# name
De-noising of gravitational-wave data: the rROF method in the cWB data analysis pipeline

# metadata.dc.contributor.other[].value
Universitat de Barcelona. Departament de Física Quàntica i Astrofísica

# metadata.dc.description.abstract[].value
[eng] Since the first experimental evidence for the existence of gravitational waves
in 2015, the amount of data in this scientific area has increased enormously.
There has also been a great deal of interest in the scientific community in
gravitational waves. The interferometers, used to capture these waves, need to
achieve a high level of instrumental sensitivity to be able to detect and
analyse the weak signals emitted by both distant sources of intrinsically high
intensity and nearby sources of much lower intensity.
High sensitivity is often accompanied by high levels of noise that difficult
data analysis. In nowadays interferometers, large amounts of data are recorded
with a high percentage of noise from which we attempt to extract the possible
gravitational waves buried therein. In this dissertation we propose to use a
denoising method based on the minimisation of the total variance of the time
series that constitute the data. Known as the ROF method, it assumes that the
largest contribution to the total variance of a function comes from noise. In
this way, a minimisation of this variance should lead to a drastic reduction in
the presence of noise. This denoising procedure should help to improve the
detection and data quality of gravitational wave analysis.
We have implemented two ROF-based denoising algorithms in a commonly used
gravitational-wave analysis software package. The analysis package is known as
coherent WaveBurst (cWB) and uses the excess energy from the coherence between
data from two or more interferometers to find gravitational waves. The denoising
methods are the one-step regularised ROF (rROF), and the iterative rROF
procedure (irROF). The latter is designed as an improvement of the former for
those cases where the noise cleaning is excessive and extracts a portion of the
signal in an unrecoverable way.
We have tested both methods using events from the gravitational-wave catalogue
of the first three observing periods of the LIGO-Virgo-KAGRA scientific
collaboration. These events, named GW1501914, GW151226, GW170817 and GW190521,
comprise different wave morphologies of compact binary systems injected at
different noise quality levels. We can see that the analysis of these wavelets
with the rROF method is defective as it incorrectly extracts a portion of the
signal at the high frequencies. However, the use of the irROF enhancement
procedure effectively removes the noise while preserving nearly intact the
wavelet function of the signals, providing a significant increase in the signalto-
noise ratio values.
One of our goals has been to use the irROF denoising method during a data
collection period to support on-the-fly signal detection. To this end, we have
extended our study by characterising the background noise of one week of data
after the application of the irROF method. We have calculated and analysed the
detection efficiencies of a selection of signals mimicking various types of
gravitational waves. The results obtained so far do not support the effect found
in the analysis of individual gravitational waves. However, we have found that
further improvements and variations of the irROF denoising method could improve
the detection efficiencies.
Our work demonstrates that, although the irROF method applied to a period of
data does not improve the detection achieved using methods that treat individual
wavelets, this improvement can be achieved by further developing and fine-tuning
some of the strategies proposed here. The methodology presented here can be used
in the implementation of other denoising methods currently in use or under
development. The present work provides a set of suggestions and proposals that
will allow to increase the detection of these gravitational waves.

# metadata.dc.description.abstract[].value
[spa] Desde la primera evidencia experimental de la existencia de ondas
gravitacionales en 2015, la cantidad de datos en esta área ha aumentado
enormemente, despertando un gran interés en la comunidad científica. Los
detectores necesitan alcanzar un gran nivel de sensibilidad instrumental para
detectar la débil señal emitida por fuentes lejanas.
Una gran sensibilidad suele estar acompañada por altos niveles de ruido. En esta
tesis proponemos usar un método de limpieza de ruido (denoising) basado en la
minimización de la variación total de las series temporales que constituyen los
datos. Conocido como método ROF, este procedimiento puede ayudar a mejorar la
detección y calidad de análisis de ondas gravitacionales.
Hemos implementado dos algoritmos de denoising basados en ROF en el paquete de
análisis de ondas gravitacionales conocido como coherent WaveBurst (cWB). Los
métodos de denoising son el ROF regularizado (rROF) de un solo paso, y el
procedimiento rROF iterativo (irROF). Este último está diseñado como una mejora
del primero para aquellos casos en los que la limpieza de ruido extrae una parte
de señal de modo irrecuperable.
Se han puesto a prueba ambos métodos usando eventos del catálogo de ondas
gravitacionales: GW1501914, GW151226, GW170817 y GW190521. Abarcan distintas
morfologías de ondas medidas en diferentes niveles de ruido. El uso del metodo
irROF elimina una fracción del ruido al tiempo que preserva casi intacta la
función de onda de las señales, proporcionando un aumento significativo en los
valores de la relación señal/ruido.
Nuestro trabajo demuestra que, si bien el método irROF aplicado a un periodo de
datos no mejora la detección, esta mejora sí puede conseguirse desarrollando
algunas de las estrategias propuestas. La metodología presentada en el presente
trabajo aporta un conjunto de sugerencias que permitirán aumentar la detección
de estas ondas gravitacionales.

# metadata.dc.rights[].value
cc by-nd (c) Barneo González, Pablo José, 2024

# metadata.dc.source[].value
Tesis Doctorals - Departament - Física Quàntica i Astrofísica

# metadata.dc.title[].value
De-noising of gravitational-wave data: the rROF method in the cWB data analysis pipeline