Near-Earth Object Observations using Synthetic Tracking

Autor: Zhai, Chengxing, Shao, Michael, Saini, Navtej, Choi, Philip, Evans, Nez, Trahan, Russell, Nazli, Kutay, Zhan, Max
Rok vydání: 2024
Předmět:
Druh dokumentu: Working Paper
Popis: Synthetic tracking (ST) has emerged as a potent technique for observing fast-moving near-Earth objects (NEOs), offering enhanced detection sensitivity and astrometric accuracy by avoiding trailing loss. This approach also empowers small telescopes to use prolonged integration times to achieve high sensitivity for NEO surveys and follow-up observations. In this study, we present the outcomes of ST observations conducted with Pomona College's 1 m telescope at the Table Mountain Facility and JPL's robotic telescopes at the Sierra Remote Observatory. The results showcase astrometric accuracy statistics comparable to stellar astrometry, irrespective of an object's rate of motion, and the capability to detect faint asteroids beyond 20.5th magnitude using 11-inch telescopes. Furthermore, we detail the technical aspects of data processing, including the correction of differential chromatic refraction in the atmosphere and accurate timing for image stacking, which contribute to achieving precise astrometry. We also provide compelling examples that showcase the robustness of ST even when asteroids closely approach stars or bright satellites cause disturbances. Moreover, we illustrate the proficiency of ST in recovering NEO candidates with highly uncertain ephemerides. As a glimpse of the potential of NEO surveys utilizing small robotic telescopes with ST, we present significant statistics from our NEO survey conducted for testing purposes. These findings underscore the promise and effectiveness of ST as a powerful tool for observing fast-moving NEOs, offering valuable insights into their trajectories and characteristics. Overall, the adoption of ST stands to revolutionize fast-moving NEO observations for planetary defense and studying these celestial bodies.
Comment: 50 pages, 19 figures
Databáze: arXiv