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Gravitational-wave observatories like LIGO are large-scale, terrestrial instruments housed in infrastructure that spans a multi-kilometer geographic area and which must be actively controlled to maintain operational stability for long observation periods. Despite exquisite seismic isolation, they remain susceptible to seismic noise and other terrestrial disturbances that can couple undesirable vibrations into the instrumental infrastructure, potentially leading to control instabilities or noise artifacts in the detector output. It is, therefore, critical to characterize the seismic state of these observatories to identify a set of temporal patterns that can inform the detector operators in day-to-day monitoring and diagnostics. On a day-to-day basis, the operators monitor several seismically relevant data streams to diagnose operational instabilities and sources of noise using some simple empirically-determined thresholds. It can be untenable for a human operator to monitor multiple data streams in this manual fashion and thus a distillation of these data-streams into a more human-friendly format is sought. In this paper, we present an end-to-end machine learning pipeline for features-based multivariate time series clustering to achieve this goal and to provide actionable insights to the detector operators by correlating found clusters with events of interest in the detector. 

Ground-based gravitational-wave (GW) detectors are a frontier large-scale experiment in experimental astrophysics. Given the elusive nature of GWs, the ground-based detectors have complex interacting systems made up of exquisitely sensitive instruments which makes them susceptible to terrestrial noise sources. As these noise transients - termed as glitches - appear in the detector's main data channel, they can mask or mimic real GW signals resulting in false alarms in the detection pipelines. Given their high rate of occurrence compared to astrophysical signals, it is vital to examine these glitches and probe their origin in the detector's environment and instruments in order to possibly eliminate them from the science data. In this paper we present a tensor factorization-based data mining approach to finding witness events to these glitches in the network of heterogeneous sensors that monitor the detectors and build a catalog which can aid human operators in diagnosing the sources of these noise transients. Available from: https://openreview.net/forum?id=O9q0ma6Oh5e 

This white paper describes the research and development needed over the next decade to realize "Cosmic Explorer," the U.S. node of a future third-generation detector network that will be capable of observing and characterizing compact gravitational-wave sources to cosmological redshifts. 

Abstract We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies.