科學家提出搜尋外星生命的新方法
Scientists suggest new way to hunt for life in space
尋找地外生命的研究已經發展到遠超簡單監聽無線電波的地步。
The search for extraterrestrial life has evolved far beyond simply listening for radio waves.
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。
Scientists are now pioneering a more sophisticated approach within the field of <span data-start="143" data-end="157" data-value="astrobiology">astrobiology</span>.
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）。
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.
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。
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.
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。
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.
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。
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.
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。
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.
