TY - JOUR
T1 - Project Hephaistos – II. Dyson sphere candidates from Gaia DR3, 2MASS, and WISE
AU - Suazo, Matías
AU - Zackrisson, Erik
AU - Mahto, Priyatam K.
AU - Lundell, Fabian
AU - Nettelblad, Carl
AU - Korn, Andreas J.
AU - Wright, Jason T.
AU - Majumdar, Suman
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/6/1
Y1 - 2024/6/1
N2 - 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.
AB - 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.
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U2 - 10.1093/mnras/stae1186
DO - 10.1093/mnras/stae1186
M3 - Article
AN - SCOPUS:85193793285
SN - 0035-8711
VL - 531
SP - 695
EP - 707
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 1
ER -