We describe our implementation of a global-parameter optimizer and Square Root Information Filter into the asteroid-modeling software shape. We compare the performance of our new optimizer with that of the existing sequential optimizer when operating on various forms of simulated data and actual asteroid radar data. In all cases, the new implementation performs substantially better than its predecessor: it converges faster, produces shape models that are more accurate, and solves for spin axis orientations more reliably. We discuss potential future changes to improve shape's fitting speed and accuracy.

Abstract: We evaluated the prospects of quantifying the parameterized post-Newtonian parameter β and solar quadrupole moment with observations of near-Earth asteroids with large orbital precession rates (9 to 27 arcsec century−1). We considered existing optical and radar astrometry, as well as radar astrometry that can realistically be obtained with the Arecibo planetary radar in the next five years. Our sensitivity calculations relied on a traditional covariance analysis and Monte Carlo simulations. We found that independent estimates of β and can be obtained with precisions of 6 × 10−4 and 3 × 10−8, respectively. Because we assumed rather conservative observational uncertainties, as is the usual practice when reporting radar astrometry, it is likely that the actual precision will be closer to 2 × 10−4 and 10−8, respectively. A purely dynamical determination of solar oblateness with asteroid radar astronomy may therefore rival the helioseismology determination.

Abstract: Analysis of Kepler mission data suggests that the Milky Way includes billions of Earth-sized planets in the habitable zone of their host stars. Current technology enables the detection of technosignatures emitted from a large fraction of the Galaxy. We describe a search for technosignatures that is sensitive to Arecibo-class transmitters located within ∼420 ly of Earth and transmitters that are 1000 times more effective than Arecibo within ∼13000 ly of Earth. Our observations focused on 14 planetary systems in the Kepler field and used the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. Each source was observed for a total integration time of 5 minutes. We obtained power spectra at a frequency resolution of 3 Hz and examined narrowband signals with Doppler drift rates between ±9 Hz s−1. We flagged any detection with a signal-to-noise ratio in excess of 10 as a candidate signal and identified approximately 850,000 candidates. Most (99%) of these candidate signals were automatically classified as human-generated radio-frequency interference (RFI). A large fraction (>99%) of the remaining candidate signals were also flagged as anthropogenic RFI because they have frequencies that overlap those used by global navigation satellite systems, satellite downlinks, or other interferers detected in heavily polluted regions of the spectrum. All 19 remaining candidate signals were scrutinized and none were attributable to an extraterrestrial source.

As part of our ongoing search for technosignatures, we collected over three terabytes of data in 2017 May with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused primarily on planetary systems in the Kepler field, but also included scans of the recently discovered TRAPPIST-1 and LHS 1140 systems. We present the results of our search for narrowband signals in this data set with techniques that are generally similar to those described by Margot et al. Our improved data processing pipeline classified over 98% of the approximately six million detected signals as anthropogenic radio frequency interference (RFI). Of the remaining candidates, 30 were detected outside of densely populated frequency regions attributable to RFI. These candidates were carefully examined and determined to be of terrestrial origin. We discuss the problems associated with the common practice of ignoring frequency space around candidate detections in radio technosignature detection pipelines. These problems include inaccurate estimates of figures of merit and unreliable upper limits on the prevalence of technosignatures. We present an algorithm that mitigates these problems and improves the efficiency of the search. Specifically, our new algorithm increases the number of candidate detections by a factor of more than four compared to the results of Margot et al.