Periodic Reporting for period 3 - LENSNOVA (Cosmic Fireworks Première: Unravelling Enigmas of Type Ia Supernova Progenitor and Cosmology through Strong Lensing)
Período documentado: 2021-06-01 hasta 2022-10-31
i) measure the expansion rate of the Universe
ii) probe the progenitors of SNe
Both of these have potentially profound consequences for physics and our understanding the Universe that we live in. For example, there is currently a discrepancy in the measurements of the expansion rate of the Universe from different methods that points toward possible new physics beyond our current standard cosmological model. This new physics could be, for example, the existence of new particles that we do not yet know, or a period of rapid expansion in the early Universe. The first goal of our project is therefore important to assess whether there is new physics. The second goal is also important for understanding the underlying physical mechanisms that cause stars to explode. For decades, the progenitors of SNe of type Ia have remained elusive, and yet SNe Ia are playing a crucial role in probing the evolution of the Universe. How the Universe became what it is today is a fundamental question that humanity has sought to answer, and the possible new physics from our astrophysical research are important topics for the society.
Given that ZTF only reaches the brighter and nearer galaxies, the chance of finding lensed SNe, especially ones that are useful for our two scientific goals, is low with ZTF. In the near future, the planned Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST) that will image substantially more distant and fainter galaxies at higher resolution than ZTF. We expect to discover at least several lensed SNe from LSST per year. Therefore, our project has investigated the observing strategy of LSST to maximize the number of lensed SNe (Huber, Suyu, Noebauer et al. 2019). The results of this work are now used to provide input for optimising the LSST observing strategy.
The time delays between the lensed SN images are needed for our two scientific aims. We have conducted thorough investigations on the follow-up observation requirements for measuring the time delays, accounting for the effects of microlensing by stars in the foreground lens galaxies (Huber, Suyu, Noebauer et al. 2019; 2020). To constrain the progenitors of SNe Ia, spectroscopic observations within about five days after SN explosion are necessary, but the spectra could be potentially distorted by microlensing. We have carried out a thorough study using numerical simulations of SN explosions to quantify the effects of microlensing, and found that the effect of microlensing is negligible at the beginning of SNe, which would allow us to accomplish our scientific goal of constraining the SN progenitors using gravitational lensing (Suyu, Huber, Cañameras et al. 2020). We have also developed fast ways to model the mass distribution of the lens system using machine learning (Schuldt, Suyu, Meinhardt et al. 2020), in order to facilitate the follow-up observations of these lens systems for achieving our scientific goals.
For cosmological studies, we have investigated the cosmological information expected from a sample of lensed SNe in the LSST survey (Suyu, Huber, Cañameras et al. 2020). We have also demonstrated the combination of supernovae and gravitational lensing to provide a measurement of the expansion rate of the Universe that is less sensitive to model assumptions (Taubenberger, Suyu, Komatsu et al. 2019; Wong, Suyu, Chen et al. 2020).
- The combination of state-of-the-art numerical simulations of supernovae with gravitational lensing to investigate lensed SNe
- Our project is the first to provide concrete observing strategies for lensed SNe, both for their detection in LSST and also for measuring their time delays from follow-up observations
- We have shown the powerful combination of SNe and gravitational lensing to obtain robust measurement of expansion rate of Universe
- By exploiting state-of-the-art machine learning tools, particularly from Deep Learning and Convolutional Neural Networks, we are able to speed up the process for lens search and lens mass modeling by orders of magnitude
Expected results until the end of the project include:
- New methods to measure time delay of lensed SNe, both from light curves and from spectra
- New lens systems in the Hyper Suprime-Cam survey, which serves as a training ground for the LSST given their similarity in image quality
- Improvement in lens mass modeling with machine learning for application to real lens systems
- Discovery of the first lensed SN from LSST, provided that LSST survey starts at least ~6 months before the end of our ERC project. This would require a no-cost extension of our ERC for 12 months given the delays in LSST due to covid.