CT achieved an average precision of 0.614 across 18 abnormality categories, compared with 0.581 for VoxelFM and **0.398 for CT-CLIP**—without a separate classification head. In a pilot study with radiologists, scan review and reporting time fell from 26.25 to 13.13 minutes: roughly half. 🧩 Part of a broader medical AI toolkit NVIDIA’s open medical model family also includes: • NV-Generate-CTMR — generates synthetic 3D CT and MRI volumes. • NV-Segment-CTMR — segments organs and lesions. • NV-Reason-CXR — analyzes chest X-rays. • NV-Reason-CT — analyzes full 3D CT scans. 🔓 Weights and code are available under OpenMDW-1.1, alongside fine-tuning and reinforcement-learning examples and a web demo. Promising early results for AI-assisted radiology—with the time savings demonstrated so far in a pilot study. @science

  Ещё
  
• 5 дней назад4,3 тыс.22 пересылки43 реакцииОткрыть в Telegram

  Медиа

О канале в цифрах

Создан

25 ноября 2016 г.

Фото

773

Видео

414

Файлы

11

Ссылки

2,9 тыс.

По данным Telegram на 1 октября 2026 г.

Отзывы

Отзывов пока нет. Ваш будет первым.

Похожие

Ещё по теме