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galaxy and CGM simulation

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: The Impact of Enhanced Halo Resolution on the Simulated Circumgalactic Medium
Authors: Cameron B. Hummels, Britton D. Smith, Philip F. Hopkins, et al.
First Author’s Institution: TAPIR, California Institute of Technology
Status: Submitted to ApJ

It can be easy to think of galaxies as islands in the universe, floating around in isolation. However, a galaxy is actually surrounded by a huge sea of low-density gas that extends out to its virial radius and beyond. This gas is known as the circumgalactic medium (CGM), and more and more research is showing that the CGM has a crucial role to play in galaxy evolution. Observing the CGM has proven difficult due to its extremely low density, though, so simulations have played a large role in understanding the physics of this region. In today’s paper, the authors detail the effects of running a CGM simulation with significantly increased resolution, capable of resolving cool gas that precipitates in the CGM and rains down on the galaxy.

What Do We Know About the CGM?

Residing just outside of the galaxy, the CGM is home to large-scale flows of gas that drive galaxy evolution. These gas flows provide fuel for star formation, regulate the interactions between dark matter halos and the intergalactic medium, and contain the energy, mass, and metals of large outflows from a galaxy. In fact, the CGM is predicted to hold at least as many baryons and heavy elements as galaxies themselves, and most of the metals in the universe are found in the CGM. These metals (meaning anything heavier than hydrogen or helium in astronomy terms), deposited by galactic outflows, serve as the dominant coolant for the CGM. They are capable of radiating energy away more easily than elements like hydrogen, so an increased abundance of metals can lead to cooler gas. Consequently, this influx of metals helps to create two phases of gas: “cool” (10,000 Kelvin) gas composed of neutral hydrogen and other elements in low-energy ionization states, and “hot” (300,000–1,000,000 Kelvin) gas that contains oxygen, nitrogen, and neon in high-energy ionization states.

Unfortunately, computational work has chronically underproduced the observed abundances of these ions across redshifts by orders of magnitude. Recent work has shown that AGN feedback can increase the abundances of oxygen and other ions in the hot gas, but the discrepancy remains for hydrogen and other ions in the cool gas. In today’s paper, the authors discuss the effect of increased simulation resolution on these discrepancies.

Resolving the Resolution Issue

Perhaps one reason that simulations struggle to reproduce observations of the CGM lies in their resolution limits. Similar to how using more pixels in a television or computer screen gives a better image, increasing the resolution in a simulation means using more cells or particles to obtain a better physical picture of what is going on. However, each increase in resolution increases the computational cost of the simulation. This means your simulation that took a few days to run could instead take a few months.

Consequently, most simulations of galaxies apply their highest resolution to regions of high density where most of the matter is. This is great for figuring out what happens in the dense disk of a galaxy, but not ideal for studying the low-density CGM. Today’s paper runs simulations that force high resolution upon the CGM, reaching resolutions that are comparable to those normally obtained in the disk of the galaxy. This technique is appropriately named Enhanced Halo Resolution (EHR). Figure 1 shows the resolutions obtained by both a normal cosmological simulation and an EHR one for a region encompassing a galaxy and its surrounding filaments.

resolution plots

Figure 1: Plots of resolution for a traditional (AMR — adaptive mesh refinement) and EHR simulation. Each of these grids is made up of many cells, and spatial resolution refers to the physical length (in kiloparsecs) of the smallest cell that is present in a region. In the left panel, many galaxies are present and a particularly massive galaxy lies at the center. Its virial radius is shown by the dotted white line. Resolution in the CGM is roughly 16 times worse than in the disk of the galaxy. On the right, the EHR simulation enforces high resolution approximately to the virial radius, ensuring that interactions within the CGM are given much more computational attention. [Hummels et al. 2019]

What Does this Computational Cost Buy You?

By better resolving the gas in the CGM, the authors note that a number of physical effects present themselves. Firstly, the balance of cool and hot gas is shifted, leaving more cool gas and less hot gas than in simulations with lower resolution. The clouds of cool gas that form are also greater in number and smaller in size. Finally, the amount of neutral hydrogen and other low-energy ions found in the cool gas increases, while the abundances of oxygen, nitrogen, and neon in high-energy ionization states fall due to the decrease in hot gas. Coupled with the aforementioned work on AGN feedback, this can bring simulations closer to the observed abundances for these ions.

simulated galaxy and CGM

Figure 2: A galaxy and the CGM in an AMR simulation and an EHR one. A significant increase in HI (neutral hydrogen) can be seen in the EHR simulation. Recall that neutral hydrogen tracks the cool gas, which condenses into many clumps on the right that weren’t resolved in a traditional AMR simulation. Many of these clumps fall back into the galaxy because they no longer have enough thermal energy to resist the gravitational pull of the galaxy. [Hummels et al. 2019]

In other words, EHR causes more gas in the CGM to cool, condense into clouds, and potentially fall back into the galaxy. This is completely analogous to water vapor in our own atmosphere, which often cools, forms clouds, and rains back down to Earth. In this way, the CGM can be conceptualized as the atmosphere of a galaxy. Figure 2 shows cool gas condensing into these clouds, some of which fall into the galaxy.

Why does an increase in resolution result in more cool gas? The answer lies in how gas mixes in simulations. With lower resolution, clouds of cool gas are typically resolved only by a few cells, inducing artificial mixing between the hot and cool gas. The authors perform a test simulation demonstrating this, shown in Figure 3.

cloud test problem

Figure 3: In this test problem, a 4-kiloparsec-wide cloud of cool gas sits in a flow of hot gas for 260 million years. In the low-resolution test, the boundary of the cloud is only resolved by a few cells. This artificially thick boundary means that much of the cool gas quickly mixes with the hot gas and eliminates the HI (neutral hydrogen). In the high-resolution case, the boundary becomes much thinner, allowing the interior cool gas to survive much longer. [Hummels et al. 2019]

Resolution clearly makes a big difference in understanding the physics of the CGM and galaxies. For example, just like plants on Earth sprout after a rain, cool gas that condenses in the CGM and falls into a galaxy can trigger star formation. Understanding the ecology and geology of Earth requires a detailed picture of the atmosphere, and perhaps unlocking the mysteries of galaxy evolution may depend just as strongly on our understanding of the CGM. 

About the author, Michael Foley:

I’m a graduate student studying Astrophysics at Harvard University. My research focuses on using simulations and observations to study stellar feedback — the effects of the light and matter ejected by stars into their surroundings. I’m interested in learning how these effects can influence further star and galaxy formation and evolution. Outside of research, I’m really passionate about education, music, and free food.

cosmic distance ladder

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: The Carnegie-Chicago Hubble Program. VIII. An Independent Determination of the Hubble Constant Based on the Tip of the Red Giant Branch
Authors: Wendy L. Freedman, Barry F. Madore, Dylan Hatt, Taylor J. Hoyt, In Sung Jang, et al.
First Author’s Institution: University of Chicago
Status: Accepted to ApJ

Author’s note: Credit for “H0tTake” goes to the conference “Tensions between the Early and the Late Universe” hosted at the Kavli Institute for Theoretical Physics!

The value of the Hubble Constant (H0) is a beast to pin down. However, it’s integral to our understanding of how the universe evolved and will continue to evolve. H0 relates the speed with which distant objects are moving away from us — due to the universe’s expansion — to how far away they are (see this Astrobite for a detailed explanation of how H0 assumed its place of importance). Measurements of H0 can be made using the early universe, from the cosmic microwave background (CMB), and the late universe, from distance measurements for stars, galaxies and other objects.

Under our current understanding of the universe, these two sorts of measurements ought to yield similar values of H0. Instead, we’ve witnessed a growing divergence between them that’s only gotten worse (or more interesting?) with time (see Figure 4, though it does come with a spoiler). Currently, early universe measurements of H0 rely on CMB observations made by the Planck satellite, while late universe measurements rely on Cepheid variables and Type Ia supernovae (Sne Ia). The discrepancy between these early and late measurements of H0 could be chalked up to new physics in the early universe that is outside our current models. But before claiming that, we’d want to rule out any hidden issues in how these measurements are being made.

On the side of the late universe, this requires using other astronomical objects to make measurements of H0  and to calibrate the distances to standard candles (objects whose brightness we understand very well), like Sne Ia. Very recently, a new measurement of H0 was announced, which used strong gravitational lens systems for distance calibration (see this Astrobite for a good summary). The paper being discussed in today’s Astrobite comes out of the Carnegie-Chicago Hubble Program, which was established to calibrate Sne Ia through alternate methods. Here, the authors use something called the Tip of the Red Giant Branch (TRGB).

The TLDR on the TRGB

color-magnitude diagram

Figure 1. A color-magnitude diagram of globular cluster Messier 55 (M55). The TRGB can be seen at the upper-right. [B.J. Mochejska, J. Kaluzny (CAMK), 1m Swope Telescope]

The TRGB consists of stars that are at a pivot point in their evolution. Red Giant Branch (RGB) stars are stars that have nearly exhausted the hydrogen in their cores. The next stage of their life is triggered when they start fusing helium in their cores instead. TRGB stars have just begun this stage of helium burning, and they can be distinguished by their characteristic redness and brightness (see Figure 1). These standard features of the TRGB make it highly suitable for measuring distances, since we know how bright it ought to appear at a certain distance.

The authors use the TRGB in lieu of Cepheids to calibrate the distances to galaxies that have hosted Sne Ia. TRGB stars have some advantages over Cepheids: they are much more common and can be found in uncrowded regions of their host galaxies, making them easier to identify. They also don’t need multiple observations to be recognized. Another useful quirk of TRGB stars is that their brightness in the I-band does not vary greatly with metallicity (the composition of the star), so the TRGBs in different galaxies shouldn’t look terribly different.

Could it (TRG)be?

In the near future, parallax measurements of Milky Way TRGB stars taken by the Gaia satellite will be available to anchor TRGB calibrations. For now, the authors use I-band observations of the Large Magellanic Cloud’s TRGB as well as parallax measurements for their analysis. The authors analyze the TRGB of 18 Sne Ia hosts, ranging from 7 to nearly 20 Mpc away, to calibrate the distances to those galaxies (see Figure 2). Their sample consists of galaxies that were not obscured by dust and had observations of their halos, where the TRGB could be cleanly measured. The TRGB calibrations were then used with a larger sample of Sne Ia to measure the distances to those Sne.

Sne Ia host galaxies

Figure 2. Nine of the eighteen Sne Ia host galaxies whose TRGB were studied. The squares represent the areas of the halo that were targeted. The hatched areas show the regions that were analyzed. [Freedman et al. 2019]

Finally, *drumroll* the authors present their measurement of the Hubble constant — 69.9 ± 0.8 ± 1.7 km s-1 Mpc-1 (the two errors are statistical and systematic respectively). This new result is shown clearly in a Hubble diagram showing their 18 TRGB calibrators and 99 Sne Ia from the Carnegie Supernova Project (see Figure 3). A Hubble diagram is a plot of distance versus speed, and the slope of the plot gives us a value of H0.

Hubble diagram

Figure 3. The Hubble diagram produced from the TRGB calibrators and Sne Ia from the Carnegie Supernova Project. The slope of the line is where the measurement of H0 comes from. The y-axis of the upper plot is the distance modulus (a measurement of distance using the relation between the absolute magnitude and apparent magnitude for an object). The y-axis of the lower plot is the difference between the points and the fit to the data. The x-axis of both plots is a quantity relating the distance modulus with redshift (see Section 7.1 of the paper). [Freedman et al. 2019]

This number falls squarely between the CMB and Cepheid-Sne Ia measurements of H0 (see Figure 4). The authors are careful to note that their result does not resolve the discrepancy in H0 values, but reiterate that additional, independent late universe measurements of H0 could change that. And the future is teeming with possibilities: aside from Gaia, the James Webb Space Telescope and LIGO and Virgo offer other avenues for measuring distances across large swaths of space, not to mention better measurements of strong lensing systems and tried-and-tested Cepheids. All in all, this is a very exciting time for cosmology!

Hubble constant over time

Figure 4. Measured values of H0 over time, showing where the TRGB measurement lands relative to the CMB and Cepheid measurements. The red star is the measurement from the paper being discussed. [Freedman et al. 2019]

About the author, Tarini Konchady:

I’m a graduate student at Texas A&M University. Currently I’m looking for Mira variables to better calibrate the distance ladder. I’m also looking for somewhere to hide my excess yarn (I’m told I may have a problem).

binary supermassive black holes

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: Discovery of a close-separation binary quasar at the heart of a z ∼ 0.2 merging galaxy and its implications for low-frequency gravitational waves
Authors: Andy D. Goulding, Kris Pardo, et al.
First Author’s Institution: Princeton University
Status: Published in ApJL

With the announcement of the experimental confirmation of gravitational waves by LIGO in 2016 in tandem with additional electromagnetic follow-up of a neutron-star merger, astronomy was quickly ushered into an era of truly multi-messenger science. Although the number of gravitational-wave events observed by LIGO since is already substantial, the sheer number of black holes (and neutron stars) predicted to exist within our universe vastly outweighs this cumulative yield. One reason why LIGO is not constantly finding strong gravitational waves from all of these black holes (the gravitational wave background or GWB) is that not every black hole exists in a pair, which is a necessary condition to spiral inwards, merge, and set off a gravitational-wave event. Predictions suggest that the timescales required for some of these events to occur are a sizable fraction of the age of our universe! 

LIGO has, however, shown direct evidence for the merging of solar-mass black holes with atypically large masses between 10–40 M☉, and although easier to observe due to their strong gravitational signal, their existence has continued to challenge theoretical explanations. Scaling up to even greater masses does not reduce the pressure on theory either, as the predicted dominant mass contribution to the yet undetected GWB is from supermassive black holes (SMBHs) on the order of 108–109 M☉.

SDSS J1010+1413

Figure 1: Hubble Space Telescope images of SDSS J1010+1413, showing wide-field galaxy morphology and a zoom-in view of the central SMBH pair with the F621M, F689M, [OIII], and F160W bands. The galaxy shows evidence of a past merger: a disturbed shape and stripped gas streams. The [OIII] extent is seen to be coincident with the continuum F689M light. [Goulding et al. 2019]

It has long been hypothesized that SMBHs inhabit the central-most regions of almost all galaxies, and they accumulate mass through the slow accretion of gas and stellar material. When galaxies undergo the often violent processes of a wholesale merger, these SMBHs are predicted to collect within the central region of a galaxy and become gravitationally bound on short Myr timescales, accelerated by dynamical friction. However, the black-hole pair can only bleed off so much energy through interacting with nearby material, and at some point within the final parsec, the merger is predicted to stall out. This so-called “final parsec problem” has yet to be resolved. For the sample of intermediate-mass-black-hole mergers suggested by the LIGO observations, it appears that nature has ways around this problem. Whether this is true for SMBH mergers has yet to be seen.

Putting the final parsec problem aside, the authors of today’s astrobite provide definitive evidence for a precursor system that may one day produce a low-frequency gravitational-wave event consistent with a strong contribution to the GWB.

As shown in Figure 1, observations with the WFC3 instrument aboard the Hubble Space Telescope revealed a pair of tightly bound SMBH candidates in a highly luminous post-merger galaxy, poetically named “SDSS J1010+1413”. Accounting for the cosmological distance, the separation between the SMBHs is found to be ~430 pc (1,400 light-years). Previous studies examining the velocities and dynamics of the galaxy confirm the outward telltale signs of a trainwreck galaxy and provide the context for the apparently resolved pair of SMBHs in its core. Special imaging with an appropriate [OIII] narrowband filter corroborates this picture by defining the extent of the extremely luminous [OIII] emission — characteristic ionized gas associated with powerfully accreting SMBHs (AKA quasars). However, coincident X-ray observations with the Chandra Space Telescope showed very little X-ray light, a fact that, when compared to infrared estimates, suggests an obscuring cloud of thick gas along the line of sight.

Despite the awesome resolution of the Hubble Space Telescope (~0.04”), such an observation of a supposed SMBH pair may be ambiguous. To increase confidence in this interpretation, the authors modeled each of the sources with a coincident point-like model and an extended Gaussian model. Even so, a single SMBH with extended [OIII] and bisecting obscuration due to a dust lane could mimic this scenario, albeit with a significantly worse fit. Given the former scenario, each SMBH is estimated to have a minimum mass of 4 x 108 M☉ based on the Eddington luminosity limit, putting them in the sweet spot of GWB contribution.

merger stages

Figure 2: Merger stages with timescales shown. From the right, dynamical friction accelerates the first stage of coalescence, followed by stellar hardening. Gas infall may help prevent stall-out and overcome the “last parsec problem”. Then, in the final stages of coalescence at < 1 pc, Pulsar Timing Arrays should be sensitive to the predicted nanohertz gravitational-wave signal prior to the merger. [Goulding et al. 2019]

Lastly, the authors make tentative predictions for a future low-frequency high-mass gravitational-wave event, as shown in Figure 2. By considering carefully motivated arguments for dynamical friction, they surmise a coalescence timescale of 0.1–2 Gyr, where the lower limit is argued from the seemingly large gas reservoir near the SMBHs, which may help dissipate energy and rapidly close their orbital separation. Ignoring the “final parsec problem”, they argue that once the system reaches < 0.1 pc separation, the gravitational wave emission will enable the pair to finally merge within ~700 Myr. Given that the lookback time to this galaxy at z ~ 0.2 is on the order of the predicted merger timescale, this discovery provides strong evidence that such galaxies could be contributing to the GWB right now.

The low-frequency nanohertz GWB signal will not be detectable by current observatories such as LIGO. However, these increasingly powerful facilities will soon be complemented by hyper-sensitive Pulsar Timing Arrays which should be able to detect a nanohertz GWB signal, as may be produced by such a pair of quasars in this and other trainwreck galaxies.

About the author, John Weaver:

I am a PhD student at the Cosmic Dawn Center at the University of Copenhagen, where I study the formation and evolution of galaxies across cosmic time with incredibly deep observations in the optical and infrared. I got my start at a little planetarium, and I’ve been doing lots of public outreach and citizen science ever since.

SOHO image of solar chromosphere

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: Chromospheric Cannonballs on the Sun
Authors: Shuhong Yang, Jun Zhang, et al.
First Author’s Institution: Chinese Academy of Sciences, China
Status: Accepted to ApJL

We’ve all been there. You’re enjoying a nice float in the pool on a hot summer’s day when suddenly you hear that dreaded word… “CANNONBALL!” The next thing you know, your uncle soars overhead and you brace yourself for the ensuing tsunami. But you might not have known that as your uncle was cannonballing, an analogous process may also have been happening on the surface of the Sun. Today’s paper reports the discovery of a phenomenon in the Sun’s atmosphere that the authors name “cannonballs” due to their circular appearance and arc-like trajectory, much like your uncle and his pooltime performance.

Our Dynamic Sun

the Sun

Figure 1: The layers of the Sun. Click to enlarge. [NSF]

Our star is an active place, despite being relatively calm in comparison to many other stars. Similarly to Earth, the Sun has an atmosphere with several layers and complex processes occurring within each one of them (Figure 1). Today’s authors specifically consider the chromosphere, the middle layer of the Sun’s three main atmospheric layers.

Situated between the cooler photosphere below and the scorchingly hot corona above, the chromosphere is responsible for transferring heat between these two layers. As a consequence, the chromosphere’s temperature increases the closer it gets to the corona. The heat transfer occurs through a number of processes, which result in a myriad of dynamical features with exceedingly pleasing names (see Figure 2): 

  • Spicules (Figure 2A): fast-moving, short-lived (~15 minutes) jets of hot plasma that can shoot tens of thousands of kilometers up into the chromosphere before collapsing and disappearing. They are typically associated with areas of high magnetic flux.
  • Surges (Figure 2B): small scale, short-lived (~2–10 minutes), upside-down-Y-shaped jets of plasma created when small magnetic field lines touch and connect. Also called chromospheric anemone jets.
  • Ellerman bombs (Figure 2C): tiny, short-lived (~5 minutes) solar flares that occur near the edges of sunspots where the magnetic field is breaking through the photosphere. Also known as Severny moustaches.

Today’s paper adds another pleasing name to the list of chromospheric phenomena in the form of cannonballs.

spicules, surges, bombs

Figure 2: From left to right: A) spicules, B) surges, and C) an Ellerman bomb. [NASA; Nishizuka et al 2011; David Darling]

How does one go about finding cannonballs in the Sun’s atmosphere in the first place? Yang et al. studied images of the Sun taken by both the New Vacuum Solar Telescope (NVST) in China and NASA’s Solar Dynamics Observatory (SDO). Specifically, they looked at sequences of images that used an H-alpha filter, a deep-red filter that measures the light emitted when the electrons in hydrogen atoms fall from the third-lowest to the second-lowest energy level. In one sequence spanning about ten minutes, the authors noticed a round, dark structure moving along a curved trajectory: cannonball! (Figure 3). They further identified similarly moving structures in two other image sequences that appeared bright as opposed to dark.

cannonball trajectory

Figure 3: Image sequence using NVST that shows the movement of a cannonball on the Sun on 28 October 2017. [Yang et al. 2019]

From the images, the authors calculated a variety of properties of the cannonball structures. They found that the three cannonballs traveled at an average speed of 55.9 km/s, which is roughly five times Earth’s escape velocity, or almost two times its orbital speed around the Sun. They also found that the structures encompassed an average volume of 1.53 billion cubic kilometers, roughly the volume of all of Earth’s oceans or 600 trillion swimming pools. Assuming the density of the cannonball is the same as the chromosphere, Yang et al. then calculated an average cannonball mass of almost 170,000 US tons—about 25,000 elephants, or roughly 1.3 million uncles.

So what are these structures? Cannonballs on the Sun are a bit more complicated than your uncle jumping into the pool or an actual ball shot from a cannon. To gather more information, Yang et al. used observations from SDO at ultraviolet (UV) and extreme ultraviolet (EUV) wavelengths, together with measurements of the solar magnetic field, that were simultaneous with the H-alpha images from NVST. The extra measurements revealed evolution in the magnetic field, as well as heightened emission in both the UV and EUV measurements near the locations of the cannonballs. This suggested that the solar magnetic field was intimately involved in the creation of these structures.

The authors propose that a solar cannonball forms when magnetic reconnection occurs within the chromosphere (Figure 4). In this process, small-scale magnetic field loops emerge and detach from stronger large-scale loops. The small loops rise up toward the large loops and reconnect, flinging chromospheric plasma along the large loops. Magnetic reconnection results in the conversion of magnetic energy into kinetic, potential, and thermal energy; this explains why the cannonballs move so quickly, and it also provides an efficient heating mechanism for the chromosphere.

cannonball formation schematic

Figure 4: Schematic of cannonball formation. A small-scale magnetic loop emerges and recombines with a large-scale loop, converting magnetic energy into kinetic, potential, and thermal energy and flinging a cannonball away in the process. [Yang et al. 2019]

As with any new discovery, more questions are raised than are answered in today’s paper. Why are the cannonballs shaped like blobs rather than jets? Why are some cannonballs dark while others are bright? Future observations will answer these questions and almost certainly raise several more. In the meantime, we can enjoy the Sun’s glow from our pool here on Earth, hopefully cannonball-free. ☀️

About the author, Stephanie Hamilton:

Stephanie is a physics graduate student and NSF graduate fellow at the University of Michigan. For her research, she studies the orbits of the small bodies beyond Neptune in order learn more about our solar system’s formation and evolution. As an additional perk, she gets to discover many more of these small bodies using a fancy new camera developed by the Dark Energy Survey Collaboration. When she gets a spare minute in the midst of hectic grad school life, she likes to read sci-fi books, binge TV shows, write about her travels or new science results, or force her cat to cuddle with her.

gas-giant formation

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: The hot Jupiter period-mass distribution as a signature of in situ formation
Authors: Elizabeth Bailey, Konstantin Batygin
First Author’s Institution: California Institute of Technology
Status: Published in ApJL

To fully understand how and where planets can form, astronomers must look to the extremes. One of the most exotic discoveries in exoplanet research has been of a class of planets known as hot Jupiters. These are gaseous worlds, hundreds of times the mass of the Earth, that orbit their host stars in mere days. Given the major role that Jupiter had in shaping our solar system, it is crucial to understand how gas-giant planets form in a variety of environments.

How to Build a Jupiter

The formation of a Jupiter-sized world is thought to be a two-step process. First, material in the protoplanetary disk conglomerates to form a solid core. If this core grows larger than about 10x the mass of the Earth, its gravitational pull becomes strong enough for the planet to accumulate a gaseous envelope. As this envelope grows, the gravitational pull gets stronger, allowing the planet to attain a huge mass fairly quickly. Eventually, the gaseous envelope becomes too hot for material to continue to condense and the growth is throttled.

For intermediate-sized worlds, radiation from the star can blast away the atmosphere if the planet is too close. This results in a dearth of close-in planets around 1/10 the mass of Jupiter. For larger worlds, however, this evaporation is ineffective. Even very highly irradiated Jupiter-sized planets only ever lose about 1% of their mass. There appears to be a very sharp cutoff, below which hot Jupiters that are too small and close to their host stars simply don’t exist. The authors of today’s paper explain this cutoff with a wonderfully simple and succinct model and use it to argue that most hot Jupiters formed at their current location, rather than having been built further out and subsequently migrating inwards.

stellar magnetic field

Figure 1: A diagram showing the structure of a star’s magnetic field (thin black lines) alongside a protoplanetary disk (thick black lines). Close to the star, the magnetic field is strong enough to disrupt the protoplanetary disk, preventing planet formation within a distance known as the “magnetic truncation radius”. [Camenzind 1990]

It turns out that there is a limit on how close to a star planets can form. Young stars have strong magnetic fields that interact with the surrounding protoplanetary disk. As the disk loses angular momentum due to its inherent viscosity, material continually falls inward onto the star. Close to the star, the magnetic field can be strong enough to force material up out of the disk and along the field lines. The distance at which this occurs is known as the magnetic truncation radius (shown in Figure 1). Interior to the truncation radius, the protoplanetary disk becomes too disrupted for planet formation to occur. If the protoplanetary disk material is vigorously falling towards the star, the disk can work its way far inward before being torn apart by the magnetic forces.

An Inner Limit for Gas Accretion

Next, the authors use this battle between the disruptive magnetic field of the star and the inwardly streaming protoplanetary disk material to explain the observed lack of close-in, less massive hot Jupiters. They make the assumption that the final mass of a hot Jupiter is set by how quickly the protoplanetary disk material is streaming inwards, or accreting. Because this also implies that the magnetic truncation radius is smaller, one should expect larger hot Jupiters to lie slightly closer to the star. This is all, of course, assuming that these worlds formed in place, rather than being constructed further from the star and then migrating inwards.

exoplanet orbital distance v. mass

Figure 2: Orbital distance vs mass for all known exoplanets. Planets fall into three distinct groups: hot Jupiters (top left), cold Jupiters (top right) and sub-Jovian worlds (bottom center). For the hot Jupiter population, there is an absence of planets below and to the left of the solid black line, which the authors argue is set by the magnetic truncation radius. [Bailey & Batygin 2018]

Figure 2 shows the distribution of known exoplanets as a function of semi-major axis (distance from the host star) and mass. The hot Jupiters are the cluster of points towards the top left of the diagram. The straight black line shows the predicted cutoff due to the magnetic truncation radius. The vast majority of hot Jupiters lie above and to the right of this line. The authors argue that the sharp cutoff is evidence that worlds are being constructed in place right up to the magnetic truncation boundary. Had these bodies formed elsewhere in the disk and moved around, the distribution would not follow this cutoff so closely.

What About Tides?

Above about 1 Jupiter mass, there are a handful of planets that do not seem to follow the cutoff denoted by the solid line. The authors explain this discrepancy as a result of tidal evolution. If a planet is massive enough and close enough to the star, its gravitational pull will distort the star slightly, similar to the way that the Moon invokes tides on the Earth. Strong tidal interactions between a star and a nearby planet can actually remove a significant amount of orbital energy. The result of this is that the planet’s orbit will shrink, possibly below the cutoff described in the previous paragraph. This should result in planets being found right up to the curved black line shown in Figure 2, below which there are indeed no observed hot Jupiters.

All of the features described in Figure 2 are consistent with the idea that the final mass and position of most hot Jupiters are set by the availability of planet-forming material at the inner edge of the disk. This is a strong indication that the gaseous envelopes of these worlds, which make up most of their mass, were constructed at or near their present locations. With that being said, it is not clear where and how the cores that seeded the gas accretion formed.

Finally, it is worth noting that there exists a small but significant population of hot Jupiters that have highly eccentric orbits. These worlds most certainly formed further out and lost orbital angular momentum to a companion planet and do not fit into the framework described here. The fact that the majority of known hot Jupiters lie above the cutoff described by the model in this paper suggests that most hot Jupiters do not undergo orbital migration. This is an important clue on the path to understanding why many exoplanetary systems appear so vastly different than our own solar system.

About the author, Spencer Wallace:

I’m a member of the UW Astronomy N-body shop working with Thomas Quinn to study simulations of planet formation. In particular, I’m interested in how this process plays out around M stars, which put out huge amounts of radiation during the pre main-sequence phase and are known to host extremely short-period planets. When I’m not thinking about planet formation, I’m an avid hiker/backpacker and play bass for the band Night Lunch.

Hubble extreme deep field

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
Authors: Ryan Hausen & Brant Robertson
First Author’s Institution: UC Santa Cruz
Status: Submitted to ApJS

Dreaming of a Better Way to Classify Galaxies

In Greek mythology, Morpheus is the God of Dreams, who shaped and formed the dreams of mortals. It is fitting, then, that Morpheus is now dabbling in classifying galaxies based on their shape, to help us mortals with our astronomy. Born of Tensorflow and Python 3, the 21st-century Morpheus is a new neural network dreamed up by the authors of today’s paper to perform galaxy classification.

Stephan's Quintet

Hubble’s view of Stephan’s Quintet, a group of five galaxies with a variety of morphologies. [NASA/ESA/Hubble SM4 ERO Team]

The shape, or morphology, of galaxies is critical to understanding their formation and evolution. As it is such an important characteristic, astronomers must have found a robust algorithm or quantitative model that determines morphology, right? Not quite — it turns out that the most accurate way to classify galaxies morphologically is to round up a pack of trained astronomers and have them look through pictures of galaxies by eye.

Unfortunately, galaxies far outnumber astronomers. The most well-known method that addresses this challenge is Galaxy Zoo, which enlists interested internet users to classify galaxies. While very successful, this approach is still limited by accuracy and scalability. To address these issues, researchers have begun to use machine-learning techniques to push morphological classification forward.

Today’s paper introduces Morpheus, a new deep-learning network to classify astronomical images. The network determines the morphological type of each pixel in an astronomical image, an approach that increases its capabilities beyond existing methods.

How to Train Your Neural Net

Neural networks like Morpheus work by learning how inputs, often images, are associated with their desired outputs, often called labels. For example, you could train a network by feeding it images of cats and dogs, labeled with the appropriate word “cat” or “dog.” Then, when you input new images of furry friends it hasn’t seen before, it should be able to assign each the appropriate label. Check out this astrobite for a great explanation.

In the case of Morpheus, the inputs are images of galaxies through multiple color filters. (This is already an improvement over previous methods, which use composite images.) The labels are the morphologies of the galaxies: disk, spheroid, and irregular, as well as point source/compact to account for unresolved sources.

The authors trained Morpheus on images of 7,629 galaxies in the CANDELS survey, in the GOODS South region. To label these training images, we still need that pack of trained astronomers: multiple experts voted on the classification of each galaxy. Morpheus goes beyond previous works by using not just the winning classification, but all of the expert votes as labels. This allows the network to learn the uncertainties in morphology, for example knowing when a certain source looks similar to both disks and spheroids. Further, it learns which pixels in the images are most relevant to the experts’ votes.

Morpheus then outputs a “classification image,” which labels each pixel with the probability that it corresponds to each classification. This allows for not only the classification of objects, but also spatially resolved morphological information and source detection.

example field classified by Morpheus

Figure 1: An example field classified by Morpheus. Left panel: A composite image of the input data. Middle panels: The dominant classification of each pixel. Right panel: The output Morpheus “classification image” color-coded by dominant morphology. The brightness of the color indicates the dominance of the most dominant morphology of each pixel, with white meaning indeterminate classification. [Adapted from Hausen & Robertson 2019]

Figure 1 shows a field region classified by Morpheus. The left panel shows a composite image of the input data, with many galaxies and other objects visible. The four panels to the right show the dominant label of each pixel for the types: spheroid (red), disk (blue), irregular (green), and point source/compact (yellow). The Morpheus classification image on the right again shows the dominant morphology of each pixel, now with the brightness corresponding to the difference between the dominant class and the second-most dominant class, so that white pixels mean similar results for multiple classes. The brightest objects in the image are well-classified into their visually apparent galaxy morphologies, while the fainter objects are mostly classified as point sources.

pixel-level classification of the GOODS South region.

Figure 2: Morpheus’s pixel-level classification of the GOODS South region. The colors correspond to the dominant classification of each pixel, with white meaning comparable classifications for the pixel. [Hausen & Robertson 2019]

Morpheus classifies the entire GOODS South field in this way. Figure 2 shows the result, with the colors again corresponding to the dominant type, with more certain classifications in brighter colors. To see Morpheus at work, check out the mesmerizing video below.

Evaluating Galaxy Classification: Morpheus vs. Astronomers

If Morpheus is classifying pixels and the astronomers classified objects, how can we compare the two to measure Morpheus’s performance? The authors do this by computing the brightness-weighted average of the pixels in the object and selecting the dominant classification. But we still expect some uncertainty in the classification, because for many sources even the “truth” (astronomer-determined labels) was unclear. As Morpheus was trained not just on the majority-voted classification but on all of the votes, Morpheus’s assignments should match the distribution of astronomer votes. This can be evaluated by looking at the confusion matrix, shown in Figure 3.

confusion matrices

Figure 3: Confusion matrices that show the distribution of morphology classifications. The left matrix shows the degeneracies in visual assignment by astronomers, and the right matrix shows Morpheus’s replication of those degeneracies in its assignments. [Hausen & Robertson 2019]

The matrix on the left shows the natural degeneracies in astronomer-classified objects, meaning how often astronomers confused two types of galaxies for each other. For example, for objects that the majority (80%) of astronomers agreed were disks (the “K15 Dominant Classification” axis), the remaining astronomers classified as spheroids 9% of the time and irregulars 11% of the time (the “Classification Distribution” axis). The matrix on the right shows the Morpheus vs. astronomer degeneracies. Continuing the above example, for objects that the majority of astronomers labeled as disks, Morpheus agreed for 76% of the objects but thought that 8% were spheroids and 16% were irregulars, close to the astronomer distribution. The two matrices clearly agree quite well overall, showing that Morpheus succeeds at reproducing the intrinsic uncertainty (represented by astronomer disagreement) in the object classifications.

The authors use many other metrics to evaluate how Morpheus performs, including inserting simulated sources to test for false negatives and completeness. These couldn’t all fit in an astrobite, so check out the paper to learn more!

The authors anticipate that Morpheus will be useful for upcoming large-scale imaging surveys, and can also be expanded to learn other information like galaxy redshift. Keep an eye open for what the Morpheus team will dream up next.

About the author, Kate Storey-Fisher:

Kate is a PhD student in the Center for Cosmology and Particle Physics at New York University. She studies the large-scale structure of the universe using cosmological simulations and galaxy surveys. She is still waiting for the galaxies to respond to the SurveyMonkey she beamed to them.

LMC

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original will be viewable at astrobites.org once the site has been fully restored.

Title: Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics Beyond ΛCDM
Authors: Adam G. Riess, Stefano Casertano, Wenlong Yuan, Lucas M. Macri, Dan Scolnic
First Author’s Institution: Space Telescope Science Institute and Johns Hopkins University
Status: Published in ApJ

Hubble’s law tells us that all galaxies, stars and planets are moving away from each other, and the more distant the object, the faster it is moving away. We quantify this expansion as a speed per distance, which gives us a unit like km/s (speed) per megaparsec (distance). This value is known as the Hubble constant, or H0.

The Hubble constant has been determined using various methods. However, two of these titan measurements disagree with each other in a way that astronomers deem significant.

The first of the measurements comes from studying the oldest electromagnetic radiation in the universe — the cosmic microwave background (CMB). See this Astrobite for a detailed explanation of how we are able to do this. The most recent results from the CMB give us a Hubble constant of roughly 67 km/s/Mpc.

The second measurement comes from using Type Ia supernovae as standard candles to calibrate distances to them (see this Astrobite for more). Essentially, by looking at these stars at various distances, we can correlate their distance with their apparent brightness. By assuming supernovae are dimmer proportional to their distance from us, we can measure the gradient of this correlation. Recent results put H0 at 73 km/s/Mpc.

So, one of the most prominent problems in cosmology boils down to a 6 km/s/Mpc difference. Certainly, each of these measurements have their own subtleties but there are two main things to note:

  • The Hubble-constant measurements using the CMB and Type Ia supernovae are independent. They do not rely on the same measurement technique, and therefore do not have any source of error in common. This makes it harder to dismiss the tension as something which comes from a shared, inaccurate measurement.
  • The Hubble-constant measurement from the CMB uses data from the early universe, while the value obtained from supernovae is a late-time or local measurement. This could potentially be an interesting explanation for the tension.

A New Addition

Today’s authors stir the Hubble cauldron a bit more with 70 space-based observations of Cepheid variables in the Large Magellanic Cloud (LMC) from the Hubble Space Telescope.

A Cepheid variable is a type of star that pulsates over some period of time. Astronomer Henrietta Swan Leavitt deduced that the rate of pulsation for these stars is correlated strongly with their luminosity (see this Astrobite for more on her work and legacy). Therefore, one can know the brightness of these stars simply by observing their pulsation rate (Figure 1). Consequently, one can determine the distance to these stars just by comparing their known luminosity to the apparent brightness. Much like supernovae, this makes Cepheid variables powerful probes of the local Hubble constant. Furthermore, by studying galaxies containing both Cepheid variables and type Ia supernovae, the Cepheid-derived distances can be used to calibrate the accuracy of supernovae-derived distances, creating a robust distance ladder, which gets us to H0.

Period-luminosity relation

Figure 1: Period-luminosity relation for the 70 Cepheid variable stars. The colours in the figure indicate the different wavelengths used for observing these Cepheids. The agreement in the slope tells us the P–L relation is not dependent on any particular wavelength. [Riess et al. 2019]

To ensure an accurate Hubble-constant measurement with Cepheid variables, various sources of uncertainty are considered by the authors. Among these are the differences in the telescope sensitivity to fainter, distant Cepheids compared to nearer ones, which can affect the measured brightness. Another source of error is the inclination of the LMC itself, which results in some Cepheids appearing closer or farther than average by a very small degree. After taking all sources into account, the total uncertainty in the distance measurement, and hence the Hubble constant, is 1.28%, which is the smallest error for any Cepheid-variable Hubble-constant measurement to date.

So What’s the Tension Now?

Combining the LMC distances with two other distance calibrators for better constraints, the authors quote a Hubble constant of 74.03 km/s/Mpc, which is in a staggering 4.4-σ tension with the CMB Hubble-constant measurement. This effectively means that the probability that the new measurement is genuine rather than a statistical fluke is above 99.999%, and therefore so is the discrepancy.

Hubble constant

Figure 2: Various measurements of the Hubble constant colour-coded by whether they use data from the early universe (blue) or the late universe (red). At the top are potential modifications to our current cosmological model which could resolve the current tension. [Riess et al. 2019]

Much has been said on the nature of the Hubble disagreement already, both on its nature and from pacifists looking to ease the tension (see examples here and here). More recently, gravitational waves have burst onto the scene with another independent measurement (though it is not statistically significant enough to fuel the flames just yet). New physics could hold the key to breaking this Hubble stalemate. For example, our universe could have a non-zero curvature, a time-dependent dark energy, or interacting dark matter. Today’s paper shows that the tension is as strong as ever, so we wait for more precise, independent measurements to help clarify the nature of our expanding universe.

About the author, Sunayana Bhargava:

I’m a third year PhD student in the Astronomy Centre at the University of Sussex. I study galaxy clusters with X-ray and optical data to learn about cosmology and the properties of dark matter.

water world

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Additional note: We are aware that astrobites.org is currently down. The AAS IT staff is working to get the site back online as quickly as possible.

Title: Detecting Ocean Glint on Exoplanets Using Multiphase Mapping
Authors: Jacob Lustig-Yaeger, Victoria Meadows, Guadalupe Tovar Mendoza, et al.
First Author’s Institution: University of Washington
Status: Published in AJ

In the coming decades, there are plentiful opportunities and ideas for space-based missions that may be able to detect life on other planets — the James Webb Space Telescope (JWST), LUVOIR, the Origins Space Telescope, HabEx, and more. But, what would those signs of life look like, and what do we need to actually detect these biosignatures with confidence? These are two of the key questions astronomers face as they prepare to choose the next big space telescopes.

Given that we only have one example of life in the universe (as of today), an exoplanet must mirror the thermal and chemical properties of Earth to be deemed habitable. One of the main ways to judge if a planet is habitable by this definition is to look at its atmosphere, finding out more about its temperature and what it’s made of. We can glean lots of information about a planet’s atmosphere through spectroscopy, such as what molecules may be present, if there are clouds or hazes, what its temperature may be, and more. In particular, modern surveys are concerned with finding water, oxygen, and other compounds that signal habitability in the atmospheres of these exoplanets. However, transmission spectroscopy (what JWST will be capable of) only allows us to see the very upper layers of an atmosphere. This isn’t very interesting for finding water, considering that on Earth, all our water vapor is concentrated in the very bottom layers of our atmosphere. Today’s paper focuses on a different avenue for finding water on exoplanets: oceans.

You may ask — why focus on finding oceans? Water is one of the key necessities for life as we know it, and a large body of water like an ocean may be one of the most unambiguous indicators of exoplanet habitability. Research groups like the Virtual Planetary Laboratory are exploring not only atmospheric biosignatures, but also other signals, such as in today’s paper where they investigate the detectability of “ocean glint”.

What Would an Exoplanet Ocean Look Like?

As an exoplanet rotates around its axis, we’re seeing different portions of the surface — sometimes, more of the disk of the planet is covered by land or ocean, and this changes its overall spectrum and albedo, as seen in Figure 1.

Earth rotational variability

Figure 1: An illustration of how Earth’s spectrum varies as different portions of the surface (e.g. different fractions of land/ocean) are in view. The spectrum is shown on the left, and colored points on the right correspond to the marked variations in the spectrum. [J. Lustig-Yaeger]

Additionally, as an exoplanet rotates around its star, we’re seeing the parts of the surface illuminated by starlight at different angles, just as we see different phases of the Moon as it rotates around us on Earth. Although we can’t resolve the surfaces of exoplanets, we can still get a sense of how reflective each slice of the surface is as we view different portions. By analyzing the light curves of simulated planets, the authors retrieve maps of surface albedo (e.g. reflectivity) in a technique called “multiphase mapping”; given that water is more reflective than land — think of the bright reflection off the ocean on a sunny beach day — these maps could help reveal where large oceans are present, as seen in Figure 2.

surface albedo maps

Figure 2: Maps of surface albedo from simulated light curves of an Earth-like exoplanet, with continents and oceans, for different viewing angles. Viewing angle corresponds to what phase the planet is in from our line of sight — 90 degrees is at “quadrature” where half the planet is illuminated, and 135 degrees is a “crescent” face where we only see a small sliver of illumination. Surface 1 shows darker blue for where the albedo indicates a higher fraction covered by ocean, and Surface 2 shows darker orange for where there is a higher fraction covered by land. [Lustig-Yaeger et al. 2018]

Oceans, when viewed at very indirect angles, reflect light differently in a phenomenon known as “glint”. As observed by the Galileo satellite as it passed Earth for a gravitational assist, Earth has this “glint” — that is, it appears brighter in crescent phases due to reflection off the oceans. This same signature could be observed in exoplanets. Interestingly, too, this phenomenon isn’t unique to water oceans — the same glint could be observed for an ocean made of hydrocarbons, such as the liquids present on Titan!

What Telescopes Could Find These Signals?

To determine what kind of telescope would be needed to detect these signatures, the authors used an atmospheric model based on observations of the Earth by NASA’s EPOXI mission to imagine “Earth as an exoplanet”, as observed from 5 parsecs away by a telescope similar to upcoming space-based direct imaging missions. These simulations can show what yield of exoplanet ocean detections can be expected from a given mission as a function of telescope size and other parameters (e.g. aperture size, coronagraph inner working angle). Generally, a larger aperture size is better for detecting these tiny planets. It is important to remember that observing exoplanet atmospheres and oceans is no easy task, given how small and faint habitable planets are, especially compared to their bright host stars. Although detecting these oceans might still be too difficult of a task for JWST, the authors find that the next generation of 6 to 15 meter space-based telescopes (e.g. LUVOIR) should be able to make these kinds of detections. The exact number of detections does depend on how common these habitable planets are in the first place (e.g. their “occurrence rates”); given that our current estimates of occurrence rates are based on limited samples, the authors assume that 20% of stars will have habitable, Earth-like planets. Under this assumption, the authors predict that future large space telescopes will be able to detect ocean glint on ~1 to 10 habitable zone exoplanets around nearby G, K, and M stars.

Detecting signs of oceans, habitability, or life is going to be a big technical challenge in the coming decades, but it is an exciting opportunity to answer some of the most looming questions in astronomy: are there other Earth-like planets? Are we alone? The combined power of multiphase mapping and ocean glint detection, as outlined in this work, will be a useful tool in our kit for determining habitability with confidence and moving us closer to answering these fundamental questions.

About the author, Briley Lewis:

Briley Lewis is a first-year graduate student and NSF Fellow at the University of California, Los Angeles studying Astronomy & Astrophysics. Her research interests are primarily in planetary systems — both exoplanets and objects in our own solar system, how they form, and how we can create instruments to learn more about them. She has previously pursued her research at the American Museum of Natural History in NYC, and also at Space Telescope Science Institute in Baltimore, MD. Outside of research, she is passionate about teaching and public outreach, and spends her free time bringing together her love of science with her loves of crafting and writing.

TESS

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: TESS Spots a Compact System of Super-Earths Around the Naked-Eye Star HR 858
Authors: Andrew Vanderburg, Chelsea X. Huang, Joseph E. Rodriguez, Juliette C. Becker, George R. Ricker, et al.
First Author’s Institution: The University of Texas at Austin
Status: Submitted to ApJL

The Transiting Exoplanet Survey Satellite (TESS) has been operating for over a year now. It is nearly halfway through its survey of the sky, currently observing Sector 11 of 26 (see Figure 1). TESS has already revealed new planets (including an Earth-sized one) and even caught some supernovae as they were getting brighter.

TESS sectors

Figure 1: A map of the sectors observed by TESS in the first year of observations, in celestial coordinates. The thick dark line is the galactic plane; the thin dark line is the ecliptic (the apparent path traced out by the Sun over a year). The different colored squares denote which of TESS’s four cameras is used to observe that part of the sky. [TESS]

The paper discussed in this Astrobite announces another new and exciting TESS detection — not one, not two, but three super-Earths orbiting a bright, nearby star. The host, HR 858, is located in the constellation of Fornax the Furnace and, as a sixth magnitude star, it is just at the edge of what can be seen with the naked eye.

Certainly Not Light on Planets

HR 858

Figure 2: HR 858 as observed by the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) (left) and TESS (right). The purple and red lines demarcate the area used to measure the brightness of the star in Sectors 3 and 4 respectively. The blue line shows the extent of one arcsecond in the observations. [Vanderburg et al. 2019]

HR 858 was observed in Sectors 3 and 4. In Sector 3, it was imaged once every 30 minutes as part of the full-frame images (the entire area one of TESS’s cameras can see) since it was near the edge of the sector. In Sector 4, HR 858 was imaged every 2 minutes, typical for bright nearby stars in TESS’s field of view (see Figure 2).

After correcting for errors (including the accidental activation of an onboard heater), the authors obtained a light curve for HR 858. If HR 858 were hosting any planets and any of those planets passed in front of it while TESS was observing it, the light curve would contain dips that corresponded to the planet transits. Two possible planet signals emerged early in the analysis, with periods of 3.59 and 5.97 days. When the light curves of Sectors 3 and 4 were combined, another candidate popped out with a period of 11.23 days (see Figure 3).

HR 858 light curve

Figure 3: The combined light curve of HR 858 from Sector 3 and 4. The x-axis shows the Barycentric Julian Date minus 2457000 days, and the y-axis shows relative brightness. The gray points show the observations used to construct the light curve. The dips in the purple line are the planet transits. For visual purposes, the 2-minute observations in Sector 4 were binned to match the 30-minute observations in Sector 3. [Vanderburg et al. 2019]

The authors ruled out false positives with archival data and follow-up observations. They found that any nearby stars were too faint to significantly impact the brightness of HR 858. Spectroscopic observations proved that HR 858 was not part of a binary star system, cementing the planet candidates as actual planets. However, the authors did notice a faint stellar companion to HR 858, HR 858 B, that moves at roughly the same speed.

A Bright Future

The planets — HR 858 b, c, and d — all have fairly short orbital periods and so are very close to their host star. Fitting their transits showed that all three were super-Earths, about twice as large as the Earth. Compact systems of rocky planets are not unheard of, but what sets this system apart is that HR 858 b and HR 858 c may be in mean motion resonance (MMR). This means that the orbital periods of the two planets are in an integer ratio with each other (specifically 3:5 for HR 858 b and c). Compact multi-planet systems in MMR are few and far between, and since MMR may play a role in planetary formation, this prospect in HR 858 is worth investigating.

There is also the possibility that the orbital plane of the planets is misaligned relative to HR 858’s own axis of rotation. The authors speculate that HR 858 B may be responsible, having interacted with the disk that formed HR 858’s planets. Long term follow-up observations should be able to verify this, as well as the likelihood of MMR between HR 858 b and c.

HR 858 is the brightest multi-planet host we have detected so far (see Figure 4). This makes it rich ground for several follow-up studies; it can definitely aid us in better understanding the interactions between stars and their planets.

Known systems with at least three transiting planets

Figure 4. Known systems with at least three transiting planets. The x-axis shows planet orbital period and the y-axis shows the apparent Gaia magnitude of the host star. The circles indicate the planets in each system and their relative sizes. The color of the circles in a system indicates the temperature of their host star. HR 858 is the brightest star in this plot. Any planets in MMR are highlighted with purple outlines. [Vanderburg et al. 2019]

About the author, Tarini Konchady:

I’m a second year graduate student at Texas A&M University. Currently I’m looking for Mira variables to better calibrate the distance ladder. I’m also looking for somewhere to hide my excess yarn (I’m told I may have a problem).

planet formation

Editor’s note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: The Boundary Between Gas-rich and Gas-poor Planets
Author: Eve J. Lee
First Author’s Institution: California Institute of Technology
Status: Accepted to ApJ

Astronomers often compare exoplanets to the planets in our own Solar System — Jupiters, Neptunes, super-Earths, etc. — because they are familiar. But the distinction can be made even simpler: planets that are gas-rich, and those that are not. Where does the boundary between the two fall, and how does it arise? Today’s paper addresses that very question.

An Excess of Sub-Saturn Planets

Figure 1. In the core accretion model of planetary formation, rocky cores form within the gas disk around the star, accrete gas as they cool, and, if they formed massive and early enough, experience runaway accretion to become gas giants. [jupiter.plymouth.edu]

The most successful theory of planet formation to date is that of core accretion (Figure 1). In this theory, planets first form as rocky cores embedded within the star’s gas disk. As the core cools, the decreased thermal pressure allows more and more gas to accrete onto the core. The outward thermal pressure of the atmosphere supports additional accreted gas in hydrostatic equilibrium until the mass of the gas envelope approaches the core mass. After this critical point, the system experiences runaway accretion and the planet becomes a gas-rich giant planet. Critically, runaway accretion occurs only if the core and atmosphere become massive enough before the end of the typical 10-million-year lifespan of the gas disk. More massive cores will accrete gas faster and therefore be more likely to trigger runaway accretion before the dissipation of the gas disk.

The core-accretion story of planet formation results in a binary picture of planets: those with large gaseous envelopes relative to their cores, and those with small envelopes. But what about the planets in the middle? The core-accretion model suggests that we should expect to find a lot of Jupiters (planets sized 8–24 R⨁, where R⨁ is Earth’s radius) and a lot of Neptunes or rocky planets (<1–4 R⨁), but not much in between. Defying theory, such in-between “sub-Saturns,” which are on the verge of runaway accretion with gas-to-core mass ratios (GCRs) of ~0.1–1.0, are observed at the same rate as gas giants!

Gassy … or Not?

The fact that sub-Saturns are observed as often as gas giants suggests that the story is a bit more complicated. The cooling of the core is not the only process that must be considered when simulating the formation of planets in a gas disk. Complex interactions between the gas in the planet’s atmosphere and the gas remaining in the disk can play a large role in a planet’s ultimate fate.

To quantify the effects of these additional processes, Lee ran a series of planetary formation simulations. She first determined the best-fit core mass distribution through comparison with observations. Notably, this paper is the first time a single core mass distribution reproduced both the observed plethora of sub-Neptunes and the similar numbers of gas giants and sub-Saturns (see Equation 5 in the paper). Considering planets with orbital periods between 10–300 days, Lee generated a range of planetary cores with masses from 0.1–100 M⨁ (where M⨁ is Earth’s mass) from the best-fit core mass model. These cores were placed in a gas disk at uniform times between 0 to 12 million years and evolved until the end of the 12 million years. The bottom line is perhaps unsurprising: the planet’s fate depended both on the initial core mass and when during the disk’s lifetime the planet formed.

More interestingly, by taking into account processes beyond cooling, Lee’s simulations resolved the discrepancy between the expected and observed number of sub-Saturns. The simulations also revealed four distinct core mass ranges that ultimately result in different planet types (see Figure 2):

  1. Core masses <0.4 M⨁ can only accrete a small amount of gas through cooling and remain sub-Neptunes and super-Earths.
  2. Core masses between 0.4–10 M⨁ accrete gas through cooling until the gas disk dissipates, while interactions between the atmosphere and gas disk decrease the amount of gas that falls onto the core. These planets do not reach runaway accretion and so remain sub-Saturns.
  3. Core masses between 10–40 M⨁ experience runaway accretion but growth is ultimately stymied by fluid interactions between the planet’s atmosphere and the gas disk. These planets become Jupiters.
  4. Core masses >40 M⨁ accrete gas so quickly that they carve deep gaps in the disk and ultimately deprive themselves of further accretion. These planets are massive Jupiters.

Figure 2. The resulting GCR given an initial core mass and time available for accretion. Each point is one planet formation simulation, and darker colors indicate that the core formed later in the disk’s lifetime. The regions A,B,C,D are described in the text. [Lee et al. 2019]

Figure 2 shows the wide variety of planets that can be formed given an initial core mass and time available for gas accretion. In particular, more massive cores can span the full GCR range depending on when they formed, becoming gas-rich or gas-poor planets. Conversely, low-mass cores will only ever become gas-poor planets. This provides a potential explanation for why metal-rich solar systems with more massive elements appear to host a wider variety of planets.

The Gassy Conclusion

Today’s paper is the first study that is consistent with observations across all core mass ranges. Furthermore, Lee shows the importance of including the fluid interactions between the planet’s atmosphere and the gas disk, resolving the discrepancy between the expected and observed number of sub-Saturns. As both observational and computational techniques improve, we will move closer to a comprehensive and complete description of planet formation.

About the author, Stephanie Hamilton:

Stephanie is a physics graduate student and NSF graduate fellow at the University of Michigan. For her research, she studies the orbits of the small bodies beyond Neptune in order learn more about our solar system’s formation and evolution. As an additional perk, she gets to discover many more of these small bodies using a fancy new camera developed by the Dark Energy Survey Collaboration. When she gets a spare minute in the midst of hectic grad school life, she likes to read sci-fi books, binge TV shows, write about her travels or new science results, or force her cat to cuddle with her.

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