Super Luminous Supernovae as standardizable candles and high redshift distance probes

We investigate the use of type Ic Super Luminous Supernovae as standardizable candles and distance indicators. Their appeal as cosmological probes stems from their remarkable peak luminosities, hot blackbody temperatures and bright restframe ultraviolet emission. We present a sample of sixteen published SLSN, from redshifts 0.1 to 1.2 and calculate accurate K-corrections to determine uniform magnitudes in two synthetic rest-frame filters with central wavelengths at 400nm and 520nm. At 400nm, we find a low scatter in their uncorrected, raw mean magnitudes with M(400)=-21.70 for the full sample of sixteen objects. We investigate the correlation between their decline rates and peak magnitude and find that the brighter events appear to decline more slowly. We define a $\Delta M(30)$ decay relation. This correlates peak magnitude and decline over 30 days and can reduce the scatter to 0.25. We further show that M(400) appears to have a strong colour dependence. Using this colour rate decay relation, a low scatter of between 0.19 and 0.26 can be found depending on sample selection. However we caution that only eight to ten objects currently have enough data to test this colour rate decline relation. We conclude that SLSN Ic are promising distance indicators at high redshift in regimes beyond those possible with SNe Ia. Although the empirical relationships are encouraging, the unknown progenitor systems and how they may evolve with redshift are of some concern. The two major measurement uncertainties are the limited numbers of low redshift objects to test these relationships and internal dust extinction in the host galaxies.Comment: The authors regret that in the published version (2014, APJ, 796, 87) there were calculation errors in many of the values in Table 1 and in particular the important values for M(400) and the decline rates. The two main conclusions of the paper are unchanged, but the quantitative rms values are larger than previously reporte

Does Early Physical Therapy and Vestibular Rehabilitation after a Sports-related Concussion Decrease the Amount of Time for Symptom Resolution When Compared to the Standard of Care?

OBJECTIVE: The objective of this selective EBM review is to determine whether or not early physical therapy and vestibular rehabilitation after a sports-related concussion (SRC) decreases the amount of time for symptom resolution compared to the standard of care. STUDY DESIGN: Systematic review of two randomized controlled trials published in 2014 and 2017, and one case series published in 2010. DATA SOURCES: All articles, which analyzed the effect of vestibular therapy on treatment of concussive symptoms, were presented in English and were taken from peer-reviewed sources using PubMed and EBSCOhost. OUTCOMES MEASURED: Days from onset of vestibular rehabilitation for persisting concussive symptoms to medical clearance for return to play (RTP) through the clinical judgement of a sports medicine provider, and subjective resolution of concussive symptoms on symptom severity scales. RESULTS: The two RCT’s performed by Schneider et. al. and Reneker et. al. yielded a significant reduction in return-to-play time and symptom resolution in patients receiving vestibular rehabilitation when compared to the control (p\u3c0.001). The case series by Alsalaheen et. al. utilized ANOVA to show a positive correlation between vestibular therapy for resolution of post-concussive symptoms (p\u3c0.001). CONCLUSIONS: Outcomes measured in these three studies demonstrate that earlier physical therapy and vestibular rehabilitation is effective in reducing a teenage or young adult athlete’s time to return-to-play after sustaining a sports-related concussion, when compared to the standard of care, as it aids in symptom resolution. Further studies can be done with larger sample sizes to reinforce the significant outcomes

Observational properties of extreme supernovae

The past ten years have opened up a new parameter space in time-domain astronomy with the discovery of transients defying our understanding of how stars explode. These extremes of the transient paradigm represent the brightest—called superluminous supernovae—and the fastest—known as fast blue optical transients—of the transient zoo. The number discovered and information gained per event have witnessed an exponential growth that has benefited observational and theoretical studies. The collected data and the understanding of such events have surpassed any initial expectation and opened up a future exploding with potential, spanning from novel tools of high-redshift cosmological investigation to new insights into the final stages of massive stars. Here, the observational properties of extreme supernovae are reviewed and put in the context of their physics, possible progenitor scenarios and explosion mechanisms

Direction of Arrival Estimation for Radio Positioning: a Hardware Implementation Perspective

Nowadays multiple antenna wireless systems have gained considerable attention due to their capability to increase performance. Advances in theory have introduced several new schemes that rely on multiple antennas and aim to increase data rate, diversity gain, or to provide multiuser capabilities, beamforming and direction finding (DF) features. In this respect, it has been shown that a multiple antenna receiver can be potentially used to perform radio localization by using the direction of arrival (DoA) estimation technique. In this field, the literature is extensive and gathers the results of almost four decades of research activities. Among the most cited techniques that have been developed, we find the so called high-resolution algorithms, such as multiple signal classification (MUSIC), or estimation of signal parameters via rotational invariance (ESPRIT). Theoretical analysis as well as simulation results have demonstrated their excellent performance to the point that they are usually considered as reference for the comparison with other algorithms. However, such a performance is not necessarily obtained in a real system due to the presence of non idealities. These can be divided into two categories: the impairments due to the antenna array, and the impairments due to the multiple radio frequency (RF) and acquisition front-ends (FEs). The former are strongly influenced by the manufacturing accuracy and, depending on the required DoA resolution, have to be taken into account. Several works address these issues in the literature. The multiple FE non idealities, instead, are usually not considered in the DoA estimation literature, even if they can have a detrimental effect on the performance. This has motivated the research work in this thesis that addresses the problem of DoA estimation from a practical implementation perspective, emphasizing the impact of the hardware impairments on the final performance. This work is substantiated by measurements done on a state-of-the-art hardware platform that have pointed out the presence of non idealities such as DC offsets, phase noise (PN), carrier frequency offsets (CFOs), and phase offsets (POs) among receivers. Particularly, the hardware platform will be herein described and examined to understand what non idealities can affect the DoA estimation performance. This analysis will bring to identify which features a DF system should have to reach certain performance. Another important issue is the number of antenna elements. In fact, it is usually limited by practical considerations, such as size, costs, and also complexity. However, the most cited DoA estimation algorithms need a high number of antenna elements, and this does not yield them suitable to be implemented in a real system. Motivated by this consideration, the final part of this work will describe a novel DoA estimation algorithm that can be used when multipath propagation occurs. This algorithm does not need a high number of antenna elements to be implemented, and it shows good performance despite its low implementation/computational complexity

The CHAIN-REDS Semantic Search Engine

e-Infrastructures, and in particular Data Repositories and Open Access Data Infrastructures, are essential platforms for e-Science and e-Research and are being built since several years both in Europe and the rest of the world to support diverse multi/inter-disciplinary Virtual Research Communities. So far, however, it is difficult for scientists to correlate papers to datasets used to produce them and to discover data and documents in an easy way. In this paper, the CHAINREDS project’s Knowledge Base and its Semantic Search Engine are presented, which attempt to address those drawbacks and contribute to the reproducibility of science

Array Pattern Synthesis for ETC Applications

The problem of antenna array synthesis for radiation pattern defined on a planar surface will be considered in this chapter. This situation could happen when the electric field r-decay factor effect cannot be neglected, for example, an antenna array mechanically tilted and a pattern defined in terms of Cartesian coordinates, as in the electronic toll collection (ETC) scenario. Two possible approaches will be presented: the first one aims at the precise synthesis of the pattern in the case both a constant power-bounded area and a sidelobe suppression region are defined and required to be synthesized. The second approach instead devotes at stretching the coverage area toward the travel length (without considering a precise definition of the communication area) to increase the available identification time with an iterative methodology. For the latter, an antenna prototype has been fabricated, and measurement results have confirmed the approach validity

A statistical approach to identify superluminous supernovae and probe their diversity

We investigate the identification of hydrogen-poor superluminous supernovae (SLSNe I) using a photometric analysis, without including an arbitrary magnitude threshold. We assemble a homogeneous sample of previously classified SLSNe I from the literature, and fit their light curves using Gaussian processes. From the fits, we identify four photometric parameters that have a high statistical significance when correlated, and combine them in a parameter space that conveys information on their luminosity and color evolution. This parameter space presents a new definition for SLSNe I, which can be used to analyse existing and future transient datasets. We find that 90% of previously classified SLSNe I meet our new definition. We also examine the evidence for two subclasses of SLSNe I, combining their photometric evolution with spectroscopic information, namely the photospheric velocity and its gradient. A cluster analysis reveals the presence of two distinct groups. `Fast' SLSNe show fast light curves and color evolution, large velocities, and a large velocity gradient. `Slow' SLSNe show slow light curve and color evolution, small expansion velocities, and an almost non-existent velocity gradient. Finally, we discuss the impact of our analyses in the understanding of the powering engine of SLSNe, and their implementation as cosmological probes in current and future surveys.Comment: 16 pages, 9 figures, accepted by ApJ on 23/01/201

A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study

In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model

Age is Not Just a Number: Perceptions of Pedagogical Content Knowledge, Transformational Teaching, Student-Professor Engagement in Learning, and Deep Learning in the Graduate Classroom

Graduate students in higher education need pedagogical strategies that prepare them with knowledge and critical thinking for their careers. Research conducted in this area concluded that teaching students how to integrate knowledge into the real-world continues to be a challenge for educators across various disciplines in higher education. While scholars have studied effective teaching practices for decades, a broad definition has not been determined. Graduate students’ perceptions of professor pedagogical content knowledge, transformational teaching, student deep learning, and age were compared to determine the behaviors that influence deep learning in business and education programs in the United States and internationally. A survey was administered to 137 students. Findings show that non-traditional learners did not feel as strongly about individualized consideration as traditional learners. The findings suggest that graduate students perceive humor, learning struggles, and relatable content differently