Author Correction: Uncovering a population of gravitational lens galaxies with magnified standard candle SN Zwicky

Goobar, A.; Johansson, Joel; Schulze, S.; Arendse, Nikki; Carracedo, Ana Sagués; Dhawan, Suhail; Mörtsell, Edvard; Fremling, C.; Yan, Lin; Perley, D.; Sollerman, J.; Joseph, R.; Hinds, K-Ryan; Meynardie, William; Andreoni, Igor; Bellm, Eric C.; Bloom, J.; Collett, Thomas E.; Drake, A. J.; Graham, M. J.; Kasliwal, M. M.; Kulkarni, S. R.; Lemon, C.; Miller, A. A.; Neill, James D.; Nordin, J.; Pierel, Justin; Richard, Johan; Riddle, Reed; Rigault, M.; Rusholme, B.; Sharma, Y.; Stein, Robert J.; Stewart, Gabrielle; Townsend, Alice; Vinkó, J.; Wheeler, Jody; Avery, Wold,

doi:10.1038/s41550-023-02034-5

Nat Astron

2023

DOI: 10.1038/s41550-023-02034-5

|View full text |Cite

Author Correction: Uncovering a population of gravitational lens galaxies with magnified standard candle SN Zwicky

A. Goobar

Joel Johansson

S. Schulze

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

gausSN: Bayesian time-delay estimation for strongly lensed supernovae

Hayes,

Thorp,

Mandel

et al. 2024

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). GausSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, GausSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that GausSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6 % of time-delay estimates from Roman and 52.9 % from Rubin-LSST have fractional errors of less than 5 %. We then apply GausSN to SN Refsdal and find the time delay for the fifth image is consistent with the original analysis, regardless of microlensing treatment. Therefore, GausSN maintains the level of precision and accuracy achieved by existing time-delay extraction methods with fewer assumptions about the underlying shape of the light curve than template-based approaches, while incorporating microlensing into the statistical error budget. GausSN is scalable for time-delay cosmography analyses given current projections of glSNe discovery rates from Rubin-LSST and Roman.

show abstract

gausSN: Bayesian time-delay estimation for strongly lensed supernovae

Hayes,

Thorp,

Mandel

et al. 2024

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

show abstract

Deep drilling in the time domain with DECam – II: characterizing the light curves of candidates in the extragalactic fields

Graham,

Rollins,

Knop

et al. 2024

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

In this second paper on the DECam deep drilling field (DDF) program we release 2,020 optical gri-band light curves for transients and variables in the extragalactic COSMOS and ELAIS fields based on time series observations with a 3-day cadence from semester 2021A through 2023A. In order to demonstrate the wide variety of time domain events detected by the program and encourage others to use the data set, we characterize the sample by presenting a brief analysis of the light curve parameters such as time span, amplitude, and peak brightness. We also present preliminary light curve categorizations, and identify potential stellar variables, active galactic nuclei, tidal disruption events, supernovae (such as Type Ia, Type IIP, superluminous, and gravitationally lensed supernovae), and fast transients. Where relevant, the number of identified transients is compared to the predictions of the original proposal. We also discuss the challenges of analyzing DDF data in the context of the upcoming Vera C. Rubin Observatory and its Legacy Survey of Space and Time, which will include DDFs. Images from the DECam DDF program are available without proprietary period and the light curves presented in this work are publicly available for analysis.

show abstract

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Rehemtulla,

Miller,

Jegou Du Laz

et al. 2024

ApJ

View full text Add to dashboard Cite

The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright (m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot, a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski, ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Author Correction: Uncovering a population of gravitational lens galaxies with magnified standard candle SN Zwicky

Cited by 3 publications

References 0 publications

gausSN: Bayesian time-delay estimation for strongly lensed supernovae

gausSN: Bayesian time-delay estimation for strongly lensed supernovae

Deep drilling in the time domain with DECam – II: characterizing the light curves of candidates in the extragalactic fields

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Contact Info

Product

Resources

About