Statistical Ranging As a Tool for Short-Arc KBO Recovery and Classification

Details Statistical Ranging As a Tool for Short-Arc KBO Recovery and Classification Statistical Ranging As a Tool for Short-Arc KBO Recovery and Classification

Sep 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006dps....38.4404w&link_type=abstract

American Astronomical Society, DPS meeting #38, #44.04; Bulletin of the American Astronomical Society, Vol. 38, p.564

Astronomy and Astrophysics

Astronomy

2

Scientific paper

We have started a project to see how a process of statistical ranging might be used to help recover short-arc KBOs and (at least qualitatively) classify such objects even if they are never actually recovered. Starting with a nominal orbit fitted to the observations of a KBO with a short observational arc, we allow the fitted orbital elements to vary (usually by 1 sigma, but constrained to allow only valid Keplerian elements). This modified orbit is used to generate a "new" observation at some later time T. An orbit is then fitted to the original observations plus the "new" observation. If the resulting orbit still fits the original observations, we save the elements, the predicted position at time T, and the RMS between the original observations and the positions resulting from the new orbit at those times. The process is repeated until we have a "reasonably" large number of test orbits (500-1000 seems to work). Using the Keplerian elements for each of the test orbits, we can assign a rough classification to each orbit from its location in the a-e plane. The resulting data set serves several purposes. For example, we can determine a "statistical" classification for an object given our data set of possible classifications. Clearly, not all KBOs that the Deep Ecliptic Survey has discovered can be recovered in the near future. A "statistical" classification can help de-bias our entire data set. The data can also be used for recoveries, especially when the distribution extends beyond the nominal error ellipse for the KBO. If an object is not found in the predicted sky area for a certain dynamical class, than statistically it is likely to belong to a different dynamical class.
This work was supported by NASA Grants NNG06GI23G, NG05GH86G, NAG5-13380 and NSF grant NSF-8406493 .

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