arXiv subjects

Marco Cavaglia

Publications and source records attributed to Marco Cavaglia.

At least 19 recordsLinked to original sources

arXiv2025

A neural network for estimating compact binary coalescence parameters of gravitational-wave events in real time

Low-latency pipelines analyzing gravitational waves from compact binary coalescence events rely on matched filter techniques. Limitations in template banks and waveform modeling, as well as non-stationary detector noise cause errors in signal parameter recovery, especially for events with high chirp masses. We present a quantile regression neural network model that provides dynamic bounds on key parameters such as chirp mass, mass ratio, and total mass. We test the model on various synthetic datasets and real events from the LIGO-Virgo-KAGRA gravitational-wave transient GTWC-3 catalog. We find that the model accuracy is consistently over 90% across all the datasets. We explore the possibility of employing the neural network bounds as priors in online parameter estimation. We find that they reduce by 9% the number of likelihood evaluations. This approach may shorten parameter estimation run times without affecting sky localizations.

gr-qc↗

arXiv2023

Detection of anomalies amongst LIGO's glitch populations with autoencoders

Gravitational-wave (GW) interferometers are able to detect a change in distance of $\sim$ 1/10,000th the size of a proton. Such sensitivity leads to large appearance rates of non-Gaussian transient noise bursts in the main detector strain, also known as glitches. These glitches come in a wide range of frequency-amplitude-time morphologies and are caused by environmental or instrumental processes, hindering searches for all sources of gravitational waves. Current approaches for their identification use supervised models to learn their morphology in the main strain, but do not consider relevant information provided by auxiliary channels that monitor the state of the interferometers nor provide a flexible framework for novel glitch morphologies. In this work, we present an unsupervised algorithm to find anomalous glitches. We encode a subset of auxiliary channels from LIGO Livingston in the fractal dimension, a measure for the complexity of the data, and learn the underlying distribution of the data using an auto-encoder with periodic convolutions. In this way, we uncover unknown glitch morphologies, and overlaps in time between different glitches and misclassifications. This led to the discovery of anomalies in $6.6 \%$ of the input data. The results of this investigation stress the learnable structure of auxiliary channels encoded in fractal dimension and provide a flexible framework to improve the state-of-the-art of glitch identification algorithms.

astro-ph.IM↗

arXiv2022

Characterization of gravitational-wave detector noise with fractals

We present a new method, based on fractal analysis, to characterize the output of a physical detector that is in the form of a set of real-valued, discrete physical measurements. We apply the method to gravitational-wave data from the latest observing run of the Laser Interferometer Gravitational-wave Observatory. We show that a measure of the fractal dimension of the main detector output (strain channel) can be used to determine the instrument status, test data stationarity, and identify non-astrophysical excess noise in low latency. When applied to instrument control and environmental data (auxiliary channels) the fractal dimension can be used to identify the origins of noise transients, non-linear couplings in the various detector subsystems, and provide a means to flag stretches of low-quality data.

gr-qc↗

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

Two-dimensional correlation function of binary black hole coalescences

We compute the two-dimensional correlation functions of the binary black hole coalescence detections in LIGO-Virgo's first and second observation runs. The sky distribution of binary black hole coalescence events is tested for correlations at different angular scales by comparing the observed correlation function to two reference functions that are obtained from mock datasets of localization error regions uniformly distributed in the sky. No excess correlation at any angular scale is found. The power-law slope of the correlation function is estimated to be $γ= 2.24\pm 0.33$ at the three-$σ$ confidence level, a value consistent with the measured distribution of galaxies.

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

Finding the origin of noise transients in LIGO data with machine learning

Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.

physics.data-an↗

arXiv2017

TensorFlow Enabled Genetic Programming

Genetic Programming, a kind of evolutionary computation and machine learning algorithm, is shown to benefit significantly from the application of vectorized data and the TensorFlow numerical computation library on both CPU and GPU architectures. The open source, Python Karoo GP is employed for a series of 190 tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data points. This body of tests demonstrates that datasets measured in tens and hundreds of data points see 2-15x improvement when moving from the scalar/SymPy configuration to the vector/TensorFlow configuration, with a single core performing on par or better than multiple CPU cores and GPUs. A dataset composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing 5.5M data points sees GPU configurations out-performing CPU configurations on average by 1.3x.

cs.DC↗

arXiv2016

Bounds on Large Extra Dimensions from the Generalized Uncertainty Principle

The Generalized Uncertainty Principle (GUP) implies the existence of a physical minimum length scale $l_m$. In this scenario, black holes must have a radius larger than $l_m$. They are hotter and evaporate faster than in standard Hawking thermodynamics. We study the effects of the GUP on black hole production and decay at the LHC in models with large extra dimensions. Lower bounds on the fundamental Planck scale and the minimum black hole mass at formation are determined from black hole production cross section limits by the CMS Collaboration. The existence of a minimum length generally decreases the lower bounds on the fundamental Planck scale obtained in the absence of a minimum length.

hep-ph↗

arXiv2015

Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on Principal Component Analysis. They are Principal Component Analysis for Transients (PCAT), and an adaptation of LALInference Burst (LIB). The third algorithm is a combination of an event generator called Wavelet Detection Filter (WDF) and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, SNR and waveform morphology.

astro-ph.IM↗

arXiv2015

Bounds on large extra dimensions from the simulation of black hole events at the LHC

If large extra dimensions exist, the Planck scale may be as low as a TeV and microscopic black holes may be produced in high-energy particle collisions at this energy scale. We simulate microscopic black hole formation at the Large Hadron Collider and compare the simulation results with recent experimental data by the Compact Muon Solenoid collaboration. The absence of observed black hole events in the experimental data allows us to set lower bounds on the Planck scale and various parameters related to microscopic black hole formation for a number ($3-6$) of extra dimensions. Our analysis sets lower bounds on the fundamental Planck scale ranging from 0.6 TeV to 4.8 TeV for black holes fully decaying into Standard Model particles and 0.3 TeV to 2.8 TeV for black holes settling down to a remnant, depending on the minimum allowed black hole mass at formation. Formation of black holes with mass less than 5.2 TeV to 6.5 TeV (SM decay) and 2.2 TeV to 3.4 TeV (remnant) is excluded at 95\% C.L. Our analysis shows consistency with and difference from the CMS results.

hep-ph↗

arXiv2009

String resonances at the Large Hadron Collider

The Large Hadron Collider promises to discover new physics beyond the Standard Model. An exciting possibility is the formation of string resonances at the TeV scale. In this article, we show how string resonances may be detected at the LHC in the $pp\toγ+jet$ channel. Our study is based on event shape variables, missing energy and momentum, maximum transverse momentum of photons and dijet invariant mass. These observables provide interesting signatures which enable us to discriminate string events from the Standard Model background.

hep-ph↗

arXiv2009

Ergoregion instability of black hole mimickers

Ultra-compact, horizonless objects such as gravastars, boson stars, wormholes and superspinars can mimick most of the properties of black holes. Here we show that these "black hole mimickers" will most likely develop a strong ergoregion instability when rapidly spinning. Instability timescales range between 10^-5 s and 1 weeks depending on the object, its mass and its angular momentum. For a wide range of parameters the instability is truly effective. This provides a strong indication that astrophysical ultra-compact objects with large rotation are black holes.

gr-qc↗

arXiv2008

Instability of hyper-compact Kerr-like objects

Viable alternatives to astrophysical black holes include hyper-compact objects without horizon, such as gravastars, boson stars, wormholes and superspinars. The authors have recently shown that typical rapidly-spinning gravastars and boson stars develop a strong instability. That analysis is extended in this paper to a wide class of horizonless objects with approximate Kerr-like geometry. A detailed investigation of wormholes and superspinars is presented, using plausible models and mirror boundary conditions at the surface. Like gravastars and boson stars, these objects are unstable with very short instability timescales. This result strengthens previous conclusions that observed hyper-compact astrophysical objects with large rotation are likely to be black holes.

gr-qc↗

arXiv2008

PP-waves on Superbrane Backgrounds

In this paper we discuss a method of generating supersymmetric solutions of the Einstein equations. The method involves the embedding of one supersymmetric spacetime into another. We present two examples with constituent spacetimes which support "charges", one of which was known previously and the other of which is new. Both examples have PP-waves as one of the embedding constituents.

hep-th↗

arXiv2008

Ergoregion instability of ultra-compact astrophysical objects

Most of the properties of black holes can be mimicked by horizonless compact objects such as gravastars and boson stars. We show that these ultra-compact objects develop a strong ergoregion instability when rapidly spinning. Instability timescales can be of the order of 0.1 seconds to 1 week for objects with mass M=1-10^6 solar masses and angular momentum J> 0.4 M^2. This provides a strong indication that ultra-compact objects with large rotation are black holes. Explosive events due to ergoregion instability have a well-defined gravitational-wave signature. These events could be detected by next-generation gravitational-wave detectors such as Advanced LIGO or LISA.

gr-qc↗

arXiv2008

Discriminating Supersymmetry and Black Holes at the Large Hadron Collider

We show how to differentiate the minimal supersymmetric extension of the standard model from black hole events at the Large Hadron Collider. Black holes are simulated with the CATFISH generator. Supersymmetry simulations use a combination of PYTHIA and ISAJET. Our study, based on event shape variables, visible and missing momenta, and analysis of dilepton events, demonstrates that supersymmetry and black hole events at the LHC can be easily discriminated.

hep-ph↗