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Valizadegan's work is just one stunning example of how AI is giving us a more detailed view of the cosmos.
瓦利扎德甘的工作只是人工智能如何讓我們更詳細地了解宇宙的一個令人驚嘆的例子。
Only a few years ago, scientists from an international team called the Event Horizon Telescope published the first image of a black hole.
就在幾年前，來自事件視界望遠鏡國際團隊的科學家發布了第一張黑洞影像。
The effort involved hundreds of researchers stitching together data from radio telescopes placed around the world.
這項工作涉及數百名研究人員將來自世界各地電波望遠鏡的數據拼接在一起。
The resulting image, however monumental, appeared blurry because of the telescopes' limitations.
由此產生的影像，無論多么巨大，由于望遠鏡的限制而顯得模糊。
A computational astrophysicist and a member of that team, Lia Medeiros built an algorithm that finds patterns in the radio data and creates a new version of the image.
計算天體物理學家兼該團隊的成員莉亞·梅德羅斯建立了一種算法，可以在無線電數據中找到模式并創建新版本的圖像。
The algorithm, called PRIMO, did not sharpen the image in the way a photographer might eliminate blurriness using Photoshop.
這種名為PRIMO的算法并沒有像攝影師使用Photoshop消除模糊那樣銳利化影像。
Rather, it put together a brand-new image, as if a Photoshop user built a fresh picture.
相反，它組合了一個全新的圖像，就像Photoshop用戶建立了一張新圖片一樣。
The result is a higher definition image than the one produced by the Event Horizon Telescope, with the black hole's features more neatly resolved.
結果是比事件視界望遠鏡產生的影像清晰度更高，黑洞的特征更清晰地解析。
Medeiros believes PRIMO could be used to construct images of other mysterious objects.
梅德羅斯相信PRIMO可以用來建構其他神秘物體的圖像。
Some of the most interesting processes of planet formation are still invisible to us, even with the best telescopes.
即使使用最好的望遠鏡，我們仍然看不到一些最有趣的行星形成過程。
Large radio telescopes can record the dust and gas in protoplanetary disks where planets form, and optical telescopes can see fully formed worlds, but the in-between stages of growth don't come through as clearly.
大型射電望遠鏡可以記錄行星形成的原行星盤中的塵埃和氣體，光學望遠鏡可以看到完全形成的世界，但中間的生長階段并沒有那么清晰。
Medeiros thinks systems like PRIMO can improve the resolution of Earth's most sensitive telescopes, perhaps bringing these mysteries into view.
梅德羅斯認為像PRIMO這樣的系統可以提高地球上最靈敏的望遠鏡的分辨率，或許可以讓人們看到這些奧秘。
Despite all the potential, Medeiros says, some scientists are still wary of machine learning. The black box problem remains.
梅德羅斯說，盡管機器學習潛力巨大，但一些科學家仍然對機器學習持謹慎態度。黑盒子問題仍然存在。
Medeiros notes she built PRIMO from scratch with transparency in mind. But not all astronomical questions can be answered with such bespoke programs.
梅德羅斯指出，她從頭開始構建PRIMO時就考慮到了透明度。但并非所有天文問題都可以通過此類定制程序得到解答。
ExoMiner is more of a black box, built on existing neural networks and then heavily refined by Valizadegan and his colleagues.
ExoMiner更像是黑盒子，建立在現有的神經網絡之上，然后由瓦利扎德甘和他的同事進行了大量改進。
But astronomers grew to trust it, especially once it began finding planets.
但天文學家在逐漸信任它，尤其是當它開始尋找行星時。
