Astronomers may have detected a 'dark' free-floating black hole

Black holes, by their nature, are invisible unless part of a stellar binary or surrounded by an accretion disk. Most stellar-sized black holes aren't, but astronomers have been searching for them through gravitational microlensing events, where the black hole brightens and distorts light from stars toward the galactic center. A UC Berkeley-led team may have found the first free-floating black hole, though more data is needed to rule out a neutron star.

If, as astronomers believe, the death of large stars leave behind black holes, there should be hundreds of millions of them scattered throughout the Milky Way galaxy. The problem is, isolated black holes are invisible.

Now, a team led by University of California, Berkeley, astronomers has for the first time discovered what may be a free-floating black hole by observing the brightening of a more distant star as its light was distorted by the object's strong gravitational field -- so-called gravitational microlensing.

The team, led by graduate student Casey Lam and Jessica Lu, a UC Berkeley associate professor of astronomy, estimates that the mass of the invisible compact object is between 1.6 and 4.4 times that of the sun. Because astronomers think that the leftover remnant of a dead star must be heavier than 2.2 solar masses in order to collapse to a black hole, the UC Berkeley researchers caution that the object could be a neutron star instead of a black hole. Neutron stars are also dense, highly compact objects, but their gravity is balanced by internal neutron pressure, which prevents further collapse to a black hole.

Whether a black hole or a neutron star, the object is the first dark stellar remnant -- a stellar "ghost" -- discovered wandering through the galaxy unpaired with another star.

"This is the first free-floating black hole or neutron star discovered with gravitational microlensing," Lu said. "With microlensing, we're able to probe these lonely, compact objects and weigh them. I think we have opened a new window onto these dark objects, which can't be seen any other way."

Determining how many of these compact objects populate the Milky Way galaxy will help astronomers understand the evolution of stars -- in particular, how they die -- and of our galaxy, and perhaps reveal whether any of the unseen black holes are primordial black holes, which some cosmologists think were produced in large quantities during the Big Bang.

The analysis by Lam, Lu and their international team has been accepted for publication in The Astrophysical Journal Letters. The analysis includes four other microlensing events that the team concluded were not caused by a black hole, though two were likely caused by a white dwarf or a neutron star. The team also concluded that the likely population of black holes in the galaxy is 200 million -- about what most theorists predicted.

Same data, different conclusions

Notably, a competing team from the Space Telescope Science Institute (STScI) in Baltimore analyzed the same microlensing event and claims that the mass of the compact object is closer to 7.1 solar masses and indisputably a black hole. A paper describing the analysis by the STScI team, led by Kailash Sahu, has been accepted for publication in The Astrophysical Journal.

Both teams used the same data: photometric measurements of the distant star's brightening as its light was distorted or "lensed" by the super-compact object, and astrometric measurements of the shifting of the distant star's location in the sky as a result of the gravitational distortion by the lensing object. The photometric data came from two microlensing surveys: the Optical Gravitational Lensing Experiment (OGLE), which employs a 1.3-meter telescope in Chile operated by Warsaw University, and the Microlensing Observations in Astrophysics (MOA) experiment, which is mounted on a 1.8-meter telescope in New Zealand operated by Osaka University. The astrometric data came from NASA's Hubble Space Telescope. STScI manages the science program for the telescope and conducts its science operations.

Because both microlensing surveys caught the same object, it has two names: MOA-2011-BLG-191 and OGLE-2011-BLG-0462, or OB110462, for short.

While surveys like these discover about 2,000 stars brightened by microlensing each year in the Milky Way galaxy, the addition of astrometric data is what allowed the two teams to determine the mass of the compact object and its distance from Earth. The UC Berkeley-led team estimated that it lies between 2,280 and 6,260 light years (700-1920 parsecs) away, in the direction of the center of the Milky Way Galaxy and near the large bulge that surrounds the galaxy's central massive black hole.

The STScI group estimated that it lies about 5,153 light years (1,580 parsecs) away.

Looking for a needle in a haystack

Lu and Lam first became interested in the object in 2020 after the STScI team tentatively concluded that five microlensing events observed by Hubble -- all of which lasted for more than 100 days, and thus could have been black holes -- might not be caused by compact objects after all.

Lu, who has been looking for free-floating black holes since 2008, thought the data would help her better estimate their abundance in the galaxy, which has been roughly estimated at between 10 million and 1 billion. To date, star-sized black holes have been found only as part of binary star systems. Black holes in binaries are seen either in X-rays, produced when material from the star falls onto the black hole, or by recent gravitational wave detectors, which are sensitive to mergers of two or more black holes. But these events are rare.

"Casey and I saw the data and we got really interested. We said, 'Wow, no black holes. That's amazing,' even though there should have been," Lu said. "And so, we started looking at the data. If there were really no black holes in the data, then this wouldn't match our model for how many black holes there should be in the Milky Way. Something would have to change in our understanding of black holes -- either their number or how fast they move or their masses."

When Lam analyzed the photometry and astrometry for the five microlensing events, she was surprised that one, OB110462, had the characteristics of a compact object: The lensing object seemed dark, and thus not a star; the stellar brightening lasted a long time, nearly 300 days; and the distortion of the background star's position also was long-lasting.

The length of the lensing event was the main tipoff, Lam said. In 2020, she showed that the best way to search for black hole microlenses was to look for very long events. Only 1% of detectable microlensing events are likely to be from black holes, she said, so looking at all events would be like searching for a needle in a haystack. But, Lam calculated, about 40% of microlensing events that last more than 120 days are likely to be black holes.

"How long the brightening event lasts is a hint of how massive the foreground lens bending the light of the background star is," Lam said. "Long events are more likely due to black holes. It's not a guarantee, though, because the duration of the brightening episode not only depends on how massive the foreground lens is, but also on how fast the foreground lens and background star are moving relative to each other. However, by also getting measurements of the apparent position of the background star, we can confirm whether the foreground lens really is a black hole."

According to Lu, the gravitational influence of OB110462 on the light of the background star was amazingly long. It took about one year for the star to brighten to its peak in 2011, then about a year to dim back to normal.

More data will distinguish black hole from neutron star

To confirm that OB110462 was caused by a super-compact object, Lu and Lam asked for more astrometric data from Hubble, some of which arrived last October. That new data showed that the change in position of the star as a result of the gravitational field of the lens is still observable 10 years after the event. Further Hubble observations of the microlens are tentatively scheduled for fall 2022.

Analysis of the new data confirmed that OB110462 was likely a black hole or neutron star.

Lu and Lam suspect that the differing conclusions of the two teams are due to the fact that the astrometric and photometric data give different measures of the relative motions of the foreground and background objects. The astrometric analysis also differs between the two teams. The UC Berkeley-led team argues that it is not yet possible to distinguish whether the object is a black hole or a neutron star, but they hope to resolve the discrepancy with more Hubble data and improved analysis in the future.

"As much as we would like to say it is definitively a black hole, we must report all allowed solutions. This includes both lower mass black holes and possibly even a neutron star," Lu said.

"If you can't believe the light curve, the brightness, then that says something important. If you don't believe the position versus time, that tells you something important," Lam said. "So, if one of them is wrong, we have to understand why. Or the other possibility is that what we measure in both data sets is correct, but our model is incorrect. The photometry and astrometry data arise from the same physical process, which means the brightness and position must be consistent with each other. So, there's something missing there. "

Both teams also estimated the velocity of the super-compact lensing object. The Lu/Lam team found a relatively sedate speed, less than 30 kilometers per second. The STScI team found an unusually large velocity, 45 km/s, which it interpreted as the result of an extra kick that the purported black hole got from the supernova that generated it.

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AI reveals unsuspected math underlying search for exoplanets

Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data to find new exploding stars, identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in the world's oldest science.

But AI, also called machine learning, can reveal something deeper, University of California, Berkeley, astronomers found: unsuspected connections hidden in the complex mathematics arising from general relativity -- in particular, how that theory is applied to finding new planets around other stars.

In a paper appearing this week in the journal Nature Astronomy, the researchers describe how an AI algorithm developed to more quickly detect exoplanets when such planetary systems pass in front of a background star and briefly brighten it -- a process called gravitational microlensing -- revealed that the decades-old theories now used to explain these observations are woefully incomplete.

In 1936, Albert Einstein himself used his new theory of general relativity to show how the light from a distant star can be bent by the gravity of a foreground star, not only brightening it as seen from Earth, but often splitting it into several points of light or distorting it into a ring, now called an Einstein ring. This is similar to the way a hand lens can focus and intensify light from the sun.

But when the foreground object is a star with a planet, the brightening over time -- the light curve -- is more complicated. What's more, there are often multiple planetary orbits that can explain a given light curve equally well -- so called degeneracies. That's where humans simplified the math and missed the bigger picture.

The AI algorithm, however, pointed to a mathematical way to unify the two major kinds of degeneracy in interpreting what telescopes detect during microlensing, showing that the two "theories" are really special cases of a broader theory that, the researchers admit, is likely still incomplete.

"A machine learning inference algorithm we previously developed led us to discover something new and fundamental about the equations that govern the general relativistic effect of light- bending by two massive bodies," Joshua Bloom wrote in a blog post last year when he uploaded the paper to a preprint server, arXiv. Bloom is a UC Berkeley professor of astronomy and chair of the department.

He compared the discovery by UC Berkeley graduate student Keming Zhang to connections that Google's AI team, DeepMind, recently made between two different areas of mathematics. Taken together, these examples show that AI systems can reveal fundamental associations that humans miss.

"I argue that they constitute one of the first, if not the first time that AI has been used to directly yield new theoretical insight in math and astronomy," Bloom said. "Just as Steve Jobs suggested computers could be the bicycles of the mind, we've been seeking an AI framework to serve as an intellectual rocket ship for scientists."

"This is kind of a milestone in AI and machine learning," emphasized co-author Scott Gaudi, a professor of astronomy at The Ohio State University and one of the pioneers of using gravitational microlensing to discover exoplanets. "Keming's machine learning algorithm uncovered this degeneracy that had been missed by experts in the field toiling with data for decades. This is suggestive of how research is going to go in the future when it is aided by machine learning, which is really exciting."

Discovering exoplanets with microlensing

More than 5,000 exoplanets, or extrasolar planets, have been discovered around stars in the Milky Way, though few have actually been seen through a telescope -- they are too dim. Most have been detected because they create a Doppler wobble in the motions of their host stars or because they slightly dim the light from the host star when they cross in front of it -- transits that were the focus of NASA's Kepler mission. Little more than 100 have been discovered by a third technique, microlensing.

One of the main goals of NASA's Nancy Grace Roman Space Telescope, scheduled to launch by 2027, is to discover thousands more exoplanets via microlensing. The technique has an advantage over the Doppler and transit techniques in that it can detect lower-mass planets, including those the size of Earth, that are far from their stars, at a distance equivalent to that of Jupiter or Saturn in our solar system.

Bloom, Zhang and their colleagues set out two years ago to develop an AI algorithm to analyze microlensing data faster to determine the stellar and planetary masses of these planetary systems and the distances the planets are orbiting from their stars. Such an algorithm would speed analysis of the likely hundreds of thousands of events the Roman telescope will detect in order to find the 1% or fewer that are caused by exoplanetary systems.

One problem astronomers encounter, however, is that the observed signal can be ambiguous. When a lone foreground star passes in front of a background star, the brightness of the background stars rises smoothly to a peak and then drops symmetrically to its original brightness. It's easy to understand mathematically and observationally.

But if the foreground star has a planet, the planet creates a separate brightness peak within the peak caused by the star. When trying to reconstruct the orbital configuration of the exoplanet that produced the signal, general relativity often allows two or more so-called degenerate solutions, all of which can explain the observations.

To date, astronomers have generally dealt with these degeneracies in simplistic and artificially distinct ways, Gaudi said. If the distant starlight passes close to the star, the observations could be interpreted either as a wide or a close orbit for the planet -- an ambiguity astronomers can often resolve with other data. A second type of degeneracy occurs when the background starlight passes close to the planet. In this case, however, the two different solutions for the planetary orbit are generally only slightly different.

According to Gaudi, these two simplifications of two-body gravitational microlensing are usually sufficient to determine the true masses and orbital distances. In fact, in a paper published last year, Zhang, Bloom, Gaudi and two other UC Berkeley co-authors, astronomy professor Jessica Lu and graduate student Casey Lam, described a new AI algorithm that does not rely on knowledge of these interpretations at all. The algorithm greatly accelerates analysis of microlensing observations, providing results in milliseconds, rather than days, and drastically reducing the computer crunching.

Zhang then tested the new AI algorithm on microlensing light curves from hundreds of possible orbital configurations of star and exoplanet and noticed something unusual: There were other ambiguities that the two interpretations did not account for. He concluded that the commonly used interpretations of microlensing were, in fact, just special cases of a broader theory that explains the full variety of ambiguities in microlensing events.

"The two previous theories of degeneracy deal with cases where the background star appears to pass close to the foreground star or the foreground planet," Zhang said. "The AI algorithm showed us hundreds of examples from not only these two cases, but also situations where the star doesn't pass close to either the star or planet and cannot be explained by either previous theory. That was key to us proposing the new unifying theory."

Gaudi was skeptical, at first, but came around after Zhang produced many examples where the previous two theories did not fit observations and the new theory did. Zhang actually looked at the data from two dozen previous papers that reported the discovery of exoplanets through microlensing and found that, in all cases, the new theory fit the data better than the previous theories.

"People were seeing these microlensing events, which actually were exhibiting this new degeneracy but just didn't realize it," Gaudi said. "It was really just the machine learning looking at thousands of events where it became impossible to miss."

Zhang and Gaudi have submitted a new paper that rigorously describes the new mathematics based on general relativity and explores the theory in microlensing situations where more than one exoplanet orbits a star.

The new theory technically makes interpretation of microlensing observations more ambiguous, since there are more degenerate solutions to describe the observations. But the theory also demonstrates clearly that observing the same microlensing event from two perspectives -- from Earth and from the orbit of the Roman Space Telescope, for example -- will make it easier to settle on the correct orbits and masses. That is what astronomers currently plan to do, Gaudi said.

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