# A deep-learning search for technosignatures of 820 nearby stars

arXiv: https://arxiv.org/abs/2301.12670 · HTML: https://mlsanity.com/paper/2301.12670/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python](https://mlsanity.com/paper/1907.10121/) (2019)
- [A CNN and LSTM-Based Approach to Classifying Transient Radio Frequency Interference](https://mlsanity.com/paper/1803.02684/) (2018)
- [A Machine-Learning-Based Direction-of-Origin Filter for the Identification of Radio Frequency Interference in the Search for Technosignatures](https://mlsanity.com/paper/2108.00559/) (2021)

## Built on by (most cited first, 4 of 4)

- [Decoding complexity: how machine learning is redefining scientific discovery](https://mlsanity.com/paper/2405.04161/) (2024)
- [A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm](https://mlsanity.com/paper/2302.13854/) (2023)
- [Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches](https://mlsanity.com/paper/2411.16556/) (2024)
- [An Exploratory Framework for Future SETI Applications: Detecting Generative Reactivity via Language Models](https://mlsanity.com/paper/2506.02730/) (2025)

## Abstract (arXiv)

The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their "technosignatures". One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radio frequency interference (RFI). Here, we present the most comprehensive deep-learning based technosignature search to date, returning 8 promising ETI signals of interest for re-observation as part of the Breakthrough Listen initiative. The search comprises 820 unique targets observed with the Robert C. Byrd Green Bank Telescope, totaling over 480, hr of on-sky data. We implement a novel beta-Convolutional Variational Autoencoder to identify technosignature candidates in a semi-unsupervised manner while keeping the false positive rate manageably low. This new approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.