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. Theoretically predicted yields of elements created by the rapid neutron capture ( r -) process carry potentially large uncertainties associated with incomplete knowledge of nuclear properties and approximative hydrodynamical modeling of the matter ejection processes. One of the dominant uncertainties in determining the ejecta composition and radioactive decay heat stems from the still unknown nuclear masses of exotic neutron-rich nuclei produced during the neutron irradiation. Aims. We investigate both the model (systematic) and parameter (statistical) uncertainties affecting nuclear mass predictions and explore their impact on r-process production, and subsequently on the composition of neutron star merger ejecta. Methods. The impact of correlated model uncertainties on masses is estimated by considering five different nuclear mass models that are known to provide an accurate description of known masses. In addition, the uncorrelated uncertainties associated with local variation of model parameters are estimated using a variant of the backward-forward Monte Carlo method, to constrain the parameter changes to experimentally known masses before propagating them consistently to the unknown masses of neutron-rich nuclei. The impact of nuclear mass uncertainties is propagated to r -process nucleosynthesis in a 1.38–1.38 M ⊙ neutron star merger model considering a large and representative number of trajectories. Results. We find that the uncorrelated parameter uncertainties lead to ejected abundance uncertainties of 20% up to A ≃ 130 and 40% between A = 150 and 200, with peaks around A ≃ 140 and A ≃ 203 giving rise to deviations around 100–300%. The correlated model uncertainties remain larger than the parameter ones for most nuclei. However, both model and parameter uncertainties have an important impact on heavy nuclei production. Conclusions. Improvements to nuclear models are still crucial in reducing uncertainties in predictions related to r -process nucleosynthesis. Both correlated model uncertainties and coherently determining parameter uncertainties are key in the sensitivity analysis.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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