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.

We conducted a search for technosignatures in 2018 and 2019 April with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused on regions surrounding 31 Sun-like stars near the plane of the Galaxy. We present the results of our search for narrowband signals in this data set, as well as improvements to our data processing pipeline. Specifically, we applied an improved candidate signal detection procedure that relies on the topographic prominence of the signal power, which nearly doubles the signal detection count of some previously analyzed data sets. We also improved the direction-of-origin filters that remove most radio frequency interference (RFI) to ensure that they uniquely link signals observed in separate scans. We performed a preliminary signal injection and recovery analysis to test the performance of our pipeline. We found that our pipeline recovers 93% of the injected signals over the usable frequency range of the receiver and 98% if we exclude regions with dense RFI. In this analysis, 99.73% of the recovered signals were correctly classified as technosignature candidates. Our improved data processing pipeline classified over 99.84% of the ∼26 million signals detected in our data as RFI. Of the remaining candidates, 4539 were detected outside of known RFI frequency regions. The remaining candidates were visually inspected and verified to be of anthropogenic nature. Our search compares favorably to other recent searches in terms of end-to-end sensitivity, frequency drift rate coverage, and signal detection count per unit bandwidth per unit integration time.

Theoretical values of the Hamaker constant have been calculated for metal nanoparticles using Lifshitz theory. The theory describes the Hamaker constant in terms of the permittivity of the interacting bodies. Metal nanoparticles exhibit an internal size effect that alters the dielectric permittivity of the particle when its size falls below the mean free path of the conducting electrons. This size dependence of the permittivity leads to size-dependence of the Hamaker constant for metal nanoparticles. Additionally, the electron damping and the plasma frequency used to model the permittivity of the particle exhibit temperature-dependence, which lead to temperature dependence of the Hamaker constant. In this work, both the size and temperature dependence for gold, silver, copper, and aluminum nanoparticles is demonstrated. The results of this study might be of interest for studying the colloidal stability of nanoparticles in solution.

We analyzed 82,548 carefully curated observations of 4420 asteroids with Wide-field Infrared Survey Explorer (WISE) four-band data to produce estimates of diameters and infrared emissivities. We also used these diameter values in conjunction with absolute visual magnitudes to infer estimates of visible-band geometric albedos. We provide solutions to 131 asteroids not analyzed by the NEOWISE team and to 1778 asteroids not analyzed with four-band data by the NEOWISE team. Our process differs from the NEOWISE analysis in that it uses an accurate solar flux, integrates the flux with actual bandpass responses, obeys Kirchhoff’s law, and does not force emissivity values in all four bands to an arbitrary value of 0.9. We used a regularized model-fitting algorithm that yields improved fits to the data. Our results more closely match stellar-occultation diameter estimates than the NEOWISE results by a factor of ∼2. Using 24 high-quality stellar-occultation results as a benchmark, we found that the median error of four-infrared-band diameter estimates in a carefully curated data set is 9.3%. Our results also suggest the presence of a size-dependent bias in the NEOWISE diameter estimates, which may pollute estimates of asteroid size distributions and slightly inflate impact-hazard risk calculations. For more than 90% of asteroids in this sample, the primary source of error on the albedo estimate is the error on absolute visual magnitude.

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.