Identifying the hosts of binary black hole and neutron star-black hole mergers with next-generation gravitational-wave detectors

The LIGO-Virgo-KAGRA Collaboration has detected over one hundred compact binary mergers in gravitational waves, but the formation history of these binaries remains an open question. Finding the host galaxies of these mergers will provide critical information that reveals how these binaries were formed. However, without an electromagnetic counterpart, localizing gravitational wave events to their hosts is challenging with the current generation of gravitational wave detectors. Next-generation detectors will localize some compact binary mergers to a small volume that allows for direct association with their hosts. To demonstrate the promise these detectors hold, we simulate a population of binary black hole and neutron star-black hole mergers using a next-generation gravitational wave network comprised of Cosmic Explorer and Einstein Telescope. We find that ~4% of binary black hole events within a redshift of 0.5 and ~3% of neutron star-black hole events within a redshift of 0.3 will be localized to a volume smaller than 100 Mpc^3, the volume in which we expect only one likely host galaxy. With the astrophysical merger rate estimated from the LIGO-Virgo-KAGRA Collaboration's third observing run, we expect to precisely localize one binary black hole event every eight days and one neutron star-black hole event every 1.5 months. With three years of gravitational wave observations (O(100) binary black hole mergers with host associations), we will be able to distinguish whether binary black hole host galaxies trace stellar mass or star formation rate, constraining the delay time distribution and shedding light on the formation channels of binary black holes.Comment: 10 pages, 2 figures, comments welcom

A Coincidence Null Test for Poisson-Distributed Events

When transient events are observed with multiple sensors, it is often necessary to establish the significance of coincident events. We derive a universal null test for an arbitrary number of sensors motivated by the archetypal detection problem for independent Poisson-distributed events in gravitational-wave detectors such as LIGO and Virgo. In these detectors, transient events may be witnessed by myriad channels that record interferometric signals and the surrounding physical environment. We apply our null test to a broad set of simulated gravitational-wave events as well as to a real gravitational-wave detection to determine which auxiliary channels do and do not witness real gravitational waves, and therefore which are safe to use when constructing vetoes. We also describe how our approach can be used to study detector artifacts and their origin, as well as to quantify the statistical independence of candidate GW signals from noise artifacts observed in auxiliary channels.Comment: 14 pages, 7 Figure

Searching for Gravitational-Wave Counterparts using the Transiting Exoplanet Survey Satellite

In 2017, the LIGO and Virgo gravitational wave (GW) detectors, in conjunction with electromagnetic (EM) astronomers, observed the first GW multi-messenger astrophysical event, the binary neutron star (BNS) merger GW170817. This marked the beginning of a new era in multi-messenger astrophysics. To discover further GW multi-messenger events, we explore the synergies between the Transiting Exoplanet Survey Satellite (TESS) and GW observations triggered by the LIGO-Virgo-KAGRA Collaboration (LVK) detector network. TESS's extremely wide field of view of ~2300 deg^2 means that it could overlap with large swaths of GW localizations, which can often span hundreds of deg^2 or more. In this work, we use a recently developed transient detection pipeline to search TESS data collected during the LVK's third observing run, O3, for any EM counterparts. We find no obvious counterparts brighter than about 17th magnitude in the TESS bandpass. Additionally, we present end-to-end simulations of BNS mergers, including their detection in GWs and simulations of light curves, to identify TESS's kilonova discovery potential for the LVK's next observing run (O4). In the most optimistic case, TESS will observe up to one GW-found BNS merger counterpart per year. However, TESS may also find up to five kilonovae which did not trigger the LVK network, emphasizing that EM-triggered GW searches may play a key role in future kilonova detections. We also discuss how TESS can help place limits on EM emission from binary black hole mergers, and rapidly exclude large sky areas for poorly localized GW events.Comment: 16 pages, 7 figures, 2 tables. Submitted to AAS Journal

Multi-messenger astrophysics in the gravitational-wave era

The observation of GW170817, the first binary neutron star merger observed in both gravitational waves (GW) and electromagnetic (EM) waves, kickstarted the age of multi-messenger GW astronomy. This new technique presents an observationally rich way to probe extreme astrophysical processes. With the onset of the LIGO-Virgo-KAGRA Collaboration's O4 observing run and wide-field EM instruments well-suited for transient searches, multi-messenger astrophysics has never been so promising. We review recent searches and results for multi-messenger counterparts to GW events, and describe existing and upcoming EM follow-up facilities, with a particular focus on WINTER, a new near-infrared survey telescope, and TESS, an exoplanet survey space telescope.Comment: 5 pages, 1 figure, proceedings from TAUP 202

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature

Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease

Biomarkers are increasingly important in the clinical management of complex diseases, yet our ability to discover new biomarkers remains limited by our dependence on endogenous molecules. Here we describe the development of exogenously administered `synthetic biomarkers' composed of mass-encoded peptides conjugated to nanoparticles that leverage intrinsic features of human disease and physiology for noninvasive urinary monitoring. These protease-sensitive agents perform three functions in vivo: target sites of disease, sample dysregulated protease activities and emit mass-encoded reporters into host urine for multiplexed detection by mass spectrometry. Using mouse models of liver fibrosis and cancer, we show that they can noninvasively monitor liver fibrosis and resolution without the need for invasive core biopsies and can substantially improve early detection of cancer compared with clinically used blood biomarkers. This approach of engineering synthetic biomarkers for multiplexed urinary monitoring should be broadly amenable to additional pathophysiological processes and to point-of-care diagnostics

Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease

Biomarkers are becoming increasingly important in the clinical management of complex diseases, yet our ability to discover new biomarkers remains limited by our dependence on endogenous molecules. Here we describe the development of exogenously administered 'synthetic biomarkers' composed of mass-encoded peptides conjugated to nanoparticles that leverage intrinsic features of human disease and physiology for noninvasive urinary monitoring. These protease-sensitive agents perform three functions in vivo: they target sites of disease, sample dysregulated protease activities and emit mass-encoded reporters into host urine for multiplexed detection by mass spectrometry. Using mouse models of liver fibrosis and cancer, we show that these agents can noninvasively monitor liver fibrosis and resolution without the need for invasive core biopsies and substantially improve early detection of cancer compared with current clinically used blood biomarkers. This approach of engineering synthetic biomarkers for multiplexed urinary monitoring should be broadly amenable to additional pathophysiological processes and point-of-care diagnostics.National Institutes of Health (U.S.) (Bioengineering Research Partnership R01 CA124427)Kathy and Curt Marble Cancer Research FundNational Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service Award (F32CA159496-01

Low-latency gravitational wave alert products and their performance in anticipation of the fourth LIGO-Virgo-KAGRA observing run

Multi-messenger 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 Advanced LIGO (aLIGO)'s, Advanced Virgo (AdVirgo)'s and KAGRA's fourth observing run (O4). To support this effort, public semi-automated data products are sent in near real-time and include localization and source properties to guide complementary observations. Subsequent refinements, as and when available, are also relayed as updates. 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-to-end performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. In this paper, we present an overview of the low-latency infrastructure as well as an overview of the performance of the data products to be released during O4 based on a MDC. We report on expected median latencies for the preliminary alert of full bandwidth searches (29.5 s) and for the creation of early warning triggers (-3.1 s), and show consistency and accuracy of released data products using the MDC. This paper provides a performance overview for LVK low-latency alert structure and data products using the MDC in anticipation of O4

Gene Expression Patterns in Peripheral Blood Correlate with the Extent of Coronary Artery Disease

Systemic and local inflammation plays a prominent role in the pathogenesis of atherosclerotic coronary artery disease, but the relationship of whole blood gene expression changes with coronary disease remains unclear. We have investigated whether gene expression patterns in peripheral blood correlate with the severity of coronary disease and whether these patterns correlate with the extent of atherosclerosis in the vascular wall