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To compile a complete data set, these patterns must be indexed (\n                    <jats:italic>i.e.<\/jats:italic>\n                    their orientation determined), integrated and merged. Introduced here is\n                    <jats:italic>TORO<\/jats:italic>\n                    (\n                    <jats:italic>Torch<\/jats:italic>\n                    -powered robust optimization)\n                    <jats:italic>Indexer<\/jats:italic>\n                    , a robust and adaptable indexing algorithm developed using the\n                    <jats:italic>PyTorch<\/jats:italic>\n                    framework.\n                    <jats:italic>TORO<\/jats:italic>\n                    is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by\n                    <jats:italic>PyTorch<\/jats:italic>\n                    , ensuring compatibility with a wide variety of computational setups. In tests,\n                    <jats:italic>TORO<\/jats:italic>\n                    outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real-time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like\n                    <jats:italic>DIALS<\/jats:italic>\n                    real-space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans &amp; Winter (2014).\n                    <jats:italic>Acta Cryst.<\/jats:italic>\n                    D\n                    <jats:bold>70<\/jats:bold>\n                    , 2652\u20132666] and\n                    <jats:italic>XGandalf<\/jats:italic>\n                    [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat &amp; Chapman (2019).\n                    <jats:italic>Acta Cryst.<\/jats:italic>\n                    A\n                    <jats:bold>75<\/jats:bold>\n                    , 694\u2013704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins,\n                    <jats:italic>TORO<\/jats:italic>\n                    consistently matches, and in certain instances outperforms, established algorithms such as\n                    <jats:italic>XGandalf<\/jats:italic>\n                    and\n                    <jats:italic>MOSFLM<\/jats:italic>\n                    [Powell (1999).\n                    <jats:italic>Acta Cryst.<\/jats:italic>\n                    D\n                    <jats:bold>55<\/jats:bold>\n                    , 1690\u20131695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of\n                    <jats:italic>TORO<\/jats:italic>\n                    and the versatility of\n                    <jats:italic>PyTorch<\/jats:italic>\n                    code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX.\n                  <\/jats:p>","DOI":"10.1107\/s1600576724003182","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T12:07:18Z","timestamp":1715083638000},"page":"931-944","update-policy":"https:\/\/doi.org\/10.1107\/cm_01","source":"Crossref","is-referenced-by-count":11,"title":["<i>TORO Indexer<\/i>\n                    : a\n                    <i>PyTorch<\/i>\n                    -based indexing algorithm for kilohertz serial crystallography"],"prefix":"10.1107","volume":"57","author":[{"given":"Piero","family":"Gasparotto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Barba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans-Christian","family":"Stadler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Greta","family":"Assmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henrique","family":"Mendon\u00e7a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1233-9752","authenticated-orcid":true,"given":"Alun W.","family":"Ashton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Janousch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2563-7169","authenticated-orcid":true,"given":"Filip","family":"Leonarski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjam\u00edn","family":"B\u00e9jar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"329","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"jo5098_bb1","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1126\/science.1217737","volume":"337","author":"Boutet","year":"2012","journal-title":"Science"},{"key":"jo5098_bb2","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/nature09750","volume":"470","author":"Chapman","year":"2011","journal-title":"Nature"},{"key":"jo5098_bb4","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-1-4939-7000-1_10","volume":"1607","author":"Diederichs","year":"2017","journal-title":"Methods Mol. 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