Algorithm designed to map universe, solve 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.

"Science works because things behave much more simply than they have any right to," said professor of physics James Sethna. "Very complicated things end up doing rather simple collective behavior."

Sethna is the senior author of "Visualizing Probabilistic Models With Intensive Principal Component Analysis," published in the Proceedings of the National Academy of Sciences.

The algorithm, designed by first author Katherine Quinn, 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.

"A person can't just sit down and do it," Quinn said. "We need better algorithms that can extract what we're interested in, without being told what to look for. We can't just say, 'Look for interesting universes.' This algorithm is a way of untangling information in a way that can reveal the interesting structure of the data."

Further complicating the researchers' task was the fact that the data consists of ranges of probabilities, rather than raw images or numbers.

Their solution takes advantage of different properties of probability distributions to visualize a collection of things that could happen. In addition to cosmology, their model has applications to machine learning and statistical physics, which also work in terms of predictions.

To test the algorithm, the researchers used data from the European Space Agency's Planck satellite, and studied it with co-author Michael Niemack, associate professor of physics. 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.

This new method of visualizing 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. These visualizations present a promising approach for optimizing cosmological measurements in the future, Niemack said.

Next, researchers will try to expand this approach to allow for more parameters for each data point. Mapping such data could reveal new information about our universe, other possible universes or dark energy -- which appears to be the dominant form of energy in our universe but about which physicists still know little.