인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Context. The scientific impact of GW170817 strongly supports the suggestion that we need an efficient electromagnetic follow-up campaign for gravitational-wave event candidates. The success of these campaigns critically depends on a fast and accurate localization of the source. Aims. We present SKYFAST , an algorithm for the rapid identification of gravitational-wave hosts to optimize electromagnetic follow-up searches. The goal is to produce a list of the galaxies within the localization volume, ranked by their probability of being the host, along with an estimate of the inclination angle conditioned on the position of each galaxy. Methods. SKYFAST runs alongside a full parameter estimation (PE) algorithm, from which posterior samples are taken. These samples are then used to reconstruct an analytical posterior of the sky position, luminosity distance, and inclination angle using a Dirichlet process Gaussian mixture model, which is a nonparametric Bayesian method. Results. We show that SKYFAST can reconstruct an accurate localization using only a fraction (∼10%) of the total posterior samples produced by the PE. Moreover, SKYFAST generates a ranked list of the most probable hosts from a galaxy catalog of choice in a few minutes. This list includes information on the inclination angle posterior conditioned on the position of each candidate host. This breaks the degeneracy between inclination angle and luminosity distance. Conclusions. The reconstruction of the posterior using fewer samples than the full PE can lead to significant time savings, depending on the PE algorithm employed. This is crucial for identifying the electromagnetic counterpart. The inclusion of the inclination angle information conditioned on the position of each galaxy can lead to an optimized electromagnetic follow-up.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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