Disentangling stellar atmospheric parameters in astronomical spectra using generative adversarial neural networks - Application to Gaia/RVS parameterisation

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Table 1

Summary statistics obtained for parameterisation using different representations of the input space.

	RVS Spectra			Pure latent			First disentangling			Second disentangling
	MAE	Mean	σ	MAE	Mean	σ	MAE	Mean	σ	MAE	Mean	σ
T_eff(K)	121	58	131	140	6	138	93	−7	94	512	17	361
logɡ(dex)	0.231	0.017	0.277	0.282	−0.012	0.340	0.141	−0.021	0.160	0.868	−0.027	0.522
[M/H](dex)	0.099	−0.007	0.143	0.100	−0.016	0.111	0.183	−0.017	0.166	0.065	0.002	0.063
[α/Fe](dex)	0.039	−0.001	0.053	0.041	0.006	0.043	0.051	0.003	0.049	0.031	−0.004	0.033

Notes. First disentangling exercise means: T_eff, logɡ entangled; [M/H], [α/Fe] disentangled. Second disentangling exercise means: T_eff, logɡ disentangled; [M/H], [α/Fe] entangled.

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