Improved detection of transient events in wide area sky survey using convolutional neural networks

Liu, Jing Jing; Boongoen, Tossapon; Iam-On, Natthakan

doi:10.1016/j.dim.2023.100035

Cited by 1 publication

(1 citation statement)

References 44 publications

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“…Convolutional neural networks (CNNs; Fukushima 1980) are often used for image classification problems and have been used extensively in astronomy for object classification, including the real-bogus classification for transients (e.g., Cabrera-Vives et al 2017;Gieseke et al 2017;Turpin et al 2020;Goode et al 2022;Acero-Cuellar et al 2023;Chen et al 2023;Liu et al 2023). Using TensorFlow (Abadi et al 2016a(Abadi et al , 2016b, we set up a basic CNN consisting of multiple 2D convolution layers, maxpooling, dropouts, and Dense layers (all with the relu activation), and finally ending with a Dense layer with a sigmoid activation.…”

Section: Machine Learningmentioning

confidence: 99%

SAGUARO: Time-domain Infrastructure for the Fourth Gravitational-wave Observing Run and Beyond

Hosseinzadeh,

Paterson,

Rastinejad

et al. 2024

ApJ

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We present upgraded infrastructure for Searches After Gravitational waves Using ARizona Observatories (SAGUARO) during LIGO, Virgo, and KAGRA’s fourth gravitational-wave (GW) observing run (O4). These upgrades implement many of the lessons we learned after a comprehensive analysis of potential electromagnetic counterparts to the GWs discovered during the previous observing run. We have developed a new web-based target and observation manager (TOM) that allows us to coordinate sky surveys, vet potential counterparts, and trigger follow-up observations from one centralized portal. The TOM includes software that aggregates all publicly available information on the light curves and possible host galaxies of targets, allowing us to rule out potential contaminants like active galactic nuclei, variable stars, solar system objects, and preexisting supernovae, as well as to assess the viability of any plausible counterparts. We have also upgraded our image-subtraction pipeline by assembling deeper reference images and training a new neural-network-based real–bogus classifier. These infrastructure upgrades will aid coordination by enabling the prompt reporting of observations, discoveries, and analysis to the GW follow-up community, and put SAGUARO in an advantageous position to discover kilonovae in the remainder of O4 and beyond. Many elements of our open-source software stack have broad utility beyond multimessenger astronomy, and will be particularly relevant in the “big data” era of transient discoveries by the Vera C. Rubin Observatory.

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