arXiv subjects

Michele Zanolin

Publications and source records attributed to Michele Zanolin.

29 records · Page 2Linked to original sources

arXiv2022

Constraining the Time of Gravitational Wave Emission from Core-Collapse Supernovae

The advent of sensitive gravitational wave (GW) detectors, coupled with wide-field, high cadence optical time-domain surveys, raises the possibility of the first joint GW-electromagnetic (EM) detections of core-collapse supernovae (CCSNe). For targeted searches of GWs from CCSNe optical observations can be used to increase the sensitivity of the search by restricting the relevant time interval, defined here as the GW search window (GSW). The extent of the GSW is a critical factor in determining the achievable false alarm probability (FAP) for a triggered CCSN search. The ability to constrain the GSW from optical observations depends on how early a CCSN is detected, as well as the ability to model the early optical emission. Here we present several approaches to constrain the GSW, ranging in complexity from model-independent analytical fits of the early light curve, model-dependent fits of the rising or entire light curve, and a new data-driven approach using existing well-sampled CCSN light curves from {\it Kepler} and the Transiting Exoplanet Survey Satellite (TESS). We use these approaches to determine the time of core-collapse and its associated uncertainty (i.e., the GSW). We apply our methods to two Type II SNe that occurred during LIGO/Virgo Observing Run 3: SN\,2019fcn and SN\,2019ejj (both in the same galaxy at $d=15.7$ Mpc). Our approach shortens the duration of the GSW and improves the robustness of the GSW compared to techniques used in past GW CCSN searches.

astro-ph.HE↗

arXiv2021

Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae

We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) burts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events using as features a set of reconstruction parameters provided by cWB. Detected noise events are discarded yielding to a reduction of the false alarm rate (FAR) and of the false alarm probability (FAP) thus enhancing of the statistical significance. We tested the proposed method using strain data from the first half of the third observing run of advanced LIGO, and CCSNe GW signals extracted from 3D simulations. The ML model is learned using a dataset of noise and signal events, and then it is used to identify and discard noise events in cWB analyses. Noise and signal reduction levels were examined in single detector networks (L1 and H1) and two detector network (L1H1). The FAR was reduced by a factor of $\sim10$ to $\sim100$, there was an enhancement in the statistical significance of $\sim1$ to $\sim2σ$, while there was no impact in detection efficiencies.

gr-qc↗

arXiv2021

Detecting and reconstructing gravitational waves from the next Galactic core-collapse supernova in the Advanced Detector Era

We performed a detailed analysis of the detectability of a wide range of gravitational waves derived from core-collapse supernova simulations using gravitational-wave detector noise scaled to the sensitivity of the upcoming fourth and fifth observing runs of the Advanced LIGO, Advanced Virgo, and KAGRA. We use the coherent WaveBurst algorithm, which was used in the previous observing runs to search for gravitational waves from core-collapse supernovae. As coherent WaveBurst makes minimal assumptions on the morphology of a gravitational-wave signal, it can play an important role in the first detection of gravitational waves from an event in the Milky Way. We predict that signals from neutrino-driven explosions could be detected up to an average distance of 10 kpc, and distances of over 100 kpc can be reached for explosions of rapidly rotating progenitor stars. An estimated minimum signal-to-noise ratio of 10-25 is needed for the signals to be detected. We quantify the accuracy of the waveforms reconstructed with coherent WaveBurst and we determine that the most challenging signals to reconstruct are those produced in long-duration neutrino-driven explosions and models that form black holes a few seconds after the core bounce.

astro-ph.HE↗

arXiv2020

Detectability of SASI activity in supernova neutrino signals

We introduce a novel methodology for establishing the presence of Standing Accretion Shock Instabilities (SASI) in the dynamics of a core collapse supernova from the observed neutrino event rate at water- or ice-based neutrino detectors. The methodology uses a likelihood ratio in the frequency domain as a test-statistics; it is also employed to assess the potential to estimate the frequency and the amplitude of the SASI modulations of the neutrino signal. The parameter estimation errors are consistent with the minimum possible errors as evaluated from the inverse of the Fisher information matrix, and close to the theoretical minimum for the SASI amplitude. Using results from a core-collapse simulation of a 15 solar-mass star by Kuroda $\it {et\, al.}$ (2017) as a test bed for the method, we find that SASI can be identified with high confidence for a distance to the supernova of up to $\sim 6$ kpc for IceCube and and up to $\sim 3$ kpc for a 0.4 Mt mass water Cherenkov detector. This methodology will aid the investigation of a future galactic supernova.

astro-ph.HE↗

arXiv2020

Improving the background of gravitational-wave searches for core collapse supernovae: A machine learning approach

Based on the prior O1-O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e., they will be collected at times when only one detector in the network is operating in observing mode. Searches for gravitational wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals. If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherentWave-Burst pipeline. We show that the method may be used to discriminate Galactic gravitational-wave supernova signals from noise transients, decrease the false alarm rate of the search, and improve the supernova detection reach of the detectors.

astro-ph.IM↗

arXiv2018

Enhancing the Sensitivity of Searches for Gravitational Waves from Core-Collapse Supernovae with a Bayesian classification of candidate events

We demonstrate how a morphological veto involving Bayesian statistics can improve the receiver-operating characteristic (ROC) curves of the current search for core-collapse supernovae (CCSNe) as implemented by the coherent Waveburst (cWB) algorithm. Examples involving two implementations of BayesWave (BW), one that makes no assumption of the polarization state of the gravitational wave (GW) and one that uses the same elliptical polarization settings adopted in previous usages for Binary systems are provided on the set of waveforms currently adopted for the first and second Advanced LIGO (aLIGO) science runs for the targeted CCSNe search. A comparison of the performance for all-sky searches versus the targeted searches with optical triggers is provided. The average cWB+BW ROC range improvements with respect to a fixed false-alarm rate (FAR) for slowly-rotating waveforms ranged from [+1.30%, +15.76%] while the improvement for rapidly-rotating waveforms were on the order of [+1.19%, +22.05%]. The application of BW to CCSN GW triggers also shows a significant reduction of the FAR while maintaining detection efficiency and remaining sensitive to a wide range of morphological CCSNe signals. It appears that the code developed for arbitrarily polarized signals outperforms the previous code for the GW morphologies tested.

astro-ph.HE↗

arXiv2016

Measuring violations of General Relativity from single gravitational wave detection by non-spinning binary systems: higher-order asymptotic analysis

A frequentist asymptotic expansion method for error estimation is employed for a network of gravitational wave detectors to assess the amount of information that can be extracted from gravitational wave observations. Mathematically we derive lower bounds in the errors that any parameter estimator will have in the absence of prior knowledge to distinguish between the post-Einsteinian (ppE) description of coalescing binary systems and that of general relativity. When such errors are smaller than the parameter value, there is possibility to detect these violations from GR. A parameter space with inclusion of dominant dephasing ppE parameters $(β, b)$ is used for a study of first- and second-order (co)variance expansions, focusing on the inspiral stage of a nonspinning binary system of zero eccentricity detectible through Adv. LIGO and Adv. Virgo. Our procedure is an improvement of the Cramér-Rao Lower Bound. When Bayesian errors are lower than our bound it means that they depend critically on the priors. The analysis indicates the possibility of constraining deviations from GR in inspiral SNR ($ρ\sim 15-17$) regimes that are achievable in upcoming scientific runs (GW150914 had an inspiral SNR $\sim 12$). The errors on $β$ also increase errors of other parameters such as the chirp mass $\mathcal{M}$ and symmetric mass ratio $η$. Application is done to existing alternative theories of gravity, which include modified dispersion relation of the waveform, non-spinning models of quadratic modified gravity, and dipole gravitational radiation (i.e., Brans-Dicke type) modifications.

gr-qc↗

arXiv2013

Excluding Source Models with Multiple Astrophysical Observations

We describe a general method to observationally exclude a theoretical model for gravitational wave (GW) emission from a transient astrophysical source (event) by using a null detection from a network of GW detectors. In the case of multiple astrophysical events with no GW detection, statements about individual events can be combined to increase the exclusion confidence. We frame and demonstrate the method using a population of hypothetical core collapse supernovae.

astro-ph.HE↗

arXiv2011

Application of asymptotic expansions for maximum likelihood estimators' errors to gravitational waves from binary mergers: the network case

This paper describes the most accurate analytical frequentist assessment to date of the uncertainties in the estimation of physical parameters from gravitational waves generated by non spinning binary systems and Earth-based networks of laser interferometers. The paper quantifies how the accuracy in estimating the intrinsic parameters mostly depends on the network signal to noise ratio (SNR), but the resolution in the direction of arrival also strongly depends on the network geometry. We compare results for 6 different existing and possible global networks and two different choices of the parameter space. We show how the fraction of the sky where the one sigma angular resolution is below 2 square degrees increases about 3 times when transitioning from the Hanford (USA), Livingston (USA) and Cascina (Italy) network to possible 5 sites ones (while keeping the network SNR fixed). The technique adopted here is an asymptotic expansion of the uncertainties in inverse powers of the signal to noise ratio where the first order is the inverse Fisher information matrix. We show that a common approach to use simplified parameter spaces and only the Fisher information matrix can largely underestimate the uncertainties (by a factor ~7 for the one sigma sky uncertainty in square degrees at a network SNR of ~15).

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arXiv2011

Application of asymptotic expansions of maximum likelihood estimators errors to gravitational waves from binary mergers: the single interferometer case

In this paper we describe a new methodology to calculate analytically the error for a maximum likelihood estimate (MLE) for physical parameters from Gravitational wave signals. All the existing litterature focuses on the usage of the Cramer Rao Lower bounds (CRLB) as a mean to approximate the errors for large signal to noise ratios. We show here how the variance and the bias of a MLE estimate can be expressed instead in inverse powers of the signal to noise ratios where the first order in the variance expansion is the CRLB. As an application we compute the second order of the variance and bias for MLE of physical parameters from the inspiral phase of binary mergers and for noises of gravitational wave interferometers . We also compare the improved error estimate with existing numerical estimates. The value of the second order of the variance expansions allows to get error predictions closer to what is observed in numerical simulations. It also predicts correctly the necessary SNR to approximate the error with the CRLB and provides new insight on the relationship between waveform properties SNR and estimation errors. For example the timing match filtering becomes optimal only if the SNR is larger than the kurtosis of the gravitational wave spectrum.

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arXiv2010

Parameter estimation from Gravitational waves generated by non-spinning binary black holes with laser interferometers: beyond the Fisher information

In this paper we apply to gravitational waves from non-spinning binary systems a recently intro- duced frequentist methodology to calculate analytically the error for a maximum likelihood estimate (MLE) of physical parameters. While existing literature focuses on using the Cramer Rao Lower bound (CRLB) and Monte Carlo simulations, we use a power expansion of the bias and covariance in inverse powers of the signal to noise ratio. The use of higher order derivatives of the likelihood function in the expansions makes the prediction also sensitive to the secondary lobes of the MLE probability distribution. We discuss conditions for validity of the CRLB and predict new features in regions of the parameter space currently not explored. For example, we see how the bias can become the most important contributor to the parameters' errors for high mass systems (200M and above).

gr-qc↗