Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

HOLISMOKES - VII. Time-delay measurement of strongly lensed Type Ia supernovae using machine learning


Huber,  S.
Technique, Max Planck Institute for Plasma Physics, Max Planck Society;


Suyu,  S. H.
Physical Cosmology, MPI for Astrophysics, Max Planck Society;


Taubenberger,  Stefan
Stellar Astrophysics, MPI for Astrophysics, Max Planck Society;


Noebauer,  U. M.
Stellar Astrophysics, MPI for Astrophysics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Huber, S., Suyu, S. H., Ghoshdastidar, D., Taubenberger, S., Bonvin, V., Chan, J. H. H., et al. (2022). HOLISMOKES - VII. Time-delay measurement of strongly lensed Type Ia supernovae using machine learning. Astronomy and Astrophysics, 658: A157. doi:10.1051/0004-6361/202141956.

Cite as: https://hdl.handle.net/21.11116/0000-000A-2E3C-1
The Hubble constant (H0) is one of the fundamental parameters in cosmology, but there is a heated debate around the > 4σ tension between the local Cepheid distance ladder and the early Universe measurements. Strongly lensed Type Ia supernovae (LSNe Ia) are an independent and direct way to measure H0, where a time-delay measurement between the multiple supernova (SN) images is required. In this work, we present two machine learning approaches for measuring time delays in LSNe Ia, namely, a fully connected neural network (FCNN) and a random forest (RF). For the training of the FCNN and the RF, we simulate mock LSNe Ia from theoretical SN Ia models that include observational noise and microlensing. We test the generalizability of the machine learning models by using a final test set based on empirical LSN Ia light curves not used in the training process, and we find that only the RF provides a low enough bias to achieve precision cosmology; as such, RF is therefore preferred over our FCNN approach for applications to real systems. For the RF with single-band photometry in the i band, we obtain an accuracy better than 1% in all investigated cases for time delays longer than 15 days, assuming follow-up observations with a 5σ point-source depth of 24.7, a two day cadence with a few random gaps, and a detection of the LSNe Ia 8 to 10 days before peak in the observer frame. In terms of precision, we can achieve an approximately 1.5-day uncertainty for a typical source redshift of ∼0.8 on the i band under the same assumptions. To improve the measurement, we find that using three bands, where we train a RF for each band separately and combine them afterward, helps to reduce the uncertainty to ∼1.0 day. The dominant source of uncertainty is the observational noise, and therefore the depth is an especially important factor when follow-up observations are triggered. We have publicly released the microlensed spectra and light curves used in this work.