Search

We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational wave signals we process an event with a modified constrained likelihood method. Using simulated supernova signals, the properties of the Hyper-Kamiokande neutrino detector, and O3 LIGO Interferometric data, we produce the two-dimensional probability density function (PDF) of the SASI activity indicator and calculate the probability of detection $P_\mathrm{D}$ as well as the false identification probability $P_\mathrm{FI}$. We discuss the probability to establish the presence of the SASI as a function of the source distance in each observational channel, as well as jointly. Compared to a single-messenger approach, the joint analysis results in $P_\mathrm{D}$ (at $P_\mathrm{FI}=0.1$) of SASI activities that is larger by up to $\approx~40\%$ for a distance to the supernova of 5 kpc. We also discuss how accurately the frequency and duration of the SASI activity can be estimated in each channel separately. Our methodology is suitable for implementation in a realistic data analysis and a multi-messenger setting., Comment: 24 pages, 15 figures, accepted by PRD

Objective. To compare the reproductive performance of postpartum and open Bos indicus cows and to study the cost effectiveness of retaining non-pregnant animals after a short breeding season in tropical region of Mexico. Material and Methods. A total of 128 Bos indicus were included, 87 postpartum cows (PP) with ≤90 days after calving and 41 open cows (OC) with >90 days open. The study was divided into three phases: 1) Estrus synchronization followed by FTAI (day 0-10), 2) Estrus detection and AI (day 11-45) and 3) Natural mating (day 46-90). For the first phase, all animals were synchronized and AI at fixed time (day 10). Cows displaying overt signs of estrus (day 11-45) were AI. Open cows during the previous two phases were exposed to the bull. Results. Pregnancy in phase 1 was different (p<0.01) for PP and OC groups, 58.6% and 34.1%, respectively. Overall pregnancy percentage over the second service was 42.5% (p>0.05). No differences (p>0.05) were observed at phase 3, average 44.2%. By the end of the breeding season, the cost of OC, was 3 times more than PP cows. Conclusions. Pregnancy rate at first phase was higher in PP cows than OC cows. At the end of breeding season, a pregnancy rate of 80% was found. Incorporation of open cows from previous breeding season was more expensive than PP cows in all phases of the breeding program. Retaining an open cow for rebreeding one year or more could not be economically feasible., Objetivo. Comparar el desempeño reproductivo de vacas Bos indicus posparto y abiertas, así como evaluar el costo de retener vacas vacías al final de una temporada de empadre en el trópico mexicano. Material y métodos. Se incluyeron 128 vacas Bos indicus, 87 vacas posparto (PP) con ≤90 días posparto y 41 vacas abiertas (OC) con >90 días abiertos. El estudio se dividió en tres fases: 1) Sincronización de celos (día 0-10) e inseminación a tiempo fijo (FTAI), 2) Detección de celos e IA (día 11-45) y 3) Monta natural (día 46-90). Para la primera fase, todos los animales fueron sincronizados inseminados FTAI (día 10). Las vacas que mostraban signos de estro (día 11-45) fueron inseminadas (IA). Las vacas abiertas durante las dos fases anteriores fueron expuestas al toro. Resultados. La tasa de gestación en la fase 1 fue 58.6 y 34.1% (p<0.01), para PP y OC, respectivamente. Durante la fase 2, el porcentaje de gestación fue 42.5% (p>0.05), mientras que en la fase 3, la tasa de preñez fue 44.2% (p>0.05). El costo de una vaca OC fue tres veces más que las vacas PP. Conclusiones. La tasa de preñez durante la primera etapa, de la estación reproductiva, fue mayor para vacas PP que vacas OC. Al final de la estación reproductiva la tasa de gestación fue 80 %. El costo beneficio de retener animales no preñados después de una corta temporada de empadre no es económicamente factible para una unidad de producción vaca-becerro.

In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. As novel contribution, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. Resampling is performed by repeated $k$-fold cross-validation experiments, and for a white-box approach, behavior of CNNs is mathematically described in detail. Through a Morlet wavelet transform, strain time series are converted to time-frequency images, which in turn are reduced before generating input datasets. Moreover, to reproduce more realistic experimental conditions, we worked only with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand. After hyperparameter adjustments, we found that resampling smooths stochasticity of mini-batch stochastic gradient descend by reducing mean accuracy perturbations in a factor of $3.6$. CNNs were quite precise to detect noise but not sensitive enough to recall GW signals, meaning that CNNs are better for noise reduction than generation of GW triggers. However, applying a post-analysis, we found that for GW signals of SNR $\geq 21.80$ with H1 data and SNR $\geq 26.80$ with L1 data, CNNs could remain as tentative alternatives for detecting GW signals. Besides, with receiving operating characteristic curves we found that CNNs show much better performances than those of Naive Bayes and Support Vector Machines models and, with a significance level of $5\%$, we estimated that predictions of CNNs are significant different from those of a random classifier. Finally, we elucidated that performance of CNNs is highly class dependent because of the distribution of probabilistic scores outputted by the softmax layer., Comment: 29 pages, 28 figures

In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. As novel contribution, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. Resampling is performed by repeated $k$-fold cross-validation experiments, and for a white-box approach, behavior of CNNs is mathematically described in detail. Through a Morlet wavelet transform, strain time series are converted to time-frequency images, which in turn are reduced before generating input datasets. Moreover, to reproduce more realistic experimental conditions, we worked only with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand. After hyperparameter adjustments, we found that resampling smooths stochasticity of mini-batch stochastic gradient descend by reducing mean accuracy perturbations in a factor of $3.6$. CNNs were quite precise to detect noise but not sensitive enough to recall GW signals, meaning that CNNs are better for noise reduction than generation of GW triggers. However, applying a post-analysis, we found that for GW signals of SNR $\geq 21.80$ with H1 data and SNR $\geq 26.80$ with L1 data, CNNs could remain as tentative alternatives for detecting GW signals. Besides, with receiving operating characteristic curves we found that CNNs show much better performances than those of Naive Bayes and Support Vector Machines models and, with a significance level of $5\%$, we estimated that predictions of CNNs are significant different from those of a random classifier. Finally, we elucidated that performance of CNNs is highly class dependent because of the distribution of probabilistic scores outputted by the softmax layer., Comment: 29 pages, 28 figures

We study spherical and nonspherical linear acoustic perturbations of the Michel flow, which describes the steady radial accretion of a perfect fluid into a nonrotating black hole. The dynamics of such perturbations are governed by a scalar wave equation on an effective curved background geometry determined by the acoustic metric, which is constructed from the spacetime metric and the particle density and four-velocity of the fluid. For the problem under consideration in this article the acoustic metric has the same qualitative features as an asymptotically flat, static and spherically symmetric black hole, and thus it represents a natural astrophysical analogue black hole. As for the case of a scalar field propagating on a Schwarzschild background, we show that acoustic perturbations of the Michel flow exhibit quasi-normal oscillations. Based on a new numerical method for determining the solutions of the radial mode equation, we compute the associated frequencies and analyze their dependency on the radii of the event and sonic horizons and the angular momentum number. Our results for the fundamental frequencies are compared to those obtained from an independent numerical Cauchy evolution, finding good agreement between the two approaches. When the radius of the sonic horizon is large compared to the event horizon radius, we find that the quasi-normal frequencies scale approximately like the surface gravity associated with the sonic horizon., Comment: 20 pages, 12 figures. Typos corrected. New subsection V.C discussing the eikonal limit. Matches published version

We study spherical and nonspherical linear acoustic perturbations of the Michel flow, which describes the steady radial accretion of a perfect fluid into a nonrotating black hole. The dynamics of such perturbations are governed by a scalar wave equation on an effective curved background geometry determined by the acoustic metric, which is constructed from the spacetime metric and the particle density and four-velocity of the fluid. For the problem under consideration in this article the acoustic metric has the same qualitative features as an asymptotically flat, static and spherically symmetric black hole, and thus it represents a natural astrophysical analogue black hole. As for the case of a scalar field propagating on a Schwarzschild background, we show that acoustic perturbations of the Michel flow exhibit quasi-normal oscillations. Based on a new numerical method for determining the solutions of the radial mode equation, we compute the associated frequencies and analyze their dependency on the radii of the event and sonic horizons and the angular momentum number. Our results for the fundamental frequencies are compared to those obtained from an independent numerical Cauchy evolution, finding good agreement between the two approaches. When the radius of the sonic horizon is large compared to the event horizon radius, we find that the quasi-normal frequencies scale approximately like the surface gravity associated with the sonic horizon., Comment: 20 pages, 12 figures. Typos corrected. New subsection V.C discussing the eikonal limit. Matches published version