Long-Term (9-Year) Response of Two Semiarid Grasslands to Prescribed Fire in the Southwestern USA

Historically, arid grasslands of SW USA experienced fire return intervals of 5-10 years. During the last 100 years, however, fire has been a rare event. Recent expansion of woody plants in arid grasslands has prompted managers to re-introduce fire as a tool to reduce abundance of woody plants and maintain perennial grass cover. The use of fire in desert grasslands poses unique challenges, however, due to extreme variability in rainfall patterns. Our research examines vegetation response to repeat fire in 2 desert grassland ecotones near Albuquerque, New Mexico (35.05o N 106.60o W)

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

Time Dependent Clustering Analysis of the Second BATSE Gamma-Ray Burst Catalog

A time dependent two-point correlation-function analysis of the BATSE 2B catalog finds no evidence of burst repetition. As part of this analysis, we discuss the effects of sky exposure on the observability of burst repetition and present the equation describing the signature of burst repetition in the data. For a model of all burst repetition from a source occurring in less than five days we derive upper limits on the number of bursts in the catalog from repeaters and model-dependent upper limits on the fraction of burst sources that produce multiple outbursts.Comment: To appear in the Astrophysical Journal Letters, uuencoded compressed PostScript, 11 pages with 4 embedded figure

AI Gamma-Ray Burst Classification: Methodology/Preliminary Results

Artificial intelligence (AI) classifiers can be used to classify unknowns, refine existing classification parameters, and identify/screen out ineffectual parameters. We present an AI methodology for classifying new gamma-ray bursts, along with some preliminary results.Comment: 5 pages, 2 postscript figures. To appear in the Fourth Huntsville Gamma-Ray Burst Symposiu

Properties of Gamma-Ray Burst Classes

The three gamma-ray burst (GRB) classes identified by statistical clustering analysis (Mukherjee et al. 1998) are examined using the pattern recognition algorithm C4.5 (Quinlan 1986). Although the statistical existence of Class 3 (intermediate duration, intermediate fluence, soft) is supported, the properties of this class do not need to arise from a distinct source population. Class 3 properties can easily be produced from Class 1 (long, high fluence, intermediate hardness) by a combination of measurement error, hardness/intensity correlation, and a newly-identified BATSE bias (the fluence duration bias). Class 2 (short, low fluence, hard) does not appear to be related to Class 1.Comment: 5 pages, 4 imbedded figures, presented at the 5th Huntsville Gamma-Ray Burst Symposiu