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

Abstract

The Hubble constant ($H_0$) is one of the fundamental parameters in cosmology, but there is a heated debate around the $>$4$\sigma$ 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 $H_0$, 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 5$\sigma$ 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 $\sim$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 $\sim$1.0 day. We have publicly released the microlensed spectra and light curves used in this work.

25 pages, 28 figures; accepted for publication in A&A

References in corpus (24)

• Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics Beyond LambdaCDM
• Cosmic Distances Calibrated to 1% Precision with Gaia EDR3 Parallaxes and Hubble Space Telescope Photometry of 75 Milky Way Cepheids Confirm Tension with LambdaCDM
• Calibration of the Tip of the Red Giant Branch (TRGB)
• TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles
• The Megamaser Cosmology Project. XIII. Combined Hubble constant constraints
• Time Dependent Monte Carlo Radiative Transfer Calculations For 3-Dimensional Supernova Spectra, Lightcurves, and Polarization
• The Halos of Satellite Galaxies: the Companion of the Massive Elliptical Lens SL2S J08544-0121
• Does Hubble Tension Signal a Breakdown in FLRW Cosmology?
• The Hubble Constant from Infrared Surface Brightness Fluctuation Distances
• A new measurement of the Hubble constant using Type Ia supernovae calibrated with surface brightness fluctuations
• COSMOGRAIL XVI: Time delays for the quadruply imaged quasar DES J0408-5354 with high-cadence photometric monitoring
• Detection of the Gravitational Lens Magnifying a Type Ia Supernova
• Over-constrained Gravitational Lens Models and the Hubble Constant
• Gravitational Microlensing
• How to Find Gravitationally Lensed Type Ia Supernovae
• HOLISMOKES -- I. Highly Optimised Lensing Investigations of Supernovae, Microlensing Objects, and Kinematics of Ellipticals and Spirals
• Turning Gravitationally Lensed Supernovae into Cosmological Probes
• Interpreting the strongly lensed supernova iPTF16geu: time delay predictions, microlensing, and lensing rates
• Improved time-delay lens modelling and $H_0$ inference with transient sources
• HOLISMOKES -- III. Achromatic Phase of Strongly Lensed Type Ia Supernovae
• Measuring accretion disk sizes of lensed quasars with microlensing time delay in multi-band light curves
• GERLUMPH Data Release 2: 2.5 billion simulated microlensing light curves
• Adventures in the microlensing cloud: large datasets, eResearch tools, and GPUs
• A Gravitationally Lensed Supernova with an Observable Two-Decade Time Delay

Cited by in corpus (16)

• Strong lensing time-delay cosmography in the 2020s
• Strongly Lensed Transient Sources: A Review
• HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
• Strong gravitational lensing and microlensing of supernovae
• JWST Photometric Time-Delay and Magnification Measurements for the Triply-Imaged Type Ia "Supernova H0pe" at z = 1.78
• GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
• The Impact of Observing Strategy on Cosmological Constraints with LSST
• LensWatch: I. Resolved HST Observations and Constraints on the Strongly-Lensed Type Ia Supernova 2022qmx ("SN Zwicky")
• Strongly lensed type Ia supernovae as a precise late-universe probe of measuring the Hubble constant and cosmic curvature
• HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
• HOLISMOKES -- XII. Time-delay Measurements of Strongly Lensed Type Ia Supernovae using a Long Short-Term Memory Network
• Detecting unresolved lensed SNe Ia in LSST using blended light curves
• Forecast of strongly lensed supernovae rates in the China Space Station Telescope surveys
• Deep Learning Unresolved Lensed Lightcurves
• HOLISMOKES -- XIV. Time-delay and differential dust extinction determination with lensed type II supernova color curves
• The Case for Space: Estimating Precise Time Delays from Ground- and Space-Based Observations of Lensed Supernovae with Glimpse