Tag Archives: Impacts

Asteroids: Main Belt

Big impact on Ceres

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Hi there! Today: Ceres. (1) Ceres is the largest object of the main asteroid belt, so large that the International Astronomical Union (IAU) classified it as a dwarf planet in 2006. As many planetary bodies, it is craterized, the largest crater being named Kerwan. This crater has a diameter of 280 km. But this is not the most remarkable one. The crater Occator, which diameter almost reaches 100 km, is particularly interesting since it exhibits bright spots, which are probably the signature of past hydrothermal activity. This raises the interest of the scientific community, since it could reveal a geophysical activity and water below the surface.
The study I present, The various ages of Occator crater, Ceres: Results of a comprehensive synthesis approach, by A. Neesemann et al., tries to be as accurate as possible on the age of Occator, in summarizing the previous studies and in using as many data as possible. These are actually data provided by the spacecraft Dawn. This paper will be published in Icarus soon.

Outline

The dwarf planet (1)Ceres
Dawn at Ceres
Occator crater
How a crater evolves
Previous estimations of Occator’s age
A young crater anyway
The study and its authors

The dwarf planet (1)Ceres

Discovery

The quest for an object between the orbits of Mars and Jupiter was initially motivated by the Titius-Bode law. This empirical law, which is now proven to be absolutely wrong, noticed a arithmetic progression between the orbital radii of the known planets, and was confirmed by the discovery of Uranus in 1781 (however, it is inconsistent with the presence of Neptune). Anyway, this convinced former astronomers that something was there, and it revealed to be true. A group led by Franz Xaver von Zach looked for an object with a semimajor axis close to 2.8 AU (astronomical units, remember that 1 AU is 150 million kilometers, which is the orbital radius of our Earth). But that group did not discover Ceres.

Ceres has been serendipitously discovered in 1801 by the Italian astronomer Giuseppe Piazzi in Palermo, Sicilia. He wanted to observe the star HR 1110, but saw a slowly moving object instead. He noticed that it looked somehow like a comet, but that it was probably better than that. Ceres was found!

Giuseppe Piazzi (1746-1826) pointing at Ceres. © Palermo Observatory
Giuseppe Piazzi (1746-1826) pointing at Ceres. © Palermo Observatory

Later, the group led by von Zach discovered many asteroids. One of them, Heinrich Olbers, is credited for the discoveries of Pallas, Vesta, and the periodic comet 13P/Olbers. He also gave his name to the Olbers paradox, which wonders why the night is so dark while we are surrounded by so many stars.

Properties

You can find below some of the orbital and physical properties of Ceres.

Semimajor axis 2.77 AU
Eccentricity 0.075
Inclination 10.6°
Revolution 4.60 yr
Rotation 9 h 4 min
Diameters (965.2 × 961.2 × 891.2) km
Density 2.161 g/cm3

These orbital elements and its size make it the largest object of the main asteroid belt. You can see a small eccentricity, and a pretty fast rotation period with respect to its orbital one (i.e., the revolution). Moreover, its equatorial section is pretty circular, i.e. if you look at its 3 diameters, the two largest ones of them are very close, and in fact the uncertainties on the measurements are even consistent with a strict equality. However, the polar diameter is much smaller. This is a consequence of its rotation, which flattens the body.

You can also notice a density, which is between the one of the water (1) and the one of silicates (3.3). This means that its composition should be a mixture of both, i.e. silicates and water ice.

Ceres seen by Dawn © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA
Ceres seen by Dawn © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA

The physical properties and the image above are due to the spacecraft Dawn. This mission is close to its end.

Dawn at Ceres

The spacecraft Dawn has been launched from Cape Canaveral, Florida, in September 2007, and reached the asteroid Vesta in 2011. After a little more than one year in orbit around Vesta, it left it and has been trapped by the gravity field of Ceres in March 2015. This mission will be completed soon.

Dawn consists of three instruments:

  • the Gamma Ray and Neutron Detector (GRaND) Instrument,
  • the Visible and Infrared Spectrometer (VIR) Instrument,
  • and the Framing Camera (FC).

Dawn is essentially an American mission, even if Germany provided the Framing Camera. The German study we discuss today uses FC data.

The orbital journey of Dawn around Ceres consists of several phases, which are different orbits. This results in variable resolutions of the images. The prime mission considered two mapping orbits, the HAMO (High Altitude Mapping Orbit) and the LAMO (for Low Altitude), at distances of 1,470 and 375 km of the surface, respectively. Since then, the mission has been extended, and the spacecraft is now at only 50 kilometers of the surface. High resolution expected.

This mapping orbits permitted to map comprehensively the surface of Ceres. Unsurprisingly, that survey revealed many craters.
We are today interested in Occator, which is not the largest one, but contains bright spots, possibly signatures of a recent hydrothermal activity.

Occator crater

Occator crater is located in the northern hemisphere of Ceres. Its diameter is some 90 km, which does not make it the largest one, but it is particularly interesting for the bright spots it shows. To be honest, there are bright spots at other locations of Ceres, but anyway Occator is remarkable for that. The spot in the center is a dome called Cerealia Facula, while the small spots are called the Vinalia Faculae. You can see them below, on these high-resolution images due to the extended mission.

Occator Crater on Ceres, with its central bright area called Cerealia Facula. © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI
Occator Crater on Ceres, with its central bright area called Cerealia Facula. © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI
Vinalia Faculae © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI
Vinalia Faculae © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI

Yes, there are domes, due to hydrothermal activity! You can find interesting analogies with Earth features here. But basically, the two possible explanations are for now:

  1. either the heat from the impact that formed the crater caused briny liquid or mushy ice to push up on the surface,
  2. or the heat from the impact could have enhanced activity related to pre-existing liquid reservoirs just below the surface.

Anyway, this reveals water! And this makes Ceres and its crater Occator fascinating.

How a crater evolves

This study wants to estimate the age of Occator, or in other words, date the impact that created it. For that, you examine its current state, and guess how long it took from the impact to the observed state.

Because of the elasticity of the surface, after some time (hundreds of millions years, maybe a little more…) the surface relaxes. The consequence is that the crater gets less deep, and its slopes get gentler. A 3-D terrain model will give you the numbers. But the dynamics of the relaxation process is barely constrained.

Another evolution is that the crater is covered by something else. This something could be other, more recent craters. If the new crater is larger than the older one, then the oldest disappears. However, if the new crater is small with respect to the old one, then you see both, and by counting the small craters, you can say “it took this time to get so many craters, so the age is at least…”. OK

But how to constrain this? You calibrate your models with better known bodies, like the Moon, and / or dynamical models of the bombardments. Previous studies have used Lunar Derived Models and Asteroid-flux Models… of course with different outcomes.

In the specific case of Occator, the hydrothermal activity revealed by the bright spots has generated ejecta blankets, as lobate deposits.

Previous estimations of Occator’s age

The quest for the age of Occator crater began with the first data on Ceres, i.e. in 2015. Here are the already published numbers

  • Nathues et al. 2015: 78 ± 5 Ma (million years). This measurement is based on crater counting, and only HAMO data. In particular, the more accurate low-altitude data were missing at that time,
  • Nathues et al. 2016: 6.9 Ma, based on the interior lobate deposits,
  • Jaumann et al. 2016: between 100 and 200 Ma, depending on how you calibrate the dating from craters,
  • Nathues et al. 2017: 34 ± 2 Ma, from the creation of the central dome, i.e. Cerealia Facula,
  • Nathues et al. 2018, stated that the dispersed bright deposits Vinalia Faculae were younger than 2 Ma, in using low-altitude high-resolution images.

The study we now discuss uses almost all of the data, and so should be more accurate.

A young crater anyway

It is interesting that a study points out all of the possible numbers, given the models, the data, and the physical process considered (crater counting, age of ejecta,…). In particular, if the hydrothermal activity has been triggered by the impact which created Occator, then dating the ejecta should tell us something accurate.

The authors find an age of 21.9 ± 0.7 Ma for the crater in using the Lunar Derived Model, and between 1 and 64 Ma in using the Asteroid-flux Derived Model. You see, lots of uncertainties… as they say, the model ages are a matter of perspective. But anyway, this is a very young and interesting crater!

The study and its authors

And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.

Moon

An Artificial Intelligence identifies the Lunar craters

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Hi there! Today we discuss about an algorithm. I guess you have already heard of Artificial Intelligence, and especially of some futuristic anticipations in which the world would be governed by robots… Fortunately, we are very far from that.

An Artificial Intelligence (AI), Google DeepMind’s AlphaGo, beat the Go professional player Lee Sedol in March 2016. Another AI, IBM’s DeepBlue, beat the then chess world champion Garry Kasparov in May 1997. These realizations of specific tasks belong to Narrow AI, while General AI would be Star Trek’s Data, i.e. a man-made system able to interact with its environments, and react better than a human would do.

Brent Spiner performing Data in Star Trek. © CBS Television Distribution
Brent Spiner performing Data in Star Trek. © CBS Television Distribution

In the context of planetary sciences, AI has recently permitted the discovery of exoplanets, from data analysis. The study I present today, Lunar crater identification via Deep Learning, by Ari Silburt et al., is another example of the way Artificial Intelligence may assist science. In this paper, the author challenge the computer to identify craters from images of the Moon and of Mercury, and the results appear to be very promising. This study has recently been accepted for publication in Icarus.

Outline

Craters in the Solar System
Artificial Intelligence, Machine Learning, and Deep Learning
Use of Digital Elevation Models of the Moon
Algorithm of crater identification
The relevant parameters
Success for the Moon
Successful transfer-learning to Mercury
The study and its authors

Craters in the Solar System

Let us forget AI for a short while. This is a blog of planetary sciences, remember? AI is a tool, not a goal. This study challenges a tool, which goal will be to identify craters. Hence, the goal is the craters.

Craters are ubiquitous in the Solar System, since it is intensively bombarded. This is especially true for the inner Solar System, since the impactors, i.e. small rocky bodies, are gravitationally attracted by the Sun. And while passing by, they may hit us, or the Moon, Mercury, Mars,… And we are lucky, since the current bombardment is much less intense than it was in the youth of the Solar System. Anyway, we have been intensively bombarded, we still are, but our atmosphere protects us in destroying the impactors, which reach the terrestrial surface as meteorites. A huge impactor is thought to be responsible for the extinctions of the dinosaurs, and may be one day… no, better not to think about it.

There are not so many craters at the surface of the Earth, partly because our atmosphere has eroded them, and partly because the geophysical activity relaxed them. But what about atmosphereless bodies like our Moon? Craters are everywhere!

And craters are the clock of the surface. If you see only craters, it means that the surface has not changed since the impact. The surface did not heat, did not melt, there was no geophysical activity creating failures, ridges,… For instance, in the satellites of Jupiter, you see almost no craters on Io and Europa, since there are active bodies. You see some on Ganymede, which is less active, and much more on Callisto, which is quieter.

The surfaces of the Galilean satellites of Jupiter Europa (left), Ganymede (middle), and Callisto (right), seen by Galileo. © NASA
The surfaces of the Galilean satellites of Jupiter Europa (left), Ganymede (middle), and Callisto (right), seen by Galileo. © NASA

This is why it is worthwhile to catalogue the craters of a given planetary body. But since it is a difficult and exhausting task, it is probably a good idea to tell a computer how to do it. This is where AI and Deep Learning come into play.

Artificial Intelligence, Machine Learning, and Deep Learning

As I told you, we are here interested in narrow AI: we want a computer to perform a specific task, and only that task, better than a human would do. And we want the computer to learn how to do it: this is Machine Learning. We give images of craters, tell the computer that they are craters, and we hope it to identify craters on images, which have not been studied yet, i.e. new data. Very well.

A common algorithm for that is Deep Learning. I do not want to go into specifics, but this uses several layers of neurons. Here, neurons should be understood as computing nodes performing a specific sub-tasks, and interacting with each others. The analogy with the human brain is obvious. In this specific case, the authors used convolutional neural networks, in which neurons layers are structured to perform a discrete convolution (a mathematical operation) between their inputs and a filter, which is represented by weights. These weights permit to ponder the relative roles of the different inputs of the system, i.e. which input is more relevant than another one…

The inputs are the planetary data.

Use of Digital Elevation Models of the Moon

Fortunately, we dispose of numerous topographic data of the Moon, which make it the ideal target for elaborating such an algorithm.

The authors used digital elevation maps (DEM) of the Moon, resulting from 2 different missions:

  1. the Lunar Reconnaissance Orbiter (LRO). This is an American mission, which orbits the Moon since 2009. It has made a 3-D map of the Moon’s surface at 100-meter resolution and 98.2% coverage, thanks to the Lunar Orbiter Laser Altimeter (LOLA), and the Lunar Reconnaissance Orbiter Camera (LROC).
  2. The Japanese mission SELENE (Selenological and Engineering Explorer), which is also known as Kaguya. It was composed of 3 spacecraft, i.e. a main orbiter and two satellites. It operated during 20 months, between September 2007 and June 2009. It was then intentionally crashed near the crater Gill.

The authors used such data to train the system, i.e. to make itself an expert in crater identification.

Algorithm of crater identification

Historically, the first identifications of craters were made by visually examining the images. Of course, this is an exhaustive task, and the human being has failures. Moreover, if a small crater looks like a circle, larger ones, i.e. with a diameter larger than 20 km, may have a central peak, may contain other craters, and/or may be altered by the topography (mountains…).

The consequence is that beside the time spent to perform this task, you would have false detections (you think this is a crater, but it is not), and miss some craters, especially the smallest ones. If someone else does the same task, from the same data, (s)he would get a significantly different list. Comparing these lists would be a way to estimate the identification errors.

So, you can see that the use of the numerical tool is inescapable. But how would you do that? Some algorithms identify the circles on the images thanks to a Hough transform (I do not want to go into specifics, but this is a mathematical transformation of your images which tells you “there is a circle there!”), some identify the edges of the craters, some do both… And Deep Learning is learning by itself how to identify craters.

This consists essentially of 3 steps:

  1. training,
  2. validation,
  3. tests.

The algorithm detects crater rims from their pixel intensities, then fits a circle on them, and give as outputs the coordinates of the center and the mean radius of the crater. The authors then compared the results with existing catalogues.

The relevant parameters

The detection of the craters uses parameters, for instance the threshold for the detection of variation of pixel intensities. And the efficiency of the algorithm is measured with

  1. the true positives Tp (the algorithm tells you there is a crater, and you know there is actually one),
  2. the false positives Fp (the algorithm tells you there is a crater, but there is none),
  3. the false negatives Fn (the algorithm tells you nothing, while you know there is a crater),

and these quantities are recombined as

  • the precision P = Tp/(Tp+Fp) (if the algorithm tells you there are 100 craters, how many are actually present?),
  • the recall R = Tp/(Tp+Fn) (over 100 craters, how many are detected by the algorithm?)
  • F1 = 2PR/(P+R), which permits to use a single-parameter metric.

    • The goal is of course to maximize F1.

    Beside this, the authors also compared the coordinates and radii of the detected craters with the ones present in the catalogues, i.e. which had been previously determined by other methods. And all of this works pretty well!

    Success for the Moon

    The algorithm detected 92% of the known craters. Moreover, it also announced to the authors the detection of 361 new craters, and showed to be particularly efficient for craters with a diameter smaller than 5 km. Not only these small craters are a challenge for the human eye, but their regular shape makes the automatic detection more reliable. So, you here have an example of a task, for which the computer could be more efficient than the human. Among these 361 new craters, the authors estimate 11% of them to be false positives (Fp). This last number has some uncertainty, since the validation of a crater is made by a human eye, and the outcome depends on the brain, i.e. the human, behind the eye.

    This is very promising but would that work on another body? It seems so…

    Successful transfer-learning to Mercury

    Finally, the authors asked the computer to identify craters on the surface of Mercury. Remember that the computer was trained with Lunar data. This is called a domain shift, and this is a challenge, since the surface of Mercury has not exactly the same properties of the Lunar one. The bombardment activity was different, Mercury was possibly partly resurfaced, the material itself is different…

    The Moon is on the left, and Mercury on the right.
    The Moon is on the left, and Mercury on the right.

    But the results are pretty good, i.e. many craters are actually detected.

    The algorithm needs some refinements. For instance, it may be lured by circular depressions, which are not true craters (false positives). But the results are very encouraging, in particular for identifying craters on bodies, for which no catalogue exists at this date. The last space missions have given Digital Elevation Models for Venus, Mars, Vesta, and Ceres, and this algorithm may prove very useful to identify their craters.

    Deep Learning is the future!

    The study and its authors

    And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.

Meteors

Saturn sends us meteorites

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Hi there! First I would like to thank you for following me on Facebook. The Planetary Mechanics page has reached 1,000 followers!

OK, now back to business. Did you know that our Earth is intensively bombarded from space? You have recently heard of this Chinese space station, Tiangong-1… in that case, it was man-made stuff. But we are intensively bombarded by natural space material. Most of it is so small that it is destroyed when entering the atmosphere, but sometimes it arrives to us as stones… And in extreme cases, the impactor is so large that its impact may generate an extinction event. The Chicxulub crater, in Mexico, is thought to result from the impact, which aftermath provoked the extinction of the dinosaurs, some 66 Myr ago.

The meteorites I speak about today are the ones, which fall on the Earth every year. This is the opportunity to discuss about Identification of meteorite source regions in the Solar System, which has recently been accepted for publication in Icarus. In that study, the authors determine the origin of 25 meteorites, from their observed trajectories just before they hit us.

Outline

Meteorites bombard the Earth
Observation networks
Identifying the source
7 candidates as reservoirs of meteorites
Saturn sends meteorites to the Earth!
The study and its authors

Meteorites bombard the Earth

We estimate that currently 60 tons of cosmic material fall on the Earth every day. This seems huge, but actually most of it arrives to us as dust, since the original object does not survive its entry into the atmosphere. In fact, the larger the meteorite, the less frequent it falls on us. 4-m objects arrive every ~16 months, 10-m ones every ~10 years, and 100-m ones every ~5,200 years. And they arrive somewhere on Earth… do not forget that most of the surface of our planet is water. So, don’t worry.

The contact of such a small object with the atmosphere may generate an airburst, which itself could be detected, in many frequencies. I mean, you may hear it, you may see it (make a wish), it can also disturb the radio emissions. This motivated the existence of several observation programs, dedicated to the detection of meteors.

Observation networks

Programs of observation exist at least since 1959, originally under the impulse of Ondřejov Observatory (Czech Republic). These are usually national programs, e.g.

and there are probably more. These are networks of camera, which systematically record the sky, accumulating data which are then automatically treated to detect meteors. The detection of a meteors from different location permit to determine its trajectory.

Detection of a fireball by FRIPON, in September 2016. © FRIPON
Detection of a fireball by FRIPON, in September 2016. © FRIPON

Identifying the source

As I said, multiple detections, at different locations, of a fireball, permit to derive its trajectory. This trajectory gives in particular the radiant, which is the direction from which the meteorite, or the impactor, seems to come. The authors are also interested in the velocity of the object.

The velocity and the radiant are determined with respect to the Earth. Once they are determined, the authors translated them into heliocentric elements, i.e. they determined the pre-impact trajectory of the object with respect to the Sun. And this makes sense, since Solar System objects orbit the Sun! This trajectory is made of orbital elements, i.e. semimajor axis, eccentricity, inclination, and the uncertainties associated. Don’t forget that the observations have an accuracy, which you must consider when you use the data. The magnitude of the fireball tells us something on the size of the impactor as well.

From these data, the authors wondered from where the object should come from.

7 candidates as reservoirs of meteorites

The authors identified 7 possible sources for these impactors. These regions are the densest parts of the Main Asteroid Belt.
These are:

  1. the Hungaria family. These asteroids have a semimajor axis between 1.78 and 2 astronomical units, and an inclination between 16° and 34° with respect to the ecliptic, i.e. the orbit of the Earth,
  2. the ν6 resonance: these are bodies, which eccentricity raise because excited by Saturn. They orbit at a location, where they are sensitive to the precessional motion of the pericentre of Saturn. The raise of their eccentricity make these bodies unstable, and good candidates for Earth-crossers. Their semimajor axis is slightly smaller than 2 AU.
  3. the Phocaea family: this is a collisional family of stony asteroids. Their semimajor axes lie between 2.25 and 2.5 AU, their eccentricities are larger than 0.1, and their inclinations are between 18° and 32°. They are known to be a source of Mars-crossers.
  4. the 3:1 MMR (mean-motion resonance with Jupiter): these bodies perform exactly 3 orbits around the Sun while Jupiter makes one. They lie at 2.5 AU. The perturbation by Jupiter tends to empty this zone, which is called a Kirkwood gap.
  5. the 5:2 MMR, at 2.82 AU. This is another Kirkwood gap.
  6. the 2:1 MMR, at 3.27 AU, also known as Hecuba gap,
  7. the Jupiter Family Comets. These are comets, which orbital periods around the Sun are shorter than 20 years, and which inclinations are smaller than 30° with respect to the ecliptic. They are likely to be significantly perturbed by Jupiter.

For each of the 25 referenced meteorites, the authors computed the probability of each of these regions to be the source, in considering the orbital elements (semimajor axis, eccentricity, and inclination) and the magnitude of the object. Indeed, the magnitude is correlated with the size, which is itself correlated with the material constituting it. The reason is that these Earth-crossers orbit the Sun on eccentric orbits, and at their pericentre, i.e. the closest approach to the Sun, they experience tides, which threaten their very existence. In other words, they might be disrupted. Particularly, a large body made of weak material cannot survive.

And now, the results!

Saturn send meteorites to the Earth!

The authors find that the most probable source for the meteorites is the ν6 secular resonance, i.e. with Saturn. In other words, Saturn sends meteorites to the Earth! Beside this, the Hungaria family and the 3:1 mean-motion resonance with Jupiter are probable sources as well. On the contrary, you can forget the Phocaea family and the 2:1 MMR as possible sources.
It appears that the inner belt is more likely to be the source of meteorites than the outer one. Actually, the outer belt mostly contains carbonaceous asteroids, which produce weak meteoroids.

The authors honestly recall that previous studies found similar results. Theirs also contains an analysis of the influence of the uncertainty on the trajectories, and of the impact velocity with the Earth. This influence appears to be pretty marginal.

Anyway, the future will benefit from more data, i.e. more detections and trajectory recoveries. So, additional results are to be expected, just be patient!

The study and its authors

  • You can find the study here, on the website of Icarus. This study is in open access, which means that the authors paid extra fees to make the study available to us. Many thanks to them!
  • You can visit here the website of Mikael Granvik, the first author of the study,
  • and the one of the second author, Peter Brown.

And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.

Planets

Impacts on Jupiter

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Hi there! Today is a little different. I present you a study of the impacts on Jupiter. This study, Small impacts on the giant planet Jupiter, by Hueso et al., has recently been accepted for publication in Astronomy and Astrophysics.
This is something different from usual by the implication of amateur astronomers. The professional scientific community sometimes needs their help, because they permit to tend to a global coverage of an expected event, like a stellar occultation. This is here pretty different since impacts on Jupiter are not predicted, so they are observed by chance. And the more observations, the more chance.
Thanks to these data, the authors derived an estimation of the impact rate on Jupiter.

Outline

The fall of Shoemaker-Levy 9
Impacting Jupiter
Observing an impact
Treating the data
A 45-m impactor every year
The study and its authors

The fall of Shoemaker-Levy 9

Before getting to the point, let me tell you the story of the comet Shoemaker-Levy 9. This comet has been discovered around Jupiter in March 1993 by Carolyn and Eugene Shoemaker, David Levy, and Philippe Bendjoya. Yes, this was discovered as a satellite of Jupiter, but on an unstable orbit. This comet was originally not a satellite of Jupiter, and when passing by Jupiter captured it. And finally, Shoemaker-Levy 9 crashed on Jupiter between July, 16 and July, 22 1994. Why during 6 days? Because the comet got fragmented. 23 fragments have been detected, which crashed close to the South Pole of Jupiter in 1994. This resulted in flashes more visible than the Red Spot, and scars which could be seen during several months. Moreover, Shoemaker-Levy 9 polluted the atmosphere of Jupiter with water.

Impacting Jupiter

Shoemaker-Levy 9 is a spectacular and well-known example of impact on Jupiter. But Jupiter is in fact regularly impacted. Cassini even mentioned a black dot on Jupiter in 1690, which could result from an impact. This is how things work.

Jupiter attracts the impactors

As you know, Jupiter is the most massive body in the Solar System, beside the Sun of course. As such, it attracts the small objects passing by, i.e. it tends to focus the trajectories of the impactors. So, the impactors are caught in the gravitational field of Jupiter, but usually on a hyperbolic orbit, since they come from very far away. As a consequence their orbits are unstable, and they usually will be ejected, or crash onto Jupiter. Let us assume we crash on Jupiter.

Jupiter destroys the impactors

Before the crash, the distance to Jupiter decreases, of course, and its gravitational action becomes stronger and stronger. A consequence is that the differential action of Jupiter on different parts of a given body, even a small one, gets stronger, and tends to disrupt it (tidal disruption). This is why Shoemaker-Levy 9 has been fragmented.

The impactors do not leave any crater

When the fragments reach Jupiter, they reach in fact its upper atmosphere. Since this atmosphere is very large and thick, the impactors do not create visible craters, but only perturbations in the atmosphere. We see at least a flash (a bright fireball), and then we may see kind of clouds, which are signatures of the atmospheric pollution due to the impactors. I mentioned a flash, actually they may be several of them, because the impactor is fragmented.

Let us now discuss on the observations of such events.

Observing an impact

Jupiter is usually easy to observe from the Earth, but only 9 months each year. It is too close to the Sun during the remaining time. While visible, everybody is free to point a telescope at it, and record the images. Actually amateur astronomers do it, and some impacts were detected by them. Once you have recorded a movie, then you should watch it slowly and carefully to detect an impact. Such an event lasts a few seconds, which is pretty tough to detect on a movie which lasts several hours.

The authors studied 5 events, at the following dates:

  1. June 3, 2010, detected twice, in Australia and in the Philippines,
  2. August 20, 2010, detected thrice, in Japan,
  3. September 9, 2012, detected twice, in the USA
  4. March 17, 2016, detected twice, in Austria and Ireland,
  5. May 26, 2017, detected thrice, in France and in Germany.

Once an observer detects such an event, he/she posts the information on an astronomy forum, to let everybody know about it. This is how several observers can get in touch. If you are interested, you can also consult the page of the Jupiter bolides detection project.

The detection of impacts can be improved in observing Jupiter through blue filters and wide filters centered on the methane absorption band at 890 nm, because Jupiter is pretty dark at these wavelengths, making the flash more visible. Moreover, one of the authors, Marc Delcroix, made an open-source software, DeTeCt, which automatically detects the flashes from observations of Jupiter.

All of these events were discovered by amateurs, and professionals exploited the data to characterize the impactors.

Treating the data

Once the impacts have been detected, the information and images reach the professionals. In order to characterize the impactor, they estimate the intensity and duration of the flash by differential photometry between images during the event and images before and after, to subtract the luminosity of Jupiter. Then they plot a lightcurve of the event, which could show several maximums if we are lucky enough. From the intensity and duration they get to the energy of the impact. And since they can estimate the velocity of the impact, i.e. 60 km/s, which is a little larger than the escape velocity of Jupiter (imagine you want to send a rocket from Jupiter… you should send it with a velocity of at least 60 km/s, otherwise it will fall back on the planet), they get to the size of the impactor.

A 45-m impactor every year

The most frequent impacts are probably the ones by micrometeorites, as on Earth, but we will never be able to observe them. They can only be estimated by dynamical models, i.e. numerical simulations, or by on-site measurements by spacecrafts.

The authors showed that the diameters of the impactors, which were involved in the detected events, could be from the meter to 20 meters, depending on their density, which is unknown. Moreover, they estimate that events by impactors of 45 m should occur and could be detectable every year, but that impacts from impactors of 380 meters would be detectable every 6 to 30 years… if observed of course. And this is why the authors insist that many amateurs participate to such surveys, use the DeTeCt software, report their observations, and share their images.

The study and its authors

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Asteroids: Main Belt

Analyzing a crater of Ceres

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Hi there! The space mission Dawn has recently visited the small planets Ceres and Vesta, and the use of its different instruments permits to characterize their composition and constrain their formation. Today we focus on the crater Haulani on Ceres, which proves to be pretty young. This is the opportunity for me to present you Mineralogy and temperature of crater Haulani on Ceres by Federico Tosi et al. This paper has recently been published in Meteoritics and Planetary Science.

Outline

Ceres’s facts
The crater Haulani
Dawn at Ceres
Different indicators
VIR and FC data
A young and bright region
Possible thanks to lab experiments
The study and its authors

Ceres’s facts

Ceres is the largest asteroid of the Solar System, and the smallest dwarf planet. A dwarf planet is a planetary body that is large enough, to have been shaped by the hydrostatic equilibrium. In other words, this is a rocky body which is kind of spherical. You can anyway expect some polar flattening, due to its rotation. However, many asteroids look pretty much like potatoes. But a dwarf planet should also be small enough to not clear its vicinity. This means that if a small body orbits not too far from Ceres, it should anyway not be ejected.

Ceres, or (1)Ceres, has been discovered in 1801 by the Italian astronomer Giuseppe Piazzi, and is visited by the spacecraft Dawn since March 2015. The composition of Ceres is close to the one of C-Type (carbonaceous) asteroids, but with hydrated material as well. This reveals the presence of water ice, and maybe a subsurface ocean. You can find below its main characteristics.

Discovery 1801
Semimajor axis 2.7675 AU
Eccentricity 0.075
Inclination 10.6°
Orbital period 4.60 yr
Spin period 9h 4m 27s
Dimensions 965.2 × 961.2 × 891.2 km
Mean density 2.161 g/cm3

The orbital motion is very well known thanks to Earth-based astrometric observations. However, we know the physical characteristics with such accuracy thanks to Dawn. We can see in particular that the equatorial section is pretty circular, and that the density is 2.161 g/cm3, which we should compare to 1 for the water and to 3.3 for dry silicates. This another proof that Ceres is hydrated. For comparison, the other target of Dawn, i.e. Vesta, has a mean density of 3.4 g/cm3.

It appears that Ceres is highly craterized, as shown on the following map. Today, we focus on Haulani.

Topographic map of Ceres, due to Dawn. Click to enlarge. © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA
Topographic map of Ceres, due to Dawn. Click to enlarge. © NASA/JPL-Caltech/UCLA/MPS/DLR/IDA

The crater Haulani

The 5 largest craters found on Ceres are named Kerwan, Yalode, Urvara, Duginavi, and Vinotonus. Their diameters range from 280 to 140 km, and you can find them pretty easily on the map above. However, our crater of interest, Haulani, is only 34 km wide. You can find it at 5.8°N, 10.77°E, or on the image below.

The crater Haulani, seen by <i>Dawn</i>. © NASA / JPL-Caltech / UCLA / Max Planck Institute for Solar System Studies / German Aerospace Center / IDA / Planetary Science Institute
The crater Haulani, seen by Dawn. © NASA / JPL-Caltech / UCLA / Max Planck Institute for Solar System Studies / German Aerospace Center / IDA / Planetary Science Institute

The reason why it is interesting is that it is supposed to be one of the youngest, i.e. the impact creating it occurred less than 6 Myr ago. This can give clues on the response of the material to the impact, and hence on the composition of the subsurface.
Nothing would have been possible without Dawn. Let us talk about it!

Dawn at Ceres

The NASA mission Dawn has been launched from Cape Canaveral in September 2007. Since then, it made a fly-by of Mars in February 2009, it orbited the minor planet (4)Vesta between July 2011 and September 2012, and orbits Ceres since March 2015.

This orbit consists of several phases, aiming at observing Ceres at different altitudes, i.e. at different resolutions:

  1. RC3 (Rotation Characterization 3) phase between April 23, 2015 and May 9, 2015, at the altitude of 13,500 km (resolution: 1.3 km/pixel),
  2. Survey phase between June 6 and June 30, 2015, at the altitude of 4,400 km (resolution: 410 m /pixel),
  3. HAMO (High Altitude Mapping Orbit) phase between August 17 and October 23, 2015, at the altitude of 1,450 km (resolution: 140 m /pixel),
  4. LAMO (Low Altitude Mapping Orbit) / XMO1 phase between December 16, 2015 and September 2, 2016, at the altitude of 375 km (resolution: 35 m /pixel),
  5. XMO2 phase between October 5 and November 4, 2016, at the altitude of 1,480 km (resolution: 140 m / pixel),
  6. XMO3 phase between December 5, 2016 and February 22, 2017, at the altitude varying between 7,520 and 9,350 km, the resolution varying as well, between
  7. and is in the XMO4 phase since April 24, 2017, with a much higher altitude, i.e. between 13,830 and 52,800 km.

The XMOs phases are extensions of the nominal mission. Dawn is now on a stable orbit, to avoid contamination of Ceres even after the completion of the mission. The mission will end when Dawn will run out of fuel, which should happen this year.

The interest of having these different phases is to observe Ceres at different resolutions. The HAMO phase is suitable for a global view of the region of Haulani, however the LAMO phase is more appropriate for the study of specific structures. Before looking into the data, let us review the indicators used by the team to understand the composition of Haulani.

Different indicators

The authors used both topographic and spectral data, i.e. the light reflected by the surface at different wavelengths, to get numbers for the following indicators:

  1. color composite maps,
  2. reflectance at specific wavelengths,
  3. spectral slopes,
  4. band centers,
  5. band depths.

Color maps are used for instance to determine the geometry of the crater, and the location of the ejecta, i.e. excavated material. The reflectance is the effectiveness of the material to reflect radiant energy. The spectral slope is a linear interpolation of a spectral profile by two given wavelengths, and band centers and band depths are characteristics of the spectrum of material, which are compared to the ones obtained in lab experiments. With all this, you can infer the composition of the material.

This requires a proper treatment of the data, since the observations are affected by the geometry of the observation and of the insolation, which is known as the phase effect. The light reflection will depend on where is the Sun, and from where you observe the surface (the phase). The treatment requires to model the light reflection with respect to the phase. The authors use the popular Hapke’s law. This is an empirical model, developed by Bruce Hapke for the regolith of atmosphereless bodies.

VIR and FC data

The authors used data from two Dawn instruments: the Visible and InfraRed spectrometer (VIR), and the Framing Camera (FC). VIR makes the spectral analysis in the range 0.5 µm to 5 µm (remember: the visible spectrum is between 0.39 and 0.71 μm, higher wavelengths are in the infrared spectrum), and FC makes the topographical maps.
The combination of these two datasets allows to correlate the values given by the indicators given above, from the spectrum, with the surface features.

A young and bright region

And here are the conclusions: yes, Haulani is a young crater. One of the clues is that the thermal signature shows a locally slower response to the instantaneous variations of the insolation, with respect to other regions of Ceres. This shows that the material is pretty bright, i.e. it has been less polluted and so has been excavated recently. Moreover, the spectral slopes are bluish, this should be understood as a jargony just meaning that on a global map of Ceres, which is colored according to the spectral reflectance, Haulani appears pretty blue. Thus is due to spectral slopes that are more negative than anywhere else on Ceres, and once more this reveals bright material.
Moreover, the bright material reveals hydrothermal processes, which are consequences of the heating due to the impact. For them to be recent, the impact must be recent. Morever, this region appears to be calcium-rich instead of magnesium-rich like anywhere else, which reveals a recent heating. The paper gives many more details and explanations.

Possible thanks to lab experiments

I would like to conclude this post by pointing out the miracle of such a study. We know the composition of the surface without actually touching it! This is possible thanks to lab experiments. In a lab, you know which material you work on, and you record its spectral properties. And after that, you compare with the spectrum you observe in space.
And this is not an easy task, because you need to make a proper treatment of the observations, and once you have done it you see that the match is not perfect. This requires you to find a best fit, in which you adjust the relative abundances of the elements and the photometric properties of the material, you have to consider the uncertainties of the observations… well, definitely not an easy task.

The study and its authors

And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.