This dataset describes candidate signal detections obtained at the Green Bank Telescope in 2020–2023 and processed with the UCLA SETI data processing pipeline. We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ∼11,680 stars and planetary systems in the ∼9′ beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ∼15 times smaller at ∼6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake figure of merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is continuously transmitting a narrowband signal with an equivalent isotropic radiated power (EIRP) > 1013 W. For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 1016 W) is at most 3 × 10−4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al.

Wolfram Notebook modeling code and input data from Table S1. Associated output from manuscript is included as a reference for test runs.

Wolfram Notebook modeling code and input data from Table S1. Associated output from manuscript is included as a reference for test runs.

This dataset describes a curated set of Wide-field Infrared Survey Explorer (WISE) observations of 4420 asteroids. 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 in absolute visual magnitude. This research has made use of the NASA/IPAC Infrared Science Archive, which is funded by the National Aeronautics and Space Administration and operated by the California Institute of Technology.