HOLISMOKES -- XII. Time-delay Measurements of Strongly Lensed Type Ia Supernovae using a Long Short-Term Memory Network
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909422699151360 |
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| author | Huber, S. Suyu, S. H. |
| author_facet | Huber, S. Suyu, S. H. |
| contents | Strongly lensed Type Ia supernovae (LSNe Ia) are a promising probe to measure the Hubble constant ($H_0$) directly. To use LSNe Ia for cosmography, a time-delay measurement between the multiple images, a lens-mass model, and a mass reconstruction along the line of sight are required. In this work, we present the machine learning network LSTM-FCNN which is a combination of a Long Short-Term Memory Network (LSTM) and a fully-connected neural network (FCNN). The LSTM-FCNN is designed to measure time delays on a sample of LSNe Ia spanning a broad range of properties, which we expect to find with the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) and for which follow-up observations are planned. With follow-up observations in $i$ band (cadence of one to three days with a single-epoch $5σ$ depth of 24.5 mag), we reach a bias-free delay measurement with a precision around 0.7 days over a large sample of LSNe Ia. The LSTM-FCNN is far more general than previous machine learning approaches such as the Random Forest (RF), where a RF has to be trained for each observational pattern separately, and yet the LSTM-FCNN outperforms the RF by a factor of roughly three. Therefore, the LSTM-FCNN is a very promising approach to achieve robust time delays in LSNe Ia, which is important for a precise and accurate constraint on $H_0$ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08029 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HOLISMOKES -- XII. Time-delay Measurements of Strongly Lensed Type Ia Supernovae using a Long Short-Term Memory Network Huber, S. Suyu, S. H. Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Strongly lensed Type Ia supernovae (LSNe Ia) are a promising probe to measure the Hubble constant ($H_0$) directly. To use LSNe Ia for cosmography, a time-delay measurement between the multiple images, a lens-mass model, and a mass reconstruction along the line of sight are required. In this work, we present the machine learning network LSTM-FCNN which is a combination of a Long Short-Term Memory Network (LSTM) and a fully-connected neural network (FCNN). The LSTM-FCNN is designed to measure time delays on a sample of LSNe Ia spanning a broad range of properties, which we expect to find with the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) and for which follow-up observations are planned. With follow-up observations in $i$ band (cadence of one to three days with a single-epoch $5σ$ depth of 24.5 mag), we reach a bias-free delay measurement with a precision around 0.7 days over a large sample of LSNe Ia. The LSTM-FCNN is far more general than previous machine learning approaches such as the Random Forest (RF), where a RF has to be trained for each observational pattern separately, and yet the LSTM-FCNN outperforms the RF by a factor of roughly three. Therefore, the LSTM-FCNN is a very promising approach to achieve robust time delays in LSNe Ia, which is important for a precise and accurate constraint on $H_0$ |
| title | HOLISMOKES -- XII. Time-delay Measurements of Strongly Lensed Type Ia Supernovae using a Long Short-Term Memory Network |
| topic | Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2403.08029 |