A new classification of gamma-ray bursts and its cosmological implications

Los estallidos de rayos gamma (del inglés gamma-ray bursts o GRBs para abreviar) se han clasificado tradicionalmente en GRBs cortos y duros, y GRBs largos y blandos, de acuerdo a la distribución biomodal de sus duraciones. Esta clasificación propone una frontera, a T90=2 s, para separar los GRBs cortos de los largos. En realidad, las distribuciones de duraciones de GRBs cortos y largos deben tener un intervalo de duraciones donde se solapan, y la clasificación clásica es incapaz de clasificar GRBs en esta región. Esta tesis propone una nueva clasificación de GRBs. Mediante el uso de múltiples variables intrínsecas al GRB y mediante el uso de algoritmos de clasificación automática propios de la inteligencia artificial, como son el análisis de clústers y las redes neuronales, se consigue una clasificación objetiva de GRBs con la que podemos discriminar a qué clase pertenece cada GRB incluso en la región de solapamiento de la distribución de duraciones. Además, esos algoritmos sugieren la existencia de 3 clases de GRBs diferentes. Presentamos pues, dos clasificaciones de GRBs, una de dos clases (2-I y 2-II) y otras en tres clases (3-I, 3-II y 3-III). La clasificación en dos clases recupera las características de la clasificación clásica de GRBs, mientras que la clasificación en tres clases propone una nueva clase de GRBs de duración intermedia y blandos. En esta clasificación los GRBs de clase 3-III tienen como principal característica una evolución de la luminosidad con el redshift. Son estos GRBs los candidatos ideales a provenir del colapso de estrellas muy masivas a través del fenómeno conocido como colapsar. Por otra parte, los GRBs de clase 3-I y 3-II se dan a distancias medias menores que los de clase 3-III, siendo candidatos ideales a provenir de la fusión de sistemas dobles compactos

A new classification of gamma-ray bursts and its cosmological implications

[spa] Los estallidos de rayos gamma (del inglés gamma-ray bursts o GRBs para abreviar) se han clasificado tradicionalmente en GRBs cortos y duros, y GRBs largos y blandos, de acuerdo a la distribución biomodal de sus duraciones. Esta clasificación propone una frontera, a T90=2 s, para separar los GRBs cortos de los largos. En realidad, las distribuciones de duraciones de GRBs cortos y largos deben tener un intervalo de duraciones donde se solapan, y la clasificación clásica es incapaz de clasificar GRBs en esta región. Esta tesis propone una nueva clasificación de GRBs. Mediante el uso de múltiples variables intrínsecas al GRB y mediante el uso de algoritmos de clasificación automática propios de la inteligencia artificial, como son el análisis de clústers y las redes neuronales, se consigue una clasificación objetiva de GRBs con la que podemos discriminar a qué clase pertenece cada GRB incluso en la región de solapamiento de la distribución de duraciones. Además, esos algoritmos sugieren la existencia de 3 clases de GRBs diferentes. Presentamos pues, dos clasificaciones de GRBs, una de dos clases (2-I y 2-II) y otras en tres clases (3-I, 3-II y 3-III). La clasificación en dos clases recupera las características de la clasificación clásica de GRBs, mientras que la clasificación en tres clases propone una nueva clase de GRBs de duración intermedia y blandos. En esta clasificación los GRBs de clase 3-III tienen como principal característica una evolución de la luminosidad con el redshift. Son estos GRBs los candidatos ideales a provenir del colapso de estrellas muy masivas a través del fenómeno conocido como colapsar. Por otra parte, los GRBs de clase 3-I y 3-II se dan a distancias medias menores que los de clase 3-III, siendo candidatos ideales a provenir de la fusión de sistemas dobles compactos

Temporal and spatial stratification for the estimation of nocturnal long-term noise levels

Noise pollution in cities is mainly caused by the vehicular traffic but, depending on the place under assessment, it could be affected by the land use. For noise assessment and strategic noise mapping, the night period equivalent level (), which evaluates sleep disturbance, is one of the requirements of the European Directive 2002/49/EC to be presented for the equivalent time of one year. This research aims to find the influence of the land use in the weekdays stratification to improve the accuracy of the long-term noise level estimation for the night period. It is found that depending on the land use of the place under assessment, the weekdays temporal and spatial stratification could be affected by leisure activities. From a statistical analysis based on a clustering procedure of samples in 19 points, it is observed that both, temporal and spatial stratification depend on the intensity of the surrounding leisure activity, and not on traffic. Following these stratification criteria, a sampling method is presented that reduces by 47% the number of days needed to estimate the annual levels with respect to random samplingPostprint (author's final draft

Annual traffic noise levels estimation based on temporal stratification

© 2017. This version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper proposes a temporal sampling strategy that increases the accuracy of long-term noise level estimation and allows to establish the estimation error according to the number of sampled days. Days of the week are stratified into working days and weekend days. This research shows how to use measurements of Leq on working days to estimate the corresponding values for weekend days. This is possible because working days have higher noise levels and less variability than weekend days. The improvement in accuracy allows for a reduction in the number of required sampled days compared to taking samples randomly, which would help to reduce the uncertainty in environmental noise assessment. As a reference, to obtain a 90% confidence interval of ±1 dB for Lday, the proposed sampling strategy reduces the required measurement days by more than 38%. For LDEN, the reduction is close to 18% of the total number of days. The proposed strategy could be adapted to different environments by simply changing a few parameters.Peer ReviewedPostprint (author's final draft

A ground-borne vibration assessment model for rail systems at-grade

Due to the increasing number and kilometres of new railways lines, either high speed railway lines or commuter lines, as well as the increasing in human sensitivity versus ground-borne vibration generated for this mean of transport, a sustained growth in complaints due to the annoyance caused by railway vibrations has been detected. In order to predict the field vibrations caused by new railway lines in the project stage, which will be useful to design appropriate countermeasures, in the present work a ground-borne vibration model for rail systems at-grade developed by the authors is validated with experimental measurements in an existing commuter railway line. It checked that this model is a very useful tool to predict the vibration field that will be caused by a railway infrastructure in the planning stage of the project.Peer ReviewedPostprint (published version

Estimating the rate and luminosity function of all classes of GRBs

The aim of the present work is to estimate the rate and luminosity functions of short, intermediate and long gamma-ray bursts (GRBs) by fitting their intensity distributions wih parameterized explosion rates and luminosity functions. The results show that the parameters of the rate and luminosity function for long GRBs can be calculated with an accuracy of 10-30%. However, some parameters of intermediate and short GRBs have large uncertainties. An important conclusion is that there was initially a large outburst in the frequency of long GRBs, and consequently a large outburst in the star-formation rate, if they come from collapsars. Finally, a simulated intensity distribution has been constructed to test the ability of the method to recover the simulated parameters.Peer ReviewedPostprint (published version

Optimization of the active control of turboprop cabin noise

Peer ReviewedPostprint (published version

Reclassification of gamma-ray bursts

We have applied two different automatic classifier algorithms to the BATSE Current GRB Catalog data and we obtain three different classes of GRBs. Our results confirm the existence of a third, intermediate class of GRBs, with mean duration \sim 25-50 s, as deduced from a cluster analysis and from a neural network algorithm. Our analyses imply longer durations than those found by Mukherjee et al. (1998) and Horvath (1998), whose intermediate class had durations \sim 2-10 s. From the neural network analysis no difference in hardness between the two longest classes is found, and from both methods we find that the intermediate-duration class constitutes the most homogeneous sample of GRBs in its space distribution while the longest-duration class constitutes the most inhomogeneous one with \sim 0.1, being thus the deepest population of GRBs with z_max \sim 10. The trend previously found in long bursts, of spatial inhomogeneity increasing with hardness, only holds for this new longest-duration class.Comment: 8 pages, 11 figures, to appear in MNRA