Periodic Reporting for period 3 - LENSNOVA (Cosmic Fireworks Première: Unravelling Enigmas of Type Ia Supernova Progenitor and Cosmology through Strong Lensing)
Okres sprawozdawczy: 2021-06-01 do 2022-10-31
Our universe contains very bright and exotic objects. Two examples are quasars which are supermassive black holes at centers 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. Such changes of quasars and SNe are detectable well within a human’s lifetime. Quasars and SNe that are gravitationally lensed into multiple images provide a unique and 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). Typically, a galaxy lens would produce either two or four multiple images of a background source. 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. 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.
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.
To achieve our two goals of measuring the cosmic expansion rate and constraining supernova progenitors via gravitationally lensed SNe, we need to find these rare astrophysical events. The chance of lensed SNe occurring is extremely low since supernovae are inherently rare (a galaxy like our Milky Way has about two SN events per century) and gravitational lensing with two astrophysical objects aligned along the same sightline is also a rare phenomenon. However, there are current and upcoming astronomical surveys monitoring large areas of the night sky and imaging billions of galaxies, which would allow the detection of lensed SNe. Our strategy is to find lens systems, and then wait for one of the background sources to explode. We have thus carried out a systematic search of gravitationally lensed galaxies in the Pan-STARRS survey that covers 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 tool, we have found more than 300 new lens candidates in the Pan-STARRS survey that could serve as potential hosts of lensed SNe (Cañameras, Schuldt, Suyu et al. 2020). We are now monitoring these lens candidates with the Zwicky Transient Facility (ZTF), a survey that maps out the northern sky every 2-3 days, in order to detect a SN occurring in one of the lens systems.
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).
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).
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
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.
- 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.