# Turning Gravitationally Lensed Supernovae into Cosmological Probes

## TL;DR

This paper introduces a software tool for measuring time delays in gravitationally lensed supernovae, demonstrating that early detection improves accuracy and that Gaussian Process Regression effectively accounts for microlensing uncertainties.

## Contribution

The paper presents an open source simulation and analysis package for lensed supernovae, enhancing time delay measurements and uncertainty characterization, especially regarding microlensing effects.

## Key findings

- Early detection before peak improves measurement accuracy and precision.
- Using Gaussian Process Regression effectively estimates microlensing uncertainties.
- Simulations suggest improved time delay measurements with pre-peak detection.

## Abstract

Recently, there have been two landmark discoveries of gravitationally lensed supernovae: the first multiply-imaged SN, "Refsdal", and the first Type Ia SN resolved into multiple images, SN iPTF16geu. Fitting the multiple light curves of such objects can deliver measurements of the lensing time delays, which are the difference in arrival times for the separate images. These measurements provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Over the next decade, accurate time delay measurements will be needed for the tens to hundreds of lensed SNe to be found by wide-field time-domain surveys such as LSST and WFIRST. We have developed an open source software package for simulations and time delay measurements of multiply-imaged SNe, including an improved characterization of the uncertainty caused by microlensing. We describe simulations using the package that suggest a before-peak detection of the leading image enables a more accurate and precise time delay measurement (by ~1 and ~2 days, respectively), when compared to an after-peak detection. We also conclude that fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data, but that employing a Gaussian Process Regression (GPR) technique is sufficient for determining the uncertainty due to microlensing.