The Impact of Peculiar Velocities on the Estimation of the Hubble Constant from Gravitational Wave Standard Sirens

In this work we investigate the systematic uncertainties that arise from the calculation of the peculiar velocity when estimating the Hubble constant ($H_0$) from gravitational wave standard sirens. We study the GW170817 event and the estimation of the peculiar velocity of its host galaxy, NGC 4993, when using Gaussian smoothing over nearby galaxies. NGC 4993 being a relatively nearby galaxy, at $\sim 40 \ {\rm Mpc}$ away, is subject to a significant effect of peculiar velocities. We demonstrate a direct dependence of the estimated peculiar velocity value on the choice of smoothing scale. We show that when not accounting for this systematic, a bias of $\sim 200 \ {\rm km \ s ^{-1}}$ in the peculiar velocity incurs a bias of $\sim 4 \ {\rm km \ s ^{-1} \ Mpc^{-1}}$on the Hubble constant. We formulate a Bayesian model that accounts for the dependence of the peculiar velocity on the smoothing scale and by marginalising over this parameter we remove the need for a choice of smoothing scale. The proposed model yields$H_0 = 68.6 ^{+14.0}_{-8.5}~{\rm km\ s^{-1}\ Mpc^{-1}}$. We demonstrate that under this model a more robust unbiased estimate of the Hubble constant from nearby GW sources is obtained.Comment: 9 pages, 5 figure

LSTM and CNN application for core-collapse supernova search in gravitational wave real data

$Context.$ Core-collapse supernovae (CCSNe) are expected to emit gravitational wave signals that could be detected by current and future generation interferometers within the Milky Way and nearby galaxies. The stochastic nature of the signal arising from CCSNe requires alternative detection methods to matched filtering. $Aims.$ We aim to show the potential of machine learning (ML) for multi-label classification of different CCSNe simulated signals and noise transients using real data. We compared the performance of 1D and 2D convolutional neural networks (CNNs) on single and multiple detector data. For the first time, we tested multi-label classification also with long short-term memory (LSTM) networks. $Methods.$ We applied a search and classification procedure for CCSNe signals, using an event trigger generator, the Wavelet Detection Filter (WDF), coupled with ML. We used time series and time-frequency representations of the data as inputs to the ML models. To compute classification accuracies, we simultaneously injected, at detectable distance of 1\,kpc, CCSN waveforms, obtained from recent hydrodynamical simulations of neutrino-driven core-collapse, onto interferometer noise from the O2 LIGO and Virgo science run. $Results.$ We compared the performance of the three models on single detector data. We then merged the output of the models for single detector classification of noise and astrophysical transients, obtaining overall accuracies for LIGO ($\sim99\%$) and ($\sim80\%$) for Virgo. We extended our analysis to the multi-detector case using triggers coincident among the three ITFs and achieved an accuracy of $\sim98\%$.Comment: 10 pages, 13 figures. Accepted by A&A journa

Machine Learning for Multi-Messenger Astronomy

The direct observation of gravitational waves (GWs) in 2015 marked the beginning of a new era of GW astronomy, unlocking an independent probe for studying the Universe. GW170817, was the first event detected in both GWs and electromagnetic (EM) observations. The implications of this multi-messenger event in the field of physics are far-reaching. Multi-messenger events can independently estimate the Hubble constant. In Chapter 2, I demonstrate the presence of a potential systematic error associated with the peculiar velocity of the host galaxy of nearby GWs, biasing the H0 estimate. I study the GW170817 event and formulate a Bayesian model that accounts for this error. Under the proposed model an unbiased estimate of the Hubble constant from nearby GW sources is obtained, H0 = 68.6+14.0−8.5 kms−1 Mpc−1, which is crucial when considering the H0 tension. In Chapter 3, I present the study of detecting and classifying GWs from core collapse supernovae (CCSNe), which are promising multi-messenger events yet to be observed. Simulated CCSNe signals were injected into real detector noise data. I implement a two-step approach comprised of wavelet-based transient detection and machine learning for classification. I compared the performance of 1D, 2D CNNs (convolutional neural networks) and LSTM (long short-term memory) and showed that 2D CNNs perform the best overall. Large galaxy surveys, play an instrumental role in EM observations of multi messenger events and studies of their properties. DESI is expected to observe 35 million galaxies. In Chapter 4, I apply Variational Autoencoders to detect anomalous spectra in DESI data. The dataset used in this analysis is composed of ∼ 208,000 spectra. The outliers identified fall into two broad categories: spectra with unique physical features and spectra with artefacts. The latter can be used to improve the DESI spectroscopic pipeline while the former can lead to the identification of transients, unusual objects and potential scientific discoverie

Impact of the COVID-19 pandemic on total, sex- and age-specific all-cause mortality in 20 countries worldwide during 2020: results from the C-MOR project

International audienceBackground - To understand the impact of the COVID-19 pandemic on mortality, this study investigates overall, sex- and age-specific excess all-cause mortality in 20 countries, during 2020. Methods - Total, sex- and age-specific weekly all-cause mortality for 2015–2020 was collected from national vital statistics databases. Excess mortality for 2020 was calculated by comparing weekly 2020 observed mortality against expected mortality, estimated from historical data (2015–2019) accounting for seasonality, long- and short-term trends. Crude and age-standardized rates were analysed for total and sex-specific mortality. Results - Austria, Brazil, Cyprus, England and Wales, France, Georgia, Israel, Italy, Northern Ireland, Peru, Scotland, Slovenia, Sweden, and the USA displayed substantial excess age-standardized mortality of varying duration during 2020, while Australia, Denmark, Estonia, Mauritius, Norway, and Ukraine did not. In sex-specific analyses, excess mortality was higher in males than females, except for Slovenia (higher in females) and Cyprus (similar in both sexes). Lastly, for most countries substantial excess mortality was only detectable (Austria, Cyprus, Israel, and Slovenia) or was higher (Brazil, England and Wales, France, Georgia, Italy, Northern Ireland, Sweden, Peru and the USA) in the oldest age group investigated. Peru demonstrated substantial excess mortality even in the <45 age group. Conclusions - This study highlights that excess all-cause mortality during 2020 is context dependent, with specific countries, sex- and age-groups being most affected. As the pandemic continues, tracking excess mortality is important to accurately estimate the true toll of COVID-19, while at the same time investigating the effects of changing contexts, different variants, testing, quarantine, and vaccination strategies