On the use of variability time-scales as an early classifier of radio transients and variables

We have shown previously that a broad correlation between the peak radio luminosity and the variability time-scales, approximately L ~ t^5, exists for variable synchrotron emitting sources and that different classes of astrophysical source occupy different regions of luminosity and time-scale space. Based on those results, we investigate whether the most basic information available for a newly discovered radio variable or transient - their rise and/or decline rate - can be used to set initial constraints on the class of events from which they originate. We have analysed a sample of ~ 800 synchrotron flares, selected from light-curves of ~ 90 sources observed at 5-8 GHz, representing a wide range of astrophysical phenomena, from flare stars to supermassive black holes. Selection of outbursts from the noisy radio light-curves has been done automatically in order to ensure reproducibility of results. The distribution of rise/decline rates for the selected flares is modelled as a Gaussian probability distribution for each class of object, and further convolved with estimated areal density of that class in order to correct for the strong bias in our sample. We show in this way that comparing the measured variability time-scale of a radio transient/variable of unknown origin can provide an early, albeit approximate, classification of the object, and could form part of a suite of measurements used to provide early categorisation of such events. Finally, we also discuss the effect scintillating sources will have on our ability to classify events based on their variability time-scales.Comment: Accepted for publication in MNRA

2s exciton-polariton revealed in an external magnetic field

We demonstrate the existence of the excited state of an exciton-polariton in a semiconductor microcavity. The strong coupling of the quantum well heavy-hole exciton in an excited 2s state to the cavity photon is observed in non-zero magnetic field due to surprisingly fast increase of Rabi energy of the 2s exciton-polariton in magnetic field. This effect is explained by a strong modification of the wave-function of the relative electron-hole motion for the 2s exciton state.Comment: 5 pages, 5 figure

Dynamics of long-range order in an exciton-polariton condensate

We report on time resolved measurements of the first order spatial coherence in an exciton polariton Bose-Einstein condensate. Long range spatial coherence is found to set in right at the onset of stimulated scattering, on a picosecond time scale. The coherence reaches its maximum value after the population and decays slower, staying up to a few hundreds of picoseconds. This behavior can be qualitatively reproduced, using a stochastic classical field model describing interaction between the polariton condensate and the exciton reservoir within a disordered potential.Comment: 7 pages, 4 figure

The Elderly Poor in the EU’s New Member States. ENEPRI Research Reports No. 60, November 2008

To what extent is the financial position of the elderly in the NMS more vulnerable than that of the old member states (or the EU-15), due to a rather unfavourable starting point and the possible impact of pension reforms? This is the main issue of the current research report. It tries to delineate the vulnerability of the income position of elderly people in the NMS, in relation to the demographic, socio-economic and institutional context of these countries. More specifically, the report focuses on: - the current level of income of the elderly in the NMS, and the degree of relative poverty; - the way this position is related to the educational and labour market status of the elderly in the NMS, their retirement behaviour, institutional arrangements (notably the pension system), and demographic developments; - specific problems regarding the income position of possibly ‘marginal’ elderly groups in the NMS (such as single elderly female pensioners)

Utility of Facebook\u27s social connectedness index in modeling COVID-19 spread: Exponential random graph modeling study

BACKGROUND: The COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI). OBJECTIVE: Building off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique. METHODS: In September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county\u27s total population, percent rurality, and distance between each county pair. RESULTS: We found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county\u27s total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26). CONCLUSIONS: These results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices

Classification of Multiwavelength Transients with Machine Learning

With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; and (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the 11 classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78 percent. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97 percent, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19 percent.Comment: 16 pages, 12 figure