arXiv subjects

Filip Morawski

Publications and source records attributed to Filip Morawski.

8 recordsLinked to original sources

arXiv2023

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 $\sim98\%$.

astro-ph.IM↗

arXiv2022

Detecting dense-matter phase transition signatures in neutron star mass-radius measurements as data anomalies using normalising flows

Observations of neutron stars may be used to study aspects of extremely dense matter, specifically a possibility of phase transitions to exotic states, such as de-confined quarks. We present a novel data analysis method for detecting signatures of dense-matter phase transitions in sets of mass-radius measurements, and study its sensitivity with respect to the size of observational errors and the number of observations. The method is based on machine learning anomaly detection coupled with normalizing flows technique: the algorithm trained on samples of astrophysical observations featuring no phase transition signatures interprets a phase transition sample as an ''anomaly''. For the sake of this study, we focus on dense-matter equations of state leading to detached branches of mass-radius sequences (strong phase transitions), use an astrophysically-informed neutron-star mass function, and various magnitudes of observational errors and sample sizes. The method is shown to reliably detect cases of mass-radius relations with phase transition signatures, while increasing its sensitivity with decreasing measurement errors and increasing number of observations. We discuss marginal cases, when the phase transition mass is located near the edges of the mass function range. Evaluated on the current state-of-art selection of real measurements of electromagnetic and gravitational-wave observations, the method gives inconclusive results, which we interpret as due to small available sample size, large observational errors and complex systematics.

astro-ph.HE↗

arXiv2021

Multimodal analysis of Gravitational Wave signals and Gamma-Ray Bursts from binary neutron star mergers

A major boost in the understanding of the universe was given by the revelation of the first coalescence event of two neutron stars (GW170817) and the observation of the same event across the entire electromagnetic spectrum. With 3rd Generation gravitational wave detectors and the new astronomical facilities, we expect many multi messenger events of the same type. We anticipate the need to analyse the data provided to us by such events, to fulfill the requirements of real-time analysis, but also in order to decipher the event in its entirety through the information emitted in the different messengers using Machine Learning. We propose a change in the paradigm in the way we will do multi-messenger astronomy, using simultaneously the complete information generated by violent phenomena in the Universe. What we propose is the application of a multimodal machine learning approach to characterize these events.

astro-ph.HE↗

arXiv2021

Anomaly Detection in Gravitational Waves data using Convolutional AutoEncoders

As of this moment, fifty gravitational waves (GW) detections have been announced, thanks to the observational efforts of the LIGO-Virgo Collaboration, working with the Advanced LIGO and the Advanced Virgo interferometers. The detection of signals is complicated by the noise-dominated nature of the data. Conventional approaches in GW detection procedures require either precise knowledge of the GW waveform in the context of matched filtering searches or coincident analysis of data from multiple detectors. Furthermore, the analysis is prone to contamination by instrumental or environmental artifacts called glitches which either mimic astrophysical signals or reduce the overall quality of data. In this paper, we propose an alternative generic method of studying GW data based on detecting anomalies. The anomalies we study are transient signals, different from the slow non-stationary noise of the detector. Presented in the manuscript anomalies are mostly based on the GW emitted by the mergers of binary black hole systems. However, the presented study of anomalies is not limited only to GW alone, but also includes glitches occurring in the real LIGO/Virgo dataset available at the Gravitational Waves Open Science Center.

astro-ph.IM↗

arXiv2020

Neural networks reconstruction of the dense-matter equation of state from neutron-star parameters

Aims: The aim of this work is to study the application of the artificial neural networks guided by the autoencoder architecture as a method for precise reconstruction of the neutron star equation of state, using their observable parameters: masses, radii and tidal deformabilities. In addition we study how well the neutron star radius can be reconstructed using the gravitational-wave only observations of tidal deformability, i.e. quantities which are not related in a straightforward way. Methods: Application of artificial neural network in the equation of state reconstruction exploits the non-linear potential of this machine learning model. Since each neuron in the network is basically a non-linear function, it is possible to create a complex mapping between the input sets of observations and the output equation of state table. Within the supervised training paradigm, we construct a few hidden layer deep neural network on a generated data set, consisting of a realistic equation of state for the neutron star crust connected with a piecewise relativistic polytropes dense core, with parameters representative to the state-of-the art realistic equations of state. Results: We demonstrate the performance of our machine learning implementation with respect to the simulated cases with varying number of observations and measurement uncertainties. Furthermore we study the impact of the neutron star mass distributions on the results. Finally, we test the reconstruction of the equation of state trained on parametric polytropic training set using the simulated mass--radius and mass--tidal-deformability sequences based on realistic equations of state. Neural networks trained with a limited data set are able to generalize the mapping between global parameters and equation of state input tables for realistic models.

astro-ph.HE↗

arXiv2020

Enhancing Gravitational-Wave Science with Machine Learning

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.

astro-ph.HE↗

arXiv2020

Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification

We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined with an event trigger generator known as Wavelet Detection Filter (WDF). We employ both a 1-D CNN search using time series gravitational-wave data as input, and a 2-D CNN search with time-frequency representation of the data as input. To test the accuracies of our 1-D and 2-D CNN classification, we add CCSN waveforms from the most recent hydrodynamical simulations of neutrino-driven core-collapse to simulated Gaussian colored noise with the Virgo interferometer and the planned Einstein Telescope sensitivity curve. We find classification accuracies, for a single detector, of over 95% for both 1-D and 2-D CNN pipelines. For the first time in machine learning CCSN studies, we add short duration detector noise transients to our data to test the robustness of our method against false alarms created by detector noise artifacts. Further to this, we show that the CNN can distinguish between different types of CCSN waveform models.

gr-qc↗

arXiv2019

Convolutional neural network classifier for the output of the time-domain F-statistic all-sky search for continuous gravitational waves

Among astrophysical sources in the Advanced LIGO and Advanced Virgo detectors' frequency band are rotating non-axisymmetric neutron stars emitting long-lasting, almost-monochromatic gravitational waves. Searches for these continuous gravitational-wave signals are usually performed in long stretches of data in a matched-filter framework e.g., the F-statistic method. In an all-sky search for a priori unknown sources, large number of templates is matched against the data using a pre-defined grid of variables (the gravitational-wave frequency and its derivatives, sky coordinates), subsequently producing a collection of candidate signals, corresponding to the grid points at which the signal reaches a pre-defined signal-to-noise threshold. An astrophysical signature of the signal is encoded in the multi-dimensional vector distribution of the candidate signals. In the first work of this kind, we apply a deep learning approach to classify the distributions. We consider three basic classes: Gaussian noise, astrophysical gravitational-wave signal, and a constant-frequency detector artifact ("stationary line"), the two latter injected into the Gaussian noise. 1D and 2D versions of a convolutional neural network classifier are implemented, trained and tested on a broad range of signal frequencies. We demonstrate that these implementations correctly classify the instances of data at various signal-to-noise ratios and signal frequencies, while also showing concept generalization i.e., satisfactory performance at previously unseen frequencies. In addition we discuss the deficiencies, computational requirements and possible applications of these implementations.

astro-ph.IM↗