A Simple BATSE Measure of GRB Duty Cycle

We introduce a definition of gamma-ray burst (GRB) duty cycle that describes the GRB's efficiency as an emitter; it is the GRB's average flux relative to the peak flux. This GRB duty cycle is easily described in terms of measured BATSE parameters; it is essentially fluence divided by the quantity peak flux times duration. Since fluence and duration are two of the three defining characteristics of the GRB classes identified by statistical clustering techniques (the other is spectral hardness), duty cycle is a potentially valuable probe for studying properties of these classes.Comment: 4 pages, 1 figure, presented at the 5th Huntsville Gamma-Ray Burst Symposiu

The BATSE Gamma-Ray Burst Spectral Catalog. I. High Time Resolution Spectroscopy of Bright Bursts using High Energy Resolution Data

This is the first in a series of gamma-ray burst spectroscopy catalogs from the Burst And Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory, each covering a different aspect of burst phenomenology. In this paper, we present time-sequences of spectral fit parameters for 156 bursts selected for either their high peak flux or fluence. All bursts have at least eight spectra in excess of 45 sigma above background and span burst durations from 1.66 to 278 s. Individual spectral accumulations are typically 128 ms long at the peak of the brightest events, but can be as short as 16 ms, depending on the type of data selected. We have used mostly high energy resolution data from the Large Area Detectors, covering an energy range of typically 28 - 1800 keV. The spectral model chosen is from a small empirically-determined set of functions, such as the well-known `GRB' function, that best fits the time-averaged burst spectra. Thus, there are generally three spectral shape parameters available for each of the 5500 total spectra: a low-energy power-law index, a characteristic break energy and possibly a high-energy power-law index. We present the distributions of the observed sets of these parameters and comment on their implications. The complete set of data that accompanies this paper is necessarily large, and thus is archived electronically at: http://www.journals.uchicago.edu/ApJ/journal/.Comment: Accepted for publication: ApJS, 125. 38 pages, 9 figures; supplementary electronic archive to be published by ApJ; available from lead author upon reques

Are the arms and head required to accurately estimate centre of mass motion during running?

Accurate measurement of centre of mass (CoM) motion can provide valuable insight into the biomechanics of human running. However, full-body kinematic measurement protocols can be time consuming and difficult to implement. Therefore, this study was performed to understand whether CoM motion during running could be estimated from a model incorporating only lower extremity, pelvic and trunk segments. Full-body kinematic data was collected whilst (n = 12) participants ran on a treadmill at two speeds (3.1 and 3.9 ms−1). CoM trajectories from a full-body model (16-segments) were compared to those estimated from a reduced model (excluding the head and arms). The data showed that, provided an offset was included, it was possible to accurately estimate CoM trajectory in both the anterior-posterior and vertical direction, with root mean square errors of 5 mm in both directions and close matches in waveform similarity (r = 0.975-1.000). However, in the ML direction, there was a considerable difference in the CoM trajectories of the two models (r = 0.774–0.767). This finding suggests that a full-body model is required if CoM motions are to be measured in the ML direction. The mismatch between the reduced and full-body model highlights the important contribution of the arms to CoM motion in the ML direction. We suggest that this control strategy, of using the arms rather than the heavier trunk segments to generate CoM motion, may lead to less variability in CoM motion in the ML direction and subsequently less variability in step width during human running

An empirical study of the “prototype walkthrough”: a studio-based activity for HCI education

For over a century, studio-based instruction has served as an effective pedagogical model in architecture and fine arts education. Because of its design orientation, human-computer interaction (HCI) education is an excellent venue for studio-based instruction. In an HCI course, we have been exploring a studio-based learning activity called the prototype walkthrough, in which a student project team simulates its evolving user interface prototype while a student audience member acts as a test user. The audience is encouraged to ask questions and provide feedback. We have observed that prototype walkthroughs create excellent conditions for learning about user interface design. In order to better understand the educational value of the activity, we performed a content analysis of a video corpus of 16 prototype walkthroughs held in two HCI courses. We found that the prototype walkthrough discussions were dominated by relevant design issues. Moreover, mirroring the justification behavior of the expert instructor, students justified over 80 percent of their design statements and critiques, with nearly one-quarter of those justifications having a theoretical or empirical basis. Our findings suggest that PWs provide valuable opportunities for students to actively learn HCI design by participating in authentic practice, and provide insight into how such opportunities can be best promoted

BATSE Gamma-Ray Burst Line Search: V. Probability of Detecting a Line in a Burst

The physical importance of the apparent discrepancy between the detections by pre-BATSE missions of absorption lines in gamma-ray burst spectra and the absence of a BATSE line detection necessitates a statistical analysis of this discrepancy. This analysis requires a calculation of the probability that a line, if present, will be detected in a given burst. However, the connection between the detectability of a line in a spectrum and in a burst requires a model for the occurrence of a line within a burst. We have developed the necessary weighting for the line detection probability for each spectrum spanning the burst. The resulting calculations require a description of each spectrum in the BATSE database. With these tools we identify the bursts in which lines are most likely to be detected. Also, by assuming a small frequency with which lines occur, we calculate the approximate number of BATSE bursts in which lines of various types could be detected. Lines similar to the Ginga detections can be detected in relatively few BATSE bursts; for example, in only ~20 bursts are lines similar to the GB 880205 pair of lines detectable. Ginga reported lines at ~20 and ~40 keV whereas the low energy cutoff of the BATSE spectra is typically above 20 keV; hence BATSE's sensitivity to lines is less than that of Ginga below 40 keV, and greater above. Therefore the probability that the GB 880205 lines would be detected in a Ginga burst rather than a BATSE burst is ~0.2. Finally, we adopted a more appropriate test of the significance of a line feature.Comment: 20 pages, AASTeX 4.0, 5 figures, Ap.J. in pres

Spectral Hardness Decay with Respect to Fluence in BATSE Gamma-Ray Bursts

We have analyzed the evolution of the spectral hardness parameter Epk as a function of fluence in gamma-ray bursts. We fit 41 pulses within 26 bursts with the trend reported by Liang & Kargatis (1996) which found that Epk decays exponentially with respect to photon fluence. We also fit these pulses with a slight modification of this trend, where Epk decays linearly with energy fluence. In both cases, we found the set of 41 pulses to be consistent with the trend. For the latter trend, which we believe to be more physical, the distribution of the decay constant is roughly log-normal, with a mean of 1.75 +/- 0.07 and a FWHM of 1.0 +/- 0.1. Regarding an earlier reported invariance in the decay constant among different pulses in a single burst, we found probabilities of 0.49 to 0.84 (depending on the test used) that such invariance would occur by coincidence, most likely due to the narrow distribution of decay constant values among pulses.Comment: 17 pages, 7 figure pages, 2 table pages, submitted to The Astrophysical Journa