Table 1.
Best-fit values of the rSFMS for our work and several other publications.
| Reference | Data (a) | Method (b) | z | b | a |
|---|---|---|---|---|---|
| 1 | 80% | OLS | 0.3 | 0.425 ± 0.014 | −5.428 ± 0.104 |
| 1 | 80% | ODR | 0.3 | 1.162 ± 0.022 | −11.014 ± 0.164 |
| 2 | 80% | OLS | 0.3 | 0.401 ± 0.015 | −5.615 ± 0.116 |
| 2 | 80% | ODR | 0.3 | 2.562 ± 0.056 | −22.248 ± 0.431 |
| 3 | 80% | – | 0 | 0.72 ± 0.04 | −7.95 ± 0.29 |
| 4 | – | OLS | < 0.15 | 0.715 ± 0.001 | −8.056 ± 0.008 |
| 4 | – | ODR | < 0.15 | 1.005 ± 0.004 | −10.338 ± 0.014 |
| 5 | log(Σ*) > 7 | OLS | 0.26 | 0.771 ± 0.032 | −7.812 ± 0.249 |
| 6 | – | LTS | 0.25 < z < 0.42 | 0.918 ± 0.005 | −9.196 ± 0.006 |
| 7 | – | OLS | 0.1 < z < 0.42 | 0.43 ± 0.05 | −5.66 ± 0.05 |
| 8 | log(Σ*) < 8.8 | – | 0.7 < z < 1.5 | 0.95 | −8.4 |
Notes. Results by Mun et al. (2024) also utilize the MAGPI survey but for a wider redshift range of 0.25 < z < 0.42.
Linear fitting method: ordinary least-square (OLS), orthogonal distance regression (ODR), or least trimmed squares (LTS).
References:
(1) This work (SF-spaxels); (2) this work (spectral decomposition); (3) Cano-Díaz et al. (2016); (4) Hsieh et al. (2017); (5) Yao et al. (2022); (6) Mun et al. (2024); (7) Jafariyazani et al. (2019); (8) Wuyts et al. (2013).
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