RESUMO
[This corrects the article DOI: 10.3389/fdata.2022.787421.].
RESUMO
Direct detection experiments are gaining in mass reach. Here we show that the inclusion of dark Compton scattering, which has typically been neglected in absorption searches, has a substantial impact on the reach and discovery potential of direct detection experiments at high bosonic cold dark matter masses. We demonstrate this for relic dark photons and axionlike particles: we improve expected reach across materials, and further use results from SuperCDMS, EDELWEISS, and GERDA to place enhanced limits on dark matter parameter space. We outline the implications for detector design and analysis.
RESUMO
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.