Dissecting Massive YSOs with Mid-Infrared Interferometry

The very inner structure of massive YSOs is difficult to trace. With conventional observational methods we often identify structures still several hundreds of AU in size. But we also need information about the innermost regions where the actual mass transfer onto the forming high-mass star occurs. An innovative way to probe these scales is to utilise mid-infrared interferometry. Here, we present first results of our MIDI GTO programme at the VLTI. We observed 10 well-known massive YSOs down to scales of 20 mas. We clearly resolve these objects which results in low visibilities and sizes in the order of 30 - 50 mas. Thus, with MIDI we can for the first time quantify the extent of the thermal emission from the warm circumstellar dust and thus calibrate existing concepts regarding the compactness of such emission in the pre-UCHII region phase. Special emphasis will be given to the BN-type object M8E-IR where our modelling is most advanced and where there is indirect evidence for a strongly bloated central star.Comment: 8 pages, 6 figures, proceedings contribution for the conference "Massive Star Formation: Observations confront Theory", held in September 2007 in Heidelberg, Germany; to appear in ASP Conf. Ser. 387, H. Beuther et al. (eds.

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.Comment: journal submission, 34 pages, 8 figure

Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions

There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations -- instead of potentially more insightful and robust causal relations. To support analyzing purely observational data for causal relations, and to assess any differences between purely predictive and causal models of the same data, this paper discusses some novel techniques based on structural causal models (such as directed acyclic graphs of causal Bayesian networks). Using these techniques, one can rigorously express, and partially validate, causal hypotheses; and then use the causal information to guide the construction of a statistical model that captures genuine causal relations -- such that correlation does imply causation. We apply these ideas to analyzing public data about programmer performance in Code Jam, a large world-wide coding contest organized by Google every year. Specifically, we look at the impact of different programming languages on a participant's performance in the contest. While the overall effect associated with programming languages is weak compared to other variables -- regardless of whether we consider correlational or causal links -- we found considerable differences between a purely associational and a causal analysis of the very same data. The takeaway message is that even an imperfect causal analysis of observational data can help answer the salient research questions more precisely and more robustly than with just purely predictive techniques -- where genuine causal effects may be confounded

VLTI observations of IRS~3: The brightest compact MIR source at the Galactic Centre

The dust enshrouded star IRS~3 in the central light year of our galaxy was partially resolved in a recent VLTI experiment. The presented observation is the first step in investigating both IRS~3 in particular and the stellar population of the Galactic Centre in general with the VLTI at highest angular resolution. We will outline which scientific issues can be addressed by a complete MIDI dataset on IRS~3 in the mid infrared.Comment: 4 pages, 3 figures, published in: The ESO Messenge

Bayesian Data Analysis in Empirical Software Engineering Research

Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics have traditionally dominated empirical data analysis, and certainly remain prevalent in empirical software engineering. This situation is unfortunate because frequentist statistics suffer from a number of shortcomings---such as lack of flexibility and results that are unintuitive and hard to interpret---that curtail their effectiveness when dealing with the heterogeneous data that is increasingly available for empirical analysis of software engineering practice. In this paper, we pinpoint these shortcomings, and present Bayesian data analysis techniques that provide tangible benefits---as they can provide clearer results that are simultaneously robust and nuanced. After a short, high-level introduction to the basic tools of Bayesian statistics, we present the reanalysis of two empirical studies on the effectiveness of automatically generated tests and the performance of programming languages. By contrasting the original frequentist analyses with our new Bayesian analyses, we demonstrate the concrete advantages of the latter. To conclude we advocate a more prominent role for Bayesian statistical techniques in empirical software engineering research and practice.Comment: To appear in IEEE Transactions on Software Engineerin

Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality

Statistical analysis is the tool of choice to turn data into information, and then information into empirical knowledge. To be valid, the process that goes from data to knowledge should be supported by detailed, rigorous guidelines, which help ferret out issues with the data or model, and lead to qualified results that strike a reasonable balance between generality and practical relevance. Such guidelines are being developed by statisticians to support the latest techniques for Bayesian data analysis. In this article, we frame these guidelines in a way that is apt to empirical research in software engineering. To demonstrate the guidelines in practice, we apply them to reanalyze a GitHub dataset about code quality in different programming languages. The dataset's original analysis (Ray et al., 2014) and a critical reanalysis (Berger at al., 2019) have attracted considerable attention -- in no small part because they target a topic (the impact of different programming languages) on which strong opinions abound. The goals of our reanalysis are largely orthogonal to this previous work, as we are concerned with demonstrating, on data in an interesting domain, how to build a principled Bayesian data analysis and to showcase some of its benefits. In the process, we will also shed light on some critical aspects of the analyzed data and of the relationship between programming languages and code quality. The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state of the art while highlighting the boundaries of its validity. The guidelines can support building solid statistical analyses and connecting their results, and hence help buttress continued progress in empirical software engineering research

Supernova Remnant in a Stratified Medium: Explicit, Analytical Approximations for Adiabatic Expansion and Radiative Cooling

We propose simple, explicit, analytical approximations for the kinematics of an adiabatic blast wave propagating in an exponentially stratified ambient medium, and for the onset of radiative cooling, which ends the adiabatic era. Our method, based on the Kompaneets implicit solution and the Kahn approximation for the radiative cooling coefficient, gives straightforward estimates for the size, expansion velocity, and progression of cooling times over the surface, when applied to supernova remnants (SNRs). The remnant shape is remarkably close to spherical for moderate density gradients, but even a small gradient in ambient density causes the cooling time to vary substantially over the remnant's surface, so that for a considerable period there will be a cold dense expanding shell covering only a part of the remnant. Our approximation provides an effective tool for identifying the approximate parameters when planning 2-dimensional numerical models of SNRs, the example of W44 being given in a subsequent paper.Comment: ApJ accepted, 11 pages, 2 figures embedded, aas style with ecmatex.sty and lscape.sty package

Detecting Extrasolar Planets with Integral Field Spectroscopy

Observations of extrasolar planets using Integral Field Spectroscopy (IFS), if coupled with an extreme Adaptive Optics system and analyzed with a Simultaneous Differential Imaging technique (SDI), are a powerful tool to detect and characterize extrasolar planets directly; they enhance the signal of the planet and, at the same time, reduces the impact of stellar light and consequently important noise sources like speckles. In order to verify the efficiency of such a technique, we developed a simulation code able to test the capabilities of this IFS-SDI technique for different kinds of planets and telescopes, modelling the atmospheric and instrumental noise sources. The first results obtained by the simulations show that many significant extrasolar planet detections are indeed possible using the present 8m-class telescopes within a few hours of exposure time. The procedure adopted to simulate IFS observations is presented here in detail, explaining in particular how we obtain estimates of the speckle noise, Adaptive Optics corrections, specific instrumental features, and how we test the efficiency of the SDI technique to increase the signal-to-noise ratio of the planet detection. The most important results achieved by simulations of various objects, from 1 M_J to brown dwarfs of 30 M_J, for observations with an 8 meter telescope, are then presented and discussed.Comment: 60 pages, 37 figures, accepted in PASP, 4 Tables adde