Measurement of supernova host galaxy properties in next generation surveys
Creators: name: Dumayne, Jamie
Publisher: name: Lancaster University
Year: 2024

description: 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 &lt; z &lt; 0.9 and 18 &lt; rAB &lt;
 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.