Measurement of supernova host galaxy properties in next generation surveys

Dumayne, Jamie and Hook, Isobel (2024) Measurement of supernova host galaxy properties in next generation surveys. PhD thesis, Lancaster University.

Text (2024dumaynephd)
2024dumaynephd.pdf - Published Version
Available under License Creative Commons Attribution.
Download (5MB)

Abstract

Upcoming supernova (SN) surveys will enable an exciting time in cosmology. The Rubin Observatory’s 10-year Legacy Survey of Space and Time (LSST) will observe near to 20 billion galaxies. Approximately 105 galaxies observed per year will contain Type Ia supernovae (SNeIa). One of the sub-surveys using the 4-metre Multi-Object Spectroscopic Telescope (4MOST), the Time Domain Extragalactic Survey, will obtain spectra of 35,000 live transients and 50,000 host-galaxies during the first 5 years of 4MOST. In this thesis we quantify the ability for these two surveys to measure host-galaxy properties, when used together. For each galaxy obsereved by 4MOST and LSST the properties can be inferred, allowing SN hostgalaxy properties to be calculated on a large scale. Measuring the properties of SN host-galaxies serves two main purposes. The first is that Type Ia SNe exhibit correlations between host-galaxy properties and the peak luminosities of the SNe, which has implications for their use as standardisable candles in cosmology. Secondly, there are known correlations between host-galaxy type and supernova type, which can be used to aid in the classification of SNe. We have used simulations to quantify the improvement in host-galaxy stellar mass (M∗) measurements when supplementing photometry from Rubin with spectroscopy from the 4MOST instrument. We provide results in the form of expected uncertainties in M∗ for galaxies with 0.1 < z < 0.9 and 18 < rAB < 25. We show that for galaxies mag 22 and brighter, combining Rubin and 4MOST data reduces the uncertainty measurements of galaxy M∗ by more than a factor of 2 compared with Rubin data alone. This applies for elliptical and Sc type hosts. We see a reduction to other galaxy properties, including: galaxy age, metallicity, star formation rate, star formation timescale and v-band extinction. We demonstrate that the reduced uncertainties in M∗ lead to a reduction of 7% in the ambiguity of the application of the “mass step” correction. This leads to a 2% reduction in the uncertainty on w. This is a negligible improvement. However, we would expect a larger effect to w0 and wa. This means more precise mass measurements could be crucial in future surveys. We then demonstrate that improved measurements of redshift, stellarmass and star formation rate aids in photometric classification of supernovae. To do this, we used a simple lookup table containing the host-galaxy properties and a machine learning algorithm. The machine learning algorithm was trained on 6 different datasets with different information included. We find the machine learning model which only has access to the SNe light curves performs the worst at producing a pure SNe Ia sample. The lookup table performs the best. Whilst a ML model which has access to SNe light curves, spectroscopic redshifts and host-galaxy properties as observed by 4MOST and LSST is able to produced the largest sample of comparable purity. We expect these results will help to improve the constraints on the dark energy of state parameters, w0 and wa. The results presented in this thesis are proof of concept of what will be possible with 4MOST and LSST, and demonstrate the implications to SN cosmology in the near future.