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Register a new userSAFARI: Searching Asteroids For Activity Revealing Indicators
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Colin Orion Chandler
Publication date
2018
fields
Physics
and research's language is
English
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Active asteroids behave dynamically like asteroids but display comet-like comae. These objects are poorly understood, with only about 30 identified to date. We have conducted one of the deepest systematic searches for asteroid activity by making use of deep images from the Dark Energy Camera (DECam) ideally suited to the task. We looked for activity indicators amongst 11,703 unique asteroids extracted from 35,640 images. We detected three previously-identified active asteroids ((62412), (1) Ceres and (779) Nina), though only (62412) showed signs of activity. Our activity occurrence rate of 1 in 11,703 is consistent with the prevailing 1 in 10,000 activity occurrence rate estimate. Our proof of concept demonstrates 1) our novel informatics approach can locate active asteroids and 2) DECam data are well-suited to the search for active asteroids.
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We investigate the case of CII 158 micron observations for SPICA/SAFARI using a three-dimensional magnetohydrodynamical (MHD) simulation of the diffuse interstellar medium (ISM) and the Meudon PDR code. The MHD simulation consists of two converging flows of warm gas (10,000 K) within a cubic box 50 pc in length. The interplay of thermal instability, magnetic field and self-gravity leads to the formation of cold, dense clumps within a warm, turbulent interclump medium. We sample several clumps along a line of sight through the simulated cube and use them as input density profiles in the Meudon PDR code. This allows us to derive intensity predictions for the CII 158 micron line and provide time estimates for the mapping of a given sky area.
We present a new classification of families identified among the population of high-inclination asteroids. We computed synthetic proper elements for a sample of 18,560 numbered and multi-opposition objects having sine of proper inclination greater than 0.295. We considered three zones at different heliocentric distances (inner, intermediate and outer region) and used the standard approach based on the Hierarchical Clustering Method (HCM) to identify families in each zone. In doing so, we used slightly different approach with respect to previously published methodologies, to achieve a more reliable and robust classification. We also used available SDSS color data to improve membership and identify likely family interlopers. We found a total of 38 families, as well as a significant number of clumps and clusters deserving further investigation.
Protoplanetary disks contain structures such as gaps, rings, and spirals, which are thought to be produced by the interaction between the disk and embedded protoplanets. However, only a few planet candidates are found orbiting within protoplanetary disks, and most of them are being challenged as having been confused with disk features. We aim to discover more proto-planetary candidates with MUSE, with a secondary aim of improving the high-resolution spectral differential imaging (HRSDI) technique by analyzing the instrumental residuals of MUSE. We analyzed MUSE observations of five young stars and applied the HRSDI technique to perform high-contrast imaging. With a 30 min integration time, MUSE can reach 5$sigma$ detection limits in apparent H$alpha$ line flux down to 10$^{-14}$ and 10$^{-15}$ erg s$^{-1}$ cm$^{-2}$ at 0.075 and 0.25, respectively. In addition to PDS 70 b and c, we did not detect any clear accretion signatures in PDS 70, J1850-3147, and V1094 Sco down to 0.1. MUSE avoids the small sample statistics problem by measuring the noise characteristics in the spatial direction at multiple wavelengths. We detected two asymmetric atomic jets in HD 163296. The HRSDI technique when applied to MUSE data allows us to reach the photon noise limit at small separations (i.e., < 0.5). With a higher spectral resolution, MUSE can achieve fainter detection limits in apparent line flux than SPHERE/ZIMPOL by a factor of $sim$5. MUSE has some instrumental issues that limit the contrast that appear in cases with strong point sources, which can be either a spatial point source due to high Strehl observations or a spectral point source due to a high line-to-continuum ratio. We modified the HRSDI technique to better handle the instrumental artifacts and improve the detection limits.
The Canada-France-Hawaii Legacy Survey (CFHTLS) comprising about 25 000 MegaCam images was data mined to search for serendipitous encounters of known Near Earth Asteroids (NEAs) and Potentially Hazardous Asteroids (PHAs). A total of 143 asteroids (109 NEAs and 34 PHAs) were found on 508 candidate images which were field corrected and measured carefully, and their astrometry was reported to Minor Planet Centre. Both recoveries and precoveries (apparitions before discovery) were reported, including data for 27 precovered asteroids (20 NEAs and 7 PHAs) and 116 recovered asteroids (89 NEAs and 27 PHAs). Our data prolonged arcs for 41 orbits at first or last opposition, refined 35 orbits by fitting data taken at one new opposition, recovered 6 NEAs at their second opposition and allowed us to ameliorate most orbits and their Minimal Orbital Intersection Distance (MOID), an important parameter to monitor for potential Earth impact hazard in the future.
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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