Supercal: Cross-Calibration of Multiple Photometric Systems to Improve Cosmological Measurements with Type Ia Supernovae

TL;DR

Supercal is a method that uses secondary standards and Pan-STARRS1 data to cross-calibrate multiple supernova photometric systems, reducing systematic uncertainties in cosmological measurements.

Contribution

It introduces a new cross-calibration technique that aligns various supernova datasets to a common system using secondary standards and PS1 data.

Findings

Discrepancies of up to 35 mmag between systems were measured.

Correcting these differences alters the dark energy parameter w by about 2.6%.

Supercal improves the consistency of supernova photometry for cosmological analysis.

Abstract

Current cosmological analyses which use Type Ia supernova (SN Ia) observations combine SN samples to expand the redshift range beyond that of a single sample and increase the overall sample size. The inhomogeneous photometric calibration between different SN samples is one of the largest systematic uncertainties of the cosmological parameter estimation. To place these different samples on a single system, analyses currently use observations of a small sample of very bright flux standards on the $HST$ system. We propose a complementary method, called `Supercal', in which we use measurements of secondary standards in each system, compare these to measurements of the same stars in the Pan-STARRS1 (PS1) system, and determine offsets for each system relative to PS1, placing all SN observations on a single, consistent photometric system. PS1 has observed $3\pi$ of the sky and has a relative calibration of better than 5 mmag (for $\sim15<griz<21$ mag), making it an ideal reference system. We use this process to recalibrate optical observations taken by the following SN samples: PS1, SNLS, SDSS, CSP, and CfA1-4. We measure discrepancies on average of 10 mmag, but up to 35 mmag, in various optical passbands. We find that correcting for these differences changes recovered values for the dark energy equation-of-state parameter, $w$, by on average $2.6\%$. This change is roughly half the size of current statistical constraints on $w$. The size of this effect strongly depends on the error in the $B-V$ calibration of the low-$z$ surveys. The Supercal method will allow future analyses to tie past samples to the best calibrated sample.