Gamma-ray bursts provide what is probably one of the messiest of all astrophysical data sets. Burst class properties are indistinct, as overlapping characteristics of individual bursts are convolved ...with effects of instrumental and sampling biases. Despite these complexities, data mining techniques have allowed new insights to be made about gamma-ray burst data. We demonstrate how data mining techniques have simultaneously allowed us to learn about gamma-ray burst detectors and data collection, cosmological effects in burst data, and properties of burst subclasses. We discuss the exciting future of this field, and the web-based tool we are developing (with support from the NASA AISR Program). We invite others to join us in AI-guided gamma-ray burst classification (http://grb.mnsu.edu/grb/).
The Fluence Duration Bias Hakkila, Jon; Meegan, Charles A; Pendleton, Geoffrey N ...
arXiv.org,
01/2000
Paper
Odprti dostop
The fluence duration bias causes fluences and durations of faint gamma-ray bursts to be systematically underestimated relative to their peak fluxes. Using Monte Carlo analysis, we demonstrate how ...this effect explains characteristics of structure of the fluence vs. 1024 ms peak flux diagram. Evidence of this bias exists in the BATSE fluence duration database, and provides a partial explanation for the existence of burst class properties.
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.
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.
High speed networking at Cray research Nicholson, Andy; Golio, Joe; Borman, David A. ...
Computer communication review,
01/1991, Letnik:
21, Številka:
1
Magazine Article
For many years, ethernet has been the mainstay for TCP/IP and local area networking, and issues specific to wide area and long haul networks have not been adequately addressed. The advent of FDDI and ...HIPPI standards, which are, respectively, one and two orders of magnitude faster then Ethernet, and high speed cross country links, are causing what used to be experimental issues to become everyday problems. This paper will cover some of these issues, as they relate to the TCP/IP protocols, and the work that has happened at Cray Research in the development of the UNICOS† operating system to address these issues.
Occurrence of Trichoderma species in apple orchard and woodland soils Roiger, D.J; Jeffers, S.N; Caldwell, R.W. (Department of Plant Pathology, 1630 Linden Drive, University of Wisconsin, Madison, WI 53706 (USA))
Soil Biology and Biochemistry (United Kingdom),
(1991), Letnik:
23, Številka:
4
Publication
