The hyperplane of early-type galaxies: using stellar population properties to increase the precision and accuracy of the fundamental plane as a distance indicator

Francesco D'Eugenio, Matthew Colless, Arjen van der Wel, Sam P. Vaughan, Khaled Said, Jesse van de Sande, Joss Bland-Hawthorn, Julia J. Bryant, Scott M. Croom, Angel R. Lopez-Sanchez, Nuria P. F. Lorente, Roberto Maiolino, Edward N. Taylor

Published 2024-06-25Version 1

We use deep spectroscopy from the SAMI Galaxy Survey to explore the precision of the fundamental plane of early-type galaxies (FP) as a distance indicator for future single-fibre spectroscopy surveys. We study the optimal trade-off between sample size and signal-to-noise ratio (SNR), and investigate which additional observables can be used to construct hyperplanes with smaller intrinsic scatter than the FP. We add increasing levels of random noise (parametrised as effective exposure time) to the SAMI spectra to study the effect of increasing measurement uncertainties on the FP-and hyperplane-inferred distances. We find that, using direct-fit methods, the values of the FP and hyperplane best-fit coefficients depend on the spectral SNR, and reach asymptotic values for a mean SNR=40 {\AA}$^{-1}$. As additional variables for the FP we consider three stellar-population observables: light-weighted age, stellar mass-to-light ratio and a novel combination of Lick indices (I$_{\rm age}$). For a SNR=45 {\AA}$^{-1}$ (equivalent to 1-hour exposure on a 4-m telescope), all three hyperplanes outperform the FP as distance indicators. Being an empirical spectral index, I$_{\rm age}$ avoids the model-dependent uncertainties and bias underlying age and mass-to-light ratio measurements, yet yields a 10 per cent reduction of the median distance uncertainty compared to the FP. We also find that, as a by-product, the Iage hyperplane removes most of the reported environment bias of the FP. After accounting for the different signal-to-noise ratio, these conclusions also apply to a 50 times larger sample from SDSS-III. However, in this case, only age removes the environment bias.