Title: Galaxy pairs in the Sloan Digital Sky Survey – VI. The orbital extent of enhanced star formation in interacting galaxies

Authors: David R. Patton, Paul Torrey, Sara L. Ellison, J. Trevor Mendel, and Jillian M. Scudder

First author’s institution: Department of Physics and Astronomy, Trent University, Canada

Ok, this is no big surprise: enviroment affects star formation in galaxies. Observations have long shown that the star formation rate (SFR) is strongly enhanced when two galaxies merge or simply interact, with strongest enhancements found in the closest galaxy pairs, such as coalescing galaxies, or systems observed near to the first pericentre passage. Enhancements in star formation result in bluer colours and lower metallicities, i.e. characteristic features of young stellar populations, and spectacular objects such as luminous infrared galaxies.

However, a question is still open, as you can guess from the title of today’s astrobite: what is the orbital extent of enhanced star formation in interacting galaxies? At which projected separation of the two galaxies does it disappear? This Letter aims at investigating the enhancement of star formation as a function of the separation in galaxy pairs. The issue is addressed in two complementary ways: from an observational perspective, analyzing galaxy pairs from the Sloan Digital Sky Survey (SDSS), and from a theoretical perspective, studying the outputs of numerical simulations of galaxy mergers.

First, a large sample of ~600,000 galaxies from the SDSS is considered, which have secure spectroscopic redshift between 0.02 and 0.2, and total stellar mass estimated from photometry. For each galaxy, the closest neighbour is singled out, by requiring that it has 1) the smallest projected separation from the galaxy, 2) a rest-frame relative velocity lower than 1000 km/s, and 3) a stellar mass which is not excessively different (a factor of 10) from that of the galaxy.

Then, based on previous measurements of the SFR (see the catalogue in Brinchmann et al 2004), only star-forming galaxies are selected from the sample, without any special requirement on the SFR of their neighbours. In this way, also “mixed” galaxy pairs are included in the resulting sample, which contains ~211,000 star forming galaxies. For each of these galaxies, the authors determine a statistical “control sample” which matches each galaxy in both physical properties (stellar mass, redshift) and environment (local density, isolation), but does not necessarily contain star forming galaxies. The details of the procedure adopted to identify such control samples are deferred to a subsequent paper.

Figure 1 (from Patton et al 2013). Mean SFR enhancement (top panel) and mean SFR (bottom panel) versus projected separation of galaxy pairs. The error bars are the standard error in the mean. Blue is for galaxy pairs from SDSS; red is for their statistical control samples. The dashed horizontal line represents zero enhancement of star formation.

The bottom panel of Figure 1 shows, as a function of projected distance, the mean SFR of all the paired galaxies (blue) and of their statistical control samples (red). The ratio of these two quantities, which is defined as the “enhancement in star formation” is plotted in the top panel, where the inset plot shows its behaviour at even larger values of the projected separation. This figure nicely shows that star formation is enhanced in interacting galaxies, that such enhancement is stronger at the smallest separations, especially less than 20 kpc, and finally that the enhancement in SFR extends to larger separations than what was previously thought, being visible out to projected separations of ~ 150 kpc. In particular, it is found that the 66% of the enhanced star formation in galaxy pairs occurs at separations greater than 30 kpc.

Takeaway message: an enhancement in star formation is not only limited to strongly interacting galaxies with a very close companion, but also to wide galaxy pairs.

Now, are these findings consistent with the predictions from numerical simulations of interacting galaxies? In order to answer to this question, the authors investigate a suite of ad-hoc simulations of galaxy mergers run with the N-body/SPH code GADGET.

The simulated galaxy pairs are simple binary systems, where the stellar masses of the two initial galaxies is set to match the median stellar mass and mass-ratio of the observed SDSS sample. The simulated mergers span a significant set of five values of orbital eccentricities, five values of impact parameters, and three values of merger disc orientation, not limiting the galaxy orbits to low values of eccentricities and to small values of impact parameters. In total, 75 (5 x 5 x 3) orbital configurations for galaxy mergers are explored, and each one can be observed from a random set of viewing angles and at random times during the orbital evolution.

The authors compute the mean SFR over the 75 orbital configurations, observing each orbit from random orientations and at random moments during the merging history. Of course, these random times imply many different values of projected separations. This measurement of SFR is then translated into a measurement of SFR enhancement by normalizing by the SFR of the same galaxy evolved in isolation.

Figure 2 (from Patton et al 2013). Mean SFR enhancement as a function of projected separation in galaxy pairs from SDSS (blue) and numerical simulations of mergers (black).

Figure 2 shows the mean enhancement in star formation rate computed from galaxy merger simulations (black), and the extremely small error bars are due to the average over many orbit orientations. The curve showing the same data derived from galaxy pairs in SDSS is overlaid in blue. Remarkably, the two curves, hence the two different approaches, yield a similar result: an enhancement in SFR is observed out to large projected distances ~150 kpc, though stronger in the SDSS data. In the simulations, the enhancement is a result of starburst activity triggered at the first pericentre passage, which persists as the galaxies move to wider separations.

Hence, the authors can safely conclude that interaction-induced star formation is not only limited to those galaxies which have a close companion, but rather it affects a larger variety of galaxies.

Title: A Shapiro delay detection in the binary system hosting the millisecond pulsar PSRJ1910-5959A
Authors: A. Corongiu, M. Burgay, A. Possenti et al.
First Author’s Institution: INAF (National Institute for Astrophysics in Italy)

Some History
Shapiro time delays are one of the four tests of general relativity possible in the solar system. Because mass curves spacetime, light traveling close to a massive object must take a longer path to reach a target than if spacetime were flat, as this video and animation show. Irwin Shapiro was the first to test this phenomenon by bouncing radar signals off Venus and Mercury in the 1960s. The time delay for these signals was only about 200 microseconds.

This paper measures a Shapiro delay in a binary pulsar system called PSRJ1910-5959A. This pulsar has been previously studied but the results here include more data that allows for a more refined analysis. (See here and here for previous Astrobites posts on pulsars). This pulsar has a spin period, how long it takes the pulsar to spin about its axis, of 3.27 ms. The companion star to the pulsar is a helium white dwarf, determined by independent spectroscopic observations with the ESO Very Large Telescope and the Hubble Space Telescope. The white dwarf orbits the pulsar with an orbital period of 0.84 days. Though this pulsar visually appears to be part of globular cluster NGC 6752, it is a matter of debate whether this is actually true or is just an illusion. If the pulsar is part of the globular cluster, this represents the first time a Shapiro delay has been detected for a pulsar in a globular cluster and offers important insights into the history of the cluster.

As the white dwarf passes between our line of sight and the pulsar, there is a slight delay in the pulses from the pulsar. Since pulsars pulse so regularly, any irregularity is a sign that something interesting is happening. This delay is on the order of microseconds; it took observations spanning 10 years to detect it. Finding a Shapiro delay is exciting because it allows for very tight constraints on the mass of the companion star and the pulsar, as well as the inclination of the system.

How They Did It
To detect this delay, the research team used the 64m Parkes Radio Telescope located in Australia. For over 10 years, they regularly monitored this pulsar to detect times of arrival for the pulses. To accurately time a pulsar, astronomers fold the data on itself at the pulse period to increase the signal to noise. This yielded the team ∼1000 usable pulse timings. Check out this post for more details on the methods radio astronomers use to measure pulsar timing.

The research team used a model called the DD binary model to precisely measure the expected time of arrival for each pulse and the residuals for each detected pulse, the amount each pulse varies from the best fit. The DD binary model includes two parameters called the range and the shape that are related to the companion mass (the white dwarf) and the orbital inclination of the system. Check out Table 1 in the paper to see all the parameters that were measured or derived for this fit, and specifically note how amazing it is that pulsar periods can be measured to thirteen places past the decimal point!
To detect the Shapiro delay, a fit to the residuals is then determined, shown in the figure to the left. By finding the best fit to all the parameters of the model, then setting the companion mass to 0 and the orbital inclination to 90 degrees, a fit is determined and the remaining residuals can be seen in the top left of the figure. The team then binned and averaged the results to find an obvious harmonic in the fit of this data (bottom left). By  fitting again and removing the parameters related to the Shapiro delay,  they form the plots on the right side of the figure. Binning again brings out another harmonic, called the third harmonic, seen in the bottom right. Placing the binary companion in an elliptical orbit can explain this first harmonic, but the third harmonic can only be due to a Shapiro delay present in the data. The solid line in the figure shows the theoretical prediction of the harmonics, which matches the data well.

Once the Shapiro delay was determined, the team used these results to determine the inclination of the system and the mass of the white dwarf. They determined a companion mass of 0.180 ± 0.018M_⊙ and an inclination of at least 88 degrees. Recall that an inclination of 90 degrees is defined as a perfectly edge-on orbit. The mass of the pulsar can then be determined, yielding a mass of 1.33 ± 0.11 M_⊙. It is interesting to compare these results to those presented in the previous papers that used photometric and spectroscopic data to determine the inclination and companion mass. The results are consistent, which give credence to both methods as ways to determine these parameters.

Other Thoughts
The proper motion of this pulsar must be measured to determine once and for all if it is part of the globular cluster. If it is, this system will prove very useful in understanding mass-radius relationships for helium white dwarfs. It is difficult to determine the mass and the radius of white dwarfs using optical observations. Doing so requires white dwarf spectral models to estimate the surface gravity and effective temperature, then infer the mass and radius. Observations that do not rely on these models are needed so we can understand the interaction of these fundamental properties better.

Title: Discrete clouds of neutral gas between the galaxies M31 and M33 Nature May 9, 2013
Authors: Spencer A. Wolfe, D. J. Pisano, Felix J. Lockman, Stacy S. McGaugh & Edward J. Shaya
First author’s institution: Department of Physics, West Virginia University

Figure 1 – Artist’s conception of the region between M31 and M33 with an image of the new high resolution observations of the clouds in between the two galaxies (inside box)

Astronomers recently found seven clouds of neutral hydrogen gas (HI – “H” and roman numeral one) spread out between the galaxies M31 and M33. Could these clouds have condensed around dark matter-rich filaments, or are they leftover gas strewn across intergalactic space from a galaxy interaction event that occurred billions of years ago? Wolfe et al. use new high resolution radio observations from the Green Bank Telescope (GBT) to sort out the origin of these mysterious clouds.

The presence of neutral hydrogen gas in the region between M31 and M33 was confirmed last year with the GBT by Lockman et al. (2012). The velocity of the gas is similar to the systemic velocities of M31 and M33, confirming that it is not Milky Way gas. But the sensitivity of these initial observations was not very high. Longer integration time were needed to get sensitive high resolution images of the gas. The high resolution determines whether the gas is diffuse or clumpy. This is important for determining the origin of the gas – intergalactic filament or debris from tidal interaction between the two galaxies.

Intergalactic filaments between galaxies can serve as a bridge to funnel gas into galaxies. This has been proposed as a mechanism to fuel further star formation in spiral galaxies for a few more billion years from the gas in the intergalactic medium. However the gas seen in the space between the galaxies could have come from a tidal interaction event. When M31 and M33 came much closer together a few billion years ago, the gravitational force of the two galaxies could have stretched gaseous material between them in a tidal tail.

Figure 2 – Illustration of the spin-flip transition that gives rise to the 21 cm line.

The HI observations of the clouds were made using the 21 cm line of neutral hydrogen. This line arises from the spin alignment transition in the ground state. As illustrated in Figure 2, when the spin alignment of the proton and the electron switch from aligned parallel to anti-parallel, the atom emits a photon corresponding to the small change in energy states – one with wavelength of 21 cm. The 21 cm line is extremely useful for mapping hydrogen gas, since the symmetric H₂ molecule does not emit strongly in the radio.

Wolfe et al. observed the region of the HI gas again with the GBT, this time with higher sensitivity and higher resolution. They found the HI gas formed seven distinct dense clumps (see Figure 3). About 50% of the HI in the region is in the clouds. The clouds are about the size of dwarf galaxies. However, there are no stellar overdensities in the region, so they are not thought to be dwarf galaxies.

Figure 3 – Map of the 21 cm emission detected by the GBT in between M31 and M33. Six of the seven clouds are visible in this image (labeled numerically). The seventh is visible when the data is smoothed to a lower resolution. The directions to M31 and M33 are marked by the arrows.

Figure 4 – This position-velocity plot shows the angular distance from M31 (x-axis) vs. the velocity (y-axis) for M31, M33 (blue squares), high velocity clouds (red circles), and the new clouds detected with the GBT (black plus signs). The new clouds occupy a distinct region in position-velocity space compared to other Local Group objects.

There are several factors that make these clouds look distinctly different from the high velocity clouds (HVCs) that surround M31 and M33. First, they are much further away from either galaxy than any of the HVCs. The plot of position vs. velocity (Figure 4) shows the HVCs are clustered around their host galaxies in the position axis, while the gas clouds occupy a space distinctly their own. The velocities of the clouds also distinguish them from HVCs. The new clouds have velocities similar to M31 and M33, while the HVCs tend to have a much larger spread in velocity.

The authors identify several possible origins of the clouds between M31 and M33.
1) The clouds are primordial, gas-rich objects, like dwarf galaxies.

• We already discussed several reasons above why the clouds don’t look like dwarf galaxies or HVCs. This possibility also does not explain why the clouds seem to lie along a connecting line.

2) The gas has accreted onto local overdensities of dark matter.

• If the gas came from a tidal interaction between the two galaxies, the velocity of the gas would be too high to be accreted.

3) The clouds could be tidal dwarf galaxies – a type of irregular dwarf galaxy that form in tidal tails after a tidal galaxy interaction.

• This would explain the position and velocity of the clouds, making some of our previous arguments against them being dwarf galaxies invalid. However, this does not explain the lack of stars or why they have low internal velocity distributions compared to other tidal dwarf galaxies.

4) The clouds are transient objects that condensed from an intergalactic filament.

• This explains the location of the clouds and the lack of stars in the region. This scenario has been shown to be possible from simulations by Fernandez et al. (2012).

The authors prefer this last scenario. If there is a galactic filament connecting the two galaxies, it can funnel gas into the galaxies and fuel star formation for a few more billion years.

Title: Photophoretic separation of metals and silicates: the formation of Mercury like planets and metal depletion in chondrites
Authors: Gerhard Wurm, Mario Trieloff, and Heike Rauer
First author’s institution: University of Duisburg-Essen

Iron-rich Mercury

Mercury’s high density has been a longstanding puzzle in planetary science. Its density means that it must have a significantly higher iron abundance than Venus, Earth, Mars, or the asteroids, probably in the form of a large iron core. A common popular explanation (the one I was taught four years ago in my Intro Planetary Science course) is that a giant impact came along late in the formation process and stripped the silicate mantle off of Mercury, leaving behind an iron-rich planet. However, in the last four years NASA’s MESSENGER mission has challenged that idea: measurements of elements like potassium—which would be extremely depleted after such a giant impact—show no depletion, so the giant impact hypothesis has been largely ruled out. A new hypothesis is required.

In the past four years, we’ve also found some irradiated rocky exoplanets, most of which are even hotter than Mercury, and there is some evidence to suggest that the two hottest rocky exoplanets with measured masses (Corot-7b and Kepler-10b) may also be more iron-rich than the Earth. While still in the realm of small-number statistics, this is suggestive that there is some more universal process shaping the formation and evolution of rocky planets. In this paper, the authors propose an explanation for the formation of both Mercury and these iron-rich exoplanets.

Sorting the particles in a disk

This cartoon illustrates how the general sorting mechanism proposed in this model works. The top panel shows that at the illuminated edge of the disk, silicates are pushed more efficiently than metals by this process. The bottom panel illustrates the resulting composition difference in the protoplanetary disk.

The basic idea proposed here is that there is a mechanism in the disk that sorts metals from silicates, making the inner part of the disk more metal-rich and the outer part of the disk more metal-poor (and thus silicate-(rock-)rich). (I’ll note that here I’m using “metal” not in the astronomer’s sense—anything heavier than H/He—but in the normal Earthling usage where metals are metallic.) If this sorting mechanism were efficient on timescales shorter than the formation of planets (a few million years), then the planets that formed from the inner material would naturally be more iron-rich.

Figure 3 shows a cartoon version of how this process occurs. The disk starts out with uniformly distributed iron and silicates, and then the silicates get preferentially pushed to the outer regions of the disk.

The proposed mechanism: Photophoresis

This cartoon illustrates how the proposed mechanism photophoresis works. In the presence of irradiation, a particle will have a warm side and a cool side. When gas molecules hit the particle, those rebounding from the warm side will have higher energy (and therefore higher velocities) than those rebounding from the cool side. This imparts a net force, away from the irradiating source, on the particle.

The mechanism that they propose to do this sorting is called photophoresis. The idea is that a particle in a protoplanetary disk will be illuminated on one side (by the Sun), and that side will become warmer. The particle will then be hit by the molecules in the disk. These molecules will equilibrate to the surface temperature of the particles before being ejected with the energy (and velocity) corresponding to that temperature. Since the temperature is asymmetric—hotter on the illuminated side—the net momentum imparted onto the particle will also be asymmetric. In general, the particle will be pushed outward, away from the illuminating source.

It’s clear how this mechanism could move particles—but why does it sort them by composition? The reason is that the strength of the photophoretic force is inversely proportional to the thermal conductivity. This is straightforward to understand conceptually: if a particle has high thermal conductivity, the heat travels efficiently through the particle, so even though only one side is illuminated, the whole particle will warm up. The temperature difference between the warm and cold side will be small, and a smaller net momentum will be imparted by the molecules hitting and rebounding from the particle. If instead the material has a low thermal conductivity (an insulator), heat travels slowly through the particle, so the temperature difference between the sides of the particle will be larger, and the corresponding net momentum gained will be larger.

Since iron is a metal, it has a high thermal conductivity, about 50 W/(mK). Silicates have much lower thermal conductivity, about 1 W/(mK). This means that photophoresis will be about 50 times more efficient at transporting silicates away from the Sun than iron particles.

Based on the properties of a protoplanetary disk, the authors calculate the drift times for particles to move 1 AU to be 300,000 years for silicate grains (well within the formation timescale of planets) and 50 times longer, or 15 million years, for iron grains (similar to the timescale to form planets).

Photophoresis would thus naturally create a composition gradient in the disk, with metal-enrichment in the inner parts and silicate-enrichment in the outer parts. This could explain the iron-enrichment of Mercury, as well as the apparent slight iron-enrichment of the rocky exoplanets Corot-7b and Kepler-10b.  Of course, many other physical mechanisms are at play during planet formation, but the authors suggest that exploring this mechanism further could be fruitful in explaining both solar and extrasolar planetary systems.

Caption: H. A. Sawyer loading plates into the Harvard 16” Metcalf Doublet telescope. Picture from http://hea-www.harvard.edu/DASCH/telescopes.php

• Paper Title: 100-year DASCH Light Curves of Kepler Planet-Candidate Host Stars
• Authors: S. Tang et al
• First Author’s Affiliation: Harvard-Smithsonian Center for Astrophysics, Cambridge, MA; Kavli Institute for Theoretical Physics, Santa Barbara, CA; California Institute of Technology, Pasadena, CA
• Journal: Publications of the Astronomical Society of the Pacific (Submitted)

Introduction: the DASCH project

Astronomy has advanced in leaps and bounds over the last few hundred years. Perhaps the single greatest advance has been the switch from observing with our eyes to observing with cameras. Where once we inspected the heavens with our eyes and relied on sketches to record what we saw, now we attach imaging mechanisms directly to the telescope. Not only does this allow us to collect more photons, imaging mechanisms also give us the ability to store data for later analysis. A little more than a century ago, astronomers at Harvard made the switch to using photographic plates to image the heavens. Each plate, once analyzed, was cataloged, archived, and forgotten…until now.

Researchers at Harvard recently recognized the promise of the data being held in these archives. Over a century’s worth of observations of the sky are recorded in these plates. By contrast, most objects observed as part of other projects have no more than a few decades worth of observations at best. This dataset offers us the remarkable opportunity to study how stars have evolved over almost a century. Who knows what long-term trends or cycles might be identified?

To realize the potential of this dataset and answer questions like these, the DASCH project (Digital Access to a Sky Century) project was created. This project aims to scan and digitize the entirety of Harvard’s plate archive, converting into usable form for today’s astronomers and their analytical techniques.

Role of This Work: Variability in the Kepler Field

A number of exciting discoveries have already been made with this data, including the discovery of a 5-year dust accretion event on the eclipsing binary KU Cyg and the observation of century-scale variability in K-giants. The paper explored in today’s Astrobite explores what the DASCH dataset can tell us about exoplanets. In particular, it looks at Kepler planet candidate host stars, and asks if we can distinguish variability in them. The variability of the host stars is important to understanding the planet environment. Long-term variability in stellar output would influence the composition and size of a planet’s atmosphere. Similarly, if a star is prone to occasional catastrophic flares, this would impact the atmospheres of orbiting planets – and their prospects for life. Interactions between close-orbiting giant planets and their parent stars might influence such flare events. The DASCH data can place constraints on long-term variability and flare rates.

Data and Results

The authors constructed time series lightcurves for the 997 stars with planet candidates from the 2011 release of the Kepler data. Of these, 261 stars had at least 10 good observations over the course of the DASCH data, and 109 of them had at least 100. Brighter stars had more observations, as they showed up in more plates.

Figure 1 presents sample lightcurves for the Kepler stars. Figure 2 presents the variability of the 240 Kepler host stars with more than 10 DASCH points and no contaminating stars as a function of magnitude (brightness). Note the decrease in scatter with higher magnitude (lower brightness); this is because the fainter stars were only observed in the high-quality deep field plates, improving the precision of observation.

Figure 1: Sample lightcurves for 4 stars thought to host planets from DASCH. The green dots are the modern-day Kepler lightcurves for these objects. The DASCH data is much less precise, but covers a far larger temporal range.

Figure 2: Variability for the Kepler planet hosts (y-axis) as a function of magnitude (brightness, x-axis). The black line is the median variability for the stars in the Kepler field. The red and green lines are the 1 and 2 sigma contours for the variability of the stars in the Kepler field. None of the planet hosts are particularly variable.

None of the stars studied show flares or long-term trends at the 3-sigma level. This is a remarkable statement: the Kepler planet hosts are shown to be stable to within 0.4 magnitudes over a timescale of 100 years! This result rules out large-scale flares or variance in insolation on this scale. This is promising from a habitability perspective, as life prefers stable environments.

We look forward to further innovative discoveries from DASCH!

Title: Why Does Nature Form Exoplanet Easily
Author: Kevin Heng
Institution: University of Bern, Center for Space and Habitability

It’s an exciting time for planet hunters. Over the last few years, the search for extrasolar planets (“exoplanets” for short) has become one of the hottest topics in astronomy. During every exoplanet talk that I’ve attended lately, the speaker started out by announcing the total number of exoplanets discovered to date, which they quickly follow with the caveat along the lines of “Well, that’s the total number as of Wednesday, but it’s probably out of date by now.” Thanks to the Kepler Space Telescope, COROT, and other ground-based observatories, planet hunters are finding these things so fast we should probably start barcoding them.

If you keep up with us here at Astrobites, you’ve no doubt read all about habitable zones, hot Jupiters, and super Earths (after all, “Exoplanets” is one of the most common tags for our posts, seconded only by “Observations”). Today, I’d like to summarize a recent arXiv submission that takes some time to appreciate the apparent abundance of exoplanets in our galaxy, especially because we don’t rightly know why they are so abundant in the first place. The paper is a recapitulation of our theoretical understanding of exoplanet formation, which appears to be not that great. Based on our current theoretical understanding, forming exoplanets is no walk in the park, but Nature makes it look easy. The author encapsulates this conundrum in one of the most poetically terse abstracts I’ve ever read: “The ubiquity of worlds beyond our Solar System confounds us.”

Many of the planetary systems known to date.

Planets Everywhere

Kepler has only been hunting planets since 2009, but it has already discovered nearly 3,000 extrasolar planets candidates (114 of which have been confirmed extrasolar planets). This tells us that their formation must not be rare; exoplanets seem to be commonplace objects. What’s more, their characteristics vary greatly. Some are smaller than the Earth, while others make our Jupiter look wimpy. Some form near their stars, bathed in radiation and scorching heat; others live frigid lives in large orbits. Some are gas giants, some are rocky, which means that even the density of exoplanets vary greatly, from that of styrofoam to that of iron. The bottom line is that these things can be found just about anywhere. Is it really that easy to build a planet?

There are two basic ways to think about planet formation. The “bottom-up” approach is called Core Accretion, in which small objects combine to form larger objects. The “top-down” approach is Gravitational Instability, involving a large cloud of gas and dust that fragment into smaller chunks. These paradigms are not absolute, and there could be other processes at work, but these are the basic models under consideration at the moment.

Building a Rocky Planet…Or Not

It makes some sense to think of the rocky planets as a product of core accretion and the gaseous planets as the result of gravitational instability. That’s a natural enough hypothesis to start out with, anyway. But as it turns out, building rocky planets and the small cores of gaseous planets is really hard with our current models of core accretion; harder than the prevalence of these objects in Nature would suggest. You have to start out with some micrometer-sized dust grains and end up with a fully-grown planet. This growth spans something like nine orders of magnitude. If that weren’t daunting enough already, the dust also encounters a form of wind due to the surrounding gas moving slower than the dust grains. This introduces a drag that causes the dust to spiral in towards the star on a time scale that is much shorter than that needed to grow a planet to maturity. This is known as the Meter-Sized Barrier. It is called that because it most greatly affects meter-sized dust grains located about 1 AU from the host star, however it is still a problem for dust grains at any distance from the star. To get around this, models have been made more complex, taking into account countless interactions between the dust grains and its environment, but none of these models are testable yet. The author remains skeptical of these approaches, citing Occam’s Razor to suggest that such a robust, commonly-occurring process must not be prone to such minuscule perturbations to ensure its completion.

The gravitational instability paradigm is not so limited in its plausibility, but a fully-formed working theory has yet to be developed. It’s not yet known if instability itself is the sole driver of planetary formation or if it perhaps works in conjunction with core accretion. It has been speculated that vortices could develop–much like they do when you swish your glass of beer or cup of cocoa–and trap dust in them, allowing them to amalgamate into exoplanets.

Taking a Look

It’s not clear which of these scenarios, if any, is at work when forming planets. It turns out to be very difficult simply by looking at the end-result like we’ve been doing. With the Kepler Space Telescope, we’ve found several systems with strange properties unlike others. The paper outlines a few of them. Our own solar system, for example, does not seem to be “normal”. In many systems, the gas giants tend to sit closer to their star while the rocky planets remain farther away. Then again, the so-called hot Jupiters are not thought to be common either, but Kepler found a lot of them right away because they were easier to detect. Perhaps we’ll eventually find more systems like our own, with rocky planets near the star and gaseous ones farther out. What we would really like is the ability to study planetesimals as they aggregate to form planets. Although that’s not thought to be feasible right now, the author notes that one astronomer has suggested studying objects as they pass in front of a white dwarf instead of an ordinary main-sequence star. That way, smaller objects will block some of the light and Kepler may be able to see them. Perhaps we could see some of the pieces before they become the bigger beasts we’re discovering oh so rapidly.

Most astronomers that I come across on a daily basis – be them undergraduate students, graduate students, or professors – have a never-ending love for astronomy.  It can be seen in the late nights worked and their incessant need to talk about their research.  I think all authors, here at astrobites, fit this category well.  We love astronomy.  We love doing research and we especially love talking about it.  So I wanted to dedicate a post to this single question: why do you love astronomy?  Here’s what a few of the authors have to say.

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I get to play a part in stitching together the story of the universe, a story that has been built chapter by chapter for a very long time. The telescopes I get to use are some of the largest, most beautiful, most impressive feats of human engineering. I get to share my career with everyone, because by understanding what is out there, we understand what is down here. But most importantly, I love astronomy because it is hard and challenging, but always rewarding. – Josh Fuchs

I came to astronomy by accident when, as an aspiring science fiction writer, I wanted to thoroughly research the details and caveats associated with ending the world by throwing Earth into a black hole.  As I learned more in high school and undergraduate courses, it was partially the big questions in astronomy – what is the universe made of?  is there anyone else out there? – but more so the possibility of answering them within my lifetime that got me hooked on research.  I discovered that I love analyzing and interpreting data.  Every time we make a new measurement, there is the potential to learn something new about the universe.  Yet one of the major reasons that I have stayed in astronomy is the people.  I love working in a place where I can discuss project results and future ideas with people who have different skills and different knowledge from me.  Graduate school has made this more possible than ever before for me, since I am continually surrounded by loads of amazing scientists! – Lauren Weiss

A general interest in astronomy led me to gradate school, which is where I’d say I found my love for astronomy. I took my first visit to a beautiful mountain top observatory, I used a telescope to gather my own data for the first time, and a friend showed me the first constellations I’d learned since the Big Dipper. But the first time I realized that I loved astronomy was one night last semester, when showing undergraduates these constellations I’d just learned while talking about stellar evolution and sharing my experiences of grad school. – Elisabeth Newton

Take a moment to imagine standing beneath a beautiful starry sky.  You peak out into the Universe as a single player in a vast cosmic play.  That feeling of incommensurable beauty and awe is at the root of my love for astronomy.  But it also extends far beyond this.  We, as human beings, have the potential to understand those points of light, scattered so far away.  We have built instruments that peak into the far distant corners of the Universe, and labs that can recreate the conditions immediately following the big bang.  Driven by sheer curiosity, we are beginning to understand the unfathomable. We are answering questions left and right. But what is even more exciting, what drives all astronomers forward, are the questions we haven’t even thought to ask yet. – Shannon Hall

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Feel free to comment and tell us why YOU love astronomy.

Title: BEER analysis of Kepler and CoRoT light curves: Discovery of a hot Jupiter with superrotation evidence in Kepler data
Authors: Faigler, S., Tal-Or, L., Mazeh, T., Latham, D. W., and Buchhave, L. A.
First Author’s Institution: School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel

The Background
The Kepler telescope is great at finding transiting planets by looking for small, periodic dips in stellar light curves. Unfortunately, it is also great at finding things that look like transiting planets but are actually something else. Brown dwarfs, the size of Jovian planets but tens of times as massive, can mimic a planetary transit, as can eclipsing binary stars situated behind a lone Kepler target. As we’ve discussed, the false positive rate is believed to be low: about 90 percent of planet candidates are likely true planets. While reassuring to those studying planet populations, this lingering ten percent chance is troubling to anyone interested in an individual system. To confirm individual transiting systems in the pre-Kepler era, radial velocity observations have often been collected to measure the Doppler “wobble” of the host star. However, since many of Kepler‘s planets are quite small and its stars very faint, radial velocity observations can be very difficult and time-consuming to obtain.

The Method
Recently, some groups have attempted to confirm planetary systems without additional data beyond that collected and publicly available from the Kepler mission. The most successful method so far has been the analysis of transit timing variations, when mutual gravitational interactions between planets in one system change the velocities of planets, causing their transits to occur systematically earlier or later than expected. While successful, this method requires at least two planets in orbit: a “lonely” planet would not have anything to perturb it during its orbit to induce timing variations. So-called “Hot Jupiters,” bodies with the mass of Jupiter but in few-day periods, tend to be in such lone systems, meaning transit timing can not be used to confirm these systems.

Faigler et al. apply a technique to confirm giant planet systems: instead of analyzing the individual transits of these planets, the authors study the rest of the light curve, looking at what happens when the planet is not transiting. They note three effects which can be observed as the planet orbits its host star. The first is reflected light. During an orbit, we can observe reflected starlight bouncing off the surface of the planet. Much like we observe for the Moon and inner planets, the amount of reflected light we can observe changes during the orbit as the companion moves from “new planet” at the transit to “full planet” half a period later. Since the amount of reflected light depends on the surface area of the planet, its magnitude is related to the radius of the planet.

Artist’s impression of a “contact binary,” where two stars orbit close enough that they share one envelope. This is an extreme case of ellipticity: as the stars orbit, the projected visible surface area changes, affecting the observed brightness.

The second effect is ellipsoidal modulation. A hot Jupiter, being massive and close to its star, can efficiently raise tides on the star. The planet’s strong gravity pulls on the part of the star closest to it, causing the star to lose its sphericity and become somewhat elliptical. This is often seen in close binary star systems, as shown to the right. During an orbit, the observable surface of the star changes in size, causing the observed brightness to change. The magnitude of this effect depends on a planet’s mass, as a more massive planet can induce a larger ellipticity.

The third effect is relativistic Doppler beaming. The Doppler effect, in addition to in(de)creasing the frequency of light as an object moved toward (away) from an observer, also in(de)creases the total observed flux from an object. In a given measure of time, you will encounter more photons from an object moving toward you, as some of them have a smaller distance to travel. You may have empirically observed this while listening to the sound emitted by a train or fast-moving car. Like the previous effect, Doppler beaming depends on the mass of the companion. By observing beaming, we can receive the same information as we would receive from observing the radial velocity wobble of a star, which is simply a different manifestation of the Doppler effect.

The Results
The authors of this paper have developed an analysis package to find and measure these effects. The BEaming, Ellipsoidal, and Reflection (BEER) algorithm has been successfully applied to Kepler data to detect binary stars, while similar methods have been used to find brown dwarfs. For the first time, the authors apply the BEER algorithm to a collection of stars observed by Kepler. One of these, romantically named KIC 4570949, is the subject of this paper. This star was originally misclassified as a binary star system by the Kepler Science Team. After removing the variability in the light curve caused by instrumental effects and the stellar intrinsic variability (mostly caused by starspots rotating into and out of the field of view), the authors found clear evidence for all three effects: the change in flux from the star over an orbit is shown below. As both ellipsoidal modulation and Doppler beaming depend on the planet’s mass, they try to fit each to determine the mass of the planet. Interestingly, from the ellipsoidal motions they measure a mass of 2.1 ± 0.4 Jupiter masses, while the beaming observations are best fit by a mass of 7.2 ± 1.4 Jupiter masses, a difference of 3 standard deviations. Something odd is going on!

Observed flux from the star KIC 4570949 during one orbit of its planetary companion. A change in flux caused by beaming, ellipsoidal modulation, and reflection can be observed. Also observed is the “secondary eclipse” at a phase of 0.5, when the planet passes behind the star.

To settle this discrepancy, the authors collected 18 radial velocity measurements of this star. This observations are fit to a planetary orbit, and they find a best fit for the planet’s mass of 2.00 ± 0.26 Jupiter masses, consistent with the ellipsoidal observation. Thus, the Doppler beaming measurement is likely in error. To rectify this issue, the authors invoke the idea of superrotation, where a tidally locked planet has an equatorial jet stream moving very quickly and transporting the thermal radiation from a star eastward so that the hottest part of a planet is several degrees of longitude away from the region directly irradiated by the star. Since the BEER reflection signal is presumed to be in phase with the planet’s motion, superrotation will artificially reduce the observed reflected light component and enhance the beaming component. By inserting a superrotation phase shift of 10 degrees into their algorithm, the authors find a new best fit of 2.1 ± 0.4 Jupiter masses, in line with the radial velocity and ellipsoidal observations.

Since radial velocity observations are so time-consuming for faint stars like the ones Kepler observes, techniques to confirm and measure the masses of exoplanets without additional observations are extremely important. Transit timing variations have been successfully applied to multi-planet systems, but studying the out-of-transit stellar flux variations induced by planetary companions will be a very important technique to confirm and characterize hot Jupiter systems uncovered by transit searches, but present and future.

• Title: The Mysterious Sickle Object in the Carina Nebula: A Stellar Wind Induced Bow Shock Grazing a Clump?
• Authors: Judith Ngoumou, Thomas Preibisch, Thorsten Ratzka, and Andreas Burkert
• First Author’s Institution: Universitäts-Sternwarte München, Ludwig-Maximilians-Universität

One of the simplest joys in astronomy is finding an oddly-shaped blob in the sky and trying to figure out what it is and how it got there. The authors of this paper were examining images of the Carina Nebula when they spotted a “peculiar arc-shaped feature” and decided to study it. The story of what happened next is a great example of how astronomers use every scrap of data available for an object to understand what’s going on.

Step One: Discovery

The Carina Nebula is a beautiful star-forming region about 2300 parsecs (7500 light years) from here. The authors were intrigued by a crescent-shaped structure in images of the Carina Nebula. This structure, which the authors dubbed “The Sickle”, is asymmetric and associated with a B1.5 V star called MJ 218. In Fig 1, MJ 218 is the brightest star inside the curve of the Sickle (if the Sickle were Pac-Man, MJ 218 would be a dot about to get chomped). The authors did a literature search to see if anyone else had studied this structure before. The Carina Nebula is a pretty popular target for observers, so they found plenty of data for the Sickle at multiple wavelengths.

Fig 1: HAWK-I image of the Sickle (inside green dashed lines) and surrounding area. This RGB composite image is made up of J-, H-, and K_s-band data (shown in blue, green, and red, respectively).

Step Two: Multi-Wavelength Observations

The downside of astronomy is that we can’t reach out and touch the objects we study (with a few notable exceptions), so the only information we have about them comes from light. The upside of astronomy is that there’s an enormous amount of information contained in that light, especially if you look at different regions in the electromagnetic spectrum. The authors found optical images from the Hubble Space Telescope (HST) in the literature and took their own near-infrared observations (shown in Fig 1) with the HAWK-I instrument at the European Southern Observatory’s Very Large Telescope. These observations and measurements of the star’s proper motion showed that MJ 218 is moving in the direction of the Sickle.

Previous radio observations of this area did not detect the Sickle or the star, but the authors found something interesting in far-infrared Herschel data and their own submillimeter observations with LABOCA on the APEX telescope. These data, shown in Fig 2 as contours overlaid on the HAWK-I image, revealed a “clump” of dust and gas on the opposite side of the Sickle from the star. This led the authors to wonder if these three objects — the Sickle, MJ 218, and the clump — were interacting.

Fig 2: The HAWK-I image of the Sickle from Fig 1 with 70µm (in green) and 870 µm (in red) contours overlaid (from Herschel and APEX/LABOCA, respectively).

On the other side of the spectrum, observations from Chandra showed high X-ray emission coming from the star MJ 218, which is very unusual for a B1.5 V type star. This suggests that MJ 218 has a low-mass companion emitting X-rays. The authors went back to the HST data to try to find this companion but were unsuccessful, probably because MJ 218 is so bright that it outshines its little friend.

Step Three: Interpretation

Now that they had gathered all these data at different wavelengths, the authors needed to put the puzzle pieces together to find an explanation of the strange shape of the Sickle. Their first theory was that MJ 218 was sitting just inside the edge of the dusty clump, and the Sickle was the edge of an expanding bubble of stellar wind surrounding the star. They tested this theory by modeling what the clump would look like if you stuck a B1.5 V star next to it. After about 10,000 years (in model time, not computer time), the model showed a crescent shape the size of the Sickle (see Fig 3).

Fig 3: The density plot produced by modeling a stellar wind bubble next to a dust clump. This plot does not show the star, just the density of the dust in the clump and Sickle.

This seems like it solves the mystery of the Sickle — it’s a stellar wind bubble! But the authors realized that there were problems with the theory that the star was sitting inside the clump. For example, MJ 218 couldn’t have been born inside the clump — the clump would long since have been blown away by the wind.

The authors took note of the fact that the star is moving at a high velocity in the direction of the Sickle and developed a second theory — perhaps the Sickle is a bow shock, the region where the stellar wind interacts with the interstellar medium (ISM). They measured the distance between MJ 218 and the Sickle and found it to be consistent with the distance you’d expect to see between a star moving at that velocity and its bow shock.

So the theory that the Sickle is a bow shock formed by MJ 218 was consistent with the data. But this theory didn’t involve the clump at all; the clump isn’t needed to form a bow shock, so it might not be interacting with MJ 218 and the Sickle. It could just be hanging out in the background and only appear next to the Sickle by coincidence.

But the authors still had one piece of information about the Sickle that they hadn’t explained yet — it’s asymmetric around the direction that MJ 218 is moving (see Fig 4). This could indicate that the clump is influencing the shape of the Sickle. They modeled how a bow shock would form next to a clump of dust and found that they could reproduce this asymmetry, implying that the Sickle is a bow shock interacting with the clump. Finally, they had a theory that could explain the interaction of all three objects! MJ 218′s motion through the ISM is forming a bow shock, the Sickle. The star happens to be passing a clump of dust and gas, which gives the bow shock an asymmetric shape.

Fig 4: Model of a bow shock (yellow curve) created by a star moving at a certain velocity (white arrow) past a dusty clump, overlaid on a Hubble image of the Sickle.

Step Four: More Questions!

Like most happy endings in astronomy, the authors’ bow shock solution to the mystery of the Sickle opens up more questions. What is the clump made of? How will the bow shock affect the internal regions of the clump? Could it trigger gravitational collapse inside the clump and lead to star formation? As always, more data, modeling, and some hard thinking will be needed to answer these new questions.

Title: First Result from the Alpha Magnetic Spectrometer on the International Space Station: Precision Measurement of the Positron Fraction in Primary Cosmic Rays of 0.5–350 GeV
Authors: M. Aguilar et al. (AMS Collaboration)

In this astrobite, we discuss the recent result of the AMS-02 experiment, a particle detector on-board the International Space Station that has measured an excess in the fraction of high-energy positrons (the electron’s antiparticle) coming from cosmic rays when compared to the expected fraction due to known phenomena.

The AMS-02 experiment detects cosmic rays, photons and charged particles with very high energies that arrive at our Solar System. Among those particles, a tiny fraction are electrons and positrons. Positrons are an example of antimatter: for every particle, there exists a corresponding antiparticle, with the same mass but opposite charge and spin. We think cosmic rays are produced in supernova remnants and travel through the Galaxy to us. Along the way, they might interact with the interstellar medium gas, producing other charged particles that we also detect.

AMS on-board the International Space Station, where it started taking data on May 2011. Credit: NASA.

To distinguish among the different particles that make up the cosmic rays, the experimental device consists, firstly, of a magnet to separate particles from antiparticles. The magnetic field bends the trajectories of the particles and antiparticles differently because they have opposite charges. The most important part of the experiment is a device called the silicon tracker, which measures the trajectory of the particle, determining its charge, its incoming direction and its momentum. From these measurements, cosmic rays can be distinguished from particles that are simply roaming the Earth’s magnetic field. In this set-up, it is very important to distinguish between positrons and protons, since protons are far more abundant and can be easily mistaken for positrons when both particles travel at relativistic velocities. Two devices are in charge of performing the distinction. In one of them, positrons and electrons produce X-rays while protons do not. There is furthermore a second device, where positrons produce a distinct shower of particles, very different from incoming protons. This also allows for measuring the particle energy.

AMS-02 has measured the fraction of positrons with respect to the total number of positrons and electrons as a function of their energy. Their results are shown in the figure below. Similar measurements had been undertaken by PAMELA and Fermi-LAT, for example, also shown in the figure. AMS-02 has confirmed the results from these previous experiments to higher precision. The most interesting feature in this figure is the increase in the fraction of positrons above 10 GeV.

The positron fraction reported by AMS-02 as a function of energy (in red). Results from previous experiments are also shown. Credit: AMS collaboration.

The overall cosmic ray energy spectrum decays with energy. There are much fewer cosmic rays with high energy than with low energy. As a result, if all positrons had the same origin as all other cosmic rays, they should follow a similar behaviour, with an energy spectrum at least as steep as the cosmic ray energy spectrum. We see in the figure that this is not the case: while the positron energy spectrum decays at low energy, the behaviour is the opposite above 10 GeV. Hence, there must be other physical mechanisms producing these high energy positrons.

Where could the excess of positrons come from? One possible explanation is that the positrons are coming from pulsars. A pulsar is a rotating neutron star with a very intense magnetic field. Electrons torn from the surface of the star are accelerated when their trajectories are bent by the strong magnetic field and they can give rise to very high energy photons that can in turn produce electron-positron pairs through a process called pair production. Another possibility is the production of positrons by microquasars. These objects are a type of X-ray binary systems where a compact object accretes matter from a companion and where particles are ejected through a relativistic jet. There is observational evidence that the jets of microquasars could contain electron-positron pairs. These pairs would be produced in the highly energetic accretion disk and channelled out through the relativistic jet. These astrophysical explanations are disfavoured by the observed isotropy in the positron fraction. The positrons are not coming from a particular direction on the sky.

An exciting possibility is that the excess positrons is caused by the annihilation of dark matter particles or their decay from an excited state. (We discussed dark matter annihilation recently on this astrobite.) Although it is too soon to attribute the excess of positrons in the AMS-02 results to dark matter, there is no doubt a thorough exploration of possible physical scenarios is necessary to explain these puzzling results. AMS-02 will continue taking data and extending the energy range of the measurement of the fraction of positrons. This will help distinguish between the different scenarios.

If you want to learn more on this topic, this is a nice article reviewing the AMS results and you can find additional information on the PAMELA website as well. For an account of the different processes that could produce positrons in our Galaxy, you can refer to this work.