127 research outputs found
Image-based deep learning for classification of noise transients in gravitational wave detectors
No full textThe detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers will probe a much larger volume of space and expand the capability of discovering new gravitational wave emitters. The characterization of these detectors is a primary task in order to recognize the main sources of noise and optimize the sensitivity of interferometers. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. Deep learning techniques are a promising tool for the recognition and classification of glitches. We present a classification pipeline that exploits convolutional neural networks to classify glitches starting from their time-frequency evolution represented as images. We evaluated the classification accuracy on simulated glitches, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization
Refugio de povero gentilhuomo di Giovan Francesco Colle, dedicato ad Alfonso I d' Este
No full textRicette realizzate dal cuoco napoletano Giovan Francesco Colle presso la corte estense al tempo di Alfonso I d'Este, all'inizio del '500 con indicazioni sulle proprietà organolettiche dei cibi e il loro valore nutritivo
Machine Learning for the Characterization of Gravitational Wave Data
No full textThe low-latency characterization of detector noise is a crucial step in the detection of gravitational waves. In particular, a rapid classification and identification of transient noise sources, usually referred to as glitches, is very important when candidate signals are sent as gravitational alerts to the astronomical community.
Machine learning is emerging as a promising alternative to standard methodologies used so far for the data characterization in the gravitational wave community. In particular, deep learning approach looks very promising in tackling the problem of rapid classification of noise transients in the second-generation interferometers like Advanced LIGO and Advanced Virgo.
We will then discuss some possible approaches for establishing the quality of data, reducing the noise and classifying transient noise sources. We will also present some results based on simulated and real data, showing the performance of deep learning and its feasibility as a new and efficient approach to data characterization in gravitational wave interferometers. At the same time, we will show how to use machine learning techniques to search for unmodeled or unknown signals
Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of the Intersection of Confidence Intervals Rule
No full textGravitational-wave data (discovered first in 2015 by the Advanced LIGO interferometers and awarded by the Nobel Prize in 2017) are characterized by non-Gaussian and non-stationary noise. The ever-increasing amount of acquired data requires the development of efficient denoising algorithms that will enable the detection of gravitational-wave events embedded in low signal-to-noise-ratio (SNR) environments. In this paper, an algorithm based on the local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule for the filter support selection is proposed to denoise the gravitational-wave burst signals from core collapse supernovae. The LPA-RICI denoising method’s performance is tested on three different burst signals, numerically generated and injected into the real-life noise data collected by the Advanced LIGO detector. The analysis of the experimental results obtained by several case studies (conducted at different signal source distances corresponding to the different SNR values) indicates that the LPA-RICI method efficiently removes the noise and simultaneously preserves the morphology of the gravitational-wave burst signals. The technique offers reliable denoising performance even at the very low SNR values. Moreover, the analysis shows that the LPA-RICI method outperforms the approach combining LPA and the original intersection of confidence intervals (ICI) rule, total-variation (TV) based method, the method based on the neighboring thresholding in the short-time Fourier transform (STFT) domain, and three wavelet-based denoising techniques by increasing the improvement in the SNR by up to 118.94% and the peak SNR by up to 138.52%, as well as by reducing the root mean squared error by up to 64.59%, the mean absolute error by up to 55.60%, and the maximum absolute error by up to 84.79%
Applications of machine learning in gravitational-wave research with current interferometric detectors
No full textAbstract This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days, but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning data after it is taken have already become crucial tools for achieving the best sensitivity of the LIGO–Virgo–KAGRA network. In data analysis, machine learning has already been applied as an alternative to traditional methods for signal detection, source localization, noise reduction, and parameter estimation. For some signal types, it can already yield improved efficiency and robustness, though in many other areas traditional methods remain dominant. As the field evolves, the role of machine learning in advancing gravitational-wave research is expected to become increasingly prominent. This report highlights recent advancements, challenges, and perspectives for the current detector generation, with a brief outlook to the next generation of gravitational-wave detectors
Transport and proximity effect in unconventional ferromagnet/superconductor heterostructures
No full text2009 - 2010This dissertation collects results of my own work about heterostructures with unconventional ferromagnets
and superconductors. It both introduces the matter by reviewing part of the existing literature
and it includes original results. In particular charge and spin transport in ferromagnet/superconductor
bilayer, Josephson effect in superconductor/ferromagnet/superconductor junctions, and proximity effect
in ferromagnet/triplet superconductor structures are examined. All work has been supervised by Prof.
Canio Noce and Dr. Mario Cuoco from Dipartimento di Fisica “E. R. Caianiello”, Università degli Studi di
Salerno. I have also benefited from collaborations with Prof. Alfonso Romano and Dr. Paola Gentile from
the same department. Part of the work has been supervised by Prof. Asle Sudbø and Prof. Jacob Linder
from Department of Physics, Norwegian University of Science and Technology. I have also benefited from
collaboration with Henrik Enoksen from the same department. [edited by Author]IX n.s
Prospects for multi-messenger detection of binary neutron star mergers in the fourth LIGO-Virgo-KAGRA observing run
No full textThe joint detection of GW170817 and GRB 170817A opened the era of
multi-messenger astronomy with gravitational waves (GWs) and provided the first
direct probe that at least some binary neutron star (BNS) mergers are
progenitors of short gamma-ray bursts (S-GRBs). In the next years, we expect to
have more multi-messenger detections of BNS mergers, thanks to the increasing
sensitivity of GW detectors. Here, we present a comprehensive study on the
prospects for joint GW and electromagnetic observations of merging BNSs in the
fourth LIGO--Virgo--KAGRA observing run with \emph{Fermi}, \emph{Swift},
INTEGRAL and SVOM. This work combines accurate population synthesis models with
simulations of the expected GW signals and the associated S-GRBs, considering
different assumptions about the GRB jet structure. We show that the expected
rate of joint GW and electromagnetic detections could be up to 6
yr when \emph{Fermi}/GBM is considered. Future joint observations will
help us to better constrain the association between BNS mergers and S-GRBs, as
well as the geometry of the GRB jets.Comment: 10 pages, 2 figures. Accepted for publication on MNRAS. We have
corrected a typo in Eq. 8; all the results are unchange
Determining the core-collapse supernova explosion mechanism with current and future gravitational-wave observatories
No full textGravitational waves are emitted from deep within a core-collapse supernova, which may enable us to
determine the mechanism of the explosion from a gravitational-wave detection. Previous studies suggested
that it is possible to determine if the explosion mechanism is neutrino-driven or magneto-rotationally
powered from the gravitational-wave signal. However, long duration magneto-rotational waveforms, that
cover the full explosion phase, were not available during the time of previous studies, and explosions were
just assumed to be magneto-rotationally driven if the model was rapidly rotating. Therefore, we perform an
updated study using new 3D long-duration magneto-rotational core-collapse supernova waveforms that
cover the full explosion phase, injected into noise for the Advanced LIGO, Einstein Telescope and NEMO
gravitational-wave detectors. We also include a category for failed explosions in our signal classification
results. We then determine the explosion mechanism of the signals using three different methods: Bayesian
model selection, dictionary learning, and convolutional neural networks. The three different methods are
able to distinguish between neutrino-driven explosions and magneto-rotational explosions, even if the
neutrino-driven explosion model is rapidly rotating. However they can only distinguish between the
nonexploding and neutrino-driven explosions for signals with a high signal to noise ratio
Smoothie: A model for linearity optimization of FET devices in RF applications
No full textElectrical Engineering, Mathematics and Computer Scienc
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