Key Points:

• Discovering Solar System small bodies (asteroids and comets) is not easy
• Currently deployed algorithms impose a strict constraint on how telescopes operate and what datasets can be used to discover these objects
• Tracklet-less Heliocentric Orbit Recovery (THOR) is a state-of-the-art algorithm designed to remove this restriction and open up the possibility of more discoveries
• Built on top of a well understood physical framework, and by leveraging the latest technologies in high performance cloud computing and machine learning, THOR is capable of discovering moving objects in datasets that would otherwise be unsuitable or unoptimized for Solar System small body discovery

Our Solar System is the current frontier of human and robotic exploration. Part of planetary science and Solar System astronomy is tasked with informing decisions regarding where and what to explore next. The DIRAC Institute has been helping answer these questions by developing state-of-art algorithms that enable the discovery of asteroids and comets — small bodies in our Solar System that very well may be the next points of interest on the ever growing map of our Solar System. 

Discovering small bodies is not an easy task. Unlike most astrophysical objects, small bodies move on appreciable time scales; asteroids and comets can move at a wide variety of different speeds and our motion, the motion of the observer, also complicates the problem. As bigger telescopes are being built, the number of observations that need to be processed also increases dramatically. The problem is akin to throwing a bucket full of sand on a table and taking a picture of the table, and then having a friend shake the table, taking a new picture. Your task is to figure out which grain moved where using just those two images. If you imagine having unlimited computing resources, the way you would approach this problem is by letting a telescope observe the sky, and every time a new detection occurs, you test all other unidentified detections for a possible linkage with this one detection. These linkages are known as orbits and define how an object moves in space. To discover a moving object is to know, with a high degree of certainty, its orbit. When a telescope generates millions of new detections in a single evening it is simply not possible to test every combination of un-linked detections for an orbit. 

Astronomers figured out a way so solve this problem: the “tracklet”. A “tracklet” is a combination of two or more detections, with the time between detections typically no more than 30 minutes apart. A tracklet, which is essentially just a motion vector, constrains the position and speed of a potential moving object. In a 30-minute time span, an asteroid or comet can only have moved a certain distance, and so, by limiting the time between two exposures on the sky, a survey telescope limits the number of combinations of detections it would need to test for an orbit down the road. Typically, to discover a moving object a telescope needs to observe three tracklets (three pairs of at least two detections) over a two-week window. 

Tracklets are problematic. By requiring tracklets for moving object discovery, it requires telescopes to operate in a very specific way. Telescopes must come back to the same area of the sky to take at least a second exposure within 30 minutes. Effectively, to discover moving objects by building tracklets you are limiting a telescope to, at best, observe only half the amount of sky it could observe in a single night. Datasets from past missions or surveys, that did not have this specific cadence of operation are also unsuitable to do retrospective searches of moving objects since they don’t allow for the building of tracklets.

Tracklet-less Heliocentric Orbit Recovery. Aside from the awesome acronym, THOR aims to solve these problems by removing the need for “tracklets” to be observed. The algorithm makes use of certain aspects of the motion of small bodies in the Solar System. THOR assumes a series of test orbits, when assuming a test orbit you know exactly where in space that potential object will be at any point in time in the past or future. Which means you can look for that potential object in datasets from any survey regardless of the time between detections (ie, no tracklets needed!). Naively, if there are 800,000 objects you would need to test 800,000 orbits to discover them all. However, small bodies in the Solar System tend to have orbits that are similar. THOR utilizes this fact, and so as opposed to needing one orbit to discover one object, a single orbit can be used to discover hundreds or even thousands of objects. The power of the THOR framework is that all you need to discover more moving objects is another well-selected test orbit. 

What has THOR achieved thus far? We ran THOR on two weeks worth of detections from the Zwicky Transient Facility (a survey in operation from the Palomar Observatory in California). ZTF’s internal tracklet-based moving object algorithm in that two week period was able to recover about 14,000 previously known moving objects. THOR running on the exact same dataset recovered a little over 21,000 objects (97% of the objects with at least five detections), a factor of 1.5 improvement. We are now working to deploy a newer, better, and faster version of THOR on all of the detections coming from ZTF. 

THOR is a completely open-source project. Find it here on Github: https://github.com/moeyensj/thor