Are we alone in the universe?

UCLA SETI

The mission of UCLA SETI is to find evidence of other civilizations in the Galaxy. We conduct searches for radio technosignatures with the largest fully steerable telescope on Earth, the 100 meter Green Bank Telescope in West Virginia. Our searches are sensitive to signals emitted thousands of light years away, enabling contact from a large fraction of the Milky Way Galaxy. We have sampled over 55,000 stars and detected over 82 million candidate signals to date, with more observations planned in the near future.

We are excited to expand our search capabilities by launching a collaboration with citizen scientists, and we are grateful for your involvement in the search! This collaboration is made possible by grants from The Planetary Society and the NASA Citizen Science Seed Funding Program.

The search for life in the universe

The search for life in the universe represents one of humanity's most profound scientific endeavors. All life on Earth is related to a common ancestor, and the discovery of other forms of life will revolutionize our understanding of living systems. On a more philosophical level, it will transform humanity's perception of its place in the cosmos. Observations with the NASA Kepler telescope have shown that there are billions of potentially habitable worlds in our Galaxy (Bryson et al. 2021). The profusion of planets, coupled with the abundance of life’s building blocks in the universe, suggests that life itself may be abundant.

Biosignatures and technosignatures

The search for life in the universe is conducted by seeking evidence for biosignatures or technosignatures. A biosignature is any substance or phenomenon that provides scientific evidence of past or present life. A technosignature is any measurable property or effect that provides scientific evidence of past or present technology. Searches for technosignatures are conducted in the visible, infrared, and radio parts of the electromagnetic spectrum. In this project, we focus on radio technosignatures.

Advantages of radio technosignatures

The search for radio technosignatures can expand the search for life in the universe from primitive to complex life and from the solar neighborhood to the entire Galaxy (Margot et al., 2019). Specifically, it has four advantages: (1) a cost that is orders of magnitude lower than the search for biosignatures, (2) a search volume that is a million times larger than the relatively small, local bubble conducive to the search for biosignatures, (3) a higher level of confidence in the interpretation of detections because no natural process can be invoked to explain the signatures, (4) the potential for profound advances in knowledge if a signal includes information that can be decoded.

Project objective 1: Identify the most promising signals in SETI data

Our search for technosignatures uses the 100 meter Green Bank Telescope (GBT) in an observing mode that is sensitive to Arecibo-class transmitters located within 415 light years of Earth and to transmitters that are 1000 times more effective located within 13,000 light years of Earth (Margot et al., 2023). Our observations sample 800 MHz of bandwidth in L band (1.1–1.9 GHz), a region of the radio spectrum that contains “a unique, objective standard of frequency, which must be known to every observer in the universe: the outstanding radio emission line at 1,420 Mc./s. (λ=21 cm) of neutral hydrogen” (Cocconi and Morrison, 1959). For each hour of telescope time, our search covers 8 directions on the sky that include thousands of stars, and it yields ~5 million narrowband signal detections, approximately 99.5% of which are automatically classified by our data processing pipeline as anthropogenic radio frequency interference (RFI). The remaining 25,000 detections per hour constitute promising candidate technosignatures. This citizen science platform is designed to identify the most promising signals among those.

Project objective 2: Help build artificial intelligence tools to recognize RFI

Radio Frequency Interference (RFI) remains the biggest challenge to the search for technosignatures. Although most RFI can be classified with classic computational tools (e.g., Siemion et al., 2013), machine learning (ML) tools (e.g., Pinchuk and Margot, 2021) offer considerable potential to improve the robustness, accuracy, and speed of the classification. Training an ML algorithm to improve the classification of candidate signals as RFI or extraterrestrial signals requires a labeled training set. This citizen science platform is designed to help generate this labeled training set.

Dynamic spectra

Our data processing pipeline is reminiscent of standard analysis procedures in radar astronomy and pulsar astronomy. It yields dynamic spectra (sometimes known as “spectrograms” or “waterfall plots”), which are 2D images where lines represent consecutive times, columns represent consecutive frequencies, and pixel intensity conveys signal power. Dynamic spectra with dimensions of 500 x 446 pixels, which span 298 Hz in frequency and 150 s in time, reveal the time-frequency structure of each candidate signal.

Learn more

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