Enabling real-time multi-messenger astrophysics discoveries with deep learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics

Working Papers: Astronomy and Astrophysics Panel Reports

The papers of the panels appointed by the Astronomy and Astrophysics survey Committee are compiled. These papers were advisory to the survey committee and represent the opinions of the members of each panel in the context of their individual charges. The following subject areas are covered: radio astronomy, infrared astronomy, optical/IR from ground, UV-optical from space, interferometry, high energy from space, particle astrophysics, theory and laboratory astrophysics, solar astronomy, planetary astronomy, computing and data processing, policy opportunities, benefits to the nation from astronomy and astrophysics, status of the profession, and science opportunities

Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges

In the domain of Natural Language Processing (NLP), Named Entity Recognition (NER) stands out as a pivotal mechanism for extracting structured insights from unstructured text. This manuscript offers an exhaustive exploration into the evolving landscape of NER methodologies, blending foundational principles with contemporary AI advancements. Beginning with the rudimentary concepts of NER, the study spans a spectrum of techniques from traditional rule-based strategies to the contemporary marvels of transformer architectures, particularly highlighting integrations such as BERT with LSTM and CNN. The narrative accentuates domain-specific NER models, tailored for intricate areas like finance, legal, and healthcare, emphasizing their specialized adaptability. Additionally, the research delves into cutting-edge paradigms including reinforcement learning, innovative constructs like E-NER, and the interplay of Optical Character Recognition (OCR) in augmenting NER capabilities. Grounding its insights in practical realms, the paper sheds light on the indispensable role of NER in sectors like finance and biomedicine, addressing the unique challenges they present. The conclusion outlines open challenges and avenues, marking this work as a comprehensive guide for those delving into NER research and applications

Long-lived space observatories for astronomy and astrophysics

NASA's plan to build and launch a fleet of long-lived space observatories that include the Hubble Space Telescope (HST), the Gamma Ray Observatory (GRO), the Advanced X Ray Astrophysics Observatory (AXAF), and the Space Infrared Telescope Facility (SIRTF) are discussed. These facilities are expected to have a profound impact on the sciences of astronomy and astrophysics. The long-lived observatories will provide new insights about astronomical and astrophysical problems that range from the presence of planets orbiting nearby stars to the large-scale distribution and evolution of matter in the universe. An important concern to NASA and the scientific community is the operation and maintenance cost of the four observatories described above. The HST cost about $1.3 billion (1984 dollars) to build and is estimated to require$160 million (1986 dollars) a year to operate and maintain. If HST is operated for 20 years, the accumulated costs will be considerably more than those required for its construction. Therefore, it is essential to plan carefully for observatory operations and maintenance before a long-lived facility is constructed. The primary goal of this report is to help NASA develop guidelines for the operations and management of these future observatories so as to achieve the best possible scientific results for the resources available. Eight recommendations are given