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Kepler-90

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Identifying Exoplanets with Deep Learning: A Five-Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90

Published January 2018

Main takeaway:

Using machine learning, Google Brain engineer Christopher Shallue and NASA Sagan Postdoctoral Fellow Andrew Vanderburg (The University of Texas at Austin, Harvard-Smithsonian CfA) have discovered two new planets within previously known Kepler multi-planet systems.

Why it’s interesting:

training light curves

Examples of light curves used to train the authors’ neural network models. [Shallue & Vanderburg 2018]

In today’s field of exoplanet astronomy, observatories like Kepler and TESS have guaranteed us plenty of data. But the transit signal of an Earth-sized planet around a Sun-like star remains at the edge of detectability, and our best bet for reliably picking such signals out of the noise is automation. Shallue and Vanderburg’s study demonstrates the power of training a deep convolutional neural network to identify planet signals in data like Kepler’s.

What was found:

Shalle and Vanderburg’s models identified two signals from among data from previously known Kepler multi-planet systems: one planet that is part of a five-planet resonant chain around Kepler-80, and one planet orbiting Kepler-90. Kepler-90 was previously known to host seven planets, so this discovery of an eighth has brought Kepler-90 to a tie with our own Sun for the star known to host the largest number of planets.

Citation

Christopher J. Shallue and Andrew Vanderburg 2018 AJ 155 94. doi:10.3847/1538-3881/aa9e09

universe expansion

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Milky Way Cepheid Standards for Measuring Cosmic Distances and Application to Gaia DR2: Implications for the Hubble Constant

Published July 2018

Main takeaway:

Recent Gaia-measured parallaxes and Hubble photometry of 50 Milky-Way Cepheid variable stars — pulsating stars used as yardsticks to measure cosmic distances — have provided the most precise measurement yet of the local rate of expansion of our universe.

Why it’s interesting:

Since astronomers first discovered the universe is expanding, there has been tension between the observed local rate of expansion (which is measured by tracking the distances to and recession speeds of objects around us) and the expansion rate inferred for the early universe (which is derived from observations of the cosmic microwave background). The new and more precise local measurements, made by a team of astronomers led by Adam Riess (Space Telescope Science Institute and Johns Hopkins University), increases that tension further.

Possible explanations for the tension:

expanding universe

A schematic illustrating one model for the expansion of our universe. Click to enlarge. [NASA/WMAP Science Team]

Why might the local and early-universe expansion rates be different? One possibility is that the universe’s expansion is accelerating over time — dark energy might drive space apart more quickly now than the expansion rate early in the universe’s history. Other possibilities include unexpected physics that render our models — and, therefore, inferences of the early-universe expansion rate — incorrect, like the existence of previously unknown subatomic particles. Further high-precision measurements like those from Gaia will help us to better understand this mystery.

Citation

Adam G. Riess et al 2018 ApJ 861 126. doi:10.3847/1538-4357/aac82e

Laser from Earth

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Optical Detection of Lasers with Near-Term Technology at Interstellar Distances

Published November 2018

Main takeaway:

Could we communicate with distant extraterrestrial intelligence using lasers? Two scientists from the Massachusetts Institute of Technology, James Clark and Kerri Cahoy, have determined that we could produce a detectable laser signal out to 20,000 light-years using current or near-term technology.

Why it’s interesting:

The challenges of communicating with hypothesized life beyond our solar system are numerous. One of the most fundamental questions is whether we are technologically capable of producing a strong signal that could be easily detected at large distances. In their feasibility study, Clark and Cahoy show that we can — and, moreover, that such a signal could have a broad enough beam that we could target nearby exoplanets with uncertain orbits (like the planet Proxima Centauri b) or the entire habitable zones of more distant systems (like the TRAPPIST-1 system).

Other challenges to communication:

European Extremely Large Telescope

The European Extremely Large Telescope, a proposed upcoming telescope with a 39-meter mirror. A telescope of this size could be used to focus a megawatt laser to communicate with distant intelligence. [Swinburne Astronomy Productions/ESO]

To be spotted by a hypothetical civilization orbiting a distant star, our communicating laser beam must be bright enough to stand out above the background light of our own star, the Sun. If this is possible — which Clark and Cahoy suggest would be with a megawatt-class laser focused by a telescope of tens of meters in diameter — then we still run up against low odds of an actual conversation within human lifetimes due to the long time it would likely take to send and receive signals. Nonetheless, it would be a good start!

Citation

James R. Clark and Kerri Cahoy 2018 ApJ 867 97. doi:10.3847/1538-4357/aae380

star-forming galaxy

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Dark Galaxy Candidates at Redshift ~3.5 Detected with MUSE

Published May 2018

Main takeaway:

A team of scientists led by Raffaella Marino (ETH Zürich, Switzerland) have used the Multi Unit Spectroscopic Explorer (MUSE) instrument at ESO’s Very Large Telescope to discover six candidate “dark galaxies”, galaxies that contain a large amount of gas but don’t yet contain any stars.

Why it’s interesting:

We still don’t fully understand what the fuel for the first stars in the universe was — how did the diffuse intergalactic medium first come together to trigger star formation in early galaxies? One theory is that there was an epoch in the early phase of galaxy formation during which galaxies were gas-rich but still inefficient at forming stars. By discovering signs of these dark galaxies in the early universe, Marino and collaborators have added supporting evidence to this theory.

Why we haven’t found these dark galaxies before now:

dark galaxy candidates

Example observations of three of the dark galaxy candidates. Left panels show the presence of hydrogen gas; right panels show the lack of detection of stellar continuum emission. [Adapted from Marino et al. 2018]

Since dark galaxies aren’t forming stars yet, they aren’t emitting easily observable starlight. Marino and collaborators instead searched for another kind of emission to identify candidates: fluorescence of the vast reservoirs of hydrogen gas caused by the ultraviolet radiation of nearby quasars, active galactic centers powered by supermassive black holes. Due to the deep imaging made possible by the MUSE instrument, the authors were able to identify six strong candidates for dark galaxies at redshifts of z > 3.5.

Citation

Raffaella Anna Marino et al 2018 ApJ 859 53. doi:10.3847/1538-4357/aab6aa


magnetar outburst

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Revival of the Magnetar PSR J1622–4950: Observations with MeerKAT, Parkes, XMM-Newton, Swift, Chandra, and NuSTAR

Published April 2018

Main takeaway:

A ultra-magnetized neutron star that has been quiescent for three years has now reawakened, according to a study led by Fernando Camilo (SKA South Africa). New radio and X-ray observations of the magnetar PSR J1622–4950 reveal pulses of radiation from this source for the first time since 2014.

Why it’s interesting:

Unlike pulsars, which are neutron stars with emission powered by the decay of their rotation, magnetars are neutron stars powered by the decay of their extremely strong magnetic fields. Of the nearly two dozen confirmed magnetars, only four have been discovered to exhibit radio pulses in addition to X-rays — and J1622–4950 is one of them. Exploring this source is therefore important for understanding the physics at work, as well as the similarities and differences between magnetars and pulsars.

The additional intrigue of a new telescope:

MeerKAT

The MeerKAT array in South Africa. [SARAO]

The radio observations of J1622–4950 were made in large part by the brand new MeerKAT radio telescope in South Africa, an array of 64 dishes that is now the largest and most sensitive radio telescope in the southern hemisphere. The MeerKAT observations of J1622–4950 were made in April through October 2017, while the telescope was still in the process of being built — only 16 of the 64 dishes were used. Camilo and collaborators’ study mark the first scientific publication based on MeerKAT data … and we can hope for many more in the future!

Citation

F. Camilo et al 2018 ApJ 856 180. doi:10.3847/1538-4357/aab35a

RX J1131-1231

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Probing Extragalactic Planets Using Quasar Microlensing

Published February 2018

Main takeaway:

Two scientists from University of Oklahoma, Xinyu Dai and Eduardo Guerras, have discovered a population of free-floating planets beyond our own galaxy. They achieved this by analyzing the microlensing of a background, bright, supermassive black hole in Chandra X-ray Observatory images.

Why it’s interesting:

quasar microlensing
Diagram of the process of quasar microlensing (click to enlarge). In the current study, the lensing objects are not foreground stars, but instead foreground free-floating planets. [Jason Cowan/Astronomy Technology Center/NASA]

If Dai and Guerras’s models and interpretations are correct, then this marks the first time we’ve ever discovered planets outside of our own galaxy. If these unbound planets in the lensing galaxy are Moon- to Jupiter-sized, the authors’ models suggest a population of about 2,000 planets per main-sequence star in the lens galaxy — all of which lie 3.8 billion light-years away from us.

What this means for future observations:

With microlensing, the details of a foreground object (here, the lens galaxy) can be determined as it lenses a background source (here, the supermassive black hole of a bright, distant quasar). By modeling the signal, we can tease out a surprising amount information — even about small objects that lie at enormous distances. Dai and Guerras’s discovery shows the power of microlensing for making detections that are well out of reach of other approaches, like direct observations or transits.

Citation

Xinyu Dai and Eduardo Guerras 2018 ApJL 853 L27. doi:10.3847/2041-8213/aaa5fb

NGC 2655

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Discovery of a Low-Surface-Brightness Galaxy in the NGC 2655 Field

Published January 2018

Main takeaway:

NGC 2655 field

In the field pictured in the top panel, NGC 2655 is the large galaxy that lies near the bottom left. The red crosshairs indicate the location of the new low-surface-brightness galaxy. Bottom panels show confirmation data and a FITS-cutout from the Pan-STARRS1 Surveys. [Adapted from Steiling and Crowson 2018]

A new low-surface-brightness galaxy has been found within the same field as the lenticular galaxy NGC 2655. This discovery was made by Frederick Steiling and followed up by Dan Crowson, both amateur astronomers and members of the Astronomical Society of Eastern Missouri. The new, dim galaxy is located about 22 arcminutes from NGC 2655.

Why it’s interesting:

The last few years has marked a boom in the discovery of extremely dim, faint, diffuse galaxies. By definition, these bodies are at least one magnitude lower in surface brightness than the ambient night sky; they have very few stars and are generally more than 95% non-baryonic dark matter by mass. This makes them excellent laboratories for exploring the properties of dark matter and dark-matter dominated galaxies.

What else we might learn:

Most low-surface-brightness galaxies are isolated, not lying near any other major galaxies. This has been used as an explanation for why they are so faint: without interactions with other galaxies to trigger star formation, they’ve stayed relatively dark. The newly discovered galaxy, on the other hand, does not appear to be isolated: it lies near the NGC 2655 group of galaxies, a group thought to have recently undergone interactions or mergers. If the low-surface-brightness galaxy is a part of the group (as opposed to appearing nearby by coincidence, which is also a possibility), this unusual association could teach us more about the behavior of diffuse galaxies.

Citation

Frederick Steiling and Dan Crowson 2018 Res. Notes AAS 2 11. doi:10.3847/2515-5172/aabf92

Image of two bright blue stars side by side and one dimmer circled star below, against a background of more distant stars.

Editor’s note: In these last two weeks of 2018, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Alpha Centauri Beyond the Crossroads

Published January 2018

Main takeaway:

A scientist from University of Colorado Boulder, Tom Ayres, has compiled observations from the Chandra X-ray Observatory tracking the X-ray emission from the two stars of the Alpha Centauri binary system. Alpha Centauri is the closest star system to us, at just 4.37 light-years away.

Why it’s interesting:

Alpha Centauri A and B are both Sun-like dwarf stars with coronae very similar to our Sun’s. By studying the X-ray activity of these stars, we can learn more about how stars like the Sun bombard their environments with harsh radiation. This is useful both from the perspective of protecting our own interests — since this so-called space weather can affect astronauts, satellites, our power grid, etc. — and from the perspective of learning about potential habitability around our nearby stellar neighbors.

On Alpha Centauri’s recent crossroads:

X-ray light curves

X-ray light curves of Alpha Centauri A (blue), Alpha Centauri B (red), and the Sun (gray) for 1995–2018. [Ayres 2018]

The latest Chandra observations are cool because we can distinctly see the stellar activity cycles in both Alpha Centauri A and B, in much the same way that our own Sun has an 11-year cycle. For Alpha Centauri B, the cycle is about 8 years; for Alpha Centauri A, it appears to be closer to 19 years. In 2016, the system reached what Ayres describes as a crossroads: not only did Alpha Centauri A hit a maximum in activity and Alpha Centauri B hit a minimum, but around the same time, we also witnessed the crossing of the apparent trajectories of the two stars on the sky.

Citation

T. R. Ayres 2018 Res. Notes AAS 2 17. doi:10.3847/2515-5172/aaa88f

GW170817

Just last year, the three observatories of the Laser Interferometer Gravitational-Wave Observatory (LIGO)–Virgo Collaboration detected the gravitational-wave signature of two neutron stars colliding. What can we learn from the months of observations made since?

GW170817 signal

On 17 August, 2017, LIGO detected this “chirp” as two neutron stars spiraled inward and collided. This brief gravitational-wave blip, known as GW170817, has been followed up with months of multiwavelength observations. [LSC/Alex Nitz]

When Worlds Collide

Immediately following the detection of gravitational-wave event GW170817, teams of astronomers around the world rushed to pinpoint and characterize the electromagnetic radiation from the source.

These early observations were hugely important for validating our understanding of what happens when neutron stars collide, but the work didn’t end there; in the months that followed, repeated measurements of the flux across the electromagnetic spectrum have provided us with the tools to probe what happened in the aftermath of the merger.

These late-time observations should allow us to distinguish between two competing post-merger scenarios, in which the resultant relativistic jet either pushes past the previously ejected material surrounding the remnant (the “jet-dominated outflow” model) or fails to escape the slow-moving shroud of material (the “cocoon-dominated outflow” model) and is choked.

Mooley et al. 2018 Fig. 1

Radio spectral indices from 6 to 10 months post merger. Combining all the radio data gives a spectral index of -0.53. The black line shown for reference is the radio-to-X-ray spectral index measurement. [Mooley et al. 2018]

Tuning in to GW170817

In order to characterize the nature of the outflow and determine which scenario describes GW170817, a team led by Kunal Mooley (National Radio Astronomy Observatory/Caltech) analyzed the decline of GW170817’s radio emission over time. The authors combined data from multiple radio sources — MeerKAT, Very Large Array (VLA), Giant Metrewave Radio Telescope (uGMRT), and the Australia Telescope Compact Array (ATCA) — to cover the radio emission from 0.65 to 12 GHz.

After steadily rising for 5–6 months after the event, the radio emission peaked and quickly began to decline, making the transition from rising to falling in just a few weeks. The authors focused on two important features of the radio light curve: how rapidly the flux density decreases after the peak (the power-law decay index) and how “sharp” the peak of the light curve is.

Mooley et al. 2018 Fig. 2

Radio data used in this study. All measurements have been scaled to 3 GHz. The black line is the best-fit model. [Mooley et al. 2018]

Jet vs. Cocoon

Models tell us that if GW170817’s jet were choked by a slow-moving cocoon of material, the radio observations would reveal a power-law decay index of -0.88. If instead the jet punches free of the material as in the jet-dominated outflow model, its flux density would decrease much more rapidly, exhibiting a power-law decay index of -2.17.
So which model do the radio observations of GW170817 support? All of the post-peak data are well-described by a single power-law decay with an index of -2.4. This strongly supports the jet model over the cocoon model, and it suggests that the majority of the energy in the post-merger outflow is carried away by the jet.

The sharpness of the light-curve peak is dependent upon the viewing angle and the width of the jet. Based on a simple jet model, the authors find that the jet is likely very narrow (with an opening angle of less than 10°) and the viewing angle is less than 28°. Future modeling will explore the effects that structure in the jet can have on how sharply peaked the radio light curve is and further our understanding of these highly energetic collisions.

Citation

“A Strong Jet Signature in the Late-time Light Curve of GW170817,” K. P. Mooley et al 2018 ApJL 868 L11. doi:10.3847/2041-8213/aaeda7

TESS first light

How do we find the signals of exoplanets lurking in the vast quantity of data that comes out of a mission like Kepler or the Transiting Exoplanet Survey Satellite (TESS)? A new study has some suggestions for how best to get computers to do the heavy lifting for us.

Managing a Mess of Data

false positives

Two common false positives — grazing eclipsing binaries (left) and background eclipsing binaries (right) — can mimic the signal of a transiting planet. [NASA/Ames Research Center]

Recent years have seen a boom in exoplanet research — in large part due to the enormous data sets produced by transiting exoplanet missions like Kepler and, now, TESS. But the >3,000 confirmed Kepler planets weren’t all just magically apparent in the data! Instead, the discovery of planets is the result of careful classification of transit-like signals amid a sea of false positives from things like stellar eclipses and instrumental noise.

Given the number of light curves that need classifying, we can use any automated help we can get. Enter machine learning, a process by which computers can be trained to identify patterns and make decisions. Using a tool called deep learning, scientists have already shown that machines can do a pretty good job of automatically classifying Kepler transit signals as either exoplanets or false positives. But can we do even better?

light curves and centroids

Local (left) and global (right) views of the light curves (cyan) and centroids (maroon) for an example confirmed planet (top) and background eclipsing binary (bottom). Click for a closer look. [Ansdell et al. 2018]

The recent 2018 NASA Frontier Development Lab provided an excellent opportunity to find out. This eight-week research incubator was aimed at applying cutting-edge machine-learning algorithms to challenges in the space sciences. As part of this lab, two machine-learning experts were paired with two space-science researchers to try to improve machine-learning models for exoplanet transit classification. The results are presented in a new publication led by scientist Megan Ansdell (Center for Integrative Planetary Science, UC Berkeley).

Insider Knowledge

Ansdell and collaborators started with a basic machine-learning model that classified signals based on straightforward local and global views of the light curves. To improve upon it, they added scientific domain knowledge — information or insight that might not be generally known, but can be provided by a domain expert. 

Exonet recall and precision

Recall (top; the fraction of true planets recovered) and precision (bottom; the fraction of classifications that are correct) of the Exonet model, as a function of MES, a measure of the signal-to-noise of candidate transits. [Ansdell et al. 2018]

In particular, the authors used their knowledge of what types of false positives might come up. To help distinguish background eclipsing binary stars from planet transit signals, the team included data with each light curve showing how the line centroids — the pixel positions of the center of light — moved over time. To help the model identify false positives like giant-star eclipsing binaries, the authors fed in known stellar parameters with the light curves.

More Planets to Come

How did Ansdell and collaborators do? Using their modified model, “Exonet”, a computer can classify a Kepler data set with 97.5% accuracy and 98% average precision. That means that 97.5% of its classifications — exoplanet or false-positive — are correct, and an average of 98% of transits classified as planets are true planets. Not bad, for a machine!

One of the added benefits of the authors’ model is that it is ideal for generalization — for example, from Kepler to TESS data. The authors are currently working on a study using Exonet to classify simulated TESS data. And yesterday’s first public data release from TESS has provided plenty of fresh data to work with in the future!

Citation

“Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning,” Megan Ansdell et al 2018 ApJL 869 L7. doi:10.3847/2041-8213/aaf23b

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