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[This corrects the article DOI: 10.3389/fdata.2022.787421.].
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In recent years, certain luminous extragalactic optical transients have been observed to last only a few days1. Their short observed duration implies a different powering mechanism from the most common luminous extragalactic transients (supernovae), whose timescale is weeks2. Some short-duration transients, most notably AT2018cow (ref. 3), show blue optical colours and bright radio and X-ray emission4. Several AT2018cow-like transients have shown hints of a long-lived embedded energy source5, such as X-ray variability6,7, prolonged ultraviolet emission8, a tentative X-ray quasiperiodic oscillation9,10 and large energies coupled to fast (but subrelativistic) radio-emitting ejecta11,12. Here we report observations of minutes-duration optical flares in the aftermath of an AT2018cow-like transient, AT2022tsd (the 'Tasmanian Devil'). The flares occur over a period of months, are highly energetic and are probably nonthermal, implying that they arise from a near-relativistic outflow or jet. Our observations confirm that, in some AT2018cow-like transients, the embedded energy source is a compact object, either a magnetar or an accreting black hole.
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In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
