인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
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
Aims. This work aims to determine how the galaxy main sequence (MS) changes using seven different commonly used methods to select the star-forming galaxies within VIPERS data over 0.5 ≤ z < 1.2. The form and redshift evolution of the MS was then compared between selection methods. Methods. The star-forming galaxies were selected using widely known methods: a specific star-formation rate (sSFR); Baldwin, Phillips, and Terlevich (BPT) diagram; a 4000 Å spectral break (D4000) cut; and four colour-colour cuts (near-ultra-violet – V verses r − J (NUVrJ), near-ultra-violet – V verses r − K (NUVrK), u − r , and U − V verses V − J (UVJ)). The main sequences were then fitted for each of the seven selection methods using a Markov chain Monte Carlo forward modelling routine, fitting both a linear main sequence and a MS with a high-mass turnover to the star-forming galaxies. This was done in four redshift bins of 0.50 ≤ z < 0.62, 0.62 ≤ z < 0.72, 0.72 ≤ z < 0.85, and 0.85 ≤ z < 1.20. Results. The slopes of all star-forming samples were found to either remain constant or increase with redshift, and the scatters were approximately constant. There is no clear redshift dependency of the presence of a high-mass turnover for the majority of samples, with the NUVrJ and NUVrK being the only samples with turnovers only at low redshift. No samples have turnovers at all redshifts. Star-forming galaxies selected with sSFR and u − r are the only samples to have no high-mass turnover in all redshift bins. The normalisation of the MS increases with redshift, as expected. The scatter around the MS is lower than the ≈0.3 dex typically seen in MS studies for all seven samples. Conclusions. The lack (or presence) of a high-mass turnover is at least partially a result of the method used to select star-forming galaxies. However, whether a turnover should be present or not is unclear.
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.