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Register a new userPencil-Beam Surveys for Trans-Neptunian Objects: Novel Methods for Optimization and Characterization
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Digital co-addition of astronomical images is a common technique for increasing signal-to-noise and image depth. A modification of this simple technique has been applied to the detection of minor bodies in the Solar System: first stationary objects are removed through the subtraction of a high-SN template image, then the sky motion of the Solar System bodies of interest is predicted and compensated for by shifting pixels in software prior to the co-addition step. This shift-and-stack approach has been applied with great success in directed surveys for minor Solar System bodies. In these surveys, the shifts have been parameterized in a variety of ways. However, these parameterizations have not been optimized and in most cases cannot be effectively applied to data sets with long observation arcs due to objects real trajectories diverging from linear tracks on the sky. This paper presents two novel probabilistic approaches for determining a near-optimum set of shift-vectors to apply to any image set given a desired region of orbital space to search. The first method is designed for short observational arcs, and the second for observational arcs long enough to require non-linear shift-vectors. Using these techniques and other optimizations, we derive optimized grids for previous surveys that have used shift-and-stack approaches to illustrate the improvements that can be made with our method, and at the same time derive new limits on the range of orbital parameters these surveys searched. We conclude with a simulation of a future applications for this approach with LSST, and show that combining multiple nights of data from such next-generation facilities is within the realm of computational feasibility.
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Two populations of minor bodies in the outer Solar System remain particularly elusive: Scattered Disk objects and Sedna-like objects. These populations are important dynamical tracers, and understanding the details of their spatial- and size-distributions will enhance our understanding of the formation and on-going evolution of the Solar System. By using newly-derived limits on the maximum heliocentric distances that recent pencil-beam surveys for Trans-Neptunian Objects were sensitive to, we determine new upper limits on the total numbers of distant SDOs and Sedna-like objects. While generally consistent with populations estimated from wide-area surveys, we show that for magnitude-distribution slopes of {alpha} > 0.7-1.0, these pencil-beam surveys provide stronger upper limits than current estimates in literature.
We test whether the population of extreme trans-Neptunian objects (eTNOs) detected in the Y4 Dark Energy Survey (DES) data exhibit azimuthal asymmetries which might be evidence of gravitational perturbations from an unseen super-Earth in a distant orbit. By rotating the orbits of the detected eTNOs, we construct a synthetic population which, when subject to the DES selection function, reproduces the detected distribution of eTNOs in the orbital elements $a,e,$ and $i$ as well as absolute magnitude $H$, but has uniform distributions in mean anomaly $M$, longitude of ascending node $Omega,$ and argument of perihelion $omega.$ We then compare the detected distributions in each of $Omega, omega,$ and $varpiequivOmega+omega$ to those expected from the isotropic population, using Kuipers variant of the Kolmogorov-Smirnov test. The three angles are tested for each of 4 definitions of the eTNO population, choosing among $a>(150,250)$ AU and perihelion $q>(30,37)$ AU. These choices yield 3--7 eTNOs in the DES Y4 sample. Among the twelve total tests, two have the likelihood of drawing the observed angles from the isotropic population at $p<0.05.$ The 3 detections at $a>250, q>37$ AU, and the 4 detections at $a>250, q>30$ AU, have $Omega$ distribution with $p=0.03$ of coming from the isotropic construction, but this is not strong evidence of anisotropy given the 12 different tests. The DES data taken on their own are thus consistent with azimuthal isotropy and do not require a Planet 9 hypothesis. The limited sky coverage and object count mean, however, that the DES data by no means falsify this hypothesis.
Trans-Neptunian objects (TNOs) and Centaurs are remnants of our planetary system formation, and their physical properties have invaluable information for evolutionary theories. Stellar occultation is a ground-based method for studying these small bodies and has presented exciting results. These observations can provide precise profiles of the involved body, allowing an accurate determination of its size and shape. The goal is to show that even single-chord detections of TNOs allow us to measure their milliarcsecond astrometric positions in the reference frame of the Gaia second data release (DR2). Accurated ephemerides can then be generated, allowing predictions of stellar occultations with much higher reliability. We analyzed data from stellar occultations to obtain astrometric positions of the involved bodies. The events published before the Gaia era were updated so that the Gaia DR2 catalog is the reference. Previously determined sizes were used to calculate the position of the object center and its corresponding error with respect to the detected chord and the International Celestial Reference System (ICRS) propagated Gaia DR2 star position. We derive 37 precise astrometric positions for 19 TNOs and 4 Centaurs. Twenty-one of these events are presented here for the first time. Although about 68% of our results are based on single-chord detection, most have intrinsic precision at the submilliarcsecond level. Lower limits on the diameter and shape constraints for a few bodies are also presented as valuable byproducts. Using the Gaia DR2 catalog, we show that even a single detection of a stellar occultation allows improving the object ephemeris significantly, which in turn enables predicting a future stellar occultation with high accuracy. Observational campaigns can be efficiently organized with this help, and may provide a full physical characterization of the involved object.
The thermal emission of transneptunian objects (TNO) and Centaurs has been observed at mid- and far-infrared wavelengths - with the biggest contributions coming from the Spitzer and Herschel space observatories-, and the brightest ones also at sub-millimeter and millimeter wavelengths. These measurements allowed to determine the sizes and albedos for almost 180 objects, and densities for about 25 multiple systems. The derived very low thermal inertias show evidence for a decrease at large heliocentric distances and for high-albedo objects, which indicates porous and low-conductivity surfaces. The radio emissivity was found to be low ($epsilon_r$=0.70$pm$0.13) with possible spectral variations in a few cases. The general increase of density with object size points to different formation locations or times. The mean albedos increase from about 5-6% (Centaurs, Scattered-Disk Objects) to 15% for the Detached objects, with distinct cumulative albedo distributions for hot and cold classicals. The color-albedo separation in our sample is evidence for a compositional discontinuity in the young Solar System. The median albedo of the sample (excluding dwarf planets and the Haumea family) is 0.08, the albedo of Haumea family members is close to 0.5, best explained by the presence of water ice. The existing thermal measurements remain a treasure trove at times where the far-infrared regime is observationally not accessible.
In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered Planet 9, may be present in the outer Solar System. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a dataset consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimised, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) $= 0.996 pm 0.001$. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.
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