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Detecting unresolved lensed SNe Ia in LSST using blended light curves
Instrumentation and Methods for Astrophysics · Submitted 23 Apr 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$).
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The abstract, sentence by sentence
Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the {\it Rubin} Observatory. In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$). Even when the light curve is well observed in only a single band among $r$, $i$, and $z$, detection is still possible with false-positive rates ranging from $\sim 4$ to $7\%$ depending on the band. Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to $\sim20$ days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. However, the abundance of such systems among those unresolved in LSST poses a significant challenge. This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. | background 1.00 | 0.05 | 0.03 | asserted 0.81 |
| s1 | Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. | background 0.98 | 0.08 | 0.04 | asserted 0.64 |
| s2 | We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the {\it Rubin} Observatory. | result 0.77 | 0.62 | 0.09 | asserted 0.49 |
| s3 | In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. | contribution 0.63 | 0.68 | 0.06 | asserted 0.64 |
| s4 | We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$). | result 1.00 | 0.97 | 0.43 | asserted 1.00 |
| s5 | Even when the light curve is well observed in only a single band among $r$, $i$, and $z$, detection is still possible with false-positive rates ranging from $\sim 4$ to $7\%$ depending on the band. | result 1.00 | 0.93 | 0.07 | asserted 0.78 |
| s6 | Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to $\sim20$ days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. | result 0.89 | 0.94 | 0.16 | asserted 1.00 |
| s7 | Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. | result 0.94 | 0.92 | 0.11 | asserted 0.97 |
| s8 | However, the abundance of such systems among those unresolved in LSST poses a significant challenge. | limitation 0.96 | 0.34 | 0.11 | asserted 0.99 |
| s9 | This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys. | future work 0.64 | 0.12 | 0.12 | hedged possibility 1.00 |
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The paper
Original abstract
Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the {\it Rubin} Observatory. In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$). Even when the light curve is well observed in only a single band among $r$, $i$, and $z$, detection is still possible with false-positive rates ranging from $\sim 4$ to $7\%$ depending on the band. Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to $\sim20$ days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. However, the abundance of such systems among those unresolved in LSST poses a significant challenge. This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys.
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References
How the literature uses it
1 arXiv paper cite this in 2 sentences
Papers citing it · 1 sentence without a confident label
Cites it as background 1 paper
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Several strategies have been developed to identify LSNe, including direct detection of multiple images [ref] , magnification-based selection using standardized Type Ia luminosities [ref] , color–magnitude cuts [ref] , and light-curve-based methods for unresolved systems [ref] .
HOLISMOKES XXI: Detecting strongly lensed type Ia supernovae from time series of multi-band LSST-like imaging data -- Part II · Introduction · 0.98
What it cites · 3 sentences without a confident label
Uses the method or tool of 1 paper
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Following [ref] , we simulate the uncertainties on the magnitudes as σ_tot^2=σ_sys^2+σ_rand^2 ,where we set the systematic noise to $σ_sys=0.005$ (the upper limit) for all filters, the random noise is given by σ_rand^2=(0.04-γ)x + γx^2 , where x=10^m-m_5 ,and the filter-dependent quantity $γ= 0.039$ for $r$ and $i$ bands and $0.040$ for $z$ and $y$ bands; see [ref] for details.
LSST Science Book, Version 2.0 · Uncertainties · 0.90
Cites as background 3 papers
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LSST, which is deeper than the ongoing Zwicky Transient Facility (ZTF) survey [ref] , is expected to observe approximately 20,000 square degrees over 10 years [ref] , thereby notably increasing the potential for discovering new unresolved LSNe.
LSST Science Book, Version 2.0 · Introduction · 0.89
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LSST, which is deeper than the ongoing Zwicky Transient Facility (ZTF) survey [ref] , is expected to observe approximately 20,000 square degrees over 10 years [ref] , thereby notably increasing the potential for discovering new unresolved LSNe.
Science-Driven Optimization of the LSST Observing Strategy · Introduction · 0.89
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LSST, which is deeper than the ongoing Zwicky Transient Facility (ZTF) survey [ref] , is expected to observe approximately 20,000 square degrees over 10 years [ref] , thereby notably increasing the potential for discovering new unresolved LSNe.
The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document · Introduction · 0.89
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