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

S. Huber, S. H. Suyu, D. Ghoshdastidar, S. Taubenberger, V. Bonvin, J. H.H. Chan, M. Kromer, U. M. Noebauer, S. A. Sim, L. Leal-Taixé

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11 Citations (Scopus)
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Abstract

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.

Original languageEnglish
Article numberA157
JournalAstronomy and Astrophysics
Volume658
DOIs
Publication statusPublished - 15 Feb 2022

Bibliographical note

Funding Information:
Acknowledgements. We thank F. Courbin, S. Schuldt and R. Cañameras for useful discussions. We also would like to thank the anonymous referee for helpful feedback, which strengthened this work. SH and SHS thank the Max Planck Society for support through the Max Planck Research Group for SHS. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771776). This research is supported in part by the Excellence Cluster ORIGINS which is funded by the Deutsche Forschungsgemein-schaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2094 – 390783311. DG acknowledges support from the Baden-Württemberg Foundation through the Baden-Württemberg Eliteprogramm for Postdocs. UMN has been supported by the Transregional Collaborative Research Center TRR33 “The Dark Universe” of the Deutsche Forschungsgemeinschaft. JHHC acknowledges support from the Swiss National Science Foundation and through European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (COSMICLENS: grant agreement No 787866). MK acknowledges support from the Klaus Tschira Foundation.

Publisher Copyright:
© S. Huber et al. 2022.

Keywords

  • Distance scale
  • Gravitational lensing: micro
  • Gravitational lensing: strong
  • Supernovae: individual: Type Ia

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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