Scientists suggest new way to hunt for life in space
科學家提出搜尋外星生命的新方法
Scientists are now pioneering a more sophisticated approach within the field of <span data-start="143" data-end="157" data-value="astrobiology">astrobiology</span>.
科學家們現正在太空生物學（astrobiology）領域內開創更精密的研究方法。
Instead of relying solely on traditional SETI methods, researchers are hunting for <span data-start="225" data-end="241" data-value="technosignatures">technosignatures</span>, such as industrial pollutants or massive space structures.
研究人員不再僅依賴傳統的搜索地外智慧生命（SETI）方法，而是致力於尋找工業汙染物或巨型太空結構等「技術簽名」（technosignatures）。
By utilizing the James Webb Space Telescope, astronomers use <span data-start="331" data-end="356" data-value="transmission spectroscopy">transmission spectroscopy</span> to scan exoplanet atmospheres for chemical signs of life, like oxygen or methane.
藉由詹韋伯太空望遠鏡，天文學家利用「穿透光譜學」（transmission spectroscopy）來掃描系外行星大氣中的生命化學徵兆，例如氧氣或甲烷。
Additionally, the focus has broadened to include our own neighborhood; missions to <span data-start="466" data-end="470" data-value="Mars">Mars</span> and the icy moons of Jupiter and Saturn aim to find evidence of simple <span data-start="497" data-end="506" data-value="microbial">microbial</span> organisms.
此外，關注焦點亦擴展至我們所在的星系鄰域；前往火星（Mars）以及木星和土星冰冷衛星的任務，旨在尋找簡單「微生物」（microbial）存在的證據。
To manage the massive influx of data, experts are now integrating <span data-start="565" data-end="573" data-value="AI">AI</span> and machine learning to distinguish genuine signals from cosmic noise.
為了管理海量的數據，專家們正在整合人工智慧（AI）與機器學習技術，以從宇宙雜訊中辨別真實的信號。
Despite the vastness of the universe and the persistent <span data-start="670" data-end="685" data-value="Fermi Paradox">Fermi Paradox</span>, these innovative strategies significantly improve our chances of discovering life beyond Earth.
儘管宇宙浩瀚且存在著持續的「費米悖論」（Fermi Paradox），這些創新策略仍顯著提高了我們發現地外生命的機會。
