Summary
Lensed supernovae offer promising tools for cosmology with the upcoming revolutionary LSST survey, however the current techniques to both find these systems and to properly use them to infer the Hubble constant (H0) are severely underdeveloped. In this fellowship, I propose PHOLOS (Probing H0 with Lensing Of Supernovae), a three-part project to make lasting contributions to all steps of this process. I will first solve the longstanding problem of finding and prioritising lensed supernovae, by simultaneous exploitation of multiple datasets to find lensed supernova canidates without spectroscopic follow-up. I will then address the issue of the mass distribution of lensing galaxies (a recent systematic worry in all time-delay cosmography studies), by modelling population level characteristics of a large sample of lensed quasars. Finally I will combine the new lensed supernovae and mass model constraints in a cosmological inference pipeline that will scale to the hundreds of systems with heterogeneous data quality expected from LSST, yielding a precise and accurate measurement of H0. The timing of this project is ideal as LSST will start collecting data in mid-2023. By expanding my knowledge and (transferable) skills, this project will prepare me to be world-leading researcher thanks to new expertise on specific datasets like LSST and Gaia, on analysis techniques and machine learning methods, and on astrophysical phenomena like supernovae, gravitational lensing, and galaxy mass profiles.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101105725 |
| Start date: | 01-01-2024 |
| End date: | 31-01-2026 |
| Total budget - Public funding: | - 206 887,00 Euro |
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Original description
Lensed supernovae offer promising tools for cosmology with the upcoming revolutionary LSST survey, however the current techniques to both find these systems and to properly use them to infer the Hubble constant (H0) are severely underdeveloped. In this fellowship, I propose PHOLOS (Probing H0 with Lensing Of Supernovae), a three-part project to make lasting contributions to all steps of this process. I will first solve the longstanding problem of finding and prioritising lensed supernovae, by simultaneous exploitation of multiple datasets to find lensed supernova canidates without spectroscopic follow-up. I will then address the issue of the mass distribution of lensing galaxies (a recent systematic worry in all time-delay cosmography studies), by modelling population level characteristics of a large sample of lensed quasars. Finally I will combine the new lensed supernovae and mass model constraints in a cosmological inference pipeline that will scale to the hundreds of systems with heterogeneous data quality expected from LSST, yielding a precise and accurate measurement of H0. The timing of this project is ideal as LSST will start collecting data in mid-2023. By expanding my knowledge and (transferable) skills, this project will prepare me to be world-leading researcher thanks to new expertise on specific datasets like LSST and Gaia, on analysis techniques and machine learning methods, and on astrophysical phenomena like supernovae, gravitational lensing, and galaxy mass profiles.Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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