Periodic Reporting for period 4 - LENSNOVA (Cosmic Fireworks Première: Unravelling Enigmas of Type Ia Supernova Progenitor and Cosmology through Strong Lensing)
Reporting period: 2022-11-01 to 2024-05-31
Our universe contains very bright and exotic objects. Two examples are quasars which are supermassive black holes at centres of galaxies that are gobbling up gas and emitting intense radiation, and supernovae (SNe) which are explosions of stars that can outshine an entire galaxy. Both of these objects change their brightness and are known as astrophysical transients. Quasars and SNe that are gravitationally lensed into multiple images provide a competitive probe of cosmology and stellar physics. Gravitational lensing occurs when a background source, by chance, lies behind a massive object, such as a galaxy. The gravitational field of the foreground galaxy acts like a lens and bends the light rays of the background source coming toward us in such a way that multiple images of the source form around the foreground lens (see Figure 1 in the attachment). Since the light paths traversed by the multiple images are different, the multiple images for the transient (time-variable) source appear at different times. The time delays between the multiple images enable us to achieve two goals with our project:
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. There is currently a discrepancy in the expansion rate of the Universe measured from different methods which points toward possible new physics beyond our current standard cosmological model, e.g. the existence of new particles that we do not yet know. 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.
Our project has laid the theoretical foundations and methodologies for achieving our two goals with upcoming astronomical surveys, particularly the Rubin Observatory Legacy Survey of Space and Time (LSST).
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. There is currently a discrepancy in the expansion rate of the Universe measured from different methods which points toward possible new physics beyond our current standard cosmological model, e.g. the existence of new particles that we do not yet know. 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.
Our project has laid the theoretical foundations and methodologies for achieving our two goals with upcoming astronomical surveys, particularly the Rubin Observatory Legacy Survey of Space and Time (LSST).
To achieve our two goals of measuring the cosmic expansion rate and constraining SN progenitors via gravitationally lensed SNe, we need to find these rare astrophysical events. The chance of lensed SNe occurring is extremely low since SNe and gravitational lensing are both inherently rare events. However, current and upcoming astronomical surveys are monitoring large areas of the night sky and imaging billions of galaxies, which would capture lensed SNe. Our strategy is to find lens systems, and then wait for one of the background sources to explode. We have searched for gravitationally lensed galaxies in the Pan-STARRS and Hyper-Suprime-Cam (HSC) surveys that cover the entire northern sky. To cope with the huge data volume, we have used Deep Learning to quickly classify images of astrophysical objects. From our newly developed tools, we have found >800 new lens candidates in the Pan-STARRS and HSC surveys that could serve as potential hosts of lensed SNe (Cañameras et al. 2020; 2021; Shu et al. 2022). We have monitored these lens candidates with the Zwicky Transient Facility (ZTF), a survey that maps out the northern sky every 2-3 days, as a way to detect a SN occurring in one of the lens systems.
Since ZTF only reaches the brighter and nearer galaxies, we have not found a suitable lensed SN in ZTF, as expected. However, we expect to discover at least several lensed SNe per year from the upcoming LSST, and the lens search methods that we have developed are readily applicable to LSST. Our project has investigated the observing strategy of LSST to maximize the number of lensed SNe (Huber et al. 2019; Lochner et al. 2022). The results of these studies have been used as input for optimising the LSST observing strategy.
The time delays between the lensed SN images are needed for our two scientific aims. We have determined follow-up observation requirements for measuring time delays (Huber et al. 2019; 2020). Accounting for microlensing, we have developed novel methods to measure time delays based on machine learning that outperform existing methods for Type Ia SNe (Huber et al. 2022; Huber & Suyu, submitted) and based on spectral evolution of Type IIP SNe (Bayer et al. 2021). In addition, our extensive simulations show that the effect of microlensing is negligible at the beginning of SNe, enabling us to constrain SN progenitors (Suyu et al. 2020). We have also developed fast ways to model the mass distribution of the lens system using machine learning (Schuldt et al. 2021; 2023ab). We have further automated our modelling procedure to enhance efficiency (Ertl et al. 2023). Both developments on the machine learning and conventional modelling approaches facilitate the analyses of lensed SNe for achieving our scientific goals, as demonstrated by the application to the two newly discovered lensed SN systems: SN Zwicky (Pierel et al. 2023) and SN H0pe (Pierel et al. 2024).
For cosmological studies, we have investigated the cosmological information expected from a sample of lensed SNe in the LSST survey (Suyu et al. 2020). We have also demonstrated the combination of SNe and gravitational lensing to provide a measurement of the expansion rate of the Universe that is less sensitive to model assumptions (Taubenberger et al. 2019; Wong et al. 2020). We have developed self-consistent lensing and dynamical model software that will be crucial to break model degeneracies for cosmological inference (Wang et al., in prep.). Utilising the first strongly lensed SN, SN Refsdal, we have demonstrated the lensed SN by a galaxy cluster accurately constrains both the expansion rate and the geometry of the Universe (Grillo et al. 2024).
Since ZTF only reaches the brighter and nearer galaxies, we have not found a suitable lensed SN in ZTF, as expected. However, we expect to discover at least several lensed SNe per year from the upcoming LSST, and the lens search methods that we have developed are readily applicable to LSST. Our project has investigated the observing strategy of LSST to maximize the number of lensed SNe (Huber et al. 2019; Lochner et al. 2022). The results of these studies have been used as input for optimising the LSST observing strategy.
The time delays between the lensed SN images are needed for our two scientific aims. We have determined follow-up observation requirements for measuring time delays (Huber et al. 2019; 2020). Accounting for microlensing, we have developed novel methods to measure time delays based on machine learning that outperform existing methods for Type Ia SNe (Huber et al. 2022; Huber & Suyu, submitted) and based on spectral evolution of Type IIP SNe (Bayer et al. 2021). In addition, our extensive simulations show that the effect of microlensing is negligible at the beginning of SNe, enabling us to constrain SN progenitors (Suyu et al. 2020). We have also developed fast ways to model the mass distribution of the lens system using machine learning (Schuldt et al. 2021; 2023ab). We have further automated our modelling procedure to enhance efficiency (Ertl et al. 2023). Both developments on the machine learning and conventional modelling approaches facilitate the analyses of lensed SNe for achieving our scientific goals, as demonstrated by the application to the two newly discovered lensed SN systems: SN Zwicky (Pierel et al. 2023) and SN H0pe (Pierel et al. 2024).
For cosmological studies, we have investigated the cosmological information expected from a sample of lensed SNe in the LSST survey (Suyu et al. 2020). We have also demonstrated the combination of SNe and gravitational lensing to provide a measurement of the expansion rate of the Universe that is less sensitive to model assumptions (Taubenberger et al. 2019; Wong et al. 2020). We have developed self-consistent lensing and dynamical model software that will be crucial to break model degeneracies for cosmological inference (Wang et al., in prep.). Utilising the first strongly lensed SN, SN Refsdal, we have demonstrated the lensed SN by a galaxy cluster accurately constrains both the expansion rate and the geometry of the Universe (Grillo et al. 2024).
We have developed new analysis tools that went beyond the state of the art, including:
- 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
- Through our new lens search methods, we have identified ~1000 new promising lens candidates, a significant fraction of the known lens candidates
- We have developed new methods to measure time delays of lensed SNe, both from light curves and from spectra. The precision in the measurement achievable with our new methods is beyond the previous state of the art.
- We have automated our lens modelling approach, enabling its efficient application to real observations of lensed SNe
- Our newly developed lensing and dynamical modelling software based on Graphical-Processing Units performs calculations an order of magnitude faster than previous software, going beyond the state of the art
- 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
- Through our new lens search methods, we have identified ~1000 new promising lens candidates, a significant fraction of the known lens candidates
- We have developed new methods to measure time delays of lensed SNe, both from light curves and from spectra. The precision in the measurement achievable with our new methods is beyond the previous state of the art.
- We have automated our lens modelling approach, enabling its efficient application to real observations of lensed SNe
- Our newly developed lensing and dynamical modelling software based on Graphical-Processing Units performs calculations an order of magnitude faster than previous software, going beyond the state of the art