Giga-z: A 100,000 Object Superconducting Spectrophotometer for LSST Follow-up

TL;DR

Giga-z, a superconducting spectrophotometer using MKID technology, can rapidly observe billions of galaxies, providing low-cost, high-accuracy redshifts up to z~6, significantly enhancing dark energy research.

Contribution

This paper introduces Giga-z, a novel wide-field spectrophotometer utilizing MKID detectors, capable of large-scale galaxy observations with unprecedented efficiency and accuracy.

Findings

Giga-z can observe ~2 billion galaxies in 3 years.

Redshift accuracy of σ_z/(1+z)=0.03 overall, 0.007 for a subset.

Improves dark energy parameter constraints by factors of 1.27 to 1.98.

Abstract

We simulate the performance of a new type of instrument, a Superconducting Multi-Object Spectrograph (SuperMOS), that uses Microwave Kinetic Inductance Detectors (MKIDs). MKIDs, a new detector technology, feature good QE in the UVOIR, can count individual photons with microsecond timing accuracy and, like X-ray calorimeters, determine their energy to several percent. The performance of Giga-z, a SuperMOS designed for wide field imaging follow-up observations, is evaluated using simulated observations of the COSMOS mock catalog with an array of 100,000 R_{423 nm} = E/\Delta E = 30 MKID pixels. We compare our results against a simultaneous simulation of LSST observations. In three years on a dedicated 4 m-class telescope, Giga-z could observe ~ 2 billion galaxies, yielding a low resolution spectral energy distribution (SED) spanning 350 - 1350 nm for each; 1000 times the number measured with any currently proposed LSST spectroscopic follow-up, at a fraction of the cost and time. Giga-z would provide redshifts for galaxies up to z ~ 6 with magnitudes m_i < 25, with accuracy \sigma_{\Delta z/(1+z)} = 0.03 for the whole sample, and \sigma_{\Delta z/(1+z)} = 0.007 for a select subset. We also find catastrophic failure rates and biases that are consistently lower than for LSST. The added constraint on Dark Energy parameters for WL+CMB by Giga-z using the FoMSWG default model is equivalent to multiplying the LSST Fisher matrix by a factor of \alpha = 1.27 (w_p), 1.53 (w_a), or 1.98 (\Delta \gamma). This is equivalent to multiplying both the LSST coverage area and the training sets by \alpha, and reducing all systematics by a factor of 1/sqrt(\alpha), advantages that are robust to even more extreme models of intrinsic alignment.