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atmospheric stripping

Can we use clues from the present to figure out how a planet has been blasted by the radiation of its host star in the past? According to a new study, it’s a definite possibility.

A History of Rotation and Radiation

rotation-period curves

Three example rotation-period curves show that stars can start with very different rotation rates and evolutionary tracks, but after 2 billion years, the tracks all converge. [Kubyshkina et al. 2019]

The history of a star’s radiation — its stellar flux evolution history — is important not just for what it tells us about the star, but also for the implications for nearby planets. Stars that emit large amounts of high-energy radiation early in their histories can wear away the atmospheres of fluffy, Neptune-like planets, leaving behind cores with thinner atmospheres like Earth’s or even no atmosphere at all.

How can we tell how much flux a star has historically emitted? This is tricky! We know that stars that rotate faster produce more high-energy radiation. But though stars are born with vastly different spin rates, they all lose angular momentum and spin down over time.

For a while, all these spin-down tracks are unique. But after about 2 billion years of slowing down, the tracks for stellar rotation evolution converge — if you’re looking at a star older than 2 billion years, you can no longer directly tell from its current properties how fast it rotated in the past, or how much flux it consequently emitted over its history.

But if a star has a sub-Neptune-sized planet in a close orbit? According to a new study led by Daria Kubyshkina (Austrian Academy of Sciences), we might then be able to make some inferences!

Clues from a Modern Atmosphere

For stars hosting close-in planets with hydrogen-dominated atmospheres, a bit of creative modeling of a planet’s atmospheric evolution can allow us to infer its host’s flux evolution history.

recovery of injected signal

Recovery of an injected signal in the authors’ analysis. In this example, the mass of the planet is assumed to be exactly known. The posterior distributions for the other parameters of the system (blue curves) well match the prior distributions of the input parameters (red curves). From top to bottom, the parameters are the stellar rotation period at 150 Myr, age of the system and present-time rotation period, and orbital separation and stellar mass. [Adapted from Kubyshkina et al. 2019]

The amount of hydrogen atmosphere a planet has left later in its life — which can be estimated from the planet’s observed radius — reveals how much stellar flux it has received over its lifetime. Kubyshkina and collaborators build a framework that combines this information with knowledge about the system’s properties today to make a best guess at the star’s entire flux evolution history. The more parameters we know for the system, the better its history can be constrained.

Hunting Down Masses

This framework has applications beyond just understanding the past rotation and radiation of the host star.

Say we observe a system containing multiple planets; here one planet mass is well known, but the others are not. Under the authors’ analysis, the mass of the first planet can be used to place strong constraints on the rotation history of the star. But this history can then be used in conjunction with the observed radii for the system’s other planets to obtain realistic estimates for their masses!

The authors show the power of this analysis by applying it to two known planetary systems, HD 3167 and K2-32, inferring the rotation history of the two stars and constraining the masses of the planets in the systems. Their work clearly demonstrates the importance of advances in theory and modeling to help us get the most out of our growing body of exoplanet observations.

Citation

“Close-in Sub-Neptunes Reveal the Past Rotation History of Their Host Stars: Atmospheric Evolution of Planets in the HD 3167 and K2-32 Planetary Systems,” D. Kubyshkina et al 2019 ApJ 879 26. doi:10.3847/1538-4357/ab1e42

galaxy formation model Meraxes

Astronomy is driven forward by a combination of novel observations and complex, inventive modeling. How can astronomers better analyze their models? A new study presents a tool for the job — and is also the first article published under a new partnership between the American Astronomical Society (AAS) and the Journal of Open Source Software.

Exploring a Complex Universe

Modeling complicated astronomical systems is an important part of how we work to understand the universe. As technology advances, models have become increasingly more complex, encompassing more and more parameters. Complex models can do a better job of describing the astronomical systems we observe, but they’re also more challenging — and time-consuming — to analyze to see how well they might fit data.

model reconstructions

Reconstruction of a multi-Gaussian model with 12 parameters using two techniques: normal MCMC parameter sampling (right column) and hybrid PRISM+MCMC parameter sampling (left column). The reconstruction is shown as a blue solid line, and the true model is shown as a dashed black line. The different rows represent increasing iterations. The hybrid reconstruction fits the known data better after fewer iterations — effectively analyzing the model 16 times faster than the normal MCMC approach. Click to enlarge. [van der Velden et al. 2019]

One approach astronomers often use to analyze many-parameter models is Markov Chain Monte Carlo (MCMC) methods. With MCMC methods, instead of fully evaluating a model throughout parameter space, you randomly sample the model in a variety of places. This provides a general idea of the behavior of the model without requiring the time and computing investment of a full analysis.

While MCMC methods are generally robust, they can be quite slow — you might spend a lot of time sampling uninteresting parts of parameter space rather than focusing on the ones that are most likely to describe your system. To address this problem, a team of scientists led by Ellert van der Velden (Swinburne University of Technology and ARC Centre of Excellence in All Sky Astrophysics, Australia) have developed a new tool: a software package they call Probabilistic Regression Instrument for Simulating Models, or PRISM.

Let’s Speed Up the Process

How does PRISM work? When given a model to analyze, PRISM uses clever statistical methods to create an approximation of the model and iteratively predict which regions of parameter space aren’t of interest. This allows the user to home in on the interesting regions and explore the general behavior of a model very quickly. This algorithm can be used either alone or in conjunction with MCMC methods to analyze models more efficiently.

PRISM’s approach isn’t new: these techniques have previously been used to analyze models in a variety of scientific disciplines, including the study of whales, oil reservoirs, galaxy formation, disease, and biological systems. But PRISM takes the techniques and neatly bundles them up into a python software package that anyone can use to analyze their models — a valuable tool for astronomers and other scientists alike!

JOSS logo

The Journal of Open Source Software is a developer friendly, open access journal for research software packages. [JOSS]

A New Partnership for Software

Want more assurance about this software? You’ve got it! Van der Velden and collaborators’ article on PRISM, published in ApJS, is just one of a pair of publications; the second is the scrutinized software itself.

Under a new agreement between the AAS and the Journal of Open Source Software (JOSS), scientists submitting articles about astronomical software to AAS journals may choose not only to have their article reviewed, but also to have the software itself reviewed at JOSS in parallel. When both review processes are complete, the reviewed software is linked with the paper describing it in AAS journals.

The article presenting PRISM is the first of these simultaneous reviews to be conducted and published, and we expect many more to come! Software plays such an integral role in the study of astronomy today, and AAS publishing is pleased to help ensure that these valuable tools are shared.

Citation

“Model Dispersion with PRISM: An Alternative to MCMC for Rapid Analysis of Models,” Ellert van der Velden et al 2019 ApJS 242 22. doi:10.3847/1538-4365/ab1f7d

“Model dispersion with PRISM; an alternative to MCMC for rapid analysis of models,” van der Velden 2019 Journal of Open Source Software, 4(38) 1229. doi:10.21105/joss.01229

Io

For all that space telescopes are powerful tools for exploring our universe, we can achieve some remarkable science using ground-based observations! A new study explores the lessons learned from five years of monitoring Jupiter’s volcanic moon Io from the ground.

Io through filters

This set of images from Keck, all taken within 30 minutes of each other, demonstrates the range of filters used to observe Io during this campaign. [de Kleer et al. 2019]

A Dramatic Landscape

Jupiter’s innermost moon, Io, is a dramatic, roiling world of heated activity. The moon’s not-quite-circular orbit means that it receives a varying gravitational tug from Jupiter, generating friction and warming up the moon’s interior. This heat then escapes from Io’s surface in the form of active volcanic vents, tremendous explosions, and scalding lava flows.

Continuous monitoring of all of these activities — Io’s hotspots, or locations of thermal emission — is essential to understand how heat is dissipated in this violently active moon. We’ve had the opportunity to explore Io’s volcanism up close as the Voyager, Galileo, Cassini, and New Horizons missions have each passed by the moon, revealing more than 150 active volcanoes on Io’s surface. But these brief flybys don’t provide the important long-term, high-cadence observations of Io’s hotspots needed to truly track its activity.

Luckily, space-based astronomy is not the only solution!

View from the Ground

Over the last five years, scientists have carefully monitored Io’s thermal emission using the Keck and Gemini North telescopes located in Hawaii.

Think their observations couldn’t possibly be as useful as the up-close data from space telescopes? Think again! The powerful adaptive optics on Keck and Gemini North allowed the team to resolve down to distances of 100–500 km on Io’s surface in infrared— a scale not far from the resolution attained by the Near-Infrared Mapping Spectrometer on Galileo during its flybys.

What’s more, the flexible scheduling of Gemini North and a dedicated observing program at Keck made it possible for the team to gather 271 nights of observations of Io over 5 years. In a new study led by Katherine de Kleer (California Institute of Technology), the team now details what they’ve learned from this campaign.

Lessons from Hotspots

Io hotspot activity

Spatial distribution of hotspot thermal emission detected on Io in 2013–2018. [de Kleer et al. 2019]

Five years of observing have produced a grand total of 980 detections of more than 75 unique hotspots. A few points of interest from these observations:

  • The brightest eruptions are generally short-lived (lasting only a few days) and very hot (above 800 K, or nearly 1,000°F). They also almost all cluster in Io’s trailing hemisphere — the side of the moon located away from its direction of motion. This trend remains unexplained.
  • A number of new hotspots have only been detected in the past three years. Some of these likely existed before but only emit sporadically; others may have arisen more recently.
  • 113 detections of the extremely active Loki Patera hint at a periodicity to this volcano of ~470 days — behavior that could be tied to Io’s orbital properties.

The authors have made all of their hotspot data available for public download and invite the astronomy community to extend their work. Between future analysis of these data and further observations of Io, we can certainly look forward to more insights into this heated, dynamic world. 

Citation

“Io’s Volcanic Activity from Time Domain Adaptive Optics Observations: 2013–2018,” Katherine de Kleer et al 2019 AJ 158 29. doi:10.3847/1538-3881/ab2380

Solar flare

Powerful solar flares are dazzlingly bright in ultraviolet and X-ray images of the Sun. Despite their demands for attention, there’s still a lot that we don’t know about these unpredictable eruptions.

Clues from 121.6 nm

Solar flare at three wavelengths

These Solar Dynamics Observatory images of the Sun show a solar flare in three extreme ultraviolet wavelengths. From left to right: 17.1, 30.4, and 13.1 nanometers. [NASA/GSFC/SDO]

Solar flares shoot energetic particles and photons from across the electromagnetic spectrum into interplanetary space. In order to understand how energy is released in solar flares, we need to first know how energy is injected.

To explore where flares get their energy, a team led by Jie Hong (Nanjing University, China) focused on a familiar feature of the ultraviolet solar spectrum: the 121.6-nm hydrogen Lyman-α emission line, produced by the roiling, turbulent hydrogen gas in the Sun’s atmosphere. The shape and behavior of the Lyman-α emission line can be used to learn about many different types of activity in the Sun’s chromosphere and corona — including solar flares.

Hong et al. 2019 Fig. 6

Evolution of Lyman-α profiles over time. The top row shows the time evolution of the profiles in the non-thermal heating case. The asymmetry of the peaks transitions from long to short wavelengths as time and energy increase. The bottom row shows the thermal heating case (left) and the thermal heating plus a soft electron beam case (right). In the thermal heating case, the double peak morphs into a single peak. Click to enlarge. [Hong et al. 2019]

Modeling Flare Emission

Hong and collaborators used radiative hydrodynamics to model solar flares heated by different mechanisms. Their goal was to explore how the type of heating might change the shape of the Lyman-α line we observe.

In particular, they examined two means of heating the flares: a thermal mechanism where the energy comes from conduction from nearby plasma, and a non-thermal mechanism where the heat is provided by a beam of energetic electrons generated by magnetic reconnection. In the non-thermal case, they also varied the strength of the heating by an order of magnitude.

After allowing the modeled flares to evolve for eight or ten seconds, the researchers looked for subtle changes in the shape of the Lyman-α profile that could be linked to the underlying heating mechanism.

The asymmetries and peaks of the modeled emission lines showed distinctive patterns and behavior over time — fingerprints, Hong and collaborators argue, that could help identify the source of heat for an observed flare.

Flare Photography

Solar Orbiter

An artist’s impression of Solar Orbiter silhouetted against the Sun. The spacecraft’s tilted orbit will provide never-before-seen images of the Sun’s poles. [Spacecraft: ESA/ATG medialab; Sun: NASA/SDO/P. Testa (CfA)]

Hong and collaborators note that their modeling efforts will complement future solar observations, helping to clarify the complex picture of flare evolution.

In particular, they look forward to the joint NASA-ESA Solar Orbiter mission, set to launch in 2020, which will be the first spacecraft to snap extreme-ultraviolet pictures of the Sun from out of the ecliptic plane, and China’s Advanced Space-Based Solar Observatory (ASO-S), which is scheduled for launch in 2022. ASO-S will carry a dedicated Lyman-α imager.

After decades of observations, it looks like the field of flare research is still heating up!

Citation

“The Response of the Lyα Line in Different Flare Heating Models,” Jie Hong et al 2019 ApJ 879 128. doi:10.3847/1538-4357/ab262e

supernova

Massive stars can die in a lot of different ways! A new study explores one possible channel in more detail.

Detectives Are on the Case

supernova

Artist’s illustration of a star exploding in a supernova at the end of its lifetime. [NASA/CXC/M. Weiss]

Studying supernovae is a little like being a detective in an odd sort of murder mystery. You’ve witnessed the death of a massive star — and from this evidence, you must determine what type of star died, how it died, and even what interactions it had before its death.

As we enter the era of ever more expansive sky surveys, we can expect to amass not just evidence of typical stellar deaths, but also some more unusual ones. In the process, piecing together the evidence to solve each mystery becomes progressively more challenging — but also more intriguing!

In a recent study, a team of scientists led by Alejandro Vigna-Gómez (U. of Birmingham, UK; Monash U., Australia; U. of Copenhagen, Denmark) have explored one particular oddball type of theorized stellar death: pulsational pair-instability supernovae (PISNe).

Gravity (Usually) Wins

According to theory, PISNe occur when a very massive (hundreds of solar masses) star gets hot enough to start producing pairs of electrons and positions. This process saps the star’s internal energy, leading to its sudden collapse as the force of gravity triumphs.

This collapse can end in the dramatic explosion of a PISN, or it may lead to a smaller eruption that only sheds some of the star’s mass. In the latter case, the star may go through multiple rounds of smaller eruptions before eventually running out of nuclear fuel and undergoing a final explosion — as a pulsational PISN.

massive stellar evolution scenarios

Schematic showing three possible ways massive stars can die; click to enlarge. Top and bottom panels describe outcomes of single-star evolution, depending on the star’s mass. The center channel depicts the merger of two evolved, massive stars to form an object with a large envelope of hydrogen. This can lead to a hydrogen-rich pulsational PISN. [Vigna-Gómez et al. 2019]

Starting with a Merger

If this weren’t complicated enough, Vigna-Gómez and collaborators propose one further twist on this stellar death scenario: the object exploding in a pulsational PISN needn’t simply be a massive star. Instead, it might be the product of the merger of two massive stars.

Vigna-Gómez and collaborators argue that this type of merger is expected to be common, and it would produce a very massive object with a large outer hydrogen shell. By running a series of simulations using the Modules for Experiments in Stellar Astrophysics (MESA), the authors demonstrate that such a merger product could undergo a pulsational PISN and still retain a significant portion of its hydrogen shell up to the final explosion, leaving the fingerprint of hydrogen in the supernova spectrum.

Explanation for a Zombie Star?

iPTF14hls

The light curve of iPTF14hls is extremely unusual, featuring multiple apparent explosions. [Adapted from Las Cumbres Observatory/S. Wilkinson]

Why does this particular theorized death matter? Stellar detectives are currently working to explain the deaths in a number of especially weird observed supernovae, and this model might match some of them. One example is iPTF14hls, the “zombie star” that’s made headlines for apparently erupting multiple times and defying explanation — in part because of the unexpected hydrogen signatures in its spectra.

We can’t yet say for sure whether iPTF14hls is an example of a stellar-merger-turned-pulsational-PISN — that will require more extensive modeling and analysis of observations — but Vigna-Gómez and collaborators think it’s a good candidate! And while we wait on the verdict of that mystery, we can be sure that transient surveys are busy finding many more examples of stellar deaths for us to puzzle over.

Citation

“Massive Stellar Mergers as Precursors of Hydrogen-rich Pulsational Pair Instability Supernovae,” Alejandro Vigna-Gómez et al 2019 ApJL 876 L29. doi:10.3847/2041-8213/ab1bdf

black holes in a globular cluster

When the Laser Interferometer Gravitational-Wave Observatory (LIGO) discovered its first merging black holes, astronomers were surprised: these black holes were much larger than we had expected! A new study looks at what these observations might tell us about black holes in star clusters.

X-ray binary

Artist’s impression of an X-ray binary, in which a black hole accretes matter from a stellar companion. [NASA/CXC/M.Weiss]

Unexpected Masses

Before the first gravitational-wave detection in 2015, the theoretical existence of black holes was solidly established within the framework of general relativity. Observational evidence for stellar-mass black holes came from X-ray binaries: binary star systems consisting of a compact object accreting matter from a companion star.

Though we can’t directly observe the black holes in X-ray binaries, we can infer their existence by watching the motions of the binary. By measuring the dynamics of these systems, we’ve obtained mass estimates for the inferred black holes in perhaps two dozen binaries so far; they typically range between about 5 and 20 solar masses.

stellar graveyard

A look at the masses for the black holes and neutron stars we’ve been able to measure. The black holes observed via X-rays (purple) are much less massive than those observed recently via gravitational waves (blue). [LIGO-Virgo/Frank Elavsky/Northwestern U.]

With this precedent, it was quite the surprise when LIGO’s first detection revealed the merger of two black holes of a whopping 31 and 36 solar masses. In the ten mergers LIGO and its European counterpart, Virgo, have detected since then, 16 of the 20 pre-merger black holes have had masses above the range measured for black holes in X-ray binaries.

Two Formation Channels?

What’s creating the dichotomy between the lower black-hole masses measured in X-ray binaries and the higher masses measured from mergers? Some scientists speculate that X-ray-detected and gravitational-wave-detected black holes are dominated by two different formation channels:

  1. A binary star system evolves in isolation, with at least one star eventually becoming a black hole. This channel is proposed for X-ray-detected black holes.
  2. Stars evolve individually within a cluster, and some of the resulting black holes later pair up into binaries via dynamical interactions in the cluster. This channel is proposed for gravitational-wave-detected black holes.

Can LIGO/Virgo observations tell us more about the latter scenario? A team of scientists led by Rosalba Perna (Stony Brook University) have run a series of N-body simulations of initially isolated black holes in a mini-cluster to find out.

Simulating Interactions

model vs LIGO observations

Comparison between the model prediction for the distribution of observed total mass after 10 observations for three models with different initial mass distributions and the LIGO/Virgo observations (black line). The best-fit model (green) is consistent with evolution of a cluster of low-metallicity, massive stars. [Perna et al. 2019]

Perna and collaborators show that dynamical interactions in the cluster preferentially cause the most massive of black holes to come together in more tightly bound binaries. Since tightly bound binaries spiral in more quickly, this cluster preference increases LIGO/Virgo’s chances of preferentially detecting the mergers of heavier black holes.

The team also shows that the particular shape of the distribution of masses measured at merger is dependent upon the distribution of initial black-hole masses in the cluster. By comparing LIGO/Virgo’s observations to their simulations with different initial mass distributions, Perna and collaborators show that the observations are consistent with the distribution expected for a star cluster that initially consists of massive, low-metallicity stars.

While these comparisons are always tricky with only 20 data points, this study can be easily expanded in the future, as LIGO and Virgo continue to amass more observations. For now, however, the dynamical formation channel is looking like a promising explanation for gravitational-wave-detected black holes!

Citation

“Constraining the Black Hole Initial Mass Function with LIGO/Virgo Observations,” Rosalba Perna et al 2019 ApJL 878 L1. doi:10.3847/2041-8213/ab2336

Venus Express

What happens to Venus when an enormous solar eruption slams into the planet? In 2011, the Venus Express spacecraft was on site to find out!

Benefits of a Field

Earth's magnetic field

Schematic illustration of the Earth’s global magnetic field. Venus does not have an intrinsic field. [NASA / Peter Reid / TheUniversity of Edinburgh]

The Earth’s magnetic field does an excellent job of protecting us from the damaging influence of the solar wind. Energetic particles emitted by the Sun are deflected around our planet and channeled to the poles, where they harmlessly light up the sky in haunting aurorae. Even the danger of sporadic solar eruptions — like flares and coronal mass ejections — is largely mitigated by our protective shield.

But our sister planet, Venus, is less fortunate: though similar to Earth in many ways, Venus lacks its own global magnetic field to protect it from the Sun’s onslaught.

What happens to this clouded planet when the Sun sends an enormous interplanetary coronal mass ejection its way?

Venus induced magnetosphere

The interaction of Venus with the magnetized solar wind produces an induced magnetosphere. [Ruslik0]

With a Little Help from the Sun

Venus has a trick up its sleeve: though it doesn’t carry its own magnetic field, it boasts an induced magnetosphere.

As extreme ultraviolet radiation from the Sun lights up Venus’s dayside, it ionizes the planet’s upper atmosphere, forming a plasma known as the ionosphere. When the solar wind — which carries the Sun’s magnetic field with it — encounters Venus, the thermal pressure of the ionosphere pushes back against the magnetic pressure of the solar wind, causing the field lines to drape around Venus and remain supported there.

This induced magnetosphere has a bow shock on the Sun side and a long, trailing magnetotail on the anti-Sun side. The pile-up of magnetic field between the magnetosphere and Venus’s ionosphere — the magnetic barrier — prevents the solar-wind plasma from penetrating deeper down into Venus’s atmosphere.

Front-Row Seats to Action

So Venus isn’t unprotected — but how well does this shield hold up in the face of powerful solar storms? In 2011, we had a orbiter ready to watch the stormy drama up close: the Venus Express spacecraft.

Venus Express, launched in 2005, orbited around Venus’s poles and studied the global space environment around the planet. On 5 November, 2011, an extremely strong interplanetary coronal mass ejection hit Venus while the spacecraft was in orbit — and now, in a publication led by Qi Xu (Macau University of Science and Technology, China), a team of scientists has detailed what the spacecraft learned.

Not Unflappable

Venus's flapping magnetotail

The magnetic field strength (top) and direction (bottom) measured by the Venus Express reveal the rapid flapping motion of the plasma sheet in the magnetotail in response to the interplanetary coronal mass ejection. The red line shows that the Bx component of the magnetic field changed direction 5 times within 1.5 minutes (7:49:30–7:51:00)! [Adapted from Xu et al. 2019]

Venus Express’s data show that the planet’s induced magnetosphere and its ionosphere responded dramatically to the strong solar eruption. Venus’s bow shock was compressed and broadened as the storm hit; the plasma sheet of the magnetotail flapped back and forth rapidly; the magnetic barrier increased in strength; and the ionosphere was excited, jumping to a whopping three times the quiet-Sun plasma density!

Based on their analysis, Xu and collaborators expect that interplanetary coronal mass ejections like this one substantially increase the rate of Venus’s atmospheric loss, violently driving ions from the planet’s gravitational grasp.

We still have a lot to learn about about how our sister planet reacts when solar storms strike, but these observations have shed new light on the dramatic struggle.

Citation

“Observations of the Venus Dramatic Response to an Extremely Strong Interplanetary Coronal Mass Ejection,” Qi Xu et al 2019 ApJ 876 84. doi:10.3847/1538-4357/ab14e1

SN 1987A

When massive stars explode as supernovae, they fling shredded stellar material into the interstellar medium. The explosion should also generate gravitational waves, but astronomers haven’t detected any yet. Is it just a matter of time before we detect gravitational waves from core-collapse supernovae, or are the signals still out of reach?

Cosmic Fireworks

Radice et al. 2019 Fig. 1

Expansion of the simulated supernova shock waves as a function of time since the outer layers of the star bounced off the core. All but the 13-solar-mass simulation exploded successfully. [Radice et al. 2019]

Astronomers have observed supernovae in every part of the electromagnetic spectrum and even captured elusive neutrinos from these events. Despite the wealth of observations, there’s still plenty we don’t know about core-collapse supernovae, including exactly how the explosions happen.

Gravitational-wave observations could be the key to understanding what goes on in the chaotic moments after massive stars collapse. To determine whether we can hope to detect these stellar explosions with our current gravitational-wave observatories — and what we could learn from the observations if we can detect them — a team led by David Radice (Princeton University) conducted three-dimensional simulations of eight supernovae.

Radice et al. 2019 Fig. 3

Computed gravitational-wave spectra. [Adapted from Radice et al. 2019]

When Stars Explode (Digitally)

Radice and coauthors began with models of supernova progenitor stars with masses between 9 and 60 times the mass of the Sun. For each simulated stellar collapse, the authors tracked the shock wave expansion, neutrino luminosity, and gravitational-wave emission as a function of time.

They found that gravitational waves were generated shortly after the collapse by convection in the material just outside the newly formed neutron star — a proto-neutron star — cooling at the supernova’s center. At later times (more than 0.2 seconds after the collapse), the gravitational-wave signal is dominated by oscillations of the proto-neutron star, which are driven by accreted material striking its surface.

The authors also show that the amount of energy radiated away in the form of gravitational waves is correlated with the amount of turbulent energy of the material accreting onto the proto-neutron star. This hints that gravitational-wave observations can provide an estimate of how turbulent the material behind the shock wave is.

These observations can also help us understand the nature of the proto-neutron star itself. The peak frequency of the signal is set by the proto-neutron star’s oscillation frequency, which depends on its interior structure. Clearly, gravitational waves can reveal a lot about what happens in a star’s final moments — but can we even detect them?

Catching a Wave

The authors found that the best possible signal-to-noise ratio for a supernova 33,000 light-years away ranges from 1.5 to 11.5, depending on the mass of the progenitor; for comparison, the signal-to-noise ratio for LIGO’s first detected black-hole merger was 24. While the authors acknowledge that this doesn’t mean that gravitational waves from core-collapse supernovae are undetectable with current observatories, the odds of detecting one are greater with future, more sensitive observatories.

Radice et al. 2019 Fig. 4

Simulated gravitational-wave luminosity as a function of time. [Radice et al. 2019]

In the case of the Einstein Telescope, a proposed gravitational-wave detector with a planned completion date of 2030, the calculated signal-to-noise ratio ranges from 20 to 110. Hopefully, the advent of third-generation detectors in the next couple of decades will bring an explosion of gravitational-wave data, allowing astronomers to peer into the turbulent interiors of collapsing stars.

Citation

“Characterizing the Gravitational Wave Signal from Core-collapse Supernovae,” David Radice et al. 2019 ApJL 876 L9. doi:10.3847/2041-8213/ab191a

Image reveals a tilted oval structure of an orange disk containing a number of concentric gaps and rings.

The stunning substructures of gaps and rings revealed in protoplanetary disks have been attributed to the motions of hidden, newly formed planets. But are we interpreting our observations correctly?

Models of Structure

planet formation

This simulation shows a Jupiter-mass planet forming inside a circumstellar disk. [Frédéric Masset]

When the Atacama Large Millimeter/submillimeter Array (ALMA) came online, one of its first released images was that of HL Tau, a young star surrounded by a protoplanetary disk — a disk that’s structured with a dramatic series of concentric gaps and rings. Since this early image, ALMA has continued to amass observations of disk structure in the inner tens of AU around young stars — and theorists are now left to decide what to make of these.

Multiple explanations for the origin of these structures have been proposed, including snowlines, flows driven by magnetic fields, gravitational instabilities, or dust trapping. But the most popular model suggests that the gaps are driven by the motions of young, invisible planets embedded in the disks.

Challenging Assumptions

Recent studies have suggested that multiple gaps and rings can actually be produced by a single embedded planet. Simulations show that as a planet moves through the disk, it excites multiple spiral density waves. Interactions of these waves with the disk can then carve out several narrow gaps.

But while the basic idea behind these simulations seems sound, two scientists from the Institute for Advanced Study, Ryan Miranda and Roman Rafikov (also of University of Cambridge, U.K.) suggest we need to be a little more careful in how we interpret them.

comparison simulations of planet-disk interactions

Three of the authors’ simulations comparing locally isothermal (left panel of each pair) and non-isothermal disks (right panel of each pair). For low-mass planets (top two pairs), locally isothermal simulations overestimate the contrast of structures. For high-mass planets (bottom pair), locally isothermal simulations also misrepresent the locations of rings and gaps. [Adapted from Miranda & Rafikov 2019]

Because full simulations of disk–planet interactions are computationally inhibitive, numericists make simplifying assumptions in their models. One commonly adopted simplification is to assume that the disk is locally isothermal, i.e., it has a fixed temperature profile. But while this assumption holds in the outer regions of the disk where cooling is efficient, Miranda and Rafikov point out that this isn’t a good model for the poorly cooled inner tens of AU where we observe these ring and gap structures.

Massive Interpretations

What quirks does this assumption introduce? By running a series of comparison simulations of a planet interacting with a locally isothermal and a non-isothermal disk, Miranda and Rafikov show that locally isothermal simulations tend to overestimate the contrast of ring and gap structures produced. This means that using isothermal models to interpret ALMA results would cause us to underestimate the masses of the planets causing the disk structure observed.

What’s more, the authors find that for large planets, the isothermal simulations also misrepresent the locations of the rings. The results in this article suggest a strong need for caution when using locally isothermal simulations to explore the interactions between planets and disks. We’re certainly getting closer to understanding the many complexities of planet formation, but we’ve still got plenty of work to do!

Citation

“On the Planetary Interpretation of Multiple Gaps and Rings in Protoplanetary Disks Seen By ALMA,” Ryan Miranda and Roman R. Rafikov 2019 ApJL 878 L9. doi:10.3847/2041-8213/ab22a7

collapsar

How do we get the heavy elements — elements with atomic mass above iron, like gold, platinum, or uranium — in our universe? A new study suggests that one theorized source, collapsing massive stars, may not be the best option.

Enriching the Universe

The Big Bang produced a universe filled almost exclusively with hydrogen and helium; almost all of the heavier elements in our universe have formed since that time. How and when they formed, however, are still questions we’re working to solve.

element origins

Periodic table showing the origin of each chemical element. Those produced by the r-process are shaded orange and attributed to supernovae in this image; though supernovae are one proposed source of r-process elements, collapsars have been proposed as another. [Cmglee]

We know that the dense, hot cores of stars fuse atoms, producing elements up to iron in mass. But we need more extreme conditions for r-process nucleosynthesis — a set of rapid neutron-capture reactions that we think are responsible for producing about half the atomic nuclei heavier than iron.

Recent research has renewed interest in one potential source of r-process elements: collapsars. Collapsars are massive (>30 solar masses), rapidly rotating stars that suffer catastrophic core-collapses into black holes. In this sudden process, a spinning disk of material accretes onto the core — and conditions in the disk are just right for the r-process. But could collapsars really account for much of the r-process elements in our universe?

Clues from Collapse

abundance ratios

Abundance ratios found by the authors in a sample of low-metallicity stars. The top plot shows good agreement between the collapsar model (red) and observations (black) for the ratio of Mg (not an r-process element) to Fe. But r-process elements Ba, Eu, and Sr show much higher abundances in collapsar models than in the observations. [Macias & Ramirez-Ruiz 2019]

Collapsar r-process nucleosynthesis should leave a visible imprint on the surrounding environment, UC Santa Cruz scientists Phillip Macias and Enrico Ramirez-Ruiz (also University of Copenhagen, Denmark) point out.

In the collapsar model, r-process elements produced in the accreting disk are flung out into the star’s surroundings via disk winds. But collapsars don’t only produce r-process elements — they also create lighter elements like iron, which are spewed from the collapsars via jets. These elements should all then mix, producing a soup of enriched material with a particular ratio of abundances — which will then seed the next generation of stars.

Macias and Ramirez-Ruiz look for signs of this soup imprinted on a sample of 186 very low-metallicity stars that haven’t already been polluted by many additional generations of star formation. If collapsars are the source of most of the r-process material in the universe, then these unpolluted canvases should show the same ratio of r-process elements to iron as the authors calculate from collapsar models.

A Mismatch with the Evidence

Macias and Ramirez-Ruiz find that their stellar sample’s abundance ratios do not match those predicted by the collapsar model — the relative amount of r-process elements would need to be much higher in the observed stars for collapsars to be a good explanation.

Instead, the authors argue that the majority of r-process nucleosynthesis must occur in sources that don’t simultaneously produce iron. One possible source that satisfies this condition is neutron-star mergers, like that observed in the recent gravitational-wave event GW170817. There are challenges to this model as well — but we can hope that future observations will help us to better understand where our universe’s heavy elements come from.

Citation

“Constraining Collapsar r-process Models through Stellar Abundances,” Phillip Macias and Enrico Ramirez-Ruiz 2019 ApJL 877 L24. doi:10.3847/2041-8213/ab2049

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