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The multi-messenger detection of the gravitational-wave signal GW170817, the corresponding kilonova AT2017gfo and the short gamma-ray burst GRB170817A, as well as the observed afterglow has delivered a scientific breakthrough. For an accurate interpretation of all these different messengers, one requires robust theoretical models that describe the emitted gravitational-wave, the electromagnetic emission, and dense matter reliably. In addition, one needs efficient and accurate computational tools to ensure a correct cross-correlation between the models and the observational data. For this purpose, we have developed the Nuclear-physics and Multi-Messenger Astrophysics framework NMMA. The code allows incorporation of nuclear-physics constraints at low densities as well as X-ray and radio observations of isolated neutron stars. In previous works, the NMMA code has allowed us to constrain the equation of state of supranuclear dense matter, to measure the Hubble constant, and to compare dense-matter physics probed in neutron-star mergers and in heavy-ion collisions, and to classify electromagnetic observations and perform model selection. Here, we show an extension of the NMMA code as a first attempt of analyzing the gravitational-wave signal, the kilonova, and the gamma-ray burst afterglow simultaneously. Incorporating all available information, we estimate the radius of a 1.4 M ⊙ neutron star to be R = 11.9 8 − 0.40 + 0.35 km. The Nuclear-physics and Multi-Messenger Astrophysics framework, NMMA, combines multiple information from neutron stars and neutron star mergers. Here, the authors show an update of the NMMA framework to constrain neutron star equation of state by simultaneously analyzing multi-messenger observations.

Background Smoking contributes to myocardial infarction (MI) by causing endothelial damage, accelerating atherosclerosis, and increasing the risk of thrombosis. Given the high prevalence of smoking in the population, assessing its association with MI is essential. Hence, this study aimed to evaluate the association between active smoking and MI by assessing its prevalence in smokers versus non-smokers, based on demographic and socioeconomic characteristics. Methodology This retrospective, cross-sectional study utilized the 2022 Behavioral Risk Factor Surveillance System database. The disease variable was MI, and the risk factor was smoking. Control variables included demographic characteristics (age, gender, and race) and socioeconomic factors (education and income). Data were analyzed using cross-tabulation, with results expressed as odds ratios (ORs) and confidence intervals (CIs). Results The total number of participants involved was 407,126. Among them, 49,504 were reported as smokers, and 357,622 were reported as non-smokers. Participants who reported being current smokers had reported higher odds of reporting having MI compared to non-smokers (8.4% vs. 5.3%, OR = 1.627, CI = 1.571-1.685). This was observed across all age groups, with the highest risk observed in the smokers in the 18-24-year age group (OR = 6.15; 95% CI = 3.926-9.434), and the risk was inversely proportional to age. Gender analysis revealed that the odds of MI in current female smokers were 1.823 (95% CI = 1.724-1.927) compared to 1.464 (95% CI = 1.399-1.532) in their male counterparts. Racial stratification revealed that the smokers who belonged to the non-Hispanic/other racial group had a relatively higher OR of 2.002 (95% CI = 1.826-2.191) when compared to the Black non-Hispanic (OR = 1.737, 95% CI = 1.525-1.976) and white non-Hispanic (OR = 1.579; 95% CI = 1.516-1.645) groups. Regarding socioeconomic factors, smokers with advanced education were more likely to report MI compared to those with basic education, with ORs of 1.685 (95% CI = 1.603-1.771) and 1.324 (95% CI = 1.259-1.393), respectively. Participants earning over $50,000 annually had higher odds of MI among smokers (OR = 1.442; 95% CI = 1.339-1.553) than those earning less than $50,000 annually (OR = 1.333; 95% CI = 1.273-1.396). Conclusions The findings of the study revealed that smoking had a strong association with MI across demographic and socioeconomic groups.

The multi-messenger detection of the gravitational-wave signal GW170817, the corresponding kilonova AT2017gfo and the short gamma-ray burst GRB170817A, as well as the observed afterglow has delivered a scientific breakthrough. For an accurate interpretation of all these different messengers, one requires robust theoretical models that describe the emitted gravitational-wave, the electromagnetic emission, and dense matter reliably. In addition, one needs efficient and accurate computational tools to ensure a correct cross-correlation between the models and the observational data. For this purpose, we have developed the NMMA (Nuclear-physics and Multi-Messenger Astrophysics) framework. The code allows incorporation of nuclear-physics constraints at low densities as well as X-ray and radio observations of isolated neutron stars. It also enables us to classify electromagnetic observations, e.g., to distinguish between supernovae and kilonovae. In previous works, the NMMA code has allowed us to constrain the equation of state of supranuclear dense matter, to measure the Hubble constant, and to compare dense-matter physics probed in neutron-star mergers and in heavy-ion collisions. The extension of the NMMA code presented here is the first attempt of analysing the gravitational-wave signal, the kilonovae, and the GRB afterglow simultaneously, which reduces the uncertainty of our constraints. Incorporating all available information, we estimate the radius of a 1.4 solar mass neutron star to beR=11.98^(+0.35)_(-0.40)km.

Gamma-Ray Bursts (GRBs), being observed at high redshift (z = 9.4), vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a 'stochastic reconstruction,' wherein we fit two pre-existing models (Willingale 2007; W07 and Broken Power Law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian Processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases, on average, by 33.3% 35.03%, and 43.32%, respectively for the W07, and by 33.3%, 30.78%, 43.9% for the BPL. The slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian Process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models and for inferring the redshift of GRBs with future machine learning analysis.

Gravitational-wave detection pipelines have helped to identify over one hundred compact binary mergers in the data collected by the Advanced LIGO and Advanced Virgo interferometers, whose sensitivity has provided unprecedented access to the workings of the gravitational universe. The detectors are, however, subject to a wide variety of noise transients (or glitches) that can contaminate the data. Although detection pipelines utilize a variety of noise mitigation techniques, glitches can occasionally bypass these checks and produce false positives. One class of mitigation techniques is the signal consistency check, which aims to quantify how similar the observed data is to the expected signal. In this work, we describe a new signal consistency check that utilizes a set of bases that spans the gravitational-wave signal space and convolutional neural networks (CNN) to probabilistically identify glitches. We recast the basis response as a grayscale image, and train a CNN to distinguish between gravitational-waves and glitches with similar morphologies. We find that the CNN accurately classifies \\( 99\\%\\) of the responses it is shown. We compare these results to a toy detection pipeline, finding that the two methods produce similar false positive rates, but that the CNN has a significantly higher true positive rate. We modify our toy model detection pipeline and demonstrate that including information from the network increases the toy pipeline's true positive rate by \\(4-7\\%\\) while decreasing the false positive rate to a data-limited bound of \\( 0.1\\%\\).

Matched-filter based gravitational-wave search pipelines identify candidate events within seconds of their arrival on Earth, offering a chance to guide electromagnetic follow-up and observe multi-messenger events. Understanding the detectors' response to an astrophysical transient across the searched signal manifold is paramount to inferring the parameters of the progenitor and deciding which candidates warrant telescope time. We describe a framework that uses artificial neural networks to interpolate gravitational waves and, equivalently, the signal-to noise ratio (SNR) across sufficiently local patches of the signal manifold. Our machine-learning based model generates a single waveform in 6 milliseconds on a CPU and 0.4 milliseconds on a GPU. When using a GPU to generate batches of waveforms simultaneously, we find that we can produce \\(10^4\\) waveforms in \\( 1\\) ms. This is achieved while remaining faithful, on average, to 1 part in \\(10^4\\) (1 part in \\(10^5\\)) for binary black hole (binary neutron star) waveforms. The model we present is designed to directly utilize intermediate detection pipeline outputs in the hopes of facilitating a better real-time understanding of gravitational-wave candidates.