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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

01/23
2014

Using Big Data Analytics To Unlock the Mysteries of the Universe

Scientists learned that the universe continues to expand in 1929 when astronomer Edwin Hubble discovered that distant galaxies are moving away from us. Today, scientists continue to make remarkable galactic discoveries.

shutterstock 63555622 300x247 Using Big Data Analytics To Unlock the Mysteries of the UniverseFor instance, a team of researchers working with the Baryon Oscillation Spectroscopic Survey (BOSS) have been able to determine the distances to galaxies more than 6 billion light years away to within an accuracy of 1%.

As astronomers and other scientists continue to push the boundaries of space exploration, big data analytics can help researchers gain deeper insights about the origins, makeup, and future of the universe.

For instance, the first stars of the universe are believed to have formed a few hundred million years after the so-called “Big Bang,” which scientists say occurred about 13.7 billion years ago.

The oldest known star with a calculable age, the Methuselah star, could be as old as 14.5 billion years, thus posing a dilemma against the estimated age of the universe.

However, the recent development of a new computational algorithm by Harvard-Smithsonian Center for Astrophysics astronomer Thomas Greif and two colleagues now enables scientists to trace the formation process of stars down to very small scales.

There’s one thing that most scientists agree upon: there’s more that’s unknown about the universe than what is known.

Everything that’s ever been observed about space – including all that can be seen from earth and man-made space ships with telescopes and other instruments – makes up less than 5% of the total universe, according to NASA.

Roughly 68% of the universe is what’s referred to as dark matter – matter that scientists can’t directly observe. This includes familiar or baryonic matter composed of protons, neutrons, and electrons along with non-baryonic matter that scientists can only speculate about, such as brown dwarfs and neutrino stars.

In the past, scientists have developed analytic models to compare and correlate the non-linear power spectrum of dark matter and galaxies.

More recently, researchers have used data discovery techniques in their attempts to learn more about dark matter. Historically, all research has indicated that dark matter is likely comprised of a weakly interacting massive particle (WIMP) or particles that interact through weak force and gravity.

The researchers conducted tests in 2007 and 2008 using silicon and germanium detectors that were buried in a mine in Minnesota with the aim of eliminating multiple sources of background material.

After filtering out known particles such as electrons and sources of radioactive decay, the researchers found that the results did indicate the presence of a WIMP. Although the estimated mass wasn’t as heavy as other theories about WIMPs have suggested, it’s still consistent with some theories of dark matter.

After Hubble discovered that the universe continues to expand, researchers studying distant exploding stars called Type 1a supernovas discovered in 1998 that the universe isn’t just expanding but that the rate of expansion is accelerating as a result of dark energy.

More recently, a team of scientists from Melbourne’s Swinburne University announced an independent discovery of both the existence of dark energy and its rate of expansion based on four years of data analysis.

The study, which evaluated more than 240,000 galaxies going back over 7 billion years, found that the growth of galaxy clusters and super clusters in the most distant parts of the universe has slowed down. However, in our corner of the universe where dark energy dominates, we continue to see accelerated expansion.

To date, analytical tools and models have enabled scientists to learn quite a bit about dark matter and dark energy with a high degree of accuracy. Going forward, future breakthroughs will hinge on the amount of data that can be collected.

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