Continuum Variability of Deeply Embedded Protostars as a Probe of Envelope Structure

Stars may be assembled in large growth spurts, however the evidence for this hypothesis is circumstantial. Directly studying the accretion at the earliest phases of stellar growth is challenging because young stars are deeply embedded in optically thick envelopes, which have spectral energy distributions that peak in the far-IR, where observations are difficult. In this paper, we consider the feasibility of detecting accretion outbursts from these younger stars by investigating the timescales for how the protostellar envelope responds to changes in the emission properties of the central source. The envelope heats up in response to an outburst, brightening at all wavelengths and with the emission peak moving to shorter wavelengths. The timescale for this change depends on the time for dust grains to heat and re-emit photons and the time required for the energy to escape the inner, optically-thick portion of the envelope. We find that the dust response time is much shorter than the photon propagation time and thus the timescale over which the emission varies is set by time delays imposed by geometry. These times are hours to days near the peak of the spectral energy distribution and weeks to months in the sub-mm. The ideal location to quickly detect continuum variability is therefore in the mid- to far-IR, near the peak of the spectral energy distribution, where the change in emission amplitude is largest. Searching for variability in sub-mm continuum emission is also feasible, though with a longer time separation and a weaker relationship between the amount of detected emission amplitude and change in central source luminosity. Such observations would constrain accretion histories of protostars and would help to trace the disk/envelope instabilities that lead to stellar growth.Comment: 25 pages, 6 figures, accepted for publication in the Astrophysical Journa

Preface

Preface to first issue of Orbit

An assembler and disassembler framework for Java™ programmers

AbstractThe Java™ programming language is primarily used for platform-independent programming. Yet it also offers many productivity, maintainability and performance benefits for platform-specific functions, such as the generation of machine code.We have created reliable assemblers for SPARC™ , AMD64, IA32 and PowerPC which support all user mode and privileged instructions and with 64 bit mode support for all but the latter. These assemblers are generated as Java source code by our extensible assembler framework, which itself is written in the Java language. The assembler generator also produces javadoc comments that precisely specify the legal values for each operand.Our design is based on the Klein Assembler System written in Self. Assemblers are generated from a specification, as are table-driven disassemblers and unit tests. The specifications that drive the generators are expressed as Java language objects. Thus no extra parsers are needed and developers do not need to learn any new syntax to extend the framework for additional ISAs.Every generated assembler is tested against a preexisting assembler by comparing the output of both. Each instruction’s test cases are derived from the cross product of its potential operand values. The majority of tests are positive (i.e., result in a legal instruction encoding). The framework also generates negative tests, which are expected to cause an error detection by an assembler. As with the Klein Assembler System, we have found bugs in the external assemblers as well as in ISA reference manuals.Our framework generates tens of millions of tests. For symbolic operands, our tests include all applicable predefined constants. For integral operands, the important boundary values, such as the respective minimum, maximum, 0, 1 and −1, are tested. Full testing can take hours to run but gives us a high degree of confidence regarding correctness

Civil Society Reconsidered: The Durable Nature and Community Structure of Collective Civic Action

This article develops a conceptual framework on civil society that shifts the dominant focus on individuals to collective action events— civic and protest alike—that bring people together in public to realize a common purpose. Analyzing over 4,000 events in the Chicago area from 1970 to 2000, the authors find that while civic engagement is durable overall, “sixties-style” protest declines, and hybrid events that combine public claims making with civic forms of behavior — what they call “blended social action”—increase. Furthermore, dense social ties, group memberships, and neighborly exchange do not predict community variations in collective action. The density of nonprofit organizations matters instead, suggesting that declines in traditional social capital may not be as consequential for civic capacity as commonly thought.Sociolog

Scholarly insight Autumn 2017:a Data wrangler perspective

As the OU is going through several fundamental changes, it is important that strategic decisions made by Faculties and senior management are informed by evidence-based research and insights. One way how Data Wranglers provide insights of longitudinal development and performance of OU modules is the Key Metric Report 2017. A particular new element is that data can now also be unpacked and visualised on a Nation-level. As evidenced by the Nation-level reporting, there are substantial variations of success across the four Nations, and we hope that our interactive dashboards allow OU staff to unpack the underlying data. The second way Data Wranglers provide insight to Faculties and Units is through the Scholarly insight report series. Building on the previous two reports whereby we reported on substantial variation and inconsistencies in learning designs and assessment practices within qualifications across the OU, in this Scholarly insight Autumn 2017 report we address four big pedagogical questions that were framed and co-constructed together with the Faculties and LTI units. Many Faculties and colleagues have reacted positively on our Scholarly insight Spring 2017 report, whereby for the first time we were able to show empirically that students experienced substantial variations in success within 12 large OU qualifications. As evidenced in our previous report, 55% of variation in students’ success over time was explained by OU institutional factors (i.e., how students were assessed within their respective module; how students were able to effectively transition from one learning design of one module to the next one), rather than students’ characteristics, engagement and behaviour. We have received several queries and questions from Faculties and Units about how to better understand these students’ journeys, and how qualifications and module designs could be better aligned within their respective qualification(s). As these are complex conceptual and Big Pedagogy questions, in Chapter 1 we continued these complex analyses by looking at the transitional processes of the first two modules that OU students take, and how well aligned these modules and qualification paths are. In Chapter 2, we explored the more fine-grained, qualitative, and lived experiences of 19 students across a range of qualifications to understand how OU grading practices and (in)consistencies of assessment and feedback influenced their affect, behaviour, and cognition. In addition to building on previous topics, we introduced two new Scholarly insights in Chapter 3 and Chapter 4. As the OU is increasingly using learning analytics to support our staff and students, in Chapter 3 we analysed the impact of giving Predictive Learning Analytics to over 500 Associate Lecturers across 31 modules on student retention. Finally, in Chapter 4 we explored the impact of first presentations of new modules on pass rates and satisfaction, whereby we were able to bust another myth that may have profound implications for Student First Transformation. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties , we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight

Tests of the Las Campanas Distant Cluster Survey from Confirmation Observations for the ESO Distant Cluster Survey

The ESO Distant Cluster Survey (EDisCS) is a photometric and spectroscopic study of the galaxy cluster population at two epochs, z~0.5 and z~0.8, drawn from the Las Campanas Distant Cluster Survey (LCDCS). We report results from the initial candidate confirmation stage of the program and use these results to probe the properties of the LCDCS. Of the 30 candidates targeted, we find statistically significant overdensities of red galaxies near 28. Of the ten additional candidates serendipitously observed within the fields of the targeted 30, we detect red galaxy overdensities near six. We test the robustness of the published LCDCS estimated redshifts to misidentification of the brighest cluster galaxy (BCG) in the survey data, and measure the spatial alignment of the published cluster coordinates, the peak red galaxy overdensity, and the brightest cluster galaxy. We conclude that for LCDCS clusters out to z~0.8, 1) the LCDCS coordinates agree with the centroid of the red galaxy overdensity to within 25'' (~150 h^{-1} kpc) for 34 out of 37 candidates with 3\sigma galaxy overdensities, 2) BCGs are typically coincident with the centroid of the red galaxy population to within a projected separation of 200 h^{-1} kpc (32 out of 34 confirmed candidates), 3) the red galaxy population is strongly concentrated, and 4) the misidentification of the BCG in the LCDCS causes a redshift error >0.1 in 15-20% of the LCDCS candidates. These findings together help explain the success of the surface brightness fluctuations detection method.Comment: 10 pages, 9 figures, accepted for publication in the November 10 issue of Ap

Scholarly insight Spring 2018: a Data wrangler perspective

In the movie classic Back to the Future a young Michael J. Fox is able to explore the past by a time machine developed by the slightly bizarre but exquisite Dr Brown. Unexpectedly by some small intervention the course of history was changed a bit along Fox’s adventures. In this fourth Scholarly Insight Report we have explored two innovative approaches to learn from OU data of the past, which hopefully in the future will make a large difference in how we support our students and design and implement our teaching and learning practices. In Chapter 1, we provide an in-depth analysis of 50 thousands comments expressed by students through the Student Experience on a Module (SEAM) questionnaire. By analysing over 2.5 million words using big data approaches, our Scholarly insights indicate that not all student voices are heard. Furthermore, our big data analysis indicate useful potential insights to explore how student voices change over time, and for which particular modules emergent themes might arise. In Chapter 2 we provide our second innovative approach of a proof-of-concept of qualification path way using graph approaches. By exploring existing data of one qualification (i.e., Psychology), we show that students make a range of pathway choices during their qualification, some of which are more successful than others. As highlighted in our previous Scholarly Insight Reports, getting data from a qualification perspective within the OU is a difficult and challenging process, and the proof-of-concept provided in Chapter 2 might provide a way forward to better understand and support the complex choices our students make. In Chapter 3, we provide a slightly more practically-oriented and perhaps down to earth approach focussing on the lessons-learned with Analytics4Action. Over the last four years nearly a hundred modules have worked with more active use of data and insights into module presentation to support their students. In Chapter 3 several good-practices are described by the LTI/TEL learning design team, as well as three innovative case-studies which we hope will inspire you to try something new as well. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties, we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight. We hope that you see some potential in the two innovative approaches, and perhaps you might want to try some new ideas in your module. While a time machine has not really been invented yet, with the increasing rich and fine-grained data about our students and our learning practices we are getting closer to understand what really drives our students