Tag Archives: numerical methods

The rings of Haumea

Hi there! I guess you have heard, last year, of the discovery of rings around the Trans-Neptunian Object Haumea. If not, don’t worry, I speak about it. Rings around planets are known since the discovery of Saturn (in fact a little later, since we needed to understand that these were rings), and now we know that there are rings around the 4 giant planets, and some small objects, which orbit beyond Saturn.

Once such a ring is discovered, we should wonder about its origin, its lifetime, its properties… This is the opportunity for me to present a Hungarian study, Dynamics of Haumea’s dust ring, by T. Kovács and Zs. Regály. This study has recently been accepted for publication in The Monthly Notices of the Royal Astronomical Society.

The Trans-Neptunian Object (136108)Haumea

Discovery

The discovery of (136108) Haumea was announced in July 2005 by a Spanish team, led by José Luis Ortiz, observing from Sierra Nevada Observatory (Spain). This discovery was made after analysis of observations taken in March 2003. As a consequence, this new object received the provisional name 2003 EL61.

But meanwhile, this object was observed since several months by the American team of Michael Brown, from Cerro Tololo Inter-American Observatory, in Chile, who also observed Eris. This led to a controversy. Eventually, the Minor Planet Center, which depends on the International Astronomical Union, credited Ortiz’s team for the discovery of the object, since they were the first to announce it. However its final name, Haumea, has been proposed by the American team, while usually the final name is chosen by the discoverer. Haumea is the goddess of fertility and childbirth in Hawaiian mythology. The Spanish team wished to name it Ataecina, after a popular goddess worshipped by the ancient inhabitants of the Iberian Peninsula.

Reanalysis of past observations revealed the presence of Haumea on photographic plates taken in 1955 at Palomar Observatory (we call that a precovery).

Properties

You can find below some numbers regarding Haumea.

Semi-major axis 43.218 AU
Eccentricity 0.191
Inclination 28.19°
Orbital period 284.12 yr
Spin period 3.92 h
Dimensions 2,322 × 1,704 × 1,138 km
Apparent magnitude 17.3

As a massive Trans-Neptunian Object, i.e. massive enough to have a pretty spherical shape, it is classified as an ice dwarf, or plutoid. This shape is pretty regular, but not that spherical actually. As you can see from its 3 diameters (here I give the most recent numbers), this is a triaxial object, with a pretty elongated shape… and this will be important for the study.

It orbits in the 7:12 mean-motion resonance with Neptune, i.e. it performs exactly 7 revolutions around the Sun while Neptune makes 12. This is a 5th order resonance, i.e. a pretty weak one, but which anyway permits some stability of the objects, which are trapped inside. This is why we can find some!

We can also see that it has a rapid rotation (less then 4 hours!). Moreover, it is pretty bright, with a geometrical albedo close to 0.8. This probably reveals water ice at its surface.

And Haumea has two satellites, and even rings!

Two satellites, and rings

Haumea has two known satellites, Namaka and Hi’iaka, named after two daughters of the goddess Haumea. They were discovered by the team of Michael Brown in 2005, simultaneously with its observations of Haumea, i.e. before the announcement of its discovery. You can find below some of their characteristics.

Namaka Hi’iaka
Semi-major axis 25657 km 49880 km
Eccentricity 0.25 0.05
Orbital period 18.28 d 49.46 d
Mean diameter 170 km 310 km
Keck image of Haumea and its moons. Hi'iaka is above Haumea (center), and Namaka is directly below. © Californian Institute of Technology
Keck image of Haumea and its moons. Hi’iaka is above Haumea (center), and Namaka is directly below. © Californian Institute of Technology

Usually such systems are expected to present spin-orbit resonances, e.g. like our Moon which rotates synchronously with the Earth. Another example is Pluto-Charon, which is doubly synchronous: Pluto and Charon have the same spin (rotational) period, which is also the orbital period of Charon around Pluto. Here, we see nothing alike. The rotational period of Haumea is 4 hours, while its satellites orbit much slower. We do not dispose of enough data to determine their rotation periods, maybe they are synchronous, i.e. with spin periods of 18.28 and 49.46 days, respectively… maybe they are not.

This synchronous state is reached after tidal dissipation slowed the rotation enough. Future measurements of the rotation of the two satellites could tell us something on the age of this ternary system.

And last year, an international team led by José Luis Ortiz (the same one) announced the discovery of a ring around Haumea.

Rings beyond Jupiter

In the Solar System, rings are known from the orbit of Jupiter, and beyond:

  • Jupiter has a system of faint rings,
  • should I introduce the rings of Saturn?
  • Uranus has faint rings, which were discovered in 1977,
  • the rings of Neptune were discovered in 1984, before being imaged by Voyager 2 in 1989. Interestingly, one of these rings, the Adams ring, contains arcs, i.e. zones in which the ring is denser. These arcs seem to be very stable, and this stability is not fully understood by now.
Arcs in the Adams ring (left to right: Fraternité, Égalité, Liberté), plus the Le Verrier ring on the inside. © NASA
Arcs in the Adams ring (left to right: Fraternité, Égalité, Liberté), plus the Le Verrier ring on the inside. © NASA

Surprisingly, we know since 2014 that small bodies beyond the orbit of Jupiter may have rings:

  • An international team detected rings around the Centaur Chariklo in 2014 (remember: a Centaur is a body, which orbits between the orbits of Jupiter and Neptune),
  • another team (with some overlaps with the previous one), discovered rings around Haumea in 2017,
  • observations in 2015 are consistent with ring material around the Centaur Chiron, but the results are not that conclusive.

These last discoveries were made thanks to stellar occultations: the object should occult a star, then several teams observe it from several locations. While the planetary object is too faint to be observed from Earth with classical telescopes, the stars can be observed. If at some point no light from the star is being recorded while the sky is clear, this means that it is occulted. And the spatial and temporal distributions of the recorded occultations give clues on the shape of the body, and even on the rings when present.

Why rings around dwarf planets?

Rings around giant planets orbit inside the Roche limit. Below this limit, a planetary object cannot accrete, because the intense gravitational field of the giant planet nearby would induce too much tidal stress, and prevent the accretion. But how can we understand rings around dwarf planets? Chiron presents some cometary activity, so the rings, if they exist, could be constituted of this ejected material. But understanding the behavior of dust around such a small object is challenging (partly because it is a new challenge).

In 2015, the American planetologist Matthew Hedman noticed that dense planetary rings had been only found between 8 and 20 AU, and proposed that the temperature of water ice in that area, which is close to 70 K (-203°C, -333°F), made it very weak and likely to produce rings. In other words, rings would be favored by the properties of the material. I find this explanation particularly interesting, since no ring system has been discovered in the Asteroid Main Belt. That paper was published before the discovery of rings around Haumea, which is far below the limit of 20 UA. I wonder how the Haumea case would affect these theoretical results.

In the specific case of Haumea, the ring has a width of 70 kilometers and a radius of about 2,287 kilometers, which makes it close to the 3:1 ground-track resonance, i.e. the particles constituting the ring make one revolution around Haumea, while Haumea makes 3 rotations.

Numerical simulations

Let us now focus of our study. The authors aimed at understanding the dynamics and stability of the discovered rings around Haumea. For that, they took different particles, initially on circular orbits around Haumea, at different distances, and propagated their motions.
Propagating their motions consists in using a numerical integrator, which simulates the motion in the future. There are powerful numerical tools which perform this task reliably and efficiently. These tools are classified following their algorithm and order. The order is the magnitude of the approximation, which is made at each timestep. A high order means a highly accurate simulation. Here, the authors used a fourth order Runge-Kutta scheme. It is not uncommon to see higher-order tools (orders between 8 and 15) in such studies. The motions are propagated over 1 to 1,000 years.

A gravitational and thermal physical model

The authors assumed the particles to be affected by

  • the gravitational field of Haumea, including its triaxiality. This is particularly critical to consider the ground-track resonances, while the actually observed ring is close to the 3:1 resonance,
  • the gravitational perturbation by the two small moons, Namaka and Hi’iaka,
  • the Solar radiation pressure.

This last force is not a gravitational, but a thermal one. It is due to an exchange of angular momentum between the particle, and the electromagnetic field, which is due to the Solar radiation. For a given particle size, the Solar radiation pressure has pretty the same magnitude for all of the particles, while the gravitational field of Haumea decreases with the distance. As a consequence, the furthest particles are the most sensitive to the radiation pressure. Moreover, this influence is inversely proportional to the grain size, i.e. small particles are more affected than the large ones.

And now, the results!

A probable excess of small particles

The numerical simulations show that the smaller the grains size, the narrower the final ring structure. The reason is that smaller particles will be ejected by the radiation pressure, unless they are close enough to Haumea, where its gravity field dominates.

And this is where you should compare the simulations with the observations. The observations tell you that the ring system of Haumea is narrow, this would be consistent with an excess of particles with grain size of approximately 1 μm.

So, such a study may constrain the composition of the rings, and may help us to understand its origin. Another explanation could be that there was originally no particle that far, but in that case you should explain why. Let us say that we have an argument for a ring essentially made of small particles.

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.

An Artificial Intelligence identifies the Lunar craters

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.

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.

Forming Mars

Hi there! Of course, you know the planet Mars. You can here from it these days, since it is exceptionally close to our Earth. Don’t worry, this is a natural, geometrical phenomenon.

Anyway, it is a good time to observe it. But I will not speak of observing it, today. We will discuss its formation instead, because the issue of the formation of Mars remains a challenge. This is the opportunity to present The curious case of Mars’ formation, by James Man Yin Woo, Ramon Brasser, Soko Matsumura, Stephen J. Mojzsis, and Shigeru Ida. Astronomy and Astrophysics will publish it pretty soon.

Mars is too small

The following table gives you comparative characteristics of Venus, the Earth, and Mars.

Venus Earth Mars
Semimajor axis 0.723 AU 1.000 AU 1.524 AU
Eccentricity 0.007 0.017 0.093
Inclination 3.39° 1.85°
Orbital period 224.7 d 365.25 d 686.96 d
Spin period -243.02 d 23.93 h 24.62 h
Mean diameter 12,104 km 12,742 km 6,779 km

The last line reveals a problem: Venus and the Earth are about the same size, while Mars is much smaller! But this is not the only problem: the compositions of the Earth and Mars are VERY different.

It is pretty easy to know the composition of the Earth: you just analyze samples. And for Mars? Just the same!

Interestingly, there are Martian meteorites on Earth. These are ejecta from impacts, which were ejected from Mars, and then traveled in the Solar System, until reaching our Earth.

In fact, over the tens of thousands of meteorites which have been found on Earth, a little more than one hundred were significantly different than the other ones, i.e. younger formation ages, a different oxygen isotopic composition, the presence of aqueous weathering products… Most of these meteorites were known as SNC, after the three groups they were classified into:

  • S for Shergottites, after the Shergotty meteorite (India, 1865),
  • N for Nakhlites, after the Nakhla meteorite (Egypt, 1911),
  • C for Chassignites, after the Chassigny meteorite (France, 1815).

Such a significant number of similar meteorites, which are that different from the other ones, suggests they come from a large body. Mars is an obvious candidate, which has been confirmed after the discovery that trapped gases in these meteorites are very similar to the ones, which are present in the atmosphere of Mars.

The Martian meteorite NWA (Northwest Africa) 2046, found in September 2003 in Algeria. This is a Shergottite. © Michael Farmer and Jim Strope.
The Martian meteorite NWA (Northwest Africa) 2046, found in September 2003 in Algeria. This is a Shergottite. © Michael Farmer and Jim Strope.

After that, the numerous space missions improved our knowledge of the Martian composition. And it finally appeared that both planets are essentially made of chondritic material. The Earth should accrete about 70% of enstatite chondrite (and same for the Moon), while Mars only about 50%. Chondrites are non-metallic meteorites, the enstatite chondrites being rich in the mineral enstatite (MgSiO3). These numbers are derived from the documented isotopic compositions of the Earth and Mars, i.e. the ratio of the different chemical elements. An isotope is a variant of a particular chemical element, which differs in neutron number.

If you want to convincingly simulate the formation of Mars, the product of your simulations should be similar to Mars in mass AND in composition. And this is very challenging. Let us see why, but first of all let us recall how to form planets from a disk.

Forming planets from a disk

At its early stage, a planetary system is composed of a proto-star, and a pretty flat disk, made of gas and dust. Then the dust accretes into clumps, which then collides to form planetary embryos, i.e. proto-planets. These embryos continue to grow with collisions, until forming the current planets. Meanwhile, the gas has dissipated.

Anyway, interactions between the protoplanets and between them and the gas can lead to planetary migration. This means that we cannot be sure whether the planets we know formed close to their current location. This makes room for several scenarios.

Two models of planetary formation

The obvious starting point is to assume that the planets formed close to their current locations. This so-called Classical model works pretty well for Venus, the Earth, Jupiter, Saturn… but not for Mars. The resulting Mars is too massive.

An idea for by-passing this problem is to start with a depletion of material at the location of Mars. This is equivalent to an excess inside the terrestrial orbit. In such a configuration, less material is available to the proto-Mars, which eventually has a mass, which is close to the present one.

You can get this excess of material inside the terrestrial orbit if you buy the Grand Tack scenario: when Jupiter formed, it created a gap in the inner disk, and the mutual interaction resulted in an inward migration of Jupiter, until reaching the present orbit of Mars. In moving inward (Type II migration), Jupiter pushed the material inward. Then, a 3:2 mean-motion resonance with Saturn occurred, which created another gap, and made Jupiter move outward, until its present location.

This way, you can form a planetary object, which is similar to Mars in mass and location.

But what about its composition?

The composition challenge

This is still a challenge. The composition of a planetary object is strongly affected by the one of the disk, where the object formed… which may not be its present location.

The authors added a free parameter to the model: the break location, which would split the protoplanetary disk into an inner and an outer region. The inner region would be rich in enstatite chondrites, while the outer one would be rich in ordinary chondrites.

A break location at 1.3 AU gives the best fit for the difference of composition between Mars and the Earth, for both formation scenarios (Classical and Grand Tack).

So, the Grand Tack with a break location at 1.3 AU could be the right scenario. But another possibility exists: the Classical scenario says that if Mars formed where it is, then it should be heavier. But what if Mars formed actually further from the Sun, and then migrated inward? Then, it would not need any depletion of material to have the right mass. And the break barrier should have been further than 1.3 AU. But you have to explain why it migrated inward.

Anticipating the composition

One of the good things with scenarios of formation is that thr gives more details on the outcomes, than actually observed. For instance, this study predicts the isotopic composition of 17O, 50Ti, 54Cr, 142Nd, 64Ni and 92Mo, in the Martian mantle. Further data, collected by space missions, will give additional constraints on these parameters, and test the validity of the present study. 8 missions are currently operational in orbit or on Mars, and InSight is en-route, after having been launched in May 2018. It should land on Mars on November 26, and will study its interior with a seismometer, and a heat transfer probe.

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.

The Earth will encounter Apophis

Hi there! You may have heard of (99942) Apophis. As 2004 MN4, this Near-Earth Asteroid was considered to be a potential hazard. Don’t worry, it is not anymore. Anyway, it will have some close approaches with the Earth, the next one occurring in 2029. This makes it an interesting object, and the paper I present today deals with the way this close encounter will affect the rotation of Apophis. This study, Changes of spin axis and rate of the asteroid (99942) Apophis during the 2029 close encounter with Earth: a constrained model, led by Jean Souchay, has recently been accepted for publication in Astronomy and Astrophysics.

The asteroid (99942) Apophis

The asteroid (99942) Apophis has been discovered on June 19, 2004, and re-observed the day after (I should say the night, actually), at Kitt Peak Observatory in Arizona. It was then re-discovered six months later from Siding Spring Observatory, New South Wales, Australia, on December 18, and very soon confirmed that it was the same body. On December 27, it was realized that this object had actually already been observed in March. This precovery revealed to be very useful to determine its orbit. You can find below some of its characteristics:

Semi-major axis 0.9225867 AU
Eccentricity 0.1914717
Inclination 3.33687°
Period 323.5 d
Diameter ~350 m

Its orbital dynamics makes it a member of the group Aten. You can see that its orbital period is pretty close to the one of the Earth, i.e. close to one year. This raises the question: could it collide with our Earth? I answer NOT AT ALL, but the question was raised.

Potentially Hazardous Asteroids (PHAs)

Several programs, like NEODys in Italy, or the CNEOS in America, follow Near-Earth Asteroids which could possibly hit the Earth. Up to now, the identified PHAs have been proved to actually present no risk. The Torino scale categorizes the impact hazard associated with near-Earth objects, on a scale from 0 to 10. The risks of collisions and the energies involved are considered. 0 means no risk of impact, 5 means serious threat, 8 means certain collision… and 10 is the worst case, of course, which would characterize the Chicxulub impact, believed by most scientists to be a significant factor in the extinction of the dinosaurs. In such a case, the very existence of the human kind would be jeopardized.

The Minor Planet Center maintains a list of Potentially Hazardous Asteroids, i.e. worthwhile to be scrutinized. I currently count 1,923 of them, but this list is not static.

The observations of Apophis in December 2004 rated it at the level 4, which is a record since the creation of the Torino scale in 1999. Level 4 means that a collision with regional devastation has a probability of at least 1%. On December 27, 2004, the precovery images of Apophis dating from March have ruled out this possibility, and we now know that Apophis will not collide our Earth… or at least not before centuries. The next close approach will occur on April 13, 2029, at a distance of 38,400 km, which is about one tenth of the Earth-Moon distance. Such accurate numbers have been obtained after almost 15 years of astrometric observations of Apophis, which permitted to refine the dynamical models, i.e. fit the ephemerides.

A close encounter changes the dynamics

The mass ratio between the Earth and Apophis implies that, at such a small distance, Apophis will suffer from a huge kick of the Earth. This will drastically affect its dynamics, and would have significant implications for further predictions of its orbit. An accurate determination of the orbital changes requires to consider the non-sphericity of the Earth, the influence of the Sun and the Moon, and also non-gravitational forces, like the Yarkovsky effect. This is a thermal effect, due to the proximity of the Sun. It is barely constrained since it depends on the surface properties and the rotation of the body.
Of course, the future close approaches depend on the next ones. Another one will occur in 2036, its prediction will be much more accurate after the one of 2029.

A study by the same authors anticipate that the 2029 close encounter will affect the orbit of Apophis in such a way that it will move from the dynamical group of Aten to the one of Apollo. In particular, its semimajor axis will be close to 1.1 AU. As a consequence, its orbital period will lengthen from 324 to 422 days.

The rotation is critical

As I said, the Yarkovsky effect depends on the rotational state of Apophis. And this is probably why the study we discuss today deals with the rotation.
The rotation of Apophis has actually been studied in a recent past, from lightcurves. This is something I already discussed on this blog: in recording the Solar light, which is reflected by the surface of the body, you see variations, which are signatures of the rotational motion.

The lightcurves of Apophis revealed two main periods, at 27.38 h and 30.51 hours. The authors of that study (or here) interpreted these two periods as a combination between a fast precessional motion of the rotation axis, with a period of 27.38 h, and a slow and retrograde rotation, with a period of 263 h. This means that the rotation itself is slow and retrograde, but meanwhile the orientation of the North Pole of the body is moving some 10 times faster. Moreover, the authors discovered that the rotation axis was very close to the smallest figure axis. This is called Short-Axis Mode (SAM), and this means that the rotational energy is close to a minimum. In other words, some of it has been dissipated over the ages.

This is the currently observed rotation, but what will it be after the encounter?

A numerical study

The authors performed intensive numerical tests to answer this question. For that, they started from a set of 10,000 model-Apophis, all consistent with our current knowledge of the rotation of Apophis. In other words, these model-Apophis were oriented consistently with the uncertainties of the observations. They also considered the shape, which is itself derived from the lightcurves by Pravec et al. (2014).
Then, they propagated the rotation in using famous equations of the rigid rotation due to Kinoshita (1977), supplemented by a model of the tidal deformation of Apophis by the Earth, during its close approach. Then, the authors deduced the results from the statistics of the outcomes of their 10,000 numerical simulations.

Different obliquity, same spin rate

And here is the results: the authors find that the close encounter with the Earth should not significantly affect the spin rate of Apophis. However, the orientation of its spin axis will tend to align with the one of the Earth, affecting its obliquity.

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.

Locating Ultima Thule

Hi there! First I have to explain the title. Ultima Thule usually stands for any place beyond the known world… Actually, we will not leave the Solar System. Ultima Thule is an unofficial nickname for (486958) 2014 MU69, which is the next target of New Horizons spacecraft. Do you remember this spacecraft, which gave us outstanding images of Pluto and Charon in July 2015? That’s just the same one!
After having left Pluto, New Horizons changed its trajectory to 2014 MU69, which will be reached on January 1st, 2019. 6 months to wait then.
Interestingly, 2014 MU69 was unknown when New Horizons was launched in January 2006. Its primary mission was the binary Pluto-Charon and its satellites, and of course it was worth to extend the mission to another body. But choosing this second target was a difficult task, since the distant Solar System, here the Kuiper Belt, is very difficult to observe, and is pretty sparse. This is why observations programs of the Hubble Space Telescope (HST) were dedicated, and 2014 MU69 has been discovered in 2014.
Discovering an object is one thing, determining accurately its motion in view of a rendezvous with a spacecraft is another thing. This is the topic of the study I present today, High-precision orbit fitting and uncertainty analysis of (486958) 2014 MU69, by Simon Porter et al. This study has recently been published in The Astronomical Journal.

The New Horizons spacecraft

New Horizons is the first mission of NASA’s New Frontier program. It was launched in January 2006, and made its closest approach to Pluto in July 2015. Before that, it incidentally encountered the small asteroid (132524) APL at a distance of about 100,000 km in June 2006, and benefited from the gravitational assistance of Jupiter in February 2007.

The asteroid 132524 APL seen by New Horizons. © NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute
The asteroid 132524 APL seen by New Horizons. © NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute

It carries seven science instruments:

  • the Long-Range Reconnaissance Imager (LORRI), which images the encountered bodies,
  • the Solar Wind At Pluto (SWAP) instrument, which name is very explicit regarding its goal,
  • the Pluto Energetic Particle Spectrometer Science Investigation (PEPSSI), which supplements SWAP for the detection of high-energy particles,
  • Alice, which is an ultraviolet imaging spectrometer,
  • the Ralph telescope, which is a photographic instrument,
  • the Venetia Burney Student Dust Counter (VBSDC) measures dust. This instrument has been built by students of the University of Colorado,
  • and the Radioscience Experiment (REX), which measured the temperature and the atmospheric pressure of Pluto.

As you can see from some of the names of the instrument, Pluto-Charon was definitely the primary goal of New Horizons. Anyway, Pluto is now behind, and New Horizons is en route to 2014 MU69, also nicknamed Ultima Thule.

Ultima Thule

At this time, our knowledge of Ultima Thule is very limited. This body has been discovered in 2014, from a dedicated observation program on the Hubble Space Telescope, to identify potential targets for New Horizons. Finally, 2014 MU69 has been selected, partly for technical reasons, i.e. it is not so difficult to reach from Pluto.

It was discovered in June 2014, and has an apparent magnitude of nearly 27, and an absolute one of 11. We can guess its size from its magnitude, and its diameter should be smaller than 50 km. So, a very small body. A stellar occultation happening in 2017 has revealed that its diameter should be closer to 25-30 km, and its shape may be bilobal, or it could even be a contact binary.

Observations of its dynamics revealed that it is a cold, classical Kuiper Belt object. Its eccentricity and inclination are limited, since they are not excited by any resonance with the giant planets. So, it belongs to a region of the Solar System, which is quiet from a dynamical point of view.

As I previously said, discovering it is not enough if you want a spacecraft to reach it. You must know its motion accurately, and for that you need more data. And Ultima Thule can be observed only with the Hubble Space Telescope.

Hubble Space Telescope data

The authors disposed of 5 observations of 2014 MU69, by the Wide Field Camera 3 (WFC3) of the HST. Even with the HST, imaging 2014 MU69 requires 6 minutes of exposure, i.e. you need to accumulate photons reflected by 2014 MU69 during 6 minutes to have enough signal.

The detection of 2014 MU<sub>69</sub>. © NASA, ESA, SwRI, JHU/APL, and the New Horizons KBO Search Team
The detection of 2014 MU<sub>69</sub>. © NASA, ESA, SwRI, JHU/APL, and the New Horizons KBO Search Team

Another difficulty comes from the number of stars in that region of the sky. This is due to the galactic latitude of 2014 MU69, which is close to 0, i.e. close to the Galactic plane. The images have to be treated, to remove their incoming light. This is not just a pixel, but a diffraction spot, which needs to be modeled to be properly removed.

Once you have these data, you can start to determine the orbit of 2014 MU69, i.e. make ephemerides.

From astrometry to orbit

When you catch a body on a 2-dimensional image of the sky, you get two coordinates. Basically, these coordinates translate into a right ascension, and a declination. And to build ephemerides of a body, you need to integrate the equation ruling the orbital motion. This equations is a second-order 3-dimensional ordinary differential equation.

The motion is ruled by the gravitational perturbation of the Sun and the major planets, and for the problem to be solved, you need initial conditions. These are a position and a velocity of 2014 MU69 at a given date, which you derive from your astrometric observations, i.e. the 5 couples (right ascension, declination).

Easy, isn’t it? No, it’s not! Because of the uncertainties on the measurements, your 10 data, i.e. 5 right ascensions and 5 declinations, do not exactly correspond to an initial condition. So, you have to make a fit, i.e. determine the initial condition, which best fits the observations.

There are many potential sources of uncertainties: the accuracy of the positioning of the HST, the accuracy of the coordinates of 2014 MU69 (remember: this is not a pixel, but a diffraction spot), the duration of the exposure… and also the location of the stars surrounding 2014 MU69 in the field of view. To make absolute astrometry, you need to know precisely the location of these stars, and you get their locations from a star catalogue. Currently, the astrometric satellite Gaia is making such a survey, with a never reached accuracy and comprehensiveness. The Gaia Data Release 2 has been released in April 2018, and gives positions and proper motions (i.e. you can now consider that the stars move from date to date) of more than 1 billion stars! The authors had the chance to use that catalogue. This resulted in predictions, which were accurate enough, to predict a stellar occultation, which has been observed from the Earth.

Predicting stellar occultations

When a Solar System body occultates a star, you can indirectly observe it. You observe the star with your telescope, and during a few seconds, the star disappears, and then reappears, because of this object, which light is too faint for you. Multiple observations of a stellar occultation give information on the motion of the object, and on its dimensions. The rings around Chariklo and Haumea have been discovered that way.

For 2014 MU69 (or Ultima Thule), an occultation has been successfully predicted. It has been observed 5 times on 2017 July 17, in Argentina, giving 5 solid-body chords. This permitted us to infer that 2014 MU69 could be bilobal, or even a contact binary.

A stable orbit

And from these astrometric data, the authors propagated the orbit of 2014 MU69 over 100 million years, in considering the uncertainties on the initial positions. The outcomes of the simulations safely state that 2014 MU69 is on a very stable orbit, with a mean semimajor axis of 44.23 astronomical units (39.48 for Pluto), and an orbital eccentricity smaller than 0.04. This results in an orbital period of 294 years, during which the distance to the Sun barely varies.

We are looking forward for the encounter in 6 months!

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.