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Dyson spheres are hypothetical megastructures built by advanced extraterrestrial civilizations to harvest radiation energy from stars. Here, we combine optical data from Gaia DR2 with mid-infrared data from AllWISE to set the strongest upper limits to date on the prevalence of partial Dyson spheres within the Milky Way, based on their expected waste-heat signatures. Conservative upper limits are presented on the fraction of stars at G <= 21 that may potentially host non-reflective Dyson spheres that absorb 1-90 per cent of the bolometric luminosity of their host stars and emit thermal waste-heat in the 100-1000 K range. Based on a sample of approximate to 2.7 x 10(5) stars within 100 pc, we find that a fraction less than approximate to 2 x 10(-5) could potentially host similar to 300 K Dyson spheres at 90 per cent completion. These limits become progressively weaker for less complete Dyson spheres due to increased confusion with naturally occurring sources of strong mid-infrared radiation, and also at larger distances, due to the detection limits of WISE. For the similar to 2.9 x 10(8) stars within 5 kpc in our Milky Way sample, the corresponding upper limit on the fraction of stars that could potentially be similar to 300 K Dyson spheres at 90 per cent completion is less than or similar to 8 x 10(-4).

The search for extraterrestrial intelligence is currently being pursued using multiple techniques and in different wavelength bands. Dyson spheres, megastructures that could be constructed by advanced civilizations to harness the radiation energy of their host stars, represent a potential technosignature, that in principle may be hiding in public data already collected as part of large astronomical surveys. In this study, we present a comprehensive search for partial Dyson spheres by analysing optical and infrared observations from Gaia, 2MASS, and WISE. We develop a pipeline that employs multiple filters to identify potential candidates and reject interlopers in a sample of five million objects, which incorporates a convolutional neural network to help identify confusion in WISE data. Finally, the pipeline identifies seven candidates deserving of further analysis. All of these objects are M-dwarfs, for which astrophysical phenomena cannot easily account for the observed infrared excess emission.