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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.

circumgalactic region

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: On the detectability of visible-wavelength line emission from the local circumgalactic and intergalactic medium
Author: Deborah Lokhorst, Roberto Abraham, Pieter van Dokkum, Nastasha Wijers, Joop Schaye
First Author’s Institution: Lockheed University of Toronto, Canada
Status: Published in ApJ

Generally when we look at a picture of a galaxy, we see the bright central region occupied by many stars and brightly glowing gas. This can include shining arms in spiral galaxies or the clumpy splotches of light scattered around irregular galaxies. However, there is much more to a galaxy than what is brightly glowing: every galaxy is surrounded by a thin, cool, and difficult to observe cloud of gas called the circumgalactic medium (CGM). Even farther away there is a yet thinner distribution of gas, called the intergalactic medium (IGM). These structures have such low densities that the gas doesn’t emit enough light to be visible to normal telescopes. Consequently, astronomers only have a vague picture of the geometry, composition, and conditions of these components of the universe. It’s important to understand the CGM and IGM though, because they contain the majority of the baryonic matter (i.e. normal, non-dark matter) in the universe and are crucial for regulating the flow of gas onto galaxies, which allows for things like the formation of stars. Further, the CGM is currently observed mostly with absorption-line studies, which are restricted to directions in the sky where a background light source — such as a quasar — is available. This means that observations are often quite limited in number, making it difficult to get a comprehensive view of what is happening.

In today’s paper, the authors wrangled two tempestuous creatures: the EAGLE cosmological simulation and the Dragonfly Telephoto Array. EAGLE is a numerical code that creates a simulated chunk of the universe, and Dragonfly is a 48-lensed instrument specially designed to observe emission from very dim objects. The idea is that the authors can simulate CGM and IGM around galaxies using EAGLE and predict what their emission should look like. Then, knowing the parameters of the Dragonfly array, they can determine whether this emission should be observable by such an instrument. The authors use the capabilities of a (now in-progress) upgrade to Dragonfly, which will add the capacity to use narrow-band filters.

Modeling Emission in EAGLE

The authors are interested in one emission line in particular, the Hα feature. Hα emission is formed by a process called recombination that occurs in the bubbles of ionized hydrogen called H II (pronounced “H 2”) regions that surround young, massive stars. This process occurs so commonly in the gas around young stars that it can be very bright, making it one of the most likely emission lines to be observed.

Figure 1: Maps of simulated surface brightness emission in Hα from a small subset of the EAGLE simulation volume. Yellow indicates a higher surface brightness (SB in the label). [Lokhorst et al. 2019]

Information about the state of the gas particles in the simulation, such as their density, temperature, and metallicity, are used to model the gas emission and calculate the surface brightness, or the amount of light per square arcsecond coming from the gas (today’s paper uses units of photons cm-2 s-1 sr-1). The only remaining question: Is the signal bright enough to be seen with the upcoming iteration of Dragonfly?

Does Dragonfly Need Better Eyes?

Extracting the emission of Hα around galaxies within the EAGLE volume, the authors calculate the surface brightness averaged in rings centered on each galaxy, creating radial profiles of the Hα’s glow. Splitting the galaxies into categories based on mass, they find that the inner edge of the CGM (corresponding to the red circle in Figure 2) should be visible for galaxies with stellar masses above about 1010 solar masses. This means that in the search for CGM emission, astronomers shouldn’t need to target rare, outrageously massive galaxies to find a signal. A similar analysis is done by creating a false Dragonfly observation of a single test galaxy, with noise and an instrumental spread function applied to the emission map (Figure 2). The finding is similar: portions of the inner edge of the CGM should be easily visible with only ~10 hour exposures by Dragonfly, without even the need for radial averaging used in the previous test. To push farther into the CGM, however, such averaging would appear to be necessary, since the Hα surface brightness drops as the distance from the galaxy increases.

Figure 2: Left panel shows Hα emission of a test galaxy, with the inner edge of the CGM indicated by the red circle. Superimposed in red and white is a real galaxy, NGC 300, to indicate the relative size of the bright portion of the galaxy. Each panel to the right shows the false observation with noise applied, integrated for 10, 100, and 1,000 hours. [Lokhorst et al. 2019]

The IGM has a much lower density than the CGM, so it should be intrinsically fainter in emission. Using a similar false-observation method, the authors isolate an IGM filament from the simulation (Figure 3) and determine whether the signal would be observable in a reasonable amount of time by Dragonfly. They find that even the brightest emission of the IGM, coming from dense clumps, reaches only about 1 photon cm-2 s-1 sr-1. With such a low surface brightness, it would take over 1,000 hours (almost 6 weeks!) to obtain a signal that outshines the noise. Unfortunately, this means that the Dragonfly instrument upgrade plan would need to incorporate additional lenses to observe the IGM in this way.

Figure 3: The sample IGM filament selected for creation of the false observation, where the yellow color indicates a brighter surface brightness in Hα. [Lokhorst et al. 2019]

Although it is disappointing that the upcoming Dragonfly upgrade likely won’t be able to observe the IGM, the ground it could gain on studies of the CGM are fundamental to studies of galaxies. Compared to the severe limitations on absorption-line studies, observations of the CGM in emission may reveal more about its structure, how it is affected by inflows and outflows, and its interaction with the galaxy proper.

About the author, Caitlin Doughty:

I am a fourth year graduate student at New Mexico State University. I use cosmological simulations to study galaxy evolution during the epoch of reionization, with a focus on metal absorption in the circumgalactic medium.

coronal loops

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: New Evidence that Magnetoconvection Drives Solar-Stellar Coronal Heating
Author: Sanjiv K. Tiwari, Julia K. Thalmann, Navdeep K. Panesar, Ronald L. Moore, Amy R. Winebarger
First Author’s Institution: Lockheed Martin Solar and Astrophysics Laboratory; Bay Area Environmental Research Institute
Status: Published in ApJL

The coronal heating problem is one of the biggest unsolved mysteries in solar physics. The solar corona is the region of the Sun’s atmosphere that extends past the surface — or photosphere — of the Sun. It is a diffuse cloud of plasma that is heated to temperatures several hundred times that of the photosphere, which isn’t what you’d expect as you move further away from a hot object. The coronal heating problem has been hotly debated since the 1940s and is thought to be related to the Sun’s magnetic field. However, no theory has yet been able to explain why the corona is so much hotter than the photosphere, and the possibility remains that multiple processes may be at work.  

Figure 1. Image of a sunspot showing the umbra and penumbra region. [SpaceWeatherLive]

To study coronal heating, solar physicists use various structures that operate on smaller scales. The authors of today’s paper focus on the heating processes of coronal loops, which occur when plasma in the corona flows along the solar magnetic field (see the cover image above). Coronal loops are rooted in strong concentrations of magnetic field such as sunspots — dark patches in the solar photosphere. Sunspots are composed of two regions, a dark umbra and a surrounding penumbra (Figure 1), that are surrounded by a bright region called a plage. Understanding how coronal loops are linked to their sunspot footprints and the surrounding magnetic field is critical to determining the heating mechanisms at work.

Using data from the Solar Dynamics Observatory (SDO), the authors selected two regions with both sunspots and coronal loops, known as active regions. Although we can see coronal plasma trace magnetic field lines in coronal loops, there is still no way to directly measure the coronal magnetic field. The authors used simulations to reconstruct and determine the strength of the coronal magnetic field based on the magnetic field at the base of the loops (which can be directly measured using instruments like SDO). One of the example active regions, along with its simulated coronal magnetic field, is shown in Figure 2.

Figure 2. Image of one example active region (NOAA 12108) plotted with the simulated coronal magnetic field. The line color indicates the height of the magnetic field line above the surface of the Sun. [Tiwari et al. 2017]

From these observations, the authors found that the loops present in their sample active regions are rooted in both sunspots and plage, and that the brightest loops have one foot at the edge of a sunspot umbra and another foot in a plage or a sunspot penumbra. Although they find no visible loops with both footprints in sunspot umbrae, the simulated coronal magnetic field shows field lines that connect sunspot umbrae. This indicates that the coronal loops that connect sunspot umbrae are too cool to be visible in extreme ultraviolet wavelengths (the type of light observed by SDO). The relationship between loop brightness and footprint location is shown in Figure 3.

Figure 3. Schematic drawing of an active region showing the dependence of coronal loop brightness on footpoint location. Brighter colors indicate brighter emission in SDO images. [Tiwari et al. 2017]

A key property of sunspots is that they suppress convection (or the ‘boiling’ behavior in the solar photosphere indicated by the presence of granules surrounding the sunspot in Figure 1), with sunspot umbrae suppressing convection the most. The authors find that since the brightest loops are partially rooted in non-umbral regions (i.e. the regions with more convective activity), convection is a major driving force of coronal loop heating. However, if a loop has both footprints in non-umbral regions, they will not be as bright. Therefore the strong magnetic field present in the umbral footprint, along with enhanced convection in the non-umbral footprint, is necessary for generating the brightest (and hottest) loops.

Through observations and simulations of two active regions, today’s paper shows that both convection and a strong magnetic field in the footprints of coronal loops are necessary for heating to occur. This provides yet another clue to solving the decades-old coronal heating problem. With several new instruments like the ground-based solar telescope, DKIST, and the Parker Solar Probe coming online soon, this mystery may be solved sooner than you think.

About the author, Ellis Avallone:

I am a first-year graduate student at the University of Hawaii at Manoa Institute for Astronomy, where I study the Sun. My current research focuses on how the solar magnetic field triggers eruptions that can affect us here on Earth. In my free time I enjoy rock climbing, painting, and eating copious amounts of mac and cheese.

M33

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: Measuring Star-Formation Histories, Distances, and Metallicities with Pixel Color-Magnitude Diagrams I: Model Definition and Mock Tests
Author: B. A. Cook, Charlie Conroy, Pieter van Dokkum, & Joshua S. Speagle
First Author’s Institution: Harvard-Smithosonian Center for Astrophysics
Status: Submitted to ApJ

The wealth of information we have gathered about the lives of galaxies within our universe is due in part to increasingly larger and more sophisticated surveys of the night sky. Better resolution, precipitated by rapid advancement in imaging technology and telescope design, has enabled detailed studies ranging from our local group to the edges of the cosmos.

color–magnitude diagram

Figure 1: Example stars plotted on a color–magnitude diagram. [ESO]

Meanwhile, the color–magnitude diagram (CMD) has remained one of the most called upon diagnostic figures in an astronomer’s tool belt (see Figure 1). The combination of color and apparent magnitude alone is enough to map out a stellar population without much clutter, allowing for a clean-cut track of main-sequence stars burning away their hydrogen cores as well as potentially more massive stars in later stages of their lives that have moved off the main sequence. Traditionally, the CMD has enabled astronomers to obtain estimates of the ages of star clusters by determining their main-sequence turn-off point. More useful, perhaps, is the ability to measure dust as a shift in color (due to reddening) and magnitude (due to extinction), as well as to estimate distances by leveraging the statistical advantage of having tens to hundreds of stars to compare observed magnitudes with expected magnitudes based on theoretical CMD tracks.

While many of these techniques have been known and readily applied in the past and met with great success within our Galaxy, the authors of today’s astrobite demonstrate that this old dog can be taught new tricks.

The Usefulness of Barely Resolved Galaxies

Our ability to understand the universe is limited in part by angular resolution. For the few galaxies in the nearby universe for which individual stars can be discerned, we can rely on traditional techniques to understand their stellar populations, dust, and metal content in exquisite detail. The opposite is true for extremely distant galaxies, captured in only a handful of pixels with no hope of resolving any stars whatsoever. In this latter regime, we must rely on difficult-to-calibrate theoretical models of galaxy spectra to estimate the integrated properties of these remote systems.

In between these two extremes lie the semi-resolved galaxies, and they exhibit an interesting observational quirk. Although individual stars cannot be resolved, there are enough pixels such that, for any one pixel, there may be surface brightness fluctuations caused by rare but bright stars. The fluctuations trace the number of bright stars visible due to Poisson sampling and have been exploited in the past. The authors of this paper go further by leveraging these surface brightness fluctuations to construct a pixelized analog of a color–magnitude diagram for a semi-resolved system, termed a pixelized CMD (pCMD). The traditional diagnostics to estimate star, dust, and metal content are hence accessible, albeit with more complex implementation.

Figure 2: Summary of the pCMD method. A) Metallicity and a star-formation history model dictate the stellar evolution tracks. B) Stars are randomly sampled per pixel. C) Stars are modified for dust and distance. D) The simulated image is made by summing the fluxes of all the stars drawn into that pixel, as shown in the top left corner. E) The models are convolved with models of the instrument response. F) Pixel fluxes are converted to apparent magnitude, with the original stellar evolution track shown beneath for reference. [Cook et al. 2019]

The Pixelized Color-Magnitude Diagram

The pCMD contains information not only about the galaxy, but also effects relating to the instrumentation — including the point-spread function (PSF) and photometric noise — that must be accounted for. The authors opt for a forward modeling approach: from a model of stellar photometry and knowledge of the instrument, they construct a simulated pCMD and compare it to observations, as described in Figure 2. By constructing a grid of simulated pCMDs varying in stellar content, dust, and metallicity, the authors can then determine the best-fit parameters by searching through the grid of models to find the closest match.

In the current implementation of the pCMD method (PCMDPy — available on GitHub), there are four components of the model inherent to the galaxy:

  • Stellar populations: The current stellar populations observed are the direct result of star formation dating back to the formation of that galaxy. They should trace the assembly of stellar mass over time — known as the star-formation history (SFH) — and vice versa. Hence, the distribution of stellar masses formed (the inital mass function, or IMF) will govern not only the distribution of stellar masses, but also the conversion between the number of stars and the mass of stars within a pixel. Having confident estimates the SFH of a galaxy can enable definitive studies of the assembly of galaxies in the universe, which is currently a major line of research.
  • Metallicity: The metallicity of each pixel is modeled independently of the star-formation history in the current implementation. This is a simplification, as interstellar metallicity is driven by enrichment from dying stars and supernovae.
  • Dust: Dust will have the effect of both reddening and dimming the light from a galaxy. In this implementation, the dust is modeled as a single thin screen per pixel. Although this, too, is a simplification, it is necessary, as dust geometries are virtually impossible to obtain.
  • Distance: As a given galaxy is seen at larger distances, the number of stars per pixel increases. Although the surface brightness remains constant, the rare Poisson fluctuations due to particularly bright stars decreases and the average luminosity per pixel increases. As shown in Figure 3, this increases the overall brightness level in the pCMD but decreases the scatter. Hence, pCMDs can simultaneously recover distances and stellar populations for galaxies in the semi-resolved regime.

Figure 3. Effect of surface number density and distance, where each row has the same average flux. Increasing the surface number density Npix raises the CMD and decreases the scatter due to fewer fluctuations, but it does not change its color. Changing distance has a similar effect, but it does not affect the scatter. [Cook et al. 2019]

As the distance to a galaxy increases, the fluctuations lessen, as there are more stars per pixel. At about 10 Mpc, the power of these fluctuations to constrain the stellar populations declines as the uncertainty rises sharply. However, with Hubble resolution, the authors report that mock properties can still be recovered within 68% confidence out to 100 Mpc, highlighting the utility of this newly revitalized method in providing additional constraints on galaxies previously only characterized by their integrated stellar light.

About the author, John Weaver:

I am a first year 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.

binary black hole merger

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: First measurement of the Hubble constant from a dark standard siren using the Dark Energy Survey galaxies and the LIGO/Virgo binary–black–hole merger GW170814
Author: Marcelle Soares-Santos, Antonella Palmese, et al. (DES, LIGO, and Virgo collaborations)
First Author’s Institution: Brandeis University (M. S.-S.), Fermi National Accelerator Laboratory (A. P.)
Status: Published in ApJL

Disclaimer: The author of this Astrobites post is a member of the Dark Energy Survey but researches a different topic and did not take part in this analysis.

Nearly all of the galaxies we observe in the night sky are rushing away from us. Only the Andromeda galaxy is moving toward us — we are trapped in a gravitational dance that will end in a major collision about 4.6 billion years from now. The remainder of galaxies are receding due to the expansion of the universe. But how fast are the rest of the galaxies flying away from us? This is actually a difficult question to answer, partly because it is difficult to accurately measure distances across the universe. Today’s paper details a new method to measure how quickly the universe is expanding using the gravitational-wave (GW) signals from binary black hole collisions.

The Sounds of the Universe

Gravitational waves are ripples of spacetime itself, analogous to sound waves traveling through the air. They are generated in violent collisions between compact objects like neutron stars and black holes. The LIGO and Virgo collaborations have detected 11 such collisions, ten of which have been the collisions of two black holes (see Figure 1). The frequency and amplitude of the GWs, or the pitch and volume of the “sound,” encode information about the mass of the merging system and how far away it is. Exactly how the signal evolves tells us everything we need to know about the gravitational “brightness,” or luminosity, of the event. By comparing the measured amplitude to the calculated amplitude, we get a precise distance to the source. The ability to do this with GW signals has earned their sources the name “standard sirens.”

stellar graveyard

Figure 1. Our current knowledge of the end states of massive stars, namely black holes and neutron stars. Because the GW signals are so precisely tied to the properties of the system, we can determine the masses of the initial objects before merger in addition to the mass of the final object after merger. [LIGO-Virgo/Frank Elavsky/Northwestern U.]

What does this have to do with measuring the expansion rate of the universe? Hubble’s Law tells us that the velocity v at which an object at redshift z recedes away from us depends on its distance from us: v(z) = H0d, where H0 is the expansion rate of the universe. Previous measurements of this parameter, called the Hubble constant, have used electromagnetic radiation from either the cosmic microwave background (CMB) or Type Ia supernovae. These measurements currently conflict with one another, suggesting there might be some missing physics in our understanding of the universe. Further, measuring distances in the universe is tricky. CMB and Type Ia supernovae measurements rely on the cosmic distance ladder, so errors from one rung will propagate to the next.

On the other hand, standard sirens with electromagnetic counterparts don’t rely on the cosmic distance ladder and so offer an independent way to measure H0. In this case, the electromagnetic signal pins down the host galaxy’s location, which identifies the redshift of the signal and thus its velocity. At the same time, the GW signal gives the precise distance to the source. In fact, this has already been done using the binary neutron star merger GW170817. But we need many more than one binary neutron star event to truly pin down H0 (Figure 3), and ten of the 11 LIGO/Virgo events have not had accompanying EM signals. Today’s paper shows that there is still a way to calculate H0 from these events!

Listening in the Dark

The authors of today’s paper report the first measurement of H0 using the “dark” siren GW170814, a GW signal from two colliding black holes with no accompanying electromagnetic radiation. Recall from Hubble’s Law that we need a distance and a redshift to calculate H0 — but to determine a redshift, we need to know what galaxy the source was located in. That’s a hard thing to determine with no electromagnetic counterpart signal. The probability maps produced by LIGO/Virgo for the on-sky location of the GW signal can encompass a large area containing tens of thousands of galaxies at as many different redshifts!

GW170814 happened to fall smack in the middle of the Dark Energy Survey (DES, see Figure 2). DES has produced exquisite galaxy maps of a quarter of the Southern Hemisphere sky, complete with estimated redshifts calculated from the coarse “spectrum” of DES’s five wavelength filters. Soares-Santos, Palmese, et al. devised a statistical analysis that selected potential host galaxies from DES’s galaxy maps using the LIGO/Virgo maps and calculated what H0 would be for each in turn.

Figure 2. The LIGO/Virgo highest probability region for where GW170814 originated from, overlaid on the DES survey area. [Dark Energy Survey Collaboration]

After analyzing 77,000 galaxies, the authors calculate that H0 = 75.2 +(-) 39.5(32.4) km s-1 Mpc-1. Figure 3 shows how this value compares to previous measurements using the CMB and Type-Ia supernovae. While the uncertainties are quite large using only one GW event, the authors estimate that uncertainties comparable to the CMB and supernovae measurements are possible with ~100 GW events. Improvements to the LIGO detectors were recently completed and the observatory’s third run (O3) started on April 1st. There could potentially be a dark siren event every week, meaning we might only have to wait a couple of years to measure H0 to sufficient precision using GW events!

Figure 3. Comparison of values of H0 calculated from the CMB (Planck, dark blue), supernovae (ShoES, light blue), the binary neutron star event (GW170817, grey), and the dark siren (DES GW170814, red). With ~100 GW events, we will approach the sensitivity of the traditional electromagnetic measurements, giving an independent measurement of H0. [Soares-Santos et al. 2019]

Having a new way to measure H0 is a big deal for potentially resolving the tension between the CMB and supernovae measurements. If the dark/standard siren methods, which probe the late-time universe, end up being consistent with the early-universe CMB results, that might imply that something is wrong with our cosmic distance ladder and the late-universe supernovae measurements. On the other hand, if the GW measurements are consistent with the supernovae results, we might need to add new physics to our current understanding of the universe to explain why H0 would evolve with time. Either way, the next few years will be a very exciting time in precision cosmology!

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 the 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.

protoplanetary disks

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: Protoplanetary Disk Rings and Gaps Across Ages and Luminosities
Author: Nienke van der Marel, Ruobing Dong, James di Francesco, Jonathan P. Williams, John Tobin
First Author’s Institution: Herzberg Astronomy & Astrophysics Programs, National Research Council of Canada
Status: Published in ApJ

If you’re well-versed in exoplanets (or even the formation of your own planet), you may be familiar with the term protoplanetary disk. These objects are disks of gas and dust surrounding a fairly newborn star, although newborn here means up to several million years old. Interestingly, images of protoplanetary disks captured by astronomers reveal gaps in the disks — or rather, separate rings of material, depending on your perspective. These types of disks were once expected to be completely smooth, so why are we seeing gap-like features in essentially all resolved images of them?

disk images

Figure 1: Left: Continuum emission for the disk sample. The beam size is shown in the lower left corner with the spectral type of each star in the right. HL Tau, TW Hya, and V1247 Ori, which had higher resolution, have been reduced to reflect the 20-AU beam size of the other images in order to make them comparable. Right: Enhanced image representations for each disk, which were used in the analyses. Gaps have been made easier to see using a variety of unsharp masking techniques. Transition disks are RXJ 1615, AA Tau, DM Tau, V 1247 Ori, HD 97048, HD 100546, TW Hya, HD 169142, and HD 135344B. [Adapted from van der Marel et al. 2019]

The authors of today’s paper use ALMA data to explore what could be causing these gaps. They examine images of 16 different protoplanetary and transition disks (disks where the material closest to the star has been cleared out; see Figure 1). The disks in the sample surround stars of various spectral types, and each exhibits multiple gap features. Since these gaps are present throughout the sample, some correlation between them should reveal the responsible mechanism, right? After all, we would expect that these features evolve in similar ways for most disk systems.

Before they can answer this question, the authors first determine each star + disk’s luminosity and use this information to age each star with model evolutionary tracks. The resulting age range gave them a way to classify their disks as older or younger. They also determined approximate gap locations and sizes via a type of intensity profile fitting, which essentially models the light coming from the star + disk in each image (where there is a gap, there is less light detected, etc.).

Searching for Answers

gap properties

Figure 2: Gap properties for each disk. Each red dot represents a gap in the corresponding disk, with the disks sorted by increasing age. The gap center radius is raised to the 3/2 power, to mimic orbital resonance ratios. [Adapted from van der Marel et al. 2019]

With this information, the authors searched for trends in
the data — any correlations between the stars’ properties and the disk properties that may point to an origin story. Figures 2 and 3 show these chosen parameters … as well as the obvious dearth of trends. The only visible trend seems to be the decrease in outermost ring radius for the four oldest stars when compared to the rest of the sample (see Figure 3, bottom). This sure does make it hard to imagine a common mechanism responsible for the gaps.

Two of the most common theories for gaps within disks are 1) planets and 2) stuff freezing. Certain compounds are present in the disk, and there should be a point away from the star where the temperature becomes just right for those compounds to freeze into ice crystals. This is called the snow (or frost) line. At that snow line, it is thought that as these crystals all form together, they may stick to each other and the disk’s dust, which could clear out some space and turn into the gaps we see.

The authors looked to 5 different molecules in order to test this hypothesis: N2, CO, CH4, CO2, and NH3. This is the “stuff” we were talking about freezing. They determine the location of each respective snow line for each star and find that these locations don’t seem to have anything to do with the gaps — there are a few instances where the gap and snow line overlap, but it does not seem to be a systematic trend. So it seems the snow line scenario is a no-go.

Planetary formation is another story. It is thought that as planetesimals accrete surrounding disk material, a gap forms as it carves out its orbit. The authors state that they simply don’t have enough information to disprove or support this theory. Neptune-mass planets are currently undetectable and only one disk (HD 163296) in the sample has been suspected of having a planet. They do note however that one model of planet formation (cold-start model) would allow for giant-planet formation at the location of many of the observed gaps. So, planet formation is still a possible culprit.

gap properties

Figure 3: Additional gap properties across ages and luminosities, with each red dot representing a gap. [Adapted from van der Marel et al. 2019]

In case you were wondering, authors also ponder the case of graviational instability. Gravitational instabilities in the disks could potentially cause the gaps, but this depends on the gas-to-dust mass ratio. Unfortunately this is very much uncertain and hard to measure, so the trail stops there.

So What Do We Know?

The only solid correlation present in the sample is that the outer disk ring is much closer in for the four oldest stars — i.e., the older disks are smaller in diameter. In that case, it seems we are missing some outer rings. So maybe the outer rings dissipate faster than the inner rings (due to drag forces or radiation pressure). Either that, or these outer dust grains accrete to become planetesimal sized and are therefore undetectable at the observed wavelengths.

Although the authors didn’t find the origin story they were looking for, they can say a few things with certainty. The lack of trends in their data show that disk gaps are diverse and their presence is largely independent of stellar properties, like spectral type or age.  They also found that snow lines don’t have anything to do with the gaps we observe, but planets very well might. And last but not least, transition disks seem to host these features in the same manner as the truly protoplanetary disks, implying that they evolve in the same way, even if we don’t know what that way is. This is actually quite a big step in the right direction. These clues get astronomers one step closer to to closing the gap on … gaps.

About the author, Lauren Sgro:

I am a PhD student at the University of Georgia and, as boring as it may sound, I study dust. This includes debris disk stars and other types of strange, dusty star systems. Despite the all-consuming nature of graduate school, I enjoy doing yoga and occasionally hiking up a mountain.

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