El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification

Abstract

As gravitational-wave interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with gravitational waves does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have developed a novel tool based on a temporal convolutional neural network architecture, trained on sparse early-time photometry and contextual information for Electromagnetic Counterpart Identification (El-CID). The overarching goal for El-CID is to slice through list of new transient candidates that are consistent with the GW sky localization, and determine which sources are consistent with kilonovae, allowing limited target-of-opportunity resources to be used judiciously. In addition to verifying the performance of our algorithm on an extensive testing sample, we validate it on AT2017gfo -the only EM counterpart of a binary neutron star merger discovered to date -and AT2019npv -a supernova that was initially suspected as a counterpart of the gravitationalwave event, GW190814, but was later ruled out after further analysis.2 There have been reports of potential kilonovae earlier from GRBs, see for example Tanvir et al. (2013)

SNAD Transient Miner: Finding Missed Transient Events in ZTF DR4 using k-D trees

“…We create realistic ZTF simulations using SNANA (Kessler et al, 2009), a catalog-level light curve simulator which includes effects due to telescope characteristics and observational conditions. We adopt ZTF data release 3 cadence and magnitude error distribution (see Chatterjee et al 2021 for details). Among the template models originally developed for the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC, Kessler et al, 2019;Hložek et al, 2020), we used only type SN Ia, SN II, SLSN-I, and TDE.…”

Multimodal Analysis of Gravitational Wave Signals and Gamma-Ray Bursts from Binary Neutron Star Mergers

“…The synergy between the information that only GWs can provide and the concomitant observations through other detectors of the EM and neutrino counterparts can strongly accelerate our knowledge of the Universe. It is clear that multi-messenger astronomy discloses the need for new paradigms for data analysis and introduces new challenges for real-time analysis, and there are many efforts ongoing to face them that involve the use of machine learning techniques (see, e.g., [3][4][5][6][7][8][9][10][11][12]). Multimodal machine learning MMML analysis is efficiently applied in many fields of data analysis for the more inclusive interpretation of events where several modalities are concurrent, such as in a video with audio; images with captions; or images, text, and sound [13].…”

Multimodal analysis of Gravitational Wave signals and Gamma-Ray Bursts from binary neutron star mergers

“…The synergy between the information that only GWs can provide and the concomitant observations through other detectors of the EM and neutrino counterparts can strongly accelerate our knowledge of the Universe. It is clear that multi-messenger astronomy discloses the need of new paradigms for data analysis and introduces new challenges for real-time analysis, and there are many efforts ongoing to face with them, which involve the use of machine learning techniques (see, e.g., [3][4][5][6][7][8][9][10][11][12]). Multimodal machine learning (MMML) analysis is efficiently applied in many fields of data analysis for the more inclusive interpretation of events where several modalities are concurrent, such as in a video with audio, or images with caption, or images, text and sound [13].…”