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      Artificial Greenhouse Gases as Exoplanet Technosignatures

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          Abstract

          Atmospheric pollutants such as chlorofluorocarbons and NO 2 have been proposed as potential remotely detectable atmospheric technosignature gases. Here we investigate the potential for artificial greenhouse gases including CF 4, C 2F 6, C 3F 8, SF 6, and NF 3 to generate detectable atmospheric signatures. In contrast to passive incidental by-products of industrial processes, artificial greenhouse gases would represent an intentional effort to change the climate of a planet with long-lived, low-toxicity gases and would possess low false positive potential. An extraterrestrial civilization may be motivated to undertake such an effort to arrest a predicted snowball state on their home world or to terraform an otherwise uninhabitable terrestrial planet within their system. Because artificial greenhouse gases strongly absorb in the thermal mid-infrared window of temperate atmospheres, a terraformed planet will logically possess strong absorption features from these gases at mid-infrared wavelengths (∼8–12 μm), possibly accompanied by diagnostic features in the near-infrared. As a proof of concept, we calculate the needed observation time to detect 1 [10](100) ppm of C 2F 6/C 3F 8/SF 6 on TRAPPIST-1 f with JWST MIRI’s Low Resolution Spectrometer (LRS) and NIRSpec. We find that a combination of 1[10](100) ppm each of C 2F 6, C 3F 8, and SF 6 can be detected with a signal-to-noise ratio ≧ 5 in as few as 25[10](5) transits with MIRI/LRS. We further explore mid-infrared direct-imaging scenarios with the Large Interferometer for Exoplanets mission concept and find these gases are more detectable than standard biosignatures at these concentrations. Consequently, artificial greenhouse gases can be readily detected (or excluded) during normal planetary characterization observations with no additional overhead.

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          Matplotlib: A 2D Graphics Environment

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            Array programming with NumPy

            Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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              HABITABLE ZONES AROUND MAIN-SEQUENCE STARS: NEW ESTIMATES

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                Journal
                The Astrophysical Journal
                ApJ
                American Astronomical Society
                0004-637X
                1538-4357
                June 25 2024
                July 01 2024
                June 25 2024
                July 01 2024
                : 969
                : 1
                : 20
                Article
                10.3847/1538-4357/ad4ce8
                c4392905-2caf-49ec-890b-d0451e25d638
                © 2024

                http://creativecommons.org/licenses/by/4.0/

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