Using visual analytics to develop situation awareness in astrophysics

We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists who need to analyze heterogeneous, complex data under time pressure, and make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in production use for 2 years by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture and the results of an informal usability evaluation of the production system by domain experts in the context of Endsley's three levels of situation awareness

Parallelizing Gaussian Process Calculations in R

We consider parallel computation for Gaussian process calculations to overcome computational and memory constraints on the size of datasets that can be analyzed. Using a hybrid parallelization approach that uses both threading (shared memory) and message-passing (distributed memory), we implement the core linear algebra operations used in spatial statistics and Gaussian process regression in an R package called bigGP that relies on C and MPI. The approach divides the covariance matrix into blocks such that the computational load is balanced across processes while communication between processes is limited. The package provides an API enabling R programmers to implement Gaussian process-based methods by using the distributed linear algebra operations without any C or MPI coding. We illustrate the approach and software by analyzing an astrophysics dataset with n = 67, 275 observations

Type II Supernovae as Probes of Cosmology

- Constraining the cosmological parameters and understanding Dark Energy have tremendous implications for the nature of the Universe and its physical laws. - The pervasive limit of systematic uncertainties reached by cosmography based on Cepheids and Type Ia supernovae (SNe Ia) warrants a search for complementary approaches. - Type II SNe have been shown to offer such a path. Their distances can be well constrained by luminosity-based or geometric methods. Competing, complementary, and concerted efforts are underway, to explore and exploit those objects that are extremely well matched to next generation facilities. Spectroscopic follow-up will be enabled by space- based and 20-40 meter class telescopes. - Some systematic uncertainties of Type II SNe, such as reddening by dust and metallicity effects, are bound to be different from those of SNe Ia. Their stellar progenitors are known, promising better leverage on cosmic evolution. In addition, their rate - which closely tracks the ongoing star formation rate - is expected to rise significantly with look- back time, ensuring an adequate supply of distant examples. - These data will competitively constrain the dark energy equation of state, allow the determination of the Hubble constant to 5%, and promote our understanding of the processes involved in the last dramatic phases of massive stellar evolution.Comment: Science white paper, submitted to the Decadal committee Astro201

V1647 Orionis: Reinvigorated Accretion and the Re-Appearance of McNeil's Nebula

In late 2003, the young eruptive variable star V1647 Orionis optically brightened by over 5 magnitudes, stayed bright for around 26 months, and then decline to its pre-outburst level. In August 2008 the star was reported to have unexpectedly brightened yet again and we herein present the first detailed observations of this new outburst. Photometrically, the star is now as bright as it ever was following the 2003 eruption. Spectroscopically, a pronounced P Cygni profile is again seen in Halpha with an absorption trough extending to -700 km/s. In the near-infrared, the spectrum now possesses very weak CO overtone bandhead absorption in contrast to the strong bandhead emission seen soon after the 2003 event. Water vapor absorption is also much stronger than previously seen. We discuss the current outburst below and relate it to the earlier event.Comment: 6 pages, 3 figure

Sunfall: a collaborative visual analytics system for astrophysics

Computational and experimental sciences produce and collect ever-larger and complex datasets, often in large-scale, multi-institution projects. The inability to gain insight into complex scientific phenomena using current software tools is a bottleneck facing virtually all endeavors of science. In this paper, we introduce Sunfall, a collaborative visual analytics system developed for the Nearby Supernova Factory, an international astrophysics experiment and the largest data volume supernova search currently in operation. Sunfall utilizes novel interactive visualization and analysis techniques to facilitate deeper scientific insight into complex, noisy, high-dimensional, high-volume, time-critical data. The system combines novel image processing algorithms, statistical analysis, and machine learning with highly interactive visual interfaces to enable collaborative, user-driven scientific exploration of supernova image and spectral data. Sunfall is currently in operation at the Nearby Supernova Factory; it is the first visual analytics system in production use at a major astrophysics project

Hubble Space Telescope and Ground-Based Observations of the Type Iax Supernovae SN 2005hk and SN 2008A

We present Hubble Space Telescope (HST) and ground-based optical and near-infrared observations of SN 2005hk and SN 2008A, typical members of the Type Iax class of supernovae (SNe). Here we focus on late-time observations, where these objects deviate most dramatically from all other SN types. Instead of the dominant nebular emission lines that are observed in other SNe at late phases, spectra of SNe 2005hk and 2008A show lines of Fe II, Ca II, and Fe I more than a year past maximum light, along with narrow [Fe II] and [Ca II] emission. We use spectral features to constrain the temperature and density of the ejecta, and find high densities at late times, with n_e >~ 10^9 cm^-3. Such high densities should yield enhanced cooling of the ejecta, making these objects good candidates to observe the expected "infrared catastrophe," a generic feature of SN Ia models. However, our HST photometry of SN 2008A does not match the predictions of an infrared catastrophe. Moreover, our HST observations rule out a "complete deflagration" that fully disrupts the white dwarf for these peculiar SNe, showing no evidence for unburned material at late times. Deflagration explosion models that leave behind a bound remnant can match some of the observed properties of SNe Iax, but no published model is consistent with all of our observations of SNe 2005hk and 2008A.Comment: 20 pages, 15 figure

Veterinary students' views on animal patiens and human clients, using Q methodology

Veterinarians serve two masters: animal patients and human clients. Both animal patients and human clients have legitimate interests, and conflicting moral claims may flow from these interests. Earlier research concludes that veterinary students are very much aware of the complex and often paradoxical relationship they have and will have with animals. In this article the views of veterinary students about their anticipated relationship with animal patients and human clients are studied. The main part of the article describes discourses of first-year and fourth-year students about their (future) relationship with animals and their caretakers, for which Q-methodology is used. At the end of the article, the discourses are related to the students' gender and their workplace preferences. © 2007 AAVMC

Object Classification at the Nearby Supernova Factory

We present the results of applying new object classification techniques to the supernova search of the Nearby Supernova Factory. In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better object discrimination: we reduced the number of nonsupernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming large optical surveys

Seeking supernovae in the clouds: a performance study,”

ABSTRACT Today, our picture of the Universe radically differs from that of just over a decade ago. We now know that the Universe is not only expanding as Hubble discovered in 1929, but that the rate of expansion is accelerating, propelled by mysterious new physics dubbed "Dark Energy." This revolutionary discovery was made by comparing the brightness of nearby Type Ia supernovae (which exploded in the past billion years) to that of much more distant ones (from up to seven billion years ago). The reliability of this comparison hinges upon a very detailed understanding of the physics of the nearby events. As part of its effort to further this understanding, the Nearby Supernova Factory (SNfactory) relies upon a complex pipeline of serial processes that execute various image processing algorithms in parallel on ~10TBs of data. This pipeline has traditionally been run on a local cluster. Cloud computing offers many features that make it an attractive alternative. The ability to completely control the software environment in a Cloud is appealing when dealing with a community developed science pipeline with many unique library and platform requirements. In this context we study the feasibility of porting the SNfactory pipeline to the Amazon Web Services environment. Specifically we: describe the tool set we developed to manage a virtual cluster on Amazon EC2, explore the various design options available for application data placement, and offer detailed performance results and lessons learned from each of the above design options