Improving Type Ia Supernovae as Probes of Dark Energy – Optical Spectrum(3/3)

Project Details


Type Ia supernovae (SNe Ia) are superb cosmological probes. After making empirical corrections based on how quickly the SNe fade and their observed colors, SNe Ia are “standardizable” candles from which we can probe the cosmic expansion and constrain the nature of the mysterious dark energy. Despite SNe Ia have been widely used to measure the cosmological parameters, our understanding on their progenitor systems and explosion mechanisms are still poor, which may limit and complicate their usage in cosmology. Recent studies showed evidence that the spectral parameters of SNe Ia (such as ejecta velocities and high-velocity features) can be used as effective tools to differentiate between progenitor scenarios and explosions. They can also be used as parameters in addition to SN light-curve width and color to improve the scatter of luminosities (and thus their distances). However, these studies generally have a relatively small sample size (~100 SNe) and focused on low-redshift (z<0.1) SNe Ia. In this project, we aim to address these issues by studying a large sample of high-redshift SNe Ia discovered by Pan-STARRS1 (PS1) Medium Deep Survey. Our sample contains ~700 optical spectra from ~400 well-observed SNe Ia, with the most distant ones having redshift of ~0.7. The spectroscopic study on these SNe will be crucial to constrain potential evolution of SNe Ia with redshift and further improve their precision as probes of dark energy.
StatusNot started
Effective start/end date1/10/2231/07/23

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 11 - Sustainable Cities and Communities
  • SDG 17 - Partnerships for the Goals


  • Supernovae
  • Type Ia supernovae
  • Dark energy


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.