A stochastic background of gravitational waves could be created by the superposition of a large number of independent sources. The physical processes occurring at the earliest moments of the universe certainly created a stochastic background that exists, at some level, today. This is analogous to the cosmic microwave background, which is an electromagnetic record of the early universe. The recent observations of gravitational waves by the Advanced LIGO and Advanced Virgo detectors imply that there is also a stochastic background that has been created by binary black hole and binary neutron star mergers over the history of the universe. Whether the stochastic background is observed directly, or upper limits placed on it in specific frequency bands, important astrophysical and cosmological statements about it can be made. This review will summarize the current state of research of the stochastic background, from the sources of these gravitational waves to the current methods used to observe them.
Quantum Chaos has originally emerged as the field which studies how the properties of classical chaotic systems arise in their quantum counterparts. The growing interest in quantum many-body systems, with no obvious classical meaning has led to consider time-dependent quantities that can help to characterize and redefine Quantum Chaos. This article reviews the prominent role that the out of time ordered correlator (OTOC) plays to achieve such goal.
Five constructs are taken into considerations to define pre-existing differences between subjects experience with: • Software development in general (DEV) • Test-driven developmet (TDD) • The Java programming language (OOP) • unit testing (UT) • the Eclipse IDE (IDE) for each of the four above (i.e., excluding TDD), we asked the subjects to evaluate: 1. general familiarity (5-points likert item: very experienced – very inexperienced ) 2. years used in academia (numerical integer) 3. years used in industry (numerical integer) 4. years used in own activities (numerical integer) Whereas we only have 1) regarding TDD. The alpha level is 0.0125 due to Bonferroni correction (i.e., taking into account the four measure above)
Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operational aspects of astronomy are guided and executed by expert astronomers. Reinforcement learning is a mechanism where we (as humans and astronomers) can teach agents of artificial intelligence to perform some of these tedious tasks. In this paper, we will present a state of the art overview of reinforcement learning and how it can benefit astronomy.
