5,912 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
Encuentros con Elena Poniatowska
No full textThe author analyzes testimonial literature from the perspective of female literature through his meeting with Elena Poniatowska. An analysis of reality vs. Fiction in Elena, Jesusa and Tinisima.El autor analiza, desde su encuentro con Elena Poniatowska, la vertiente de la literatura testimonial como literatura de mujeres. Un análisis interior de la relación entre realidad y ficción, entre Elena, Jesusa o Tinísima
Bio-bibliografía de y sobre Elena Poniatowska Amor
No full textThe objective of this article is to collect and present: I. the chronology of the life and work of Elena Poniatowska; II. the complete works (as principal and secondary author), and III. the bibliography on the author (books, chapters, and articles).El objetivo de este artículo es reunir y presentar: I. los datos cronológicos fundamentales de la vida y la obra de Elena Poniatowska; II. El compendio de sus obras (como autora principal y como secundaria); y III. la bibliografía sobre la escritora (libros, capítulos de libros y artículos)
El Tlacuache Núm. 698 (2015). 698 Año 13 (2015) noviembre. El Tlacuache
No full textLa mujer vista por cronistas en tiempos novohispanos por Laura Elena Hinojosa. - El Códice Mauricio de la Arena forma parte de Los códices de Tlaquiltenango por Laura Elena Hinojosa
Encounters with Elena Poniatowska
No full textEl autor analiza, desde su encuentro con Elena Poniatowska, la vertiente de la literatura testimonial como literatura de mujeres. Un análisis interior de la relación entre realidad y ficción, entre Elena, Jesusa o Tinísima.The author analyzes testimonial literature from the perspective of female literature through his meeting with Elena Poniatowska. An analysis of reality vs. Fiction in Elena, Jesusa and Tinisima
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%
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