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A stable-frequency transmitter with relative radial acceleration to a receiver will show a change in received frequency over time, known as a “drift rate.” For a transmission from an exoplanet, we must account for multiple components of drift rate: the exoplanet’s orbit and rotation, the Earth’s orbit and rotation, and other contributions. Understanding the drift rate distribution produced by exoplanets relative to Earth, can (a) help us constrain the range of drift rates to check in a Search for Extraterrestrial Intelligence project to detect radio technosignatures, and (b) help us decide validity of signals-of-interest, as we can compare drifting signals with expected drift rates from the target star. In this paper, we modeled the drift rate distribution for ∼5300 confirmed exoplanets, using parameters from the NASA Exoplanet Archive (NEA). We find that confirmed exoplanets have drift rates such that 99% of them fall within the ±53 nHz range. This implies a distribution-informed maximum drift rate ∼4 times lower than previous work. To mitigate the observational biases inherent in the NEA, we also simulated an exoplanet population built to reduce these biases. The results suggest that, for a Kepler-like target star without known exoplanets, ±0.44 nHz would be sufficient to account for 99% of signals. This reduction in recommended maximum drift rate is partially due to inclination effects and bias toward short orbital periods in the NEA. These narrowed drift rate maxima will increase the efficiency of searches and save significant computational effort in future radio technosignature searches.

 

A growing avenue for determining the prevalence of life beyond Earth is to search for “technosignatures” from extraterrestrial intelligences/agents. Technosignatures require significant energy to be visible across interstellar space and thus intentional signals might be concentrated in frequency, in time, or in space, to be found in mutually obvious places. Therefore, it could be advantageous to search for technosignatures in parts of parameter space that are mutually derivable to an observer on Earth and a distant transmitter. In this work, we used theL-band (1.1–1.9 GHz) receiver on the Robert C. Byrd Green Bank Telescope to perform the first technosignature search presynchronized with exoplanet transits, covering 12 Kepler systems. We used the Breakthrough Listen turboSETI pipeline to flag narrowband hits (∼3 Hz) using a maximum drift rate of ±614.4 Hz s⁻¹and a signal-to-noise threshold of 5—the pipeline returned ∼3.4 × 10⁵apparently-localized features. Visual inspection by a team of citizen scientists ruled out 99.6% of them. Further analysis found two signals of interest that warrant follow up, but no technosignatures. If the signals of interest are not redetected in future work, it will imply that the 12 targets in the search are not producing transit-aligned signals from 1.1 to 1.9 GHz with transmitter powers >60 times that of the former Arecibo radar. This search debuts a range of innovative technosignature techniques: citizen science vetting of potential signals of interest, a sensitivity-aware search out to extremely high drift rates, a more flexible method of analyzing on-off cadences, and an extremely low signal-to-noise threshold.

 

Characterizing the masses and orbits of near-Earth-mass planets is crucial for interpreting observations from future direct imaging missions (e.g., HabEx, LUVOIR). Therefore, the Exoplanet Science Strategy report recommended further research so future extremely precise radial velocity surveys could contribute to the discovery and/or characterization of near-Earth-mass planets in the habitable zones of nearby stars prior to the launch of these future imaging missions. Newman et al. (2023) simulated such 10 yr surveys under various telescope architectures, demonstrating they can precisely measure the masses of potentially habitable Earth-mass planets in the absence of stellar variability. Here, we investigate the effect of stellar variability on the signal-to-noise ratio (S/N) of the planet mass measurements in these simulations. We find that correlated noise due to active regions has the largest effect on the observed mass S/N, reducing the S/N by a factor of ∼5.5 relative to the no-variability scenario; granulation reduces by a factor of ∼3, while p-mode oscillations has little impact on the proposed survey strategies. We show that in the presence of correlated noise, 5 cm s⁻¹instrumental precision offers little improvement over 10 cm s⁻¹precision, highlighting the need to mitigate astrophysical variability. With our noise models, extending the survey to 15 yr doubles the number of Earth-analogs with mass S/N > 10, and reaching this threshold for any Earth-analog orbiting a star >0.76M_⊙in a 10 yr survey would require an increase in the number of observations per star from that in Newman et al. (2023).

 

Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme-precision radial-velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The Extreme-precision Spectrograph (EXPRES) Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed radial-velocity (RV) correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV rms than classic linear decorrelation, but no method is yet consistently reducing the RV rms to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets—such as solar data or data with known injected planetary and/or stellar signals—to better understand method performance and whether planetary signals are preserved.