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A neural network discovered Copernicus’ heliocentricity on its own

Can neural networks help scientists discover laws about more complex phenomena, like quantum mechanics?

  • Scientists trained a neural network to predict the movements of Mars and the Sun.
  • In the process, the network generated formulae that place the Sun at the center of our solar system.
  • The case suggests that machine-learning techniques could help reveal new laws of physics.


A neural network was able to rediscover one of the most important paradigm shifts in scientific history: Earth and other planets revolve around the Sun. The accomplishment suggests machine-learning techniques could someday help to reveal new laws of physics, maybe even within the complex realm of quantum mechanics.

The results are set to appear in the journal Physical Review Letters, according to Nature.

The neural network — a machine-learning algorithm called SciNetwas shown measurements of how the Sun and Mars appear from Earth against the fixed-star background of the night sky. SciNet's task, assigned by a team of scientists at the Swiss Federal Institute of Technology, was to predict where the Sun and Mars would be at future points in time.

Copernicus-style formulae

In the process, SciNet generated formulas that place the Sun at the center of our solar system. Remarkably, SciNet accomplished this in a way similar to how astronomer Nicolaus Copernicus discovered heliocentricity.

"In the 16th century, Copernicus measured the angles between a distant fixed star and several planets and celestial bodies and hypothesized that the Sun, and not the Earth, is in the centre of our solar system and that the planets move around the Sun on simple orbits," the team wrote in a paper published on the preprint repository arXiv. "This explains the complicated orbits as seen from Earth."

The team "encouraged" SciNet to come up with ways to predict the movements of the Sun and Mars in the simplest way possible. To do that, SciNet passes information back and forth between two sub-networks. One network "learns" from data, and the other uses that knowledge to make predictions and test their accuracy. These networks are connected to each other by only a few links, so when they communicate, information is compressed, resulting in "simpler" representations.

Renner et al.

SciNet decided that the simplest way to predict the movements of celestial bodies was through a model that places the Sun at the center of our solar system. So, the neural network didn't necessarily "discover" heliocentricity, but rather described it through mathematics that humans can interpret.

Building humanlike AI

In 2017, data scientist Brenden Lake and his colleagues wrote a paper describing what it will take to build machines that learn and think like people. One benchmark for doing so would be artificial intelligence that can describe the physical world. At the time, they said it "remains to be seen" whether "deep networks trained on physics-related data" could discover laws of physics on their own. In a narrow sense, SciNet passes this test.

"To summarize, the main aim of this work is to show that neural networks can be used to discover physical concepts without any prior knowledge," the SciNet team wrote. "To achieve this goal, we introduced a neural network architecture that models the physical reasoning process. The examples illustrate that this architecture allows us to extract physically relevant data from experiments, without imposing further knowledge about physics or mathematics."

How accountability at work can transform your organization

If you don't practice accountability at work you're letting the formula for success slip right through your hands.

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  • What is accountability? It's a tool for improving performance and, once its potential is thoroughly understood, it can be leveraged at scale in any team or organization.
  • In this lesson for leaders, managers, and individuals, Shideh Sedgh Bina, a founding partner of Insigniam and the editor-in-chief of IQ Insigniam Quarterly, explains why it is so crucial to success.
  • Learn to recognize the mindset of accountable versus unaccountable people, then use Shideh's guided exercise as a template for your next post-project accountability analysis—whether that project was a success or it fell short, it's equally important to do the reckoning.

What if Middle-earth was in Pakistan?

Iranian Tolkien scholar finds intriguing parallels between subcontinental geography and famous map of Middle-earth

Could this former river island in the Indus have inspired Tolkien to create Cair Andros, the ship-shaped island in the Anduin river?

Image: Mohammad Reza Kamali, reproduced with kind permission
Strange Maps
  • J.R.R. Tolkien himself hinted that his stories are set in a really ancient version of Europe.
  • But a fantasy realm can be inspired by a variety of places; and perhaps so is Tolkien's world.
  • These intriguing similarities with Asian topography show that it may be time to 'decolonise' Middle-earth.
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Giant whale sharks have teeth on their eyeballs

The ocean's largest shark relies on vision more than previously believed.

An eight-metre-long Whale shark swims with other fish at the Okinawa Churaumi Aquarium on February 26, 2010 in Motobu, Okinawa, Japan.

Photo by Koichi Kamoshida/Getty Images
Surprising Science
  • Japanese researchers discovered that the whale shark has "tiny teeth"—dermal denticles—protecting its eyes from abrasion.
  • They also found the shark is able to retract its eyeball into the eye socket.
  • Their research confirms that this giant fish relies on vision more than previously believed.
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A massive star has mysteriously vanished, confusing astronomers

A gigantic star makes off during an eight-year gap in observations.

Image source: ESO/L. Calçada
Surprising Science
  • The massive star in the Kinsman Dwarf Galaxy seems to have disappeared between 2011 and 2019.
  • It's likely that it erupted, but could it have collapsed into a black hole without a supernova?
  • Maybe it's still there, but much less luminous and/or covered by dust.

A "very massive star" in the Kinman Dwarf galaxy caught the attention of astronomers in the early years of the 2000s: It seemed to be reaching a late-ish chapter in its life story and offered a rare chance to observe the death of a large star in a region low in metallicity. However, by the time scientists had the chance to turn the European Southern Observatory's (ESO) Very Large Telescope (VLT) in Paranal, Chile back around to it in 2019 — it's not a slow-turner, just an in-demand device — it was utterly gone without a trace. But how?

The two leading theories about what happened are that either it's still there, still erupting its way through its death throes, with less luminosity and perhaps obscured by dust, or it just up and collapsed into a black hole without going through a supernova stage. "If true, this would be the first direct detection of such a monster star ending its life in this manner," says Andrew Allan of Trinity College Dublin, Ireland, leader of the observation team whose study is published in Monthly Notices of the Royal Astronomical Society.

So, em...

Between astronomers' last look in 2011 and 2019 is a large enough interval of time for something to happen. Not that 2001 (when it was first observed) or 2019 have much meaning, since we're always watching the past out there and the Kinman Dwarf Galaxy is 75 million light years away. We often think of cosmic events as slow-moving phenomena because so often their follow-on effects are massive and unfold to us over time. But things happen just as fast big as small. The number of things that happened in the first 10 millionth of a trillionth of a trillionth of a trillionth of a second after the Big Bang, for example, is insane.

In any event, the Kinsman Dwarf Galaxy, or PHL 293B, is far way, too far for astronomers to directly observe its stars. Their presence can be inferred from spectroscopic signatures — specifically, PHL 293B between 2001 and 2011 consistently featured strong signatures of hydrogen that indicated the presence of a massive "luminous blue variable" (LBV) star about 2.5 times more brilliant than our Sun. Astronomers suspect that some very large stars may spend their final years as LBVs.

Though LBVs are known to experience radical shifts in spectra and brightness, they reliably leave specific traces that help confirm their ongoing presence. In 2019 the hydrogen signatures, and such traces, were gone. Allan says, "It would be highly unusual for such a massive star to disappear without producing a bright supernova explosion."

The Kinsman Dwarf Galaxy, or PHL 293B, is one of the most metal-poor galaxies known. Explosive, massive, Wolf-Rayet stars are seldom seen in such environments — NASA refers to such stars as those that "live fast, die hard." Red supergiants are also rare to low Z environments. The now-missing star was looked to as a rare opportunity to observe a massive star's late stages in such an environment.

Celestial sleuthing

In August 2019, the team pointed the four eight-meter telescopes of ESO's ESPRESSO array simultaneously toward the LBV's former location: nothing. They also gave the VLT's X-shooter instrument a shot a few months later: also nothing.

Still pursuing the missing star, the scientists acquired access to older data for comparison to what they already felt they knew. "The ESO Science Archive Facility enabled us to find and use data of the same object obtained in 2002 and 2009," says Andrea Mehner, an ESO staff member who worked on the study. "The comparison of the 2002 high-resolution UVES spectra with our observations obtained in 2019 with ESO's newest high-resolution spectrograph ESPRESSO was especially revealing, from both an astronomical and an instrumentation point of view."

Examination of this data suggested that the LBV may have indeed been winding up to a grand final sometime after 2011.

Team member Jose Groh, also of Trinity College, says "We may have detected one of the most massive stars of the local Universe going gently into the night. Our discovery would not have been made without using the powerful ESO 8-meter telescopes, their unique instrumentation, and the prompt access to those capabilities following the recent agreement of Ireland to join ESO."

Combining the 2019 data with contemporaneous Hubble Space Telescope (HST) imagery leaves the authors of the reports with the sense that "the LBV was in an eruptive state at least between 2001 and 2011, which then ended, and may have been followed by a collapse into a massive BH without the production of an SN. This scenario is consistent with the available HST and ground-based photometry."

Or...

A star collapsing into a black hole without a supernova would be a rare event, and that argues against the idea. The paper also notes that we may simply have missed the star's supernova during the eight-year observation gap.

LBVs are known to be highly unstable, so the star dropping to a state of less luminosity or producing a dust cover would be much more in the realm of expected behavior.

Says the paper: "A combination of a slightly reduced luminosity and a thick dusty shell could result in the star being obscured. While the lack of variability between the 2009 and 2019 near-infrared continuum from our X-shooter spectra eliminates the possibility of formation of hot dust (⪆1500 K), mid-infrared observations are necessary to rule out a slowly expanding cooler dust shell."

The authors of the report are pretty confident the star experienced a dramatic eruption after 2011. Beyond that, though:

"Based on our observations and models, we suggest that PHL 293B hosted an LBV with an eruption that ended sometime after 2011. This could have been followed by
(1) a surviving star or
(2) a collapse of the LBV to a BH [black hole] without the production of a bright SN, but possibly with a weak transient."

Future of Learning

Changing the way we grade students could trigger a wave of innovation

How students apply what they've learned is more important than a letter or number grade.

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