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Register a new userAI-driven spatio-temporal engine for finding gravitationally lensed supernovae
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Added by
Doogesh Kodi Ramanah
Publication date
2021
fields
Physics
and research's language is
English
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We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in the discovery of such exotic transients in wide-field surveys. Our spatio-temporal engine is designed using recurrent convolutional layers, while drawing from recent advances in variational inference to quantify approximate Bayesian uncertainties via a confidence score. Using simulated Young Supernova Experiment (YSE) images as a showcase, we find that the use of time-series images yields a substantial gain of nearly 20 per cent in classification accuracy over single-epoch observations, with a preliminary application to mock observations from the Legacy Survey of Space and Time (LSST) yielding around 99 per cent accuracy. Our innovative deep learning machinery adds an extra dimension in the search for gravitationally lensed supernovae from current and future astrophysical transient surveys.
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Type Ia supernovae (SNe Ia) that are multiply imaged by gravitational lensing can extend the SN Ia Hubble diagram to very high redshifts $(zgtrsim 2)$, probe potential SN Ia evolution, and deliver high-precision constraints on $H_0$, $w$, and $Omega_m$ via time delays. However, only one, iPTF16geu, has been found to date, and many more are needed to achieve these goals. To increase the multiply imaged SN Ia discovery rate, we present a simple algorithm for identifying gravitationally lensed SN Ia candidates in cadenced, wide-field optical imaging surveys. The technique is to look for supernovae that appear to be hosted by elliptical galaxies, but that have absolute magnitudes implied by the apparent hosts photometric redshifts that are far brighter than the absolute magnitudes of normal SNe Ia (the brightest type of supernovae found in elliptical galaxies). Importantly, this purely photometric method does not require the ability to resolve the lensed images for discovery. AGN, the primary sources of contamination that affect the method, can be controlled using catalog cross-matches and color cuts. Highly magnified core-collapse supernovae will also be discovered as a byproduct of the method. Using a Monte Carlo simulation, we forecast that LSST can discover up to 500 multiply imaged SNe Ia using this technique in a 10-year $z$-band search, more than an order of magnitude improvement over previous estimates (Oguri & Marshall 2010). We also predict that ZTF should find up to 10 multiply imaged SNe Ia using this technique in a 3-year $R$-band search---despite the fact that this survey will not resolve a single system.
Recently, there have been two landmark discoveries of gravitationally lensed supernovae: the first multiply-imaged SN, Refsdal, and the first Type Ia SN resolved into multiple images, SN iPTF16geu. Fitting the multiple light curves of such objects can deliver measurements of the lensing time delays, which are the difference in arrival times for the separate images. These measurements provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Over the next decade, accurate time delay measurements will be needed for the tens to hundreds of lensed SNe to be found by wide-field time-domain surveys such as LSST and WFIRST. We have developed an open source software package for simulations and time delay measurements of multiply-imaged SNe, including an improved characterization of the uncertainty caused by microlensing. We describe simulations using the package that suggest a before-peak detection of the leading image enables a more accurate and precise time delay measurement (by ~1 and ~2 days, respectively), when compared to an after-peak detection. We also conclude that fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data, but that employing a Gaussian Process Regression (GPR) technique is sufficient for determining the uncertainty due to microlensing.
We report observations of three gravitationally lensed supernovae (SNe) in the Cluster Lensing And Supernova survey with Hubble (CLASH) Multi-Cycle Treasury program. These objects, SN CLO12Car (z = 1.28), SN CLN12Did (z = 0.85), and SN CLA11Tib (z = 1.14), are located behind three different clusters, MACSJ1720.2+3536 (z = 0.391), RXJ1532.9+3021 (z = 0.345), and Abell 383 (z = 0.187), respectively. Each SN was detected in Hubble Space Telescope (HST) optical and infrared images. Based on photometric classification, we find that SNe CLO12Car and CLN12Did are likely to be Type Ia supernovae (SNe Ia), while the classification of SN CLA11Tib is inconclusive. Using multi-color light-curve fits to determine a standardized SN Ia luminosity distance, we infer that SN CLO12Car was approximately 1.0 +/- 0.2 mag brighter than field SNe Ia at a similar redshift and ascribe this to gravitational lens magnification. Similarly, SN CLN12Did is approximately 0.2 +/- 0.2 mag brighter than field SNe Ia. We derive independent estimates of the predicted magnification from CLASH strong+weak lensing maps of the clusters: 0.83 +/- 0.16 mag for SN CLO12Car, 0.28 +/- 0.08 mag for SN CLN12Did, and 0.43 +/- 0.11 mag for SN CLA11Tib. The two SNe Ia provide a new test of the cluster lens model predictions: we find that the magnifications based on the SN Ia brightness and those predicted by the lens maps are consistent. Our results herald the promise of future observations of samples of cluster-lensed SNe Ia (from the ground or space) to help illuminate the dark-matter distribution in clusters of galaxies, through the direct determination of absolute magnifications.
Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, but are rare and difficult to find. The number of currently known lenses is on the order of 1,000. We wish to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. We selected a sample of $sim300,000$ galaxies with photometric redshifts in the range $0.2 < z_{phot} < 1.2$ and photometrically inferred stellar masses $log{M_*} > 11.2$. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform, as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses, for training purposes. Nearly 6,000 citizen volunteers participated in the experiment. In parallel, we used YattaLens, an automated lens finding algorithm, to look for lenses in the same sample of galaxies. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising $sim1,500$ candidates which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. Including lenses found by YattaLens or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses, 129 probable lenses and 581 possible lenses. YattaLens found half the number of lenses discovered via crowdsourcing. Crowdsourcing is able to produce samples of lens candidates with high completeness and purity, compared to currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms and/or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s.
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