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Abstract The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies (O(1s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context.However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial.Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes. 

ABSTRACT The identification of extragalactic fast optical transients (eFOTs) as potential multimessenger sources is one of the main challenges in time-domain astronomy. However, recent developments have allowed for probes of rapidly evolving transients. With the increasing number of alert streams from optical time-domain surveys, the next paradigm is building technologies to rapidly identify the most interesting transients for follow-up. One effort to make this possible is the fitting of objects to a variety of eFOT light curve models such as kilonovae and γ-ray burst (GRB) afterglows. In this work, we describe a new framework designed to efficiently fit transients to light curve models and flag them for further follow-up. We describe the pipeline’s workflow and a handful of performance metrics, including the nominal sampling time for each model. We highlight as examples ZTF20abwysqy, the shortest long gamma-ray burst discovered to date, and ZTF21abotose, a core-collapse supernova initially identified as a potential kilonova candidate. 

Over the past century, supernova (SN) searches have detected multiple supernovae (SNe) in hundreds of individual galaxies. So-called SN siblings discovered in the same galaxy present an opportunity to constrain the dependence of the properties of SNe on those of their host galaxies. To investigate whether there is a connection between sibling SNe in galaxies that have hosted multiple SNe and the properties of galaxies, we have acquired integrated optical spectroscopy of 59 galaxies with multiple core-collapse SNe. Perhaps surprisingly, a strong majority of host galaxy spectra fall within the composite region of the Baldwin–Phillips–Terlevich (BPT) diagram. We find a statistically significant difference (Kolmogorov–Smirnov test p-value = 0.044) between the distributions of the [Nii]λ6583/Hα of galaxies that have hosted a majority of SNe Ibc and those that have hosted a majority of Type II supernovae (SNe II), where the majority of Type Ibc supernovae (SNe Ibc) galaxies have, on average, higher ratios. The difference between the distributions of [Nii]λ6583/Hα may arise from either increased contribution from active galactic nuclei or low-ionization nuclear emission-line regions in SNe Ibc host galaxies, greater metallicity for SNe Ibc host galaxies, or both. When comparing the inferred oxygen abundance and the ionization parameter for the galaxies in the star-forming region on the BPT diagram, we find statistically significant differences between the distributions for SNe Ibc hosts and SNe II hosts (p= 0.008 and p= 0.001, respectively), as well as SNe Ib hosts and SNe II hosts (p = 0.030 and p= 0.006, respectively). We also compare the Hα equivalent width distributions, also integrated across the galaxies, and find no significant difference. 

Multimessenger searches for binary neutron star (BNS) and neutron star-black hole (NSBH) mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to gravitational wave (GW)s has resumed with ALIGO’s, AdVirgo’s and KAGRA’s fourth observing run (O4). To support this effort, public semiautomated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a mock data challenge (MDC) in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-toend performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5 s) and show consistency and accuracy of released data products using the MDC. We report the expected median latency for triggers from early warning searches (−3.1 s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LIGO-Virgo-KAGRA (LVK) low-latency alert infrastructure and data products using theMDCand serves as a useful reference for the interpretation of O4 detections. 

An advanced LIGO and Virgo’s third observing run brought another binary neutron star merger (BNS) and the first neutron-star black hole mergers. While no confirmed kilonovae were identified in conjunction with any of these events, continued improvements of analyses surrounding GW170817 allow us to project constraints on the Hubble Constant (H₀), the Galactic enrichment fromr-process nucleosynthesis, and ultra-dense matter possible from forthcoming events. Here, we describe the expected constraints based on the latest expected event rates from the international gravitational-wave network and analyses of GW170817. We show the expected detection rate of gravitational waves and their counterparts, as well as how sensitive potential constraints are to the observed numbers of counterparts. We intend this analysis as support for the community when creating scientifically driven electromagnetic follow-up proposals. During the next observing run O4, we predict an annual detection rate of electromagnetic counterparts from BNS of ${0.43}_{-0.26}^{+0.58}$( ${1.97}_{-1.2}^{+2.68}$) for the Zwicky Transient Facility (Rubin Observatory). 

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivialfraction of global greenhouse gas (GHG) budget. To date, estimatingN2O fluxes from cropland remains a challenging task because the relatedmicrobial processes (e.g., nitrification and denitrification) are controlledby complex interactions among climate, soil, plant and human activities.Existing approaches such as process-based (PB) models have well-knownlimitations due to insufficient representations of the processes oruncertainties of model parameters, and due to leverage recent advances inmachine learning (ML) a new method is needed to unlock the “black box” toovercome its limitations such as low interpretability, out-of-sample failureand massive data demand. In this study, we developed a first-of-its-kindknowledge-guided machine learning model for agroecosystems (KGML-ag) byincorporating biogeophysical and chemical domain knowledge from an advanced PBmodel, ecosys, and tested it by comparing simulating daily N2O fluxes withreal observed data from mesocosm experiments. The gated recurrent unit (GRU)was used as the basis to build the model structure. To optimize the modelperformance, we have investigated a range of ideas, including (1) usinginitial values of intermediate variables (IMVs) instead of time series asmodel input to reduce data demand; (2) building hierarchical structures toexplicitly estimate IMVs for further N2O prediction; (3) using multi-tasklearning to balance the simultaneous training on multiple variables; and (4)pre-training with millions of synthetic data generated from ecosys and fine-tuningwith mesocosm observations. Six other pure ML models were developed usingthe same mesocosm data to serve as the benchmark for the KGML-ag model.Results show that KGML-ag did an excellent job in reproducing the mesocosmN2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1from cross validation). Importantly, KGML-ag always outperformsthe PB model and ML models in predicting N2O fluxes, especially forcomplex temporal dynamics and emission peaks. Besides, KGML-ag goes beyondthe pure ML models by providing more interpretable predictions as well aspinpointing desired new knowledge and data to further empower the currentKGML-ag. We believe the KGML-ag development in this study will stimulate anew body of research on interpretable ML for biogeochemistry and otherrelated geoscience processes.