Table 3. Open in new tab Best-fitting...

Table 3.

Best-fitting parameters of G4 from SED fitting.

G4
	FAST++	Bagpipes	Bagpipes	CIGALE
Redshift	2.13^a	2.13^a	2.13^a	2.13^a
log M_⋆	10.99 ± 0.05	11.16	11.22	11.25 ± 0.10
		[11.10,11.21]	[11.17,11.30]
SFR\|$_{\rm SED}^{\dagger }$\|^d	0.02	0.00^b	0.01^b	1.3e−4 ± 7.04e−5^b^,c
		[0.00,0.00]	[0.00,0.04]
log(ΔMS)^d	−3.79	−69.68	−4.40	−6.20
log (age)^e	9.15	8.83	9.27	9.26 ± 0.20
		[8.79,8.97]	[9.20,9.30]
SFH	Delayed-τ	Double-power law	Non-parametric	Delayed-τ
A_V	0.10	0.75	0.51	0.57 ± 0.10
		[0.50,0.93]	[0.33,0.72]
χ^2f	2.1	15.8	22.4	11.3

G4
	FAST++	Bagpipes	Bagpipes	CIGALE
Redshift	2.13^a	2.13^a	2.13^a	2.13^a
log M_⋆	10.99 ± 0.05	11.16	11.22	11.25 ± 0.10
		[11.10,11.21]	[11.17,11.30]
SFR\|$_{\rm SED}^{\dagger }$\|^d	0.02	0.00^b	0.01^b	1.3e−4 ± 7.04e−5^b^,c
		[0.00,0.00]	[0.00,0.04]
log(ΔMS)^d	−3.79	−69.68	−4.40	−6.20
log (age)^e	9.15	8.83	9.27	9.26 ± 0.20
		[8.79,8.97]	[9.20,9.30]
SFH	Delayed-τ	Double-power law	Non-parametric	Delayed-τ
A_V	0.10	0.75	0.51	0.57 ± 0.10
		[0.50,0.93]	[0.33,0.72]
χ^2f	2.1	15.8	22.4	11.3

^aBest-fitting photo-z from eazy-py.^bAveraged over 100 Myr. ^cIncluding the ALMA 2 mm data point. ^dWe note the limitation of the SFR from the SED which is likely to introduce an unrealistically low value of SFR and hence ΔMS especially for the parametric SFH models. The photometry (⁠|$\mathcal {R}_s$|-band) and the best-fitting dust attenuation (A_V) suggest the SFR of |$\sim 8\, {\rm M}_{\odot }$| yr⁻¹ and log (ΔMS) ∼−1.6 (see also the main text in Section 4.1). ^eMass weighted.

^fWe quote the absolute χ² value, as the templates employed (as is the case for many SED fitting codes) are not independent of each other, and degrees of freedom are ill-defined (e.g. Smith et al. 2012).

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