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Category: mapping

JWST Just Found Something That Shocked Scientists

The release of the 2026 dark matter map marks a definitive shift in how we approach the cosmos. For decades, we were in the hunting phase, trying to prove that dark matter existed and attempting to catch a single particle in a laboratory. While we still haven’t touched a dark matter particle, we have moved into the surveying phase. We are no longer asking if it is there; we are busy measuring its dimensions, its density, and its influence on the growth of everything we can see. This map of the Sextans field is essentially the first page in a new atlas of the invisible universe.

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Timestamps:
0:00 Dark Matter.
1:05 The Cosmic Lens.
4:20 The COSMOS-Web Survey.
7:15 Mapping the Filaments.
10:22 Beyond the Standard Model.
13:15 The Architect of Life.

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Fexl Spanish: / @fexl_es.
Fexl Portuguese: / @fexlpt.
Fexl Ukraine: / @fexl_ua.

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References:
Nature Astronomy (January 2026): An ultra-high-resolution map of (dark) matter: https://www.nature.com/articles/s4155… Pre-print (Technical Breakdown): COSMOS-Web: The ultra-deep weak lensing survey: https://arxiv.org/abs/2601.17239 NASA Webb Mission Page: Webb Unveils the Dark Matter Scaffolding of the Universe: https://www.nasa.gov/missions/webb/na… COSMOS-Web Collaboration Official Site: https://cosmos.astro.caltech.edu/ NASA JPL Press Release: Seeing the Unseen: 800,000 Galaxies Mapped: https://www.jpl.nasa.gov/news/nasa-re… #fexl #space #jwst.

ArXiv Pre-print (Technical Breakdown): COSMOS-Web: The ultra-deep weak lensing survey: https://arxiv.org/abs/2601.

Finally Released! The James Webb Telescope Has Found The Object That Holds Our Universe Together

#jameswebbspacetelescope #jwst.
Finally Released! The James Webb Telescope Has Found The Object That Holds Our Universe Together.

Containing nearly 800,000 galaxies, this image from NASA’s James Webb Space Telescope is overlaid with a map of dark matter, represented in blue. Researchers used Webb data to find the invisible substance via its gravitational influence on regular matter.

You see, Scientists using data from NASA’s James Webb Space Telescope have made one of the most detailed, high-resolution maps of dark matter ever produced. It shows how the invisible, ghostly material overlaps and intertwines with “regular” matter, the stuff that makes up stars, galaxies, and everything we can see.

Published Monday, Jan. 26, in Nature Astronomy, the map builds on previous research to provide additional confirmation and new details about how dark matter has shaped the universe on the largest scales — galaxy clusters millions of light-years across — that ultimately give rise to galaxies, stars, and planets like Earth.

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What does it mean to compute? Framework maps hidden computations running inside natural dynamic systems

Some computers are easy to spot. Artificial, human-built computers like those found in smartphones and laptops are abstract dynamic systems with observable computational elements like input, output, energy cost, and logical processes. Other computers aren’t so readily recognized.

Scientists have argued that many natural dynamic systems—from cells to brains to turbulence in fluids—carry out computations, too. However, it’s not always been clear what these dynamic systems are computing, or how they might be harnessed to solve tasks, says SFI Professor David Wolpert.

Previously harmless Google API keys now expose Gemini AI data

Google API keys for services like Maps embedded in accessible client-side code could be used to authenticate to the Gemini AI assistant and access private data.

Researchers found nearly 3,000 such keys while scanning internet pages from organizations in various sectors, and even from Google.

The problem occurred when Google introduced its Gemini assistant, and developers started enabling the LLM API in projects. Before this, Google Cloud API keys were not considered sensitive data and could be exposed online without risk.

Mars volcano formed through multiple eruptive phases

“Our results show that even during Mars’ most recent volcanic period, magma systems beneath the surface remained active and complex,” said Dr. Bartosz Pieterek. [ https://www.labroots.com/trending/space/30240/mars-volcano-f…e-phases-2](https://www.labroots.com/trending/space/30240/mars-volcano-f…e-phases-2)

How did young volcanoes on Mars form? This is what a recent study published in the journal Geology hopes to address as a team of scientists investigated the complex geological processes responsible for forming the first volcanoes on Mars. This study has the potential to help scientists better understand the recent environment on Mars over the last several million years and what this could mean for finding signs of life on the Red Planet.

For the study, the researchers used a combination of mapping and orbital data to analyze the mineralogical and geological volcanic features near one of Mars’ largest volcanoes, Pavonis Mons. The goal of the study was to ascertain the eruption history of these volcanoes, specifically whether they formed from single, short-lived eruptions or perhaps something that lasted longer and was more complex. In the end, the researchers found that the processes involved in forming the volcanoes were far more complex than previously thought. Specifically, the interior volcanic activity consisted of several magma chambers that grew and developed over time, resulting in multiple eruption events and several types of minerals that erupted onto the surface over several eruption cycles.

Norway unveiled a new underwater reconnaissance system for depths of up to 6 km

Norway is implementing technologies to improve underwater sensing. Specifically, a development by the Norwegian company Kongsberg is cited as an example. Kongsberg claims to have created a device that “changes the way the Navy collects intelligence in the underwater environment.”

This is an upgrade to the Argeo Listen platform. The upgrade consists of an enhanced passive electromagnetic sensing system. This reportedly allows for more efficient detection of underwater objects with more precise measurements, followed by mapping.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge

We introduce Layered Self-Supervised Knowledge Distillation (LSSKD) framework for training compact deep learning models. Unlike traditional methods that rely on pre-trained teacher networks, our approach appends auxiliary classifiers to intermediate feature maps, generating diverse self-supervised knowledge and enabling one-to-one transfer across different network stages. Our method achieves an average improvement of 4.54\% over the state-of-the-art PS-KD method and a 1.14% gain over SSKD on CIFAR-100, with a 0.32% improvement on ImageNet compared to HASSKD. Experiments on Tiny ImageNet and CIFAR-100 under few-shot learning scenarios also achieve state-of-the-art results. These findings demonstrate the effectiveness of our approach in enhancing model generalization and performance without the need for large over-parameterized teacher networks. Importantly, at the inference stage, all auxiliary classifiers can be removed, yielding no extra computational cost. This makes our model suitable for deploying small language models on affordable low-computing devices. Owing to its lightweight design and adaptability, our framework is particularly suitable for multimodal sensing and cyber-physical environments that require efficient and responsive inference. LSSKD facilitates the development of intelligent agents capable of learning from limited sensory data under weak supervision.

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