Features RSS

image of AT2018cow's location

The recent discovery of an unusual transient event, nicknamed “the Cow”, has set the community of transient astronomers abuzz (amoo?). What do we know about this odd event so far?

Thinking Outside the Box

AT2018cow

The location of AT2018cow: a post-discovery image (top left), a pre-discovery reference image (top right), a subtracted difference image (bottom left), and a Pan-STARRS multi-color image (bottom right). [Prentice et al. 2018]

Once upon a time, supernovae seemed somewhat well characterized. But with the advent of today’s large, wide-field transient surveys that scan the visible sky every few nights, it seems like we’re now constantly discovering new supernova-like events that don’t quite fit into previous, neatly defined categories.

Among the large variety of new classes of transients uncovered by these surveys are supernova-like events whose lightcurves rise and fall much faster than standard supernovae. One example is AT2017gfo, the first confirmed kilonova, which was paired with the neutron-star merger first detected in gravitational waves in August 2017. Additional examples of these rapidly evolving transients span a wide range of peak absolute magnitudes (from –15 to –22 magnitude) and rise times (~1–10 days), making them difficult to explain through a single scenario.

AT2018cow light curves

ATLAS, Liverpool Telescope, GROND, and Swift light curves of AT2018cow. [Adapted from Prentice et al. 2018]

Now astronomers have found one more unusual, luminous, and fast-evolving transient: AT2018cow. In a new study, a team of astronomers led by Simon Prentice (Queen’s University Belfast, UK) has presented the discovery and initial analysis of the first 18 days of this event.

An Unusual Transient

The Cow was first discovered with ATLAS, a twin 0.5-m telescope system located in Hawaii, on the night of 16 June 2018. Post-discovery monitoring of the Cow with various telescopes spanning optical, near-infrared, and ultraviolet wavelengths reveals a variety of odd properties.

The Cow’s peak luminosity was remarkably high: ~1.77 x 10^44 erg/s, or about 10–100 times brighter than a typical supernova. It reached the peak very quickly, brightening by more than 5 mag in just 3.3 days, while typical supernovae have rise times of perhaps 10–20 days. In addition, the Cow had a high peak blackbody temperature (~27,000 K), low estimated ejecta mass (just 0.1–0.4 solar mass), and relatively featureless and non-evolving spectra.

Magnetar from a Collision?

magnetar

Artist’s impression of a strongly magnetized neutron star. [NASA/Penn State University/Casey Reed]

The combination of the Cow’s odd properties eliminates a number of more common progenitor explanations, such as supernova shock breakout. The authors do explore one scenario that could produce properties similar to the Cow’s, however: the formation of a magnetar — a strongly magnetized neutron star — from the merger of a binary neutron star system. Such a model, Prentice and collaborators say, would predict a transient with a peak luminosity, decline rate, and effective temperature that are all consistent with those of the Cow.

How can we confirm this picture? The next step will be to compare additional observations of AT2018cow in radio and X-ray wavelengths — which were made simultaneously with those reported here in near-infared through ultraviolet — to the magnetar models to see if the models also match those observations. If so, we may have an explanation for this unusual transient.  

Citation

“The Cow: Discovery of a Luminous, Hot, and Rapidly Evolving Transient,” S. J. Prentice et al 2018 ApJL 865 L3. doi:10.3847/2041-8213/aadd90

Great Red Spot

A camera on the Juno spacecraft has returned stunning high-resolution images of Jupiter’s Great Red Spot. What can we learn about the properties of this long-lived storm?

A Dramatic Storm

Great Red Spot Hubble

The Great Red Spot, as imaged by Hubble in 2017. [NASA/ESA/A. Simon (GSFC)]

Jupiter’s Great Red Spot, an exceptionally long-lived storm churning south of Jupiter’s equator, has been observed continuously for nearly two centuries. Though this atmospheric vortex is the largest and longest-lived of any planet in our solar system, our observations suggest that the Great Red Spot is gradually shrinking: the major axis of the ellipse was ~21° in longitude 40 years ago, and only ~14° in the last few years. Some studies suggest the Great Red Spot may even vanish within the next 20 years.

Our current understanding of the morphology of this storm comes primarily from detailed observations by spacecraft since 1979 — first by the two Voyager spacecraft as they flew by, then by the Galileo orbiter, and then by the Hubble Space Telescope. These past observations have ranged in resolution from about 15 to 150 km per pixel. Now, since the 2016 arrival of the Juno spacecraft in orbit around Jupiter, there’s a new player in town: JunoCam.

cloud-top morphologies

Identification of different Great-Red-Spot features and winds. See article text for label descriptions. [Adapted from Sánchez-Lavega et al. 2018]

JunoCam: Public Outreach and Science

JunoCam is a visible-light camera with a 58° field of view. The camera scans as the spacecraft rotates, producing images with resolution down to 7 km per pixel in some areas! JunoCam’s remarkable photos of Jupiter’s atmospheric patterns — taken as Juno skims just thousands of kilometers above Jupiter’s cloud tops — have certainly drawn the public eye. But though JunoCam’s primary intent is as a tool for public engagement, its images can serve a scientific purpose as well.

In a new study, a team of scientists led by Agustín Sánchez-Lavega (University of the Basque Country, Spain) have used the unprecedented detail of JunoCam’s observations to examine the various cloud morphologies inside the Great Red Spot.

Rich Dynamics

Great Red Spot features

Close views of the five features the authors identify within the Great Red Spot cloud tops. Click to enlarge. [Adapted from Sánchez-Lavega et al. 2018]

Sánchez-Lavega and collaborators identify five particular morphologies within the cloud tops of the Great Red Spot:

  1. Compact cloud clusters
    Several groups of compact clouds resemble altocumulus clouds observed on Earth. These may suggest condensation of ammonia.
  2. Mesoscale waves
    Interfering trains of wave packets indicate stable conditions in this region.
  3. Spiraling vortices
    A large eddy of ~500 km in radius suggest a region of intense horizontal wind shear.
  4. Central turbulent nucleus
    The red nucleus of the Great Red Spot spans ~5,200 km in length (that’s about 40% of Earth’s diameter) and ~3,150 km in width.
  5. Large dark thin filaments
    Undulating dark gray filaments 2,000–7,000 km in length circulate at high speeds around the outer park of the vortex. These may be darker aerosols or represent areas with different altitudes.

The team’s measurements of the overall wind field in the Great Red Spot demonstrate that though the Spot may be dramatically shrinking, its wind field has shown little change over 40 years of observation. The rich variety of morphologies we’re seeing therefore likely represents just the top of a dynamical system with a much deeper circulation.

We can’t wait to see what else JunoCam reveals during the Juno mission!

Citation

“The Rich Dynamics of Jupiter’s Great Red Spot from JunoCam: Juno Images,” A. Sánchez-Lavega et al 2018 AJ 156 162. doi:10.3847/1538-3881/aada81

black-hole snapshots

In 2006 an ambitious project was begun: creating the world’s largest telescope with the goal of imaging the shadow of a black hole. But how will we analyze the images this project produces?

A Planet-Sized Telescope

EHT participating telescopes

The locations of the participating telescopes of the Event Horizon Telescope (EHT) and the Global mm-VLBI Array (GMVA) as of March 2017. Jointly, these telescopes plan to image the shadow of the event horizon of the supermassive black hole at the center of the Milky Way. [ESO/O. Furtak]

The Event Horizon Telescope (EHT) is composed of radio observatories around the world. These observatories combine their data using very-long-baseline interferometry to create a virtual telescope that has an effective diameter of the entire planet!

The EHT, researchers hope, will have the power to peer in millimeter emission down to the very horizon of an accreting black hole — specifically, Sgr A*, the supermassive black hole in the Milky Way’s center — to learn about black-hole physics and general relativity in the depths of this monster’s gravitational pull.

Today, the EHT is closer than ever to its goal, as the project continues to increase its resolving power and sensitivity as more telescopes join the system. Another important aspect of this project exists, however: the ability to analyze and characterize the images it produces in a meaningful way.

PCA decomposition example

A simple example of using principal component analysis to decompose a set of images into independent eigenimages. The example images (top row) are snapshots from a simple model of a Gaussian spot moving on a circular path. The first four components of the principal component analysis decomposition — the four leading eigenimages — are shown in the bottom row, labeled with their corresponding eigenvalues. [Adapted from Medeiros et al. 2018]

Recently, a team of scientists led by Lia Medeiros (University of Arizona, University of California Santa Barbara) has demonstrated that a novel approach — principal component analysis — may be a useful tool in this process.

Principal Components

Principal component analysis is a clever mathematical approach that allows the user to convert a complicated set of observations of variables into their “principal components”. This process — commonly used in traditional statistical applications like economics and finance — can simplify the amount of information present in the observations and help identify variability.

Medeiros and collaborators demonstrate that a time sequence of simulated EHT observations — produced from high-fidelity general-relativistic magnetohydrodynamic simulations of a black hole — can be decomposed using principal component analysis into a sum of independent “eigenimages”. These eigenimages provide a means of compressing the information in the snapshots: most snapshots can be reproduced by summing just a few dozen of the leading eigenimages.

image reconstruction

A typical snapshot from a simulation (top), followed by three different reconstructions of the snapshot from the leading 10, 40, and 100 eigenimages. [Adapted from Medeiros et al. 2018]

Exploring Steady and Variable Flow

How is this useful? If images from simulations of a black hole can be represented by sums of eigenimages, so can the actual observations produced by the EHT. By comparing the two sets of observations — real and simulated — to each other within this eigenimage framework, we’ll be able to better understand the components of what we’re observing. In addition, the mathematics of principal component analysis allow for this to work even with sparse interferometric data, as is expected with EHT observations.

Furthermore, recognizing images that aren’t represented well by the leading eigenimages is equally important. These outlier images can be indicative of flaring or otherwise variable phenomena around the black hole, and identifying moments in which this occurs will help us to better understand the physics of accretion flows around black holes.

So keep an eye out for the first images from the EHT, expected soon — there’s a good chance that principal component analysis will be helping us to make sense of them!

Citation

“Principal Component Analysis as a Tool for Characterizing Black Hole Images and Variability,” Lia Medeiros et al 2018 ApJ 864 7. doi:10.3847/1538-4357/aad37a

Solar corona

The hot, tenuous solar corona is visible during a total solar eclipse, and astronomers have long studied the structure and dynamics of the ghostly coronal streamers. Now, a special observing campaign has allowed us to see the corona in unprecedented detail.

STEREO sees a CME

NASA’s STEREO has observed the solar atmosphere and solar wind — including coronal mass ejections like the one pictured here — for over a decade. [NASA/STEREO]

A STEREO View

Despite the wealth of knowledge we’ve amassed about our nearest star, there is still a lot we don’t know about the corona — the uppermost region of the solar atmosphere. Previous observations of the outer corona have indicated that the region is smooth and lacking in small-scale structure — but is it really?

To learn more about the outer corona, a team led by Craig DeForest (Southwest Research Institute) analyzed images from a special observing campaign by NASA’s Solar Terrestrial Relations Observatory-A (STEREO-A). In a departure from its typical observing mode, STEREO-A increased its imaging cadence by a factor of four and exposure time by a factor of six. Using careful image-processing techniques, DeForest and collaborators extracted hidden details from the STEREO-A images.

DeForest et al. 2018 Fig. 2

Left: An unprocessed image from the STEREO-A campaign. Right: The same frame with stray light and the F-corona (sunlight scattered off of dust grains) removed. Click to enlarge. [DeForest et al. 2018]

An Eye for Detail

The authors found that the seemingly smooth outer corona is made up of small-scale filamentary substructures that are visible down to the resolution limit of the instrument — corresponding to approximately 20,000 km.

These dense, narrow filaments might arise due to the dynamics of the corona itself, but it’s also possible that each filament can be traced back to an individual granule on the solar photosphere. This has exciting implications for future observations, which may be able to capture changes in the corona that correspond to changes in the granulation pattern on the solar surface.

The observed density variations also have consequences for how the solar wind is generated. The boundary between the solar atmosphere and the solar wind is often taken to be the Alfvén surface, where the radial velocity of the solar plasma exceeds the Alfvén speed — the speed at which hydrodynamic waves travel in a magnetized plasma. Because the Alfvén speed is a function of the plasma density, these extreme density variations over small spatial scales imply that the Alfvén surface is less of a discrete level in the solar atmosphere and more of a broad zone over which the coronal plasma gradually disconnects from the Sun and begins to flow outward as the solar wind. 

DeForest et al. 2018 Fig. 7

Top: An image of the corona in polar coordinates. Bottom: The same region, highlighting the locations of sharp brightness gradients. Click to enlarge. [DeForest et al. 2018]

Ready for Its Close-up

Luckily, we’ll have the opportunity to examine the intricate structure in the outer corona up close. DeForest and collaborators predict that NASA’s Parker Solar Probe, which will travel through the outer corona, will be able to discern the small-scale structures they discovered in the STEREO images. The spacecraft may observe changes in the density of the coronal plasma of up to an order of magnitude over the span of just ten minutes.

We won’t have to wait long to find out — launched in August 2018, Parker Solar Probe will have its first close encounter with the Sun in November 2018. By 2025, the spacecraft will be sampling the coronal plasma less than 10 solar radii above the solar photosphere.

Citation

C. E. DeForest et al 2018 ApJ 862 18. doi:10.3847/1538-4357/aac8e3

astropy

One of the greatest misconceptions about astronomy as a profession is that we all sit alone in front of a telescope eyepiece every night, gazing at the stars. In reality, today’s observational astronomy is collaborative — and it takes the form of ones and zeros on a computer.

RGB images with astropy

Two different RGB images of the region newar the Hickson 88 group, both produced with Astropy from Sloan Digital Sky Survey data. The top image uses default plot parameters; the bottom has parameters set to show a greater dynamical range. [The Astropy Collaboration et al. 2018]

A Computer-Driven World

Before the days of photography, the field of astronomy did rely on lone professionals who observed the heavens through their telescopes; after that, astronomers exposed film plates to gather data. Today, astronomy is a largely computer-driven field: observations are made by telescopes that often aren’t in the same location as the astronomers, and the images the telescopes take are stored as files full of data.

Modern observational astronomers need the coding skills to process these data and turn them into images and tables. They need to use computers to fit models to the data to better understand what they’re seeing. They need to present their results via complex plots and graphs — which are again produced using code.

As a result of this reality for astronomers, the handling of astronomical data has become in large part a community-driven, collaborative process; when good ideas are shared, each individual astronomer can spend less time reinventing the wheel. It’s in this spirit that the Astropy project was first developed. In a recent publication, the Astropy collaboration has now detailed the current status of this project.

Pooling Resources

Astropy commits

Plot of the total number of commits (contributions consisting of changes or additions) to the Astropy core package over time. [The Astropy Collaboration et al. 2018]

Many astronomers conduct their work in Python, a freely available, general-purpose programming language. Often, chunks of code that are useful to one astronomer are also useful to another — for instance, code that defines specific astronomical constants, or a module that reduces data in a certain way. Astropy is an open-source and open-development community library for such pieces of generally useful Python code for astronomy.

The Astropy project was started in 2011. Since then, the package has been used in hundreds of projects, and its scope has grown considerably. Anyone is able to contribute to this body of code, and it continues to be actively developed — as of version 2.0, the Astropy package contained over 212,244 lines of code contributed by 232 unique contributors.

Status of Astropy

In their recent publication, the authors describe some of the features currently contained in the Astropy core package — like support for coordinate transformations, reading and writing astronomical files, manipulating quantities with units attached, and modeling and visualizing data. 

example of coordinate systems in figures

Spitzer data providing another example of a figure made using an Astropy subpackage, which allows for the overlay of multiple coordinate systems and customization of which ticks and labels are shown on each axis. [Beerer et al. 2010]

The Astropy collaboration also discusses their plans for the future of the project: in addition to planned changes and additions to the core package, the next major release will also include an overhaul of the Astropy educational and learning materials, designed to make it easier for new users to start taking advantage of the resources in the Astropy package.

Critical efforts like the Astropy project not only provide and develop software tools essential to modern academic research, but they also help lower the barrier to entry for the next generation of professional astronomy researchers. With such support in a collaborative community, we can only imagine what modern astronomy will look like a few generations in the future!

Citation

“The Astropy Project: Building an Open-Science Project and Status of the v2.0 Core Package,” The Astropy Collaboration et al 2018 AJ 156 123. doi:10.3847/1538-3881/aabc4f

far infared galactic center

A supermassive black hole lurks at the center of our galaxy — and we’re still trying to understand its structure and behavior. Now scientists have made new detections of Sgr A* in far infrared, helping us to further piece together a picture of this monster.

A Missing Window

Past research suggests that most galaxies host a supermassive black hole of millions or billions of solar masses in their center — and the Milky Way is no exception. Our galactic center is dominated by Sagittarius A* (Sgr A*), a black hole that weights in at about 4 million solar masses.

electromagnetic spectrum

The electromagnetic spectrum (click to enlarge). Observations of Sgr A* have previously been lacking at far-infrared wavelengths below 250 µm. [Penubag]

We’ve observed Sgr A* across the electromagnetic spectrum through the years, noting its flux and variability in radio, millimeter and submillimeter, near-infrared, and X-ray wavelengths. A few windows are notably missing, however: dust and atmospheric obscuration have prevented observations of Sgr A* in optical and ultraviolet wavelengths, and in the far infrared at wavelengths shorter than 250 µm. 

Though we’ve learned a lot about our supermassive black hole from observations, we’re still short of a fully coherent picture. What is the structure and flow of the gas and dust surrounding the black hole? What causes the various different types of emission we’ve observed from Sgr A*? Is the variability in flux we see at different wavelengths related? Or does the emission come from multiple different sources, each with its own timescale for variability?

To further build out our understanding of this mysterious source, a team of scientists led by Sebastiano von Fellenberg (Max Planck Institute for Extraterrestrial Physics, Germany) has now obtained observations of Sgr A* at 160 and 100 µm for the first time, providing new information about Sgr A* in the missing window in the far infrared.

Hidden Variability

far-infared variability

The authors’ observations of the far-infrared variability of the region around Sgr A* (click to enlarge). Correlated variability is visible between the two observed bands, and a point source can be seen at the position of Sgr A*. [von Fellenberg et al. 2018]

Von Fellenberg and collaborators obtained these far-infrared observations not by looking directly for Sgr A* in these wavelengths, but by looking for its variability. Using ESA’s Herschel Space Observatory, the team observed the emission at 160 and 100 µm and then subtracted off the constant emission from the warm dust at the galactic center. After correcting for systematic errors, von Fellenberg and collaborators were left with a faint signature of variable emission correlated between the two wavelengths: emission from around the black hole. 

These observations have allowed the authors to place limits on Sgr A*’s far-infrared luminosity. By comparing these limits to the emission predicted by various models of accretion flow onto Sgr A*, von Fellenberg and collaborators have narrowed down the set of models that are consistent with the observations.

Though we still don’t have everything figured out about the supermassive black hole at our galaxy’s center, this work represents an important step that brings us a bit closer.

Citation

“A Detection of Sgr A* in the Far Infrared,” Sebastiano D. von Fellenberg et al 2018 ApJ 862 129. doi:10.3847/1538-4357/aacd4b

NGC 1892

Much of today’s astronomy happens via methodical searches, but sometimes serendipitous discoveries still surprise us. Such is the case with the transient CGS2004A, a possible supernova recently detected in a galaxy nearly 50 million light-years away.

Observing Explosions

Supernovae — some of the brightest phenomena in our universe — are vast explosions thought to mark the destruction of stars in the end stages of their evolution.

The history of supernova observations is long: the first recorded supernova was seen in China in 185 AD! Because supernovae are scarce (there are perhaps 1–3 per century in the Milky Way) and their brightest stages of are short-lived (lasting just a few months), only a handful of supernova were spotted by naked eye through the ages. The invention of the telescope, however, changed this: as technology improved, astronomers became able to observe bright supernovae in galaxies beyond the Milky Way.

NGC 1892

Chronological observations of NGC 1892. From the top, a Hubble image from 2001, the CGS image from 2004, Stockler de Moraes’s image from 2017, and a Magellan image from 2018. The transient is visible only in the 2004 CGS image. [Guillochon et al. 2018]

Today, around 50,000 supernovae have been observed. The field has been vastly expanded by recent automated sky surveys that methodically hunt for transients. Nonetheless, intrepid individual astronomers still contribute to this scene — as evidenced by the recent discovery by Brazilian amateur astronomer Jorge Stockler de Moraes.

An Unexpected Find

In January of 2017, Stockler de Moraes imaged the distant galaxy NGC 1892 using a 12-inch diameter telescope. When he later compared his image to an archival image from 2004 of the same galaxy, taken as part of the Carnegie-Irvine Galaxy Survey (CGS), he discovered a distinct difference between the two photos: a bright source was present in the archival image that wasn’t visible in his recent photo.

Stockler de Moraes next contacted astronomer James Guillochon (Harvard Center for Astrophysics), who first eliminated possible alternate explanations for the source — such as minor planets in our solar system that might have coincided with NGC 1892 at the time. Guillochon then worked with a team of collaborators to explore other images of the galaxy and conduct follow-up imaging, as well as analyze the transient in the CGS image.

Core Collapse

The transient — labeled CGS2004A — was found to be absent in all additional images the authors explored, both in years before and after the CGS observation. Guillochon and collaborators’ photometric analysis of the transient and our knowledge of the nature of NGC 1892, a massive, star-forming galaxy, further suggest that this transient was likely a Type IIP supernova, caused when the core of a massive star (perhaps 8–50 solar masses) suddenly collapses.

Based on the authors’ analysis, it would seem that Stockler de Moraes serendipitously discovered a stellar explosion that went unnoticed 14 years ago. Discoveries such as these help us to continue to expand our understanding of how stars evolve throughout the universe.

Bonus

For a cool way to experience the history of supernova detections over time, check out the video by astronomer Greg Salvesen below. Chronological discoveries of supernovae are displayed both visually and using sound, for the time period of 1950 to the start of 2018. You can skip ahead to ~1990 to see detection rates pick up as more surveys come online! Data is from the Open Supernova Catalog by Guillochon et al.

Citation

“Serendipitous Discovery of a 14-year-old Supernova at 16 Mpc,” James Guillochon et al 2018 Res. Notes AAS 2 165. doi:10.3847/2515-5172/aade89

A pair of black holes

The advent of gravitational-wave astrophysics has made possible the study of elusive cosmic phenomena — like the mysterious merging of stellar-mass black holes.

When Black Holes Meet

Black hole mass chart

As of November 15, 2017, six black-hole mergers have been discovered via gravitational waves. [LSC/LIGO/Caltech/Sonoma State (Aurore Simonnet)]

The cataclysmic inspiraling of a pair of black holes doomed to merge sends ripples through space-time. Thanks to the Laser Interferometer Gravitational-Wave Observatory (LIGO), we have now detected a handful of instances of these ripples — enough to take a closer look at the broader population of binary black-hole mergers.

Beyond just collecting individual merger events, we can now explore whether or not the rate at which black hole mergers occur has evolved over the course of cosmic time. The merger rate reflects the underlying star formation rate as well as the particulars of stellar evolution. Ultimately, understanding how the merger rate has changed can help us learn how black-hole binaries form.

How can we tell whether or not the rate of binary black-hole mergers has evolved with redshift? A team led by Maya Fishbach (University of Chicago) aimed to extract this information from the first six binary black-hole detections from LIGO/Virgo.

Fishbach et al. Figure 1

The cumulative probability distribution of detected black-hole binaries depends on black hole mass, detector sensitivity (with the dashed lines indicating a more sensitive detector), and the underlying redshift distribution. Evolution of the merger rate with redshift would shift these curves — which demonstrate the case of a uniform redshift distribution — to the left or right. [Fishbach et al. 2018]

LIGO Provides a Listening Ear

One challenge is that the redshift distribution of black-hole binaries that we observe from LIGO/Virgo isn’t just a function of the underlying redshift distribution — it’s also a function of the mass distribution. Since mergers of more massive black holes generate “louder” signals and are more likely to be detected, a binary black-hole population with more massive members will generate more detections at high redshift than a population with fewer massive members.

To remedy this, Fishbach and collaborators used models of realistic redshift distributions to fit the redshift and the two component masses simultaneously. Based on the six available binary black-hole detections, Fishbach and collaborators find that the observations are consistent with a merger rate that is constant in redshift.

There does appear to be a slight decrease in the merger rate density with increasing redshift, but the authors caution that this could arise if the detections of the “quieter” mergers are published later; an artificially large proportion of “louder” events could skew the redshift distribution toward low-redshift events.

Looking Ahead to Future Detections

Fishbach et al. Figure 4

Merger rate density as a function of redshift for two redshift parameterizations. Both redshift models are consistent with a constant merger rate, which is indicated by the dotted line. Click to enlarge. [Fishbach et al. 2018]

What does the future hold for estimating the black hole merger rate as a function of redshift? To explore this question, Fishbach and collaborators generated synthetic black hole populations and modeled the likely detections by LIGO/Virgo.

They find that with a few hundred binary black-hole detections per year — an estimate based off of the expected improvements to LIGO/Virgo sensitivity — any deviations from a constant merger rate should be detectable within a few years. Exciting developments to come!

Citation

Maya Fishbach et al 2018 ApJL 863 L41. doi:10.3847/2041-8213/aad800

Illustration of a rocky body next to a gas giant planet, with a distant star and another small body in the background.

One of the primary goals of exoplanet-hunting missions like Kepler is to discover Earth-like planets in their hosts’ habitable zones. But could there be other relevant worlds to look for? A new study has explored the possibility of habitable moons around giant planets.

Seeking Rocky Worlds

Since its launch, the Kepler mission has found hundreds of planet candidates within their hosts’ habitable zones — the regions where liquid water can exist on a planet surface. In the search for livable worlds beyond our solar system, it stands to reason that terrestrial, Earth-like planets are the best targets. But stand-alone planets aren’t the only type of rocky world out there!

Many of the Kepler planet candidates found to lie in their hosts’ habitable zones are larger than three Earth radii. These giant planets, while unlikely to be good targets themselves in the search for habitable worlds, are potential hosts to large terrestrial satellites that would also exist in the habitable zone. In a new study led by Michelle Hill (University of Southern Queensland and University of New England, Australia; San Francisco State University), a team of scientists explores the occurrence rate of such moons.

habitable-zone gas giants

Kepler has found more than 70 gas giants in their hosts’ habitable zones. These are shown in the plot above (green), binned according to the temperature distribution of their hosts and compared to the broader sample of Kepler planet candidates (grey). [Hill et al. 2018]

A Giant-Planet Tally

Hill and collaborators combine the known Kepler detections of giant planets located within their hosts’ optimistic habitable zones with calculated detection efficiencies that measure the likelihood that there are additional, similar planets that we’re missing. From this, the authors estimate the frequency with which we expect giant planets to occur in the habitable zones of different types of stars.

The result: a frequency of 6.5 ± 1.9%, 11.5 ± 3.1%, and 6 ± 6% for giant planets lying in the habitable zones of G, K, and M stars, respectively. This is lower than the equivalent occurrence rate of habitable-zone terrestrial planets — which means that if the giant planets all host an average of one moon, habitable-zone rocky moons are less likely to exist than habitable-zone rocky planets. However, if each giant planet hosts more than one moon, the occurrence rates of moons in the habitable zone could quickly become larger than the rates of habitable-zone planets.

Lessons from Our Solar System

planet–moon angular separation

Distribution of the estimated planet–moon angular separation for known Kepler habitable-zone giant planets. Future missions would need to be able to resolve a separation between 1 and 90 microarcsec to detect potential moons. [Hill et al. 2018]

What can we learn from our own solar system? Of the ~185 moons known to orbit planets within our solar system, all but a few are in orbit around the gas giants. Jupiter, in particular, recently upped its tally to a whopping 79 moons! Gas giants therefore seem quite capable of hosting many moons.

Could habitable-zone moons reasonably support life? Jupiter’s moon Io provides a good example of how radiative and tidal heating by the giant planet can warm a moon above the temperature of its surroundings. And Jupiter’s satellite Ganymede demonstrates that large moons can even have their own magnetic fields, potentially shielding the moons’ atmospheres from their host planets.

Overall, it seems that the terrestrial satellites of habitable-zone gas giants are a valuable target to consider in the ongoing search for habitable worlds. Hill and collaborators’ work goes on to discuss observational strategies for detecting such objects, providing hope that future observations will bring us closer to detecting habitable moons beyond our solar system.

Citation

“Exploring Kepler Giant Planets in the Habitable Zone,” Michelle L. Hill et al 2018 ApJ 860 67. doi:10.3847/1538-4357/aac384

Gravitational microlensing illustration

The gravity of massive galaxies can warp the light from distant background sources into dramatic shapes. Gravitational lensing by foreground stars, planets, and other low-mass objects may be less visually stunning, but these tiny lensing events hold big promise.

Spitzer Space Telescope

The Spitzer Space Telescope, shown in this artist’s impression, trails Earth at a distance of over 1 AU. The large distance between Spitzer and ground-based observatories makes it a good choice for carrying out microlensing measurements. [NASA/JPL-Caltech]

Multifaceted Microlensing

When light from a distance source is bent by an object passing through our line of sight, we observe a temporary brightening of the source — a gravitational microlensing event.

While microlensing may be best known as a method of discovering exoplanets, it has the potential to detect other types of astrophysical objects as well. Most notably, microlensing can reveal faint, isolated objects that wander into our line of sight: rogue planets that have been ejected from stellar systems, brown dwarfs, and compact stellar remnants like neutron stars. Microlensing may also help us learn about the enigmatic population of stellar-mass black holes, which seem to be curiously rare between two and five solar masses.

Like many promising astrophysical techniques, microlensing comes at a cost: lots of telescope time, be it ground-based, space-based, or a combination of the two. Is it possible to take advantage of this powerful technique in a less costly way?

Shin et al. 2018 Fig. 2

Light curves and best-fit model curves for actual, realistic, and idealized data sets. The grey symbols are the ground-based observations. Click to enlarge. [Adapted from Shin et al. 2018]

Putting It to the Test

In order to determine the properties of the lensing object, you need to first determine the microlens parallax by comparing observations taken at different times of the year (annual parallax), at different ground-based observatories (terrestrial parallax), or simultaneously at ground- and space-based observatories (space-based parallax). A team led by In-Gu Shin (Harvard-Smithsonian Center for Astrophysics) tested the idea that the space-based microlens parallax can be determined accurately from just a handful of observations.

In order to test this theory, Shin and collaborators paired ground-based observations of OGLE-2016-BLG-1045, a microlensing event discovered by the Optical Gravitational Lensing Experiment (OGLE), with space-based observations from the Spitzer Space Telescope.

The authors considered three cases derived from the Spitzer observations:

  1. “Actual” case: the existing Spitzer measurements, which span roughly 15 days
  2. “Realistic” case: two space-based observations near the time of the ground-based peak and one baseline observation
  3. “Ideal” case: one space-based observation at the moment the ground-based peak occurs — a moment that’s nearly impossible to capture under normal circumstances — and one baseline observation taken well after the microlensing event
Shin et al. 2018 Fig. 4

The lens mass and distance derived from the realistic, actual, and idealized data. The two rows represent the results for the two degenerate solutions. For both parameters, the realistic result is within 1σ of the actual result. Click to enlarge. [Shin et al. 2018]

Getting to Know OGLE-2016-BLG-1045

By modeling the light curves for each of the three cases, the authors find that OGLE-2016-BLG-1045 is a low-mass stellar object (0.08 solar masses) located 16,000 light-years away. The results from the “realistic” and “actual” cases agreed to within 1σ, confirming that just two or three observations can adequately constrain the properties of the lensing object.

This promising result hints that it may be possible to study a large sample of isolated objects with just a few measurements each, helping us to understand better the populations of brown dwarfs, stellar remnants, and black holes in our galaxy. Expect to see more exciting results from microlensing in the future!

Citation

I.-G. Shin et al 2018 ApJ 863 23. doi:10.3847/1538-4357/aacdf4

1 … 58 59 60 61 62 … 96