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Researchers Develop an Algorithm to Map Universe, Solve Mysteries

They applied the model data on the cosmic microwave background – radiation left over from the universe’s early days

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Mathematics. Source: Pixabay

Researchers have developed an algorithm designed to visualise models of the universe in order to solve some of physics’ greatest mysteries.

The algorithm was developed by applying scientific principles used to create models for understanding cell biology and physics to the challenges of cosmology and big data, according to the study published in the journal Proceedings of the National Academy of Sciences.

“Science works because things behave much more simply than they have any right to, very complicated things end up doing rather simple collective behaviour,” said James Sethna, Professor at the Cornell University, US.

The algorithm allows researchers to image a large set of probabilities to look for patterns or other information that might be useful, and provides them with better intuition for understanding complex models and data.

In addition to cosmology, their model has applications to Machine Learning and statistical physics, which also work in terms of predictions.

galaxy, universe
Hubble’s view of a galaxy in Ursa Major, 65 million light-years away. VOA

To test the algorithm, the researchers used data from the European Space Agency’s Planck satellite, and studied it.

They applied the model data on the cosmic microwave background – radiation left over from the universe’s early days.

The model produced a map depicting possible characteristics of different universes, of which our own universe is one point.

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“This new method of visualising the qualities of our universe highlights the hierarchical structure of the dark energy and dark matter dominated model that fits the cosmic microwave background data so well,” said study co-author Michael Niemack.

“These visualisations present a promising approach for optimising cosmological measurements in the future,” he added. (IANS)

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Researchers Develop an Algorithm to Predict Storms, Cyclones

This research is an early attempt to show feasibility of AI-based interpretation of weather-related visual information

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Hurricane
In this image provided by NOAA, Tropical Storm Gordon approaches the United States. VOA

Using Artificial Intelligence (AI), researchers have developed an algorithm to detect cloud formations that lead to storms, hurricanes and cyclones.

The study, published in the journal IEEE Transactions on Geoscience and Remote Sensing, shows a model that can help forecasters recognise potential severe storms more quickly and accurately.

The researchers created a framework based on Machine Learning (ML) — a kind of AI — that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed.

“The very best forecasting incorporates as much data as possible, there’s so much to take in as the atmosphere is infinitely complex. By using the models and the data we have, we’re taking a snapshot of the most complete look of the atmosphere,” said Steve Wistar, Senior Forensic Meteorologist at AccuWeather in the US.

For the study, researchers analysed more than 50,000 US weather satellite images and identified and labelled the shape and motion of ‘comma-shaped’ clouds.

These cloud patterns are strongly associated with cyclone formations which can lead to severe weather events including hail, thunderstorms, high winds and blizzards, they said.

cyclone kenneth, torrential rain
An aerial shot shows widespread destruction caused by Cyclone Kenneth when it struck Ibo island north of Pemba city in Mozambique, May, 1, 2019 (Representational image). VOA

Then, using computer vision and ML techniques, the researchers taught computers to automatically recognize and detect ‘comma-shaped’ clouds in satellite images.

The computers could then assist experts by pointing out in real time where, in an ocean of data, could they focus their attention in order to detect the onset of severe weather.

“Because the ‘comma-shaped’ cloud is a visual indicator of severe weather events, our scheme can help meteorologists to forecast such events,” said study lead author Rachel Zheng from Penn State University in the US.

The researchers found that their method can effectively detect ‘comma-shaped’ clouds with 99 per cent accuracy, at an average of 40 seconds per prediction.

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It was also able to predict 64 per cent of severe weather events, outperforming other existing severe weather detection methods.

This research is an early attempt to show feasibility of AI-based interpretation of weather-related visual information. (IANS)