@science
Science in telegram
Telegram channel @science: 120.1K subscribers, 5.4K views per post, score 35
35DBelow 9 in 10 channels in its category
Category midrange 42–60This channel 35
- 120.1K
- Subscribers
- 5.4K
- Median views over 30 days
- 4.5%
- Views / subscribers over 30 days
- 62
- Posts over 30 days
Data as of October 1, 2026
Science news in English: short pieces on fresh discoveries in physics, neuroscience, medicine, and artificial intelligence. Good for readers who want to keep up with research breakthroughs without wading through long papers or jargon.
catalog description About the channel, by its author
Science that matters: AI, space, biotech, physics, future tech — explained sharply
Common questions
- How many subscribers?
- Subscribers: 120.1K. Median views per post: 5.4K. Views per subscriber: 4.5%. Measured on October 1, 2026.
- How often are posts published?
- Posts in the last 30 days: 62 — that is several times a day. Measured on October 1, 2026.
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- No Telegram tick.
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120,835120,141
Oct 1120,141−16 in a day
Hover the chart or swipe it — we show the day.
| What it is made of | |
|---|---|
| Engagement | 3.4 of 30 |
| Growth quality | 12 of 20 |
| Reactions and forwards | 6.3 of 15 |
| Consistency | 3.8 of 12 |
| Trust | 4.2 of 8 |
| Reviews | not enough datano reviews yet |
Score 35 — from 5 of 6 signals: the rest are not measured yet. Methodology
What the channel wrote about
From posts September 9 – 13 · written automatically
Particle physics and biology research. Posts discussed an underground detector's potential dark matter event, how DNA molecules align through metal ion bridges, and the discovery of Denisovan fossils in a Chinese cave that fills a geographic gap in the fossil record.
Latest posts
Media
- 🌌 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 Media
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- ⚛️ 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 Media
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- 🩻 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
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The channel in numbers
- Created
- November 25, 2016
- Photos
- 773
- Videos
- 414
- Files
- 11
- Links
- 2.9K
Telegram data as of October 1, 2026
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