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Earth-like exoplanet

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! Normal AAS Nova posting will resume on Friday.

Title: A Revised Exoplanet Yield from the Transiting Exoplanet Survey Satellite (TESS)
Author: Thomas Barclay, Joshua Pepper, Elisa V. Quintana
First Author’s Institution: NASA Goddard Space Flight Center
Status: Submitted to AAS Journals

Exoplanet hunters around the world held their breath while NASA’s Transiting Exoplanet Survey Satellite (TESS) launched last Wednesday. Thankfully the launch was a success, and after 60 days of orbit manoeuvring and engineering tests, TESS is expected to begin its initial two years of science observations. The question is: how many planets do we expect TESS to find?

How To Count Chickens Before They Hatch (or Planets Before They Are Detected)

The TESS mission will observe 90% of the sky to find nearby unknown planets. However, to get mission funding, astronomers need to predict how many planets they expect to identify.

The authors of today’s paper took on this challenge with a three-step modelling plan: 1) predict which stars would be observed, 2) randomly assign planets around them and 3) test if they are detected.

  1. Stars Observed
    Determining which stars TESS is likely to observe is made easier thanks to the Candidate Target List, a ranked list of 3.8 million stars most suitable for detecting small planets (where “small” means a planet with radius smaller than 4 Earth radii). The Candidate Target List contains isolated dwarf stars brighter than TESS magnitude 13, which will be less blended in the giant TESS pixels (each pixel contains 21 arcseconds of sky, compared to 4 arcseconds in Kepler). The authors calculate how long TESS can observe these stars and those most likely to be priority targets. Data for priority targets will be available as observations every two minutes (at a two-minute cadence) whereas other stars will only have data in the full-frame images every 30 minutes.
  2. Planet Assignment
    Each star in their list is assigned 0 or more planets according to a Poisson distribution. Each planet is then given random properties, including inclination, orbital period and radius based on the general trends found by the Kepler Space Telescope. Periods are drawn from distributions between 0.5–85 days for planets orbiting A/F/G/K stars and between 0.5–200 days for planets with M-star hosts. Time of the first transit is then drawn randomly between 0 and the length of the period, which can be after observations finish, meaning no transits will be seen.
  3. Detection test
    Finally, the authors test whether the transit signal is significantly stronger than the noise. The signal strength is determined by considering the number of transits, the transit depth and duration, and the extent of contamination from nearby stars. If the signal is greater than 7.3 times the TESS photometric noise level (7.3 SNR) and at least two transits are seen, this optimistic model claims a detection.

Optimistic Planet Numbers

Figure 1: Planet numbers detected by radius using the optimistic model. Red bars indicate the numbers of planets detected using 2 minute cadence data. Numbers above blue bars show the combined number of planets found in 2 minute cadence or full frame images. Note the log scale of planet numbers. [Barclay et al. 2018]

The optimistic model above identifies ~4,500 planets around stars in the Candidate Target List, shown split into planet radius in Figure 1. For stars with V magnitude brighter than 12, the authors predict the detection of 1,317 small planets. If 20% of these are amenable to radial velocity follow up, this would triple the number of small planets with measured masses and exceed the TESS main science objective of identifying 50 small planets with measurable masses.

Predicting planet numbers means astronomers can plan the number of follow-up observations necessary to confirm planets. Not all transit-like signals will be due to transiting planets; they may be caused by instrumental effects or astrophysical false positives, such as deep eclipsing binary signals blended to give shallower transits, especially with multiple stars on the same pixel. Today’s paper predicts for every true planet found there will be one astrophysical false positive in the 2-minute cadence data, and 5.5 astrophysical false positives in the full-frame images.

More Conservative Planet Numbers

Figure 2: Planet numbers detected by radius using the conservative model. Green bars indicate the numbers of planets detected using 2 minute cadence data. Numbers above orange bars show the combined number of planets found in 2 minute cadence or full frame images. Note the log scale of planet numbers. [Barclay et al. 2018]

Identifying planets based on fewer than three transits and detecting all planets with SNR ≥ 7.3 is very difficult. The Kepler Space Telescope is capable of identifying planets from one or two transits in K2 data, but only with additional investment of limited space-based follow up, or in cases where other planets had already been discovered around that star. Analysis of Kepler data found that below SNR = 8–10, there were many spurious detections, so typically only targets with SNR > 12 were followed up.

In their conservative model, removing planets with fewer than three transits or SNR < 10 reduced the number of planets detected by 60%, as seen in Figure 2. The number of small planets detected around stars brighter than V = 12 halved to 621. The number of habitable-zone planets smaller than 2 Earth radii drops to just 6.

Adding to James Webb Space Telescope Targets

Any small planets detected by TESS may represent new targets for atmospheric characterisation with the James Webb Space Telescope (JWST). Figure 3 shows that the simulated TESS planets greatly increase the number of known small nearby planets, some of which should be amenable to atmospheric characterisation. The authors estimate that on the order of ten super-Earth planets could be found around bright M3 stars in the optimistic habitable zone, adding to JWST’s sample of temperate worlds.

Figure 3: Simulation of small planets TESS may find (orange) as a function of the star’s distance to us. Kepler planet candidates are in blue and planets detected by other telescopes in black. The size of the circle is proportional to transit depth. [Barclay et al. 2018]

Conclusion

Today’s paper will be useful for planning follow up strategies and for identifying potential numbers of planets found in TESS 2-minute cadence data and full-frame images. This is the first paper to predict planets based on the stars most likely to be observed from the Candidate Target List rather than simulated star populations. Where the authors also considered the hotter, fainter, giant or more crowded stars that TESS would observe (excluded from the Candidate Target List), planet number estimates increased to 16,000. However the higher labour intensity to follow up and much higher rates of false positives mean few of these are likely to be confirmed.

Today’s paper shows that TESS should greatly add to the numbers of known small nearby planets which we should be able to investigate further. Now we just need to find them!

About the author, Emma Foxell:

I am a PhD student at the University of Warwick. My project involves searching for transiting exoplanets around bright stars using telescopes on the ground. Outside of astronomy, I enjoy rock climbing and hiking.

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: Synthesis of Molecular Oxygen via Irradiation of Ice Grains in the Protosolar Nebula
Author: O. Mousis, T. Ronnet, J. I. Lunine, R. Maggiolo, P. Wurz, G. Danger, and A. Bouquet
First Author’s Institution: Aix-Marseille University, France
Status: Accepted to ApJ

Tailing a Comet’s Tail

Figure 1: A picture of the comet 67P/C-G, taken by Rosetta in August 2014. The resolution is 5.3 meters/pixel.  [ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA]

In August 2014, the Rosetta orbiter met up with the comet known as 67P/Churyumov-Gerasimenko (a.k.a. 67P/C-G). Rosetta stuck close by, watching and observing, as the comet orbited around the Sun. Now, nearly four years later, we’re still learning new science from everything Rosetta (and its lander Philae) discovered. In today’s astrobite, we focus on one comet discovery in particular: molecular oxygen.

Molecular oxygen (O2) is certainly important here on Earth. Plants breathe out O2 during photosynthesis, while other living creatures (like humans) breathe it in. But O2 isn’t only produced through life as we know it — which means that if we see molecular oxygen in the atmosphere of, say, an exoplanet, we shouldn’t automatically assume it came from a living source (as talked about in this article and this previous astrobite). 67P/C-G seems like an example of such a non-living, “breathing” body.

67P/C-G is a comet, which means it’s a small icy body of rock, dust, and gas orbiting about the Sun. When a comet passes close to the Sun, the Sun warms up its ices, producing a coma — the iconic tail of released gas that trails after a comet during its orbit, as seen in this gallery. With Rosetta, scientists have detected O2 abundance levels in 67P/C-G’s coma of some 1% to 10% with respect to water (which means 1% to 10% of water’s abundance). Scientists have also noted that the rate of O2 production in the coma is surprisingly correlated with the coma’s rate of water production; from this, they’ve concluded that both the O2 and the water must be coming from the same icy phase of the comet.

So how did all of this O2 get into 67P/C-G in the first place? To try to answer this question, today’s authors looked back to well before 67P/C-G formed.

early solar system

Artist’s impression of the early solar system, a disk of dust around the young Sun. [NASA/JPL-Caltech]

From Cloud to Comet

Back then — waaay back then — our solar system was young and hip and had yet to form any of its signature planets. This (brief) history of the solar system starts righteously with a dense cloud of gas and dust, hanging out in space. The dense cloud eventually collapsed, forming a swirling disk of material known as a solar nebula.  Gravity dragged more and more material towards the center of the nebula, until the pressure at the nebula’s core was so great that it fused hydrogen into helium — leading to the birth of the Sun. From there, the solar nebula transitioned into a protoplanetary disk, which refers to a puffy disk of dust grains and gas orbiting around a young star. The dust grains in this disk clumped together over time, and these clumps grew larger and larger, eventually turning into the asteroids, comets, and planets that we know and love today.

These dust grains may have had O2 tucked into their crevices and corners, and when some of these dust grains accumulated to form 67P/C-G, the O2 was trapped inside the comet as it grew. Given O2‘s correlation with water in 67P/C-G, the O2 itself seems to have come from radiolysis of water ice, which is the process in which molecules (in this case water) are broken apart by ionizing radiation (like cosmic rays).

But 67P/C-G likely formed while our solar system was still a disk of orbiting dust and gas, specifically along the disk’s midplane where heavier grains would have settled. The outer midplane of the disk would have been a cold, dark, and dense place, far from the young Sun, and ionizing radiation would have had a lot of difficulty even reaching water ice to irradiate it. So how could enough radiation have reached the water ice to produce as much O2 as we see from 67P/C-G?

Two mechanisms provide possible answers to this question. One: turbulent mixing in the disk — mixing of the disk’s materials would literally bring dust grains in the midplane up towards the disk surface. And two: the O2 wasn’t produced during the high-density disk phase at all — it was actually produced when our solar system was still a low-density cloud, when radiation would have penetrated more easily. Today’s authors explored the turbulent mixing idea, and investigated if this mechanism would be enough to produce the O2 observed for 67P/C-G today.

Caught Between Rocks and an Irradiated Place

vertical transport

Figure 2: A schematic of vertical transport due to turbulent mixing. Grains cycled from the lower midplane towards the upper regions of the disk are more exposed to external radiation. [Mousis et al. 2018]

The grains in a solar nebula or protoplanetary disk aren’t all the same size. Larger grains tend to huddle along the midplane because of gravity and drag against the gas. But smaller grains, with less weight to throw around, are more easily tossed and turned about by turbulent flows of the disk gas. This turbulent mixing moves smaller grains not only horizontally but also vertically within the disk. Figure 2 gives an illustration of this tossing-and-turning process. Today’s authors pointed out that as grains are churned vertically upwards away from the midplane, they become more exposed to external radiation from ultraviolet, X-ray, and cosmic-ray sources. They set out to model this cyclical process over a disk’s lifetime, to determine how much O2 relative to water would be produced.

O2 abundance relative to water

Figure 3: Plots of O2 abundance relative to water over the disk model’s lifetime. Each line corresponds to a different grain size (as given by the legend at the bottom right of each plot). The top panel is for a disk with more turbulence (represented by the α parameter) while the bottom panel is for a disk with less turbulence. [Mousis et al. 2018]

To do this, the authors built a simulation of vertical grain transport due to turbulent mixing. To define their disk model’s structure, they artfully combined theory, equations, and parameters from a variety of other papers and studies. For example, they adapted parametric profiles to map out the disk’s horizontal and vertical temperature and density. They used their simulation to track the vertical diffusive motion of the grains in the disk model over time for different grain sizes and levels of turbulence, and from there they calculated how much O2 in their disk model was released due to radiolysis over time.

Figure 3 shows results from their simulation over 10 million years (scientists think 67P/C-G formed within 2.2 to 7.7 million years). We can see that even the smallest/least heavy, most churnable grains didn’t reach the same level of O2 abundance observed around 67P/C-G; even in the closest case, the simulation abundances are still some two orders of magnitude below the observations. The authors felt it safe to conclude that turbulent mixing was not a main mechanism for producing the O2 observed in 67P/C-G.

So the authors agreed that the evidence points to the second mechanism, which reasoned that the O2 was produced while our solar system was still a low-density cloud. If this is true, then this affects our beliefs not only about 67P/C-G’s formation, but also about the origins and chemistry of the other comets cruising through our solar system as well. But we’ll need more simulations before we can say for sure!

About the author, Jamila Pegues:

Hi there! I’m a 2nd-year grad student at Harvard. I focus on the evolution of protoplanetary disks and extra-solar systems. I like using chemical/structural modeling and theory to explain what we see in observations. I’m also interested in artificial intelligence; I like trying to model processes of decision-making and utility with equations and algorithms. Outside of research, I enjoy running, cooking, reading stuff, and playing board/video games with friends. Fun Fact: I write trashy sci-fi novels! Stay tuned — maybe I’ll actually publish one someday!

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 Terminal Rotation Rates of Giant Planets
Author: Konstantin Batygin
First Author’s Institution: California Institute of Technology
Status: Published in AJ

Good luck getting any sleep on Jupiter! This humongous gas giant rotates faster than any other planet in the Solar System, completing a day in less than 10 hours! If you were to emigrate from Earth to the largest planet in our solar system and still aimed to get the daily 8 hours of sleep recommended for adults, that would leave you with less than two hours per day to eat, exercise, and study astrophysics. That’s not nearly enough time! Future inhabitants of Jupiter’s cloud cities should not complain, though.

When Jupiter formed, it accreted its atmosphere (over 95% of the planet’s total mass!) from the hydrogen and helium gas in the protoplanetary disk surrounding our Sun. As Jupiter ate up this gas mass, it must have begun to spin faster as it also ate up the gas’s angular momentum. Eventually, it would reach the break-up velocity, which is defined as the point when the upper layers of the atmosphere are rotating as fast as an object would if it were placed in orbit around the planet close to the surface. At these speeds, Jupiter could not possibly rotate any faster. Naively, we would expect Jupiter to still be rotating that fast today. However, if we calculate Jupiter’s rotation period based on its break-up velocity, we get that one Jovian day should not even last 3 hours!

Really, Jupiter’s inhabitants should be thankful that something was able to slow down the planet’s rotation enough for them to be able to watch the first four Harry Potter movies in one day instead of just one of them. But what?

We have long known that Saturn also rotates much slower than its break-up speed (11 hr compared to 4 hr). And as Eckhart summarized in his December astrobite, we now know gas-giant exoplanets also rotate slower than expected. In today’s paper, Konstantin Batygin attempts to solve this widespread conundrum with the solution people would most expect: Jupiter’s magnetic field.

Copying the Answer

Jupiter and Saturn aren’t the only objects in our solar system subject to this mystery. When the Sun formed, it too accreted hydrogen gas from the disk around it. As a result, we would naturally expect the Sun to be rotating even faster than Jupiter. Yet a solar day lasts nearly a month, somehow leaving the Sun with only about 1% of our solar system’s angular momentum — even though it has over 99% of the mass!

One way for the Sun, or any object, to lose angular momentum is to fling out some of its material. The leading explanation for why stars like the Sun spin down this way is called magnetic braking. In this process, the solar wind carries material out of the surface just like it does today. Then, some of that material will get caught on the Sun’s magnetic field lines, which expel it even further from the Sun — taking a significant chunk of the Sun’s angular momentum with it. To conserve angular momentum, the Sun will have to slow down how fast it rotates. Can gas-giant planets do this too?

Copying Doesn’t Work

Jupiter is not quite the same as a star. It doesn’t have a solar wind, so it can’t just fling out material. However, recent research on how gas giants accrete their atmospheres may offer an alternative idea.

While we used to assume Jupiter accrued gas directly at its equator, modern simulations show it actually should have collected gas from above its “North Pole” (and below its “South Pole”). As Figure 1 shows, not all of the material gets eaten by Jupiter. Some of it is deflected outward into a disk around Jupiter — called a circumplanetary disk. The disk then spills that material back into the larger disk around the star — the protoplanetary disk from which it came. This makes up for Jupiter not having a solar wind and gives it a way to fling out material and angular momentum.

magnetic braking

Figure 1. Concept diagram of magnetic braking for a growing Jupiter-size planet. A) The planet can accrete hydrogen gas into its atmosphere directly from the blue protoplanetary disk around the star (not the disk around the planet!). This gas follows the meridional flow arrows towards the “North Pole” of the planet. B) The rest of the hydrogen is deflected into the orange disk around the planet. The planet can also accrete some more gas from this orange disk (specifically, the upper and lower layers). However, most of the orange disk (the middle layers) will spill outward, following the white arrows through the red region — leading it back into the blue disk around the star where it started. The fact that the gas is expelled completely out of the disk around the planet makes it possible for the planet to undergo magnetic braking. [Batygin 2018, by James Keane]

Spinning Slower, Then Faster

Figure 2 shows the evolution of Jupiter’s rotation period (i.e. the length of its day) after it formed, with and without magnetic braking. In both models, Batygin assumes that Jupiter starts out with twice as large of a radius before gravity causes it to contract to its present-day size in about 1 Myr. He also assumes that the planet starts out rotating at the break-up velocity (about 8 hours).

Without any slow-down, Jupiter simply speeds up as the planet contracts in order to conserve angular momentum. But with magnetic braking, Jupiter first spins down to a rotation period of 36 hours in about 25,000 years. Then, gravitational contraction spins it back up to a rotation period of 9 hours by the time it reaches its current radius — pretty close to its present rotation period of 9 hours and 56 minutes!

Jupiter's rotation period

Figure 2. Jupiter’s rotation period in the first 2 Myr after it formed. Without magnetic braking (gold), it rotates at the break-up velocity, which gets faster as the planet shrinks in size. With magnetic breaking (blue), Jupiter spins down first, leaving it with about the right rotation period by the time it contracts to its current size. [Batygin 2018]

Now that the model in this paper has demonstrated magnetic braking can lead to about the right spin rate for Jupiter (and Saturn too), Batygin hopes future work will explore more meticulous aspects of the problem related to magnetohydrodynamics (MHD), both with an analytic approach (like with the equations in this paper) and also with simulations (which were not used directly in this model).

The astrophysicists living on Jupiter or gas-giant exoplanets in other star systems will have plenty more time per day to investigate this problem. And they may have both magnetic fields and how these planets accreted their atmospheres to thank.

About the author, Michael Hammer:

I am a 3rd-year graduate student at the University of Arizona, where I am working with Kaitlin Kratter on simulating planets, vortices, and other phenomena in protoplanetary disks. I am from Queens, NYC; but I’m not Spider-Man…

spotted star planet transit

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 Transit Light Source Effect: False Spectral Features and Incorrect Densities for M-Dwarf Transiting Planets
Author: Benjamin V. Rackham, Dániel Apai, and Mark S. Giampapa
First Author’s Institution: The University of Arizona
Status: Published in ApJ

Twice in my life, I have mistakenly purchased green sweaters. “What a lovely pearl gray!” I have thought in the soothing, dim light of a Target fitting room. “This will go so nicely with my red corduroys,” I have mused under the bland fluorescent bulbs of the check-out line. “Dammit, green again?” I have sworn in the harsh sunshine of the parking lot.

Unhappily, this is neither the most important nor the most expensive mistake an astronomer can make about lighting. Misinterpreting the color of a sweater based on the wrong perception of the ambient light? Not so bad — I just had to wait in another line to return the impostor sweater. But misinterpreting the color of planets based on misunderstanding their host stars’ light? Today’s authors warn that such a mistake could lead us to false conclusions about planetary atmospheres, compositions, and habitability.

The Wrong Kind of Light

Planets are cool (temperature-wise, but also in terms of social status), and therefore planets are dim. All of our knowledge of planets beyond our solar system is therefore somewhat indirect; it comes from analyzing the much brighter light from a planet’s host star, influenced by the planet in some way. In the most straightforward case, a big planet reflects some starlight, and we take a picture of it. Subtler planets block some starlight, or swing the star around at such speeds that the starlight changes color.

Planets with atmospheres are able to warp starlight in another way: while the body of the planet blocks a big chunk of starlight as it crosses in front of its host star, the planet’s thin, enveloping atmosphere absorbs starlight at particular colors, or wavelengths.

We can subtract a spectrum of the star taken before the planet crosses the star from a spectrum taken right in the middle of its transit — the difference, called a “transmission spectrum,” is the signature of the planet’s atmosphere. Different molecules have different spectral effects, so the transmission spectrum tells us what the planet’s atmosphere is made of.

But how do we know what out-of-transit spectrum to subtract? Usually, we take the average spectrum of the whole star. But as today’s authors remind us, stars are fickle and inconstant creatures. Their surfaces change; dark starspots bubble and bright faculae simmer.

If a planet transits directly across one of these features, it’s back-lit by a different light than the average light of the star. Subtracting the average, in this case, imparts a false signal in the transmission spectrum (see Figure 1). Just as I mistook a green sweater for gray by mistaking yellowish indoor light for white, we may end up interpreting planet spectra wrongly by misunderstanding the spectral colors of their host stars.

Figure 1. An illustration of the “transit light source effect,” by which we can derive an incorrect understanding of a transiting planet’s atmosphere by wrongly assuming that the planet transits an “average” piece of the star. Middle panel: The spectrum of the entire star, averaged together (blue) vs. the spectrum of the piece of star that the planet actually crosses (orange). Right panel: As the planet transits, its atmosphere absorbs some of the starlight it intercepts. By subtracting an in-transit spectrum from an out-of-transit spectrum, you can isolate the spectral signature of the planet’s atmosphere. But if you subtract the (wrong) blue spectrum instead of the (right) orange one, you get the wrong atmospheric signal (green) instead of the right one (gray). [Rackham et al. 2018]

The Wrong Answers

An incorrect transmission spectrum is a dangerous thing. Not only can it lead us astray with respect to the chemical makeup of the planet’s atmosphere, it can also lead us to wrong answers about much more basic properties, like the planet’s size.

Alarmingly, it’s difficult to estimate how spotted any particular star is — and therefore how wrong we’re likely to be — based on a transit observation alone. The authors focus on M-dwarf stars, whose surfaces are poorly understood and which commonly host rocky exoplanets.

As a star rotates and spots come in and out of view, they impart a sine-wave pattern to the star’s light curve, which we observe when we look for transits; a larger fraction of the surface with spots means that the amplitude of the wave will be larger. But today’s authors show that you can’t infer the spotted fraction of a star’s surface based on the amplitude alone, because a wide range of “spottinesses” generates the same amplitude.

It’s a bleak outlook! The authors examine one famous M-dwarf planetary system, the seven-rocky-planet TRAPPIST-1, and conclude that the effect of being wrong about starspots is up to 15 times bigger than the signal of the planets’ atmospheres. It’s nigh impossible to measure anything meaningful about planetary atmospheres when there’s such a large confounding influence.

Figure 2. An investigation of the transit light source effect in the 7-planet TRAPPIST-1 system. Left panel: The amplitude of the variability in TRAPPIST-1’s light curve due to starspots is observed to be 0.01 magnitudes (black dashed line), but according to the models plotted in blue and gray, this is consistent with a huge range of starspot covering fractions (x-axis). Middle panel: The ratio of observed transit depth to true transit depth for the TRAPPIST-1 planets, across a range of wavelengths, due to starspots. Starspots cause us to overestimate the radii of planets and therefore underestimate their densities. Right panel: The ratio of observed-to-true transit depths at a handful of specific wavelengths where molecular absorption features appear. The effect of stellar contamination is up to 15 times larger than the signal expected from molecules in the atmosphere of a rocky planet (light green band), which means that we can’t currently draw any meaningful conclusions about planetary atmospheres from measurements like this. [Rackham et al. 2018]

A Call to Action

Right now, we just don’t understand stellar surfaces well enough to be confident that we’re correct in our transmission spectra. To improve our understanding, we can watch planets transit spotted stars in multiple wavelength bands to get better constraints on where the starspots are and how much area they cover. Consider it a silver lining to the recent news that the James Webb Space Telescope is delayed until 2020: we have two more years to figure out stars before it looks at any planets!

About the author, Emily Sandford:

I’m a PhD student in the Cool Worlds research group at Columbia University. I’m interested in exoplanet transit surveys. For my thesis project, I intend to eat the Kepler space telescope and absorb its strength.

Type Ia supernova

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: Further insight on the hypervelocity white dwarf, LP 40-365 (GD 492): a nearby emissary from a single-degenerate Type Ia supernova
Author: Roberto Raddi, Mark Hollands, Detlev Koester, et al.
First Author’s Institution: University of Warwick, UK
Status: Accepted to ApJ

I’m sure you’ve heard of Type Ia supernovae. They’re a certain type of exploding star, most famous for the fact that their brightness can be easily calculated from the other features of the explosion. If you know how bright something is, and you measure how much of the light reaches you, that tells you how far away the light source must be; “standard candle” is the common term for objects like this. Type Ia supernovae are useful to astronomers who want to measure the distance to far-away galaxies, and they form one link in the cosmic distance ladder.

Despite how useful Type Ia supernovae are, we still don’t fully understand how they happen. We know that you need a white dwarf, and you need that white dwarf’s mass to increase until it nears a critical point (the Chandrasekhar mass limit, which is about 1.4 times the mass of the Sun). What we don’t know is what makes a white dwarf’s mass increase to reach that point. There are two models that we usually consider for how this happens. Firstly, the white dwarf could slowly pull matter from a nearby star. Secondly, two white dwarfs could collide. The two models are often called “single-degenerate” and “double-degenerate”, because “degenerate objects” is another term for white dwarfs (a term related to the physics of their structure). The advantages and disadvantages of the two models have been debated for decades. In recent years the debate seems to have leaned more towards the double-degenerate channel for most Type Ia supernovae, and the single-degenerate channel for some unusual-looking Type Ia supernovae.

Last year, a team of astronomers found a white dwarf named LP40-365. It’s moving through the galaxy incredibly fast (about 500–800 km/s), and it contains an unusual collection of elements. The authors of the discovery paper suggest that this is a leftover from a Type Ia supernova — a white dwarf that tried to go bang but survived. Today’s authors studied spectra of the star (from the Copernico telescope) in order to get a better idea of what is going on with it.

Spectrum of LP40-365

Figure 1: The spectrum of LP40-365. The top panel shows the low-resolution spectrum taken for today’s paper, while the bottom panels show two high-resolution spectra. Black lines show the spectrum itself; red the best-fit model found by today’s authors. Spectral lines from identified elements are labeled. [Raddi et al. 2018]

From their spectrum, they measure a temperature of 8,000 to 10,000 Kelvin (towards the cool end for white dwarfs) and a surface gravity between 100 and 3,000 times that of Earth (uncertainties this size on surface gravity are not too uncommon). This surface gravity is very low for a white dwarf, suggesting it has an unusually low mass — which seems to fit with the theory that the white dwarf has been partially exploded.

white dwarf abundances

Figure 2: Measured abundances for various elements shown as black dots, scaled so that 0 = the abundance of that element in the Sun. For comparison, the abundances of the same elements are shown for a typical low-mass white dwarf (red), metal-polluted white dwarf (green), and oxygen-atmosphere white dwarf (blue). [Raddi et al. 2018]

The spectrum of LP40-365 is pretty complicated, and it shows a multitude of unusual elements. It’s rare that astronomers have to talk about any material heavier than, say, oxygen; in LP40-365 today’s authors found helium, oxygen, neon, sodium, magnesium, silicon, calcium, iron, nickel, sulphur, chromium, titanium, and — importantly — manganese. They measured the abundance of each of these elements (see Figure 2). They found that the white dwarf seems to be around 2/3 helium and 1/3 neon, with other elements being at most 2% of the mass. However, they do stress the difficulties of modelling such an unusual system — the elements interact in ways that aren’t fully understood and hence aren’t included in the modelling code, and there are several features in LP40-365’s spectrum that they haven’t been able to understand.

The Supernova

The presence of manganese turns out to be an important clue as to how this white dwarf formed — specifically, what kind of Type Ia supernova created it. Manganese creation only occurs under high pressures, the type of pressure found in high-mass white dwarfs. If the supernova occurred through the single-degenerate channel, this makes sense — our white dwarf has grown to its high mass over millions of years, and it therefore had plenty of time for the pressure to increase in response to the high mass. In the double-degenerate channel, however, our two normal-mass white dwarfs collide and explode almost straight away, and the pressure needed is never reached. The presence of manganese is then a strong sign that this failed supernova formed via the single-degenerate channel.

The authors also note that a remnant with this composition — dominated by helium and neon, with very little carbon — is hard to make from the most commonly considered Type Ia supernova progenitor, a carbon-oxygen-core white dwarf. Instead, they point towards a rarer type of white dwarf that has oxygen-neon cores.

In any case, LP40-365 is the first known white dwarf to have survived a (failed) Type Ia supernova, and as such it opens up some exciting prospects for future science.

About the author, Matthew Green:

I am a PhD student at the University of Warwick. I work with white dwarf binary systems, and in particular with AM CVn-type binaries. In my spare time I enjoy writing of all kinds, as well as playing music, board games and rock climbing. For more things written by me, take a look at my website.

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: Precision Light Curves from TESS Full-Frame Images: A Difference Imaging Approach
Author: Ryan J. Oelkers and Keivan G. Stassun
First Author’s Institution: Vanderbilt University
Status: Submitted to AJ

Disclaimer by Tarini: The first author of this paper was my supervisor during an REU, and while I was briefly involved with TESS as an undergraduate I did not participate in this work.

Figure 1: Left: field of view of the four TESS cameras. Middle: the twenty-six observation sectors TESS will cover across the northern and southern hemispheres of the sky. Right: the duration for which TESS will observe different parts of the sky, with the James Webb Space Telescope (JWST) continuous viewing zone. [NASA/TESS]

The Transiting Exoplanet Survey Satellite (TESS) will be launching very soon (in about a month, according to NASA’s launch countdown clock), and it promises to yield exciting results with a near all-sky survey for exoplanets around bright, nearby stars. Over two years, TESS will observe over 400 million stars by systematically scanning sectors of the sky for 27 days at a time (see Figure 1). About 400,000 of these stars will be studied closely and will have light curves associated with them in data releases. The rest of the stars can be studied through full-frame images (FFIs), which have a 30-minute cadence and consist of TESS’s entire field of view. To clarify, this doesn’t mean that an observation is taken every thirty minutes. The TESS cameras take images every two seconds. To save on storage and transmission time those images are stacked to create new images with effective exposure times of thirty minutes.

Because TESS is designed to conduct a wide survey, its pixels span a large part of the sky — 21 arcseconds per pixel, to be exact. For comparison, the Wide Field Camera 3 on the Hubble Space Telescope spans 0.04 to 0.13 arcseconds per pixel, depending on the detector. The large arcsecond-per-pixel scale means that the stars in TESS FFIs are more likely to be distorted and blurred into each other, making it difficult to measure changes in brightness (see Figure 2). How can one get around this? The authors of this paper offer up an image-processing pipeline that does the trick.

Figure 2: Comparing the pipeline’s output for three different data sets with different arcsec/pixel scales. The x-axis records magnitude and the y-axis records the deviation from the average magnitude of a given object. The inset images show how stars appear in the typical image from that data set. From left to right, the scales are 6.4”/pix, 15”/pix, 21”/pix (TESS). [Oelkers & Stassun 2018]

Stack, Subtract, Extract

One way to measure how an object’s brightness changes is to count the number of photons it emits over time. This is easy to do when a star is in a sparsely-populated part of an image; it’s safe to assume that any light from that part of the image is coming from the star. In crowded fields, things get complicated. Stars can appear to overlap, making it difficult to tell where the light is coming from.

A way around this is Difference Imaging Analysis (DIA). To do this, you need at least two images of the same part of the sky. Usually, one has a higher signal-to-noise ratio (SNR) than the other. Blur the image with the higher SNR to match the image with the lower SNR, and subtract one from the other. This should eliminate any objects that have a constant brightness and leave behind — you guessed it — the objects that do change brightness!

To determine a DIA approach to TESS FFIs, the authors used NASA’s “End-to-End 6” (ETE-6) simulated FFIs for TESS. To create the higher-SNR image, all 1,348 ETE-6 images were aligned and stacked to create a master frame. Aside from creating a frame to subtract from, stacking several images also ensures that transient objects like asteroids are removed from the master image. To be sure that subtraction had occurred properly, the authors checked the distribution of the values of background pixels in the differenced image. If only variable objects were left behind, then the rest of the pixels ought to be around zero. This is indeed the case with the differenced images that are produced (see Figure 3).

Figure 3: Left: a typical science image, with the colors inverted and an arrow pointing towards a candidate variable object. Middle: the differenced image, with an arrow still pointing at the much more distinct variable object. Right: the distribution of background pixel values from the differenced image. The distribution follows a Gaussian centered at zero, which means that the subtraction occurred properly. [Oelkers & Stassun 2018]

Through the Light Curves, and What the Pipeline Found There

The exact number of variable events (transits and variable stars) injected into the simulated images is unknown, but there should be several hundred of each type. The authors recovered 2,275 stars that showed variability (see Figure 4), which is good news for the pipeline. With their current resources, the authors estimate that they should be able to extract light curves from TESS FFIs a few weeks after data releases. The pipeline is publicly available and can be adapted for other data sets.

Figure 4: Examples of the variable-object light curves extracted from the ETE-6 FFIs. Left: likely simulated periodic variable star. Middle: likely simulated transit or binary candidate. Right: simulated large-amplitude variable star. [Oelkers & Stassun 2018]

All in all, it seems that TESS is going to uncover loads of interesting objects. It’s expected to recover approximately 1,700 planets from its target stars — including 70 or so Earth-like planets — and variable objects by the thousands from its FFIs. And DIA will definitely make a difference.

About the author, Tarini Konchady:

I’m a first year graduate student at Texas A&M University. Currently I’m looking for variable stars 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).

SL2S0217

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: A Window on the Earliest Star Formation: Extreme Photoionization Conditions of a High-Ionization, Low-Metallicity Lensed Galaxy at z∼2∗
Author: Danielle A. Berg, Dawn K. Erb, Matthew W. Auger, Max Pettini, Gabriel B. Brammer
First Author’s Institution: University of Wisconsin Milwaukee
Status: Submitted to ApJ

A crucial component of understanding how galaxies evolve is figuring out what their earliest years are like. However, this process presents a problem to astronomers, since most early galaxies are believed to have been small and faint and are quite distant to boot. One simple workaround is to study nearby galaxies that we believe have similar properties to the progenitors of older galaxies like the Milky Way or Andromeda. Today’s paper covers the analysis of one such galaxy.

Previous observations of the galaxy SL2S0217 using the Hubble Space Telescope have revealed that it is young, small, metal-poor, and forming about 23 solar masses of new stars per year. Small galaxies like SL2S0217 can sometimes be hard to observe at large distances since they tend to be faint. However, this galaxy is serendipitously located behind a much larger galaxy, which gravitationally lenses it and magnifies its light by a factor of 17, simultaneously smearing it into an arc-like shape (see the cover image above). Using a model of the lens, astronomers have been able to reconstruct the true appearance of the galaxy, revealing a clumpy and irregular shape.

In today’s paper, the authors used spectra of this galaxy obtained from the Keck I telescope to study its strong UV emission lines (Figure 2), which likely come from a combination of nebular gas around hot stars and from the interstellar medium (ISM). These features are highly unusual for redshift z = 2 galaxies, which generally don’t show signatures of strong emission.

spectrum of emission line galaxy overlaid with stacked spectra from "normal" galaxies

Figure 2:  The spectrum of SL2SJ0217 is shown in black, while the orange line shows the stacked spectrum of 1,000 redshift z = 2 galaxies to serve as a comparison. The blue dotted lines and purple dashed lines show nebular and ISM features, respectively. The gray bars show regions of the spectrum that may be contaminated in observations by spectral features of the Earth’s atmosphere. [Adapted from Berg et al. 2018]

In order to figure out what the conditions in the real galaxy are like, the authors use the program CLOUDY17 to model the chemistry and photoionization occurring in nebular gas and the ISM, generating a simulated spectrum.  By varying the input parameters to the model and comparing the simulated and observed spectra, the authors find the best-fit model to the observed spectrum — confirming the previous finding that the nebular gas of SL2S0217 is metal-poor and embedded in a hard radiation field. This hard radiation indicates that there is an abundance of high energy photons in the galaxy.

Building on this result, their best fit is obtained when including the effects of binary-star evolution, indicating that this galaxy contains a high fraction of binary stars (which is not generally assumed when modeling stellar populations). However, one characteristic of the spectrum could not be reproduced using stellar photoionization alone: a particularly strong and narrow He II emission line (Figure 3). To reproduce this line, the authors consider additional ionization sources for the gas, separately incorporating the effects of shock and active galactic nuclei (AGN) heating into the photoionization code to see if these could potentially cause the high He II emission.  Varying the shock velocity and the radiation field of the AGN, they find the inclusion of strong ionization from shocks or AGN into the model increases the predicted He II emission, but it does so at the expense of overpredicting the emission in the other observed lines. Thus, this line remains unexplained.

spectrum of galaxy showing strong emission lines along with a simulated model fit

Figure 3:  The observed spectrum of SL2SJ0217 shown in black, while the best-fit model spectrum is shown in orange. Most emission features are well matched with the exception of the He II line, which is underpredicted. [Berg et al. 2018]

Another item of interest in the spectrum of SL2SJ0217 is its double-peaked Lyman-alpha emission (Figure 4).  Lyman-alpha emission arises when an electron in the n = 2 orbital of a neutral hydrogen atom drops into the n = 1 orbital, emitting a photon with a wavelength of 1216 Angstroms. It isn’t uncommon to see Lyman-alpha emission in a galaxy, but the double-peaked structure is less typical. Luckily, simple models have shown that a double-peaked structure can arise in Lyman-alpha emission when the photons scatter through a spherical or shell-like cloud of gas around a region of star formation. The unequal peak heights can then be attributed an inflow of gas into the cloud. Thus, SL2SJ0217 appears to be hosting an inflow, which could also explain its star formation activity.

double-peaked, red-suppressed lyman alpha emission profile

Figure 4: The double-peaked emission of SL2SJ0217 in Lyman alpha. The red dashed lines show the average velocity range of absorption features in the spectrum (not shown), the black dashed line shows the systemic velocity of the galaxy, while the solid blue line shows the blended Gaussian profiles used to fit the emission. [Berg et al. 2018]

Overall, it is clear that this galaxy has many unique characteristics distinguishing it from others at its cosmic epoch. However, the strong He II emission is unexplained and the authors speculate that better models of massive, metal-poor stars are necessary in order to fully explain the observed nebular emission. If SL2SJ0217 is indeed representative of the very first generation of galaxies, there is still some work do be done before we can understand them completely.

About the author, Caitlin Doughty:

I’m a third year graduate student at New Mexico State University working with Dr. Kristian Finlator. I use numerical simulations to study galaxy evolution during the epoch of reionization, with a focus on metal absorption in the circumgalactic medium.

brown dwarf

Editor’s note: This article, written by AAS Media Fellow Kerry Hensley, was originally published on Astrobites.

Figure 1. A comparison of the sizes of Sun-like and low-mass stars to brown dwarfs, gas giants, and terrestrial planets. Though brown dwarfs have only slightly larger radii than Jupiter, they contain more than ten times the mass. [NASA/JPL-Caltech/UCB]

Stars dutifully fuse hydrogen into helium throughout their main-sequence lifetimes, while planets quietly fuse nothing at all. In between these two extremes — large and hot enough to fuse deuterium but too small and cool to process its lighter cousin, hydrogen — lie brown dwarfs (see Figure 1). Like giant planets, they have cloudy atmospheres and sport polar aurorae. Like stars, they are powered by nuclear fusion, but unlike stars, they cool as they age, which could have interesting implications for the development of life on planets orbiting around them.

Astronomers have discovered over a thousand brown dwarfs, ranging in spectral type from the barely-sub-stellar late M dwarfs to the ultra-cool Y dwarfs, but questions about their formation, interior goings-on, and early lives remain. Of particular interest is the lower end of the mass range: where do we draw the line between brown dwarfs and planets? And where do the transitions between brown-dwarf spectral types lie?

A Curious Brown Dwarf in AB Doradus

Figure 2. The spectral energy distribution of 2M1324+6358 (black line) compared to two other T2 dwarfs. 2M1324+6358 is much brighter at long wavelengths than either of the other T2 dwarfs, which could mean that it’s an unresolved binary. [Gagné et al. 2018]

In this paper, the authors investigate an object that has defied past classification attempts: 2MASS J13243553+6358281, or 2M1324+6358 for short. Other than being the top baby name for 2018, this unwieldy name tells us where to find the object in the sky and that it was cataloged by the Two Micron All-Sky Survey. Previous observations of this object (see Figure 2) indicated that it might be a single, very young brown dwarf or an unresolved binary system composed of two brown dwarf flavors: one L-dwarf and one T dwarf.

In order to learn more about 2M1324+6358, the authors first determine whether or not it belongs to AB Doradus, a young (~150 million years old), nearby (~65 light-years away) moving group. A moving group is a collection of stars, traveling together through the Galaxy, that formed at the same time from the same cloud of gas and dust. It’s much easier to figure out the age of a group of stars than an individual star, and since all stars in a moving group formed at the same time, figuring out if an object belongs to a moving group tells us its approximate age. Combining luminosity and color measurements with distance and age gives modelers the information they need to determine the brown dwarf’s radius, temperature, and surface gravity—critical information for exploring the muddy waters between small stars and giant planets.

First, the authors use parallax to determine the distance to 2M1324+6358. The parallax measurements hint that 2M1324+6358 belongs to the moving group because it’s at the same distance from the Earth. It’s not enough to just be at the right distance, though; stars are constantly in motion, and it’s common for a star to escape its natal cluster and mosey through neighboring clusters. However, a star that’s just passing through will tend to have a different velocity from stars that belong to the cluster, so if 2M1324+6358’s distance and velocity both match AB Doradus’, it’s very likely to belong. The authors pass the object’s velocity and location to a Bayesian statistical framework and find a cluster membership probability of 98% — bingo!

2M1324+6358: One Brown Dwarf or Two?

Figure 3 shows that 2M1324+6358 is fainter than other objects of similar spectral type, which means it’s unlikely to be a binary system. As a member of the AB Doradus moving group, it must also be young — just about 150 million years old. Young brown dwarfs are thought to be highly variable, due to both stellar activity and clouds drifting through their atmospheres, which could explain the unusual spectral features that led past studies to conclude it was a binary.

Figure 3. Color-magnitude diagram showing 2M1324+6358 (J-K ~ 1.6) in relation to other likely AB Doradus moving group members and field stars. 2M1324+6358 is slightly fainter in J-band than other T dwarfs. [Gagné et al. 2018]

With the potential binary reduced to a single object, it’s also possible to estimate its radius and mass: just 20% larger than Jupiter and 11–12 times as massive, making 2M1324+6358 one of the nearest known planetary-mass brown dwarfs! While there is still much we don’t know about young brown dwarfs, studying nearby objects like 2M1324+6358 can help us understand what fills the gap between small stars and large planets.

Citation

Jonathan Gagné et al 2018 ApJL 854 L27. doi:10.3847/2041-8213/aaacfd

Pluto and Charon

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 Early In Situ Formation of Pluto’s Small Satellites
Author: Man Yin Woo and Man Hoi Lee
First Author’s Institution: The University of Hong Kong
Status: Accepted to AJ

Many Moons, Many Mysteries

Pluto’s moons. Top: Styx (left) and Kerberos (right). Middle: Nix (left) and Hydra (right). Bottom: Charon. [NASA/JHUAPL/SwRI]

Planet or otherwise, Pluto’s not some lonely chunk of rock lurking around the edges of our solar system — as previous astrobites and gorgeous pictures from the New Horizons spacecraft have shown. Pluto has not one, not two, but five satellites (natural satellites, not artificial ones) making up its complex moon system. The largest of the moons is Charon, which is half the length of Pluto and one-eighth of Pluto’s mass. The other four moons, in order from closest to farthest from Pluto, are Styx, Nix, Kerberos, and Hydra. All together, these moons make up quite a peculiar system; all five of them, for example, have orbits that are almost perfectly circular and nearly coplanar. Moreover, the smaller moons’ orbital periods relative to Charon fall near some very neat intervals of 1:3:4:5:6 — meaning that the orbital periods of Styx, Nix, Kerberos, and Hydra are about 3, 4, 5, and 6 times the orbital period of Charon, respectively.

So what astrophysical phenomenon led to such a nicely oriented orbiting system? The answer is still unclear. Scientists do think they have a pretty good explanation for how Charon formed, however. Scientists discovered Charon back in the 1970’s, and today the most widely accepted idea for Charon’s formation is the intact capture scenario. This scenario says that Charon formed back when the Kuiper Belt was a lot more crowded; at some point, Pluto collided with a Kuiper Belt object and captured the impactor — Charon — into orbit. After the collision, tidal evolution (which would have slowed down Pluto’s spin and pushed Charon’s orbit outward) helped bring Pluto and Charon into the orbital system that the two have today.

Pluto’s four smaller moons, on the other hand, are recent, 21st-century discoveries, and their strange orbits have yet to be explained. There are a number of proposed formation scenarios out there, but scientists are still trying to find a scenario that completely and consistently explains the complex moon system that we observe today.

The authors of today’s paper, Man Yin Woo and Man Hoi Lee, joined this very search. They focused on one scenario in particular: the early in-situ formation scenario. This scenario follows right along with the Charon-forming intact capture scenario described earlier: it states that the impact from the collision that formed Charon also produced a ring of debris at about 20 RP, where RP is the radius of Pluto. This ring spread outward over time, due to angular momentum transfer from the Pluto-Charon system. Pluto’s smaller moons then formed from the debris ring near their modern orbital distances, in orbits that were already nearly coplanar and circular as a result of the ring.

But the outward spread of the ring would have happened quickly, reaching today’s distances all while Charon was still close to Pluto (aka, before Charon’s tidal evolution). Woo and Lee point out that the story couldn’t just end there; Charon would then move its orbit outward due to tidal evolution — and this outward movement of Charon (as a very massive body) might have disrupted the smaller moons’ orbits. To investigate this possibility, the authors set out to simulate Charon’s tidal evolution after the early in-situ formation scenario, to explore how Charon’s movement outward might have affected the smaller moons orbiting around Pluto as well.

No Body by N-Body

The authors used N-Body simulations to play out the scene of Charon’s tidal evolution, after the four small moons had already formed from a debris disk. They used the Charon-forming intact capture scenario to help inform the large moon’s initial conditions, such as for choosing Charon’s initial eccentricity (which describes how circular an orbit is) and the moon’s starting distance from Pluto before tidal evolution. They also varied certain initial conditions, including Charon’s initial eccentricity and the form and speed of Charon’s tidal evolution, to help cover a range over how the intact capture scenario might have played out in practice.

To simulate small satellites, the authors used massless test particles, which they placed randomly between about 35 and 60 RP from the Pluto-Charon system. This range covered the orbital distance range that Pluto’s four small moons orbit in today. The authors based the test particles’ initial conditions, such as their coplanar orbits and initial eccentricities, on theory as to what satellite orbits would look like after forming from a debris disk.

The authors started with 200 test particles for each simulation run with different initial conditions. Then they let Charon tidally evolve. They kept track of how many test particles survived after the tidal evolution, as well as of surviving particles’ final eccentricities and orbital periods relative to Charon.

pluto system simulations

Results from the simulation run that most closely explains the moon system we observe around Pluto today. The x-axis gives the final mean distance to the Pluto-Charon system in units of Pluto radii. The y-axis represents the final eccentricity in log scale, with smaller log values indicating orbits closer to circular in shape. The scattered points show the test particles that survived after tidal evolution. Green points stand for test particles not affected by resonance with Charon during Charon’s tidal evolution; black points are those particles that were affected by resonance but were not trapped by it; red points are those particles that were trapped by resonance but escaped; and blue points are those particles still trapped in resonance with Charon at the end of the simulation run. The dotted black lines guide the eyes along the distances where Pluto’s moons orbit today. The blue points in this case seem to have a preference for the 4:1 and 5:1 orbital ratios of Nix and Kerberos, since they are so thickly concentrated there and nowhere else. But no points have such a significant preference for the 3:1 and 6:1 orbital ratios of Styx and Hydra. [Woo and Lee 2018]

In most of the simulation runs, the final system after evolution looked quite different compared to the moon system we see around Pluto today. In some cases, for example, the majority of the test particles were ejected from the system by Charon during the tidal evolution phase, while in other cases the orbital periods of the surviving test particles didn’t end up near the special 1:3:4:5:6 values. The most promising simulation run, as shown in the figure above, featured test particles that survived through tidal evolution and had orbital period ratios concentrated around 4:1 and 5:1. However, the only particles that reached the 6:1 distance of Hydra seemed to have no preference in doing so, and hardly any particles reached the 3:1 distance of Styx.

In short, none of the simulation runs led to the complex moon system we see around Pluto today. The authors did acknowledge that they made some crucial assumptions and simplifications for their simulations, such as their treatment of satellites as massless, and that relaxing these characteristics might change the simulation results. But overall they noted that in order to explain how Pluto’s moons came to be, we need to find a scenario that accounts for all of the complexities in the orbiting system.

And so the search continues! We can use the results from today’s paper to inform new ideas and new scenarios for how Pluto’s moons might have formed. Hopefully, we’ll be able to one day witness a simulation that reproduces every characteristic of Pluto’s orbiting moon system — and in doing so, unravel more of Pluto’s mysteries as a whole. Because even if Pluto isn’t considered a planet anymore, it’s still a beloved part of the solar system — and its origin story forms an important piece in the puzzle of how the solar system we live in today came to be.

About the author, Jamila Pegues:

Hi there! I’m a 2nd-year grad student at Harvard. I focus on the evolution of protoplanetary disks and extra-solar systems. I like using chemical/structural modeling and theory to explain what we see in observations. I’m also interested in artificial intelligence; I like trying to model processes of decision-making and utility with equations and algorithms. Outside of research, I enjoy running, cooking, reading stuff, and playing board/video games with friends. Fun Fact: I write trashy sci-fi novels! Stay tuned — maybe I’ll actually publish one someday!

Titan

Editor’s note: This article, written by AAS Media Fellow Kerry Hensley, was originally published on Astrobites.

By now, exoplanet enthusiasts will be familiar with hot Jupiters, super-Earths, mini Neptunes, and even exo-Venuses, to name just a few. As the search continues, astronomers are finding colder and smaller planets, making possible the discovery of more analogs to our solar system. In today’s paper, Lora et al. consider exoplanets similar to one of the most tantalizingly Earth-like yet alien bodies in our solar system: Titan.

Figure 1. False-color RADAR image of Titan’s north pole from the Cassini spacecraft. Click here for a larger version. [Gazetteer of Planetary Nomenclature]

Aptly named, Titan is the second largest moon in our solar system, outdone only by Jupiter’s moon Ganymede. Larger (though less massive) than Mercury, if Titan were to orbit the Sun rather than Saturn, it would be a planet in its own right. Titan’s main claim to fame is being the only known solar-system object other than the Earth to have stable liquid on its surface — in lakes and seas, as Figure 1 shows — beneath a thick, hazy atmosphere.

While Earth is amenable to hosting water in liquid, solid, and gaseous states simultaneously, Titan appears to be similarly welcoming — but to methane. Because of this similarity, it’s fun to imagine Titan as Earth’s chilly alter-ego, a cool and hazy home for life — but not life as we know it. The ample hydrocarbons and nitrogen compounds might assemble into lurking carbon-based creatures crawling beneath the haze and swimming in the hydrocarbon seas … but before we get too deep into science-fiction speculations, let’s get back to the science.

Modeling Titanic Planets

In today’s paper, Lora et al. use theoretical models to investigate the atmospheres of Titan-like exoplanets orbiting Sun-like stars as well as K and M dwarfs, which are smaller and redder than the Sun. Thanks to Cassini, we already have an idea of what Titan might look like masquerading as an exoplanet around a Sun-like star, but Titan takes about 30 years to orbit the Sun; that’s a long time to wait to detect and confirm an exoplanet! Around an M dwarf, a planet with the same effective temperature as Titan (~80 K) takes only about 2 years to complete an orbit — much easier and quicker to detect. Titan-like exoplanets don’t need to crowd M dwarfs as closely as Earth-like planets do, so they are less susceptible to the notoriously nasty space weather of cool stars and they are less likely to be tidally locked. Though neither issue immediately disqualifies M-dwarf planets from the habitability contest, considering a cooler planet appears to solve both problems at once.

For this investigation, the authors pair a general circulation model with a photochemical model. General circulation models are widely used in planetary and Earth science to investigate atmospheric dynamics and the driving forces behind climate. While general circulation models can help us understand the bulk motion of the atmosphere, a photochemical model can tell us what atoms and molecules will be present, and in what proportion. Photochemistry is driven by starlight as photons trickle through the upper atmosphere to the surface, providing energy to nudge chemical reactions along. In the case of Titan, the atmosphere is largely made up of nitrogen and methane, with trace amounts of light hydrocarbons like ethane, acetylene, and hydrogen cyanide.

The authors pass the model parameters back and forth between the general circulation model and the photochemical model until, roughly 2,000 Earth-years later in model-time, the atmosphere reaches equilibrium. Model-generated maps of equilibrium temperature are shown in Figure 2. The slight differences between stellar types arise due to the presence of atmospheric haze; the haze tends to absorb short wavelengths and transmit long wavelengths, so redder stars have a higher proportion of starlight making it to the surface. The amount of haze in each case appears to be about the same, which could mean that haze is a persistent feature of Titan-like exoplanets, regardless of what type of star they orbit.

Figure 2. Temperature maps for a Titan-like exoplanet orbiting (from left to right) a G-, K-, and M-type star. The cooler the star, the cooler the stratosphere (top, red and orange) and the warmer the troposphere (bottom, blue and green). [Lora et al. 2018]

What observational features would these atmospheres have? Using the model atmospheres, the authors generate emission spectra for each of the three host-star spectral types, which are shown in Figure 3.

Figure 3. Globally averaged emission spectra for a Titan-like planet orbiting the Sun (blue), a K dwarf (orange), and an M dwarf (red). In some cases, the strength of the emission feature is determined by the atmospheric temperature (such as the methane feature on the far right), while in other cases it reflects how much of the compound is present (such as the ethane feature around 800/cm). [Lora et al. 2018]

Changing the host star’s spectral type changes the temperature and abundance of each chemical species, which in turn changes the strength of the emission features. This work considered only the effect of host-star spectral type on Titan-size and Titan-mass planets, but future modeling will consider the effects that changing surface gravity, axial tilt, and rotation rate have on the emission spectra, preparing astronomers to investigate the full diversity of Titan-like exoplanets. With highly sensitive space observatories on the horizon (e.g. WFIRST, LUVOIR, HabEx, OST), models like these will be valuable for atmospheric characterization of cold, hazy planets.

Citation

Juan M. Lora et al 2018 ApJ 853 58. doi:10.3847/1538-4357/aaa132

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