Computational AstroStatistics: Fast and efficient tools for analysing huge astronomical data sources

Robert C. Nichol; S Chong; A. J. Connolly; S. Davies; C. Genovese; A. M. Hopkins; C. J. Miller; A. W. Moore; D. Pelleg; G. T. Richards; J. Schneider; I. Szapudi; L. Wasserman

doi:10.1007/0-387-21529-8_18

Computational AstroStatistics: Fast and efficient tools for analysing huge astronomical data sources

Robert C. Nichol, S Chong, A. J. Connolly, S. Davies, C. Genovese, A. M. Hopkins, C. J. Miller, A. W. Moore, D. Pelleg, G. T. Richards, J. Schneider, I. Szapudi, L. Wasserman

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics2 (PiCA) group. This group is dedicated to developing fast and efficient statistical algorithms for analysing huge astronomical data sources. I begin with a short review of multi-resolutional kd-trees which are the building blocks for many of our algorithms. For example, quick range queries and fast N-point correlation functions. I will present new results from the use of Mixture Models (Connolly et al. 2000) in density estimation of multi-color data from the Sloan Digital Sky Survey (SDSS). Specifically, the selection of quasars and the automated identification of X-ray sources. I will also present a brief overview of the False Discovery Rate (FDR) procedure (Miller et al. 2001a) and show how it has been used in the detection of “Baryon Wiggles” in the local galaxy power spectrum and source identification in radio data. Finally, I will look forward to new research on an automated Bayes Network anomaly detector and the possible use of the Locally Linear Embedding algorithm (LLE; Roweis & Saul 2000) for spectral classification of SDSS spectra.

Original language	English
Title of host publication	Statistical challenges in astronomy
Editors	Eric D. Feigelson, G. Jogesh Babu
Publisher	Springer, Springer Nature
ISBN (Electronic)	9780387215297
ISBN (Print)	9780387955469
DOIs	https://doi.org/10.1007/0-387-21529-8_18
Publication status	Published - 2003
Externally published	Yes
Event	The third Statistical Challenges in Modern Astronomy (SCMA III) - University Park, PA, United States Duration: 18 Jul 2021 → 21 Jul 2021

Conference

Conference	The third Statistical Challenges in Modern Astronomy (SCMA III)
Country/Territory	United States
City	University Park, PA
Period	18/07/21 → 21/07/21

Keywords

False Discovery Rate
Mixture Model
Projection Pursuit
Point Correlation Function
Virtual Observatory

Access to Document

10.1007/0-387-21529-8_18

Cite this

Nichol, R. C., Chong, S., Connolly, A. J., Davies, S., Genovese, C., Hopkins, A. M., Miller, C. J., Moore, A. W., Pelleg, D., Richards, G. T., Schneider, J., Szapudi, I., & Wasserman, L. (2003). Computational AstroStatistics: Fast and efficient tools for analysing huge astronomical data sources. In E. D. Feigelson, & G. J. Babu (Eds.), Statistical challenges in astronomy Springer, Springer Nature. https://doi.org/10.1007/0-387-21529-8_18

@inproceedings{aa784320627945a6ad5bc769d47413db,

title = "Computational AstroStatistics: Fast and efficient tools for analysing huge astronomical data sources",

abstract = "I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics2 (PiCA) group. This group is dedicated to developing fast and efficient statistical algorithms for analysing huge astronomical data sources. I begin with a short review of multi-resolutional kd-trees which are the building blocks for many of our algorithms. For example, quick range queries and fast N-point correlation functions. I will present new results from the use of Mixture Models (Connolly et al. 2000) in density estimation of multi-color data from the Sloan Digital Sky Survey (SDSS). Specifically, the selection of quasars and the automated identification of X-ray sources. I will also present a brief overview of the False Discovery Rate (FDR) procedure (Miller et al. 2001a) and show how it has been used in the detection of “Baryon Wiggles” in the local galaxy power spectrum and source identification in radio data. Finally, I will look forward to new research on an automated Bayes Network anomaly detector and the possible use of the Locally Linear Embedding algorithm (LLE; Roweis \& Saul 2000) for spectral classification of SDSS spectra.",

keywords = "False Discovery Rate, Mixture Model, Projection Pursuit, Point Correlation Function, Virtual Observatory",

author = "Nichol, \{Robert C.\} and S Chong and Connolly, \{A. J.\} and S. Davies and C. Genovese and Hopkins, \{A. M.\} and Miller, \{C. J.\} and Moore, \{A. W.\} and D. Pelleg and Richards, \{G. T.\} and J. Schneider and I. Szapudi and L. Wasserman",

year = "2003",

doi = "10.1007/0-387-21529-8\_18",

language = "English",

isbn = "9780387955469",

editor = "Feigelson, \{Eric D.\} and Babu, \{G. Jogesh\}",

booktitle = "Statistical challenges in astronomy",

publisher = "Springer, Springer Nature",

address = "United States",

note = "The third Statistical Challenges in Modern Astronomy (SCMA III) ; Conference date: 18-07-2021 Through 21-07-2021",

}

Nichol, RC, Chong, S, Connolly, AJ, Davies, S, Genovese, C, Hopkins, AM, Miller, CJ, Moore, AW, Pelleg, D, Richards, GT, Schneider, J, Szapudi, I & Wasserman, L 2003, Computational AstroStatistics: Fast and efficient tools for analysing huge astronomical data sources. in ED Feigelson & GJ Babu (eds), Statistical challenges in astronomy. Springer, Springer Nature, The third Statistical Challenges in Modern Astronomy (SCMA III), University Park, PA, United States, 18/07/21. https://doi.org/10.1007/0-387-21529-8_18

Computational AstroStatistics: Fast and efficient tools for analysing huge astronomical data sources. / Nichol, Robert C.; Chong, S; Connolly, A. J. et al.
Statistical challenges in astronomy. ed. / Eric D. Feigelson; G. Jogesh Babu. Springer, Springer Nature, 2003.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Computational AstroStatistics

T2 - The third Statistical Challenges in Modern Astronomy (SCMA III)

AU - Nichol, Robert C.

AU - Chong, S

AU - Connolly, A. J.

AU - Davies, S.

AU - Genovese, C.

AU - Hopkins, A. M.

AU - Miller, C. J.

AU - Moore, A. W.

AU - Pelleg, D.

AU - Richards, G. T.

AU - Schneider, J.

AU - Szapudi, I.

AU - Wasserman, L.

PY - 2003

Y1 - 2003

N2 - I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics2 (PiCA) group. This group is dedicated to developing fast and efficient statistical algorithms for analysing huge astronomical data sources. I begin with a short review of multi-resolutional kd-trees which are the building blocks for many of our algorithms. For example, quick range queries and fast N-point correlation functions. I will present new results from the use of Mixture Models (Connolly et al. 2000) in density estimation of multi-color data from the Sloan Digital Sky Survey (SDSS). Specifically, the selection of quasars and the automated identification of X-ray sources. I will also present a brief overview of the False Discovery Rate (FDR) procedure (Miller et al. 2001a) and show how it has been used in the detection of “Baryon Wiggles” in the local galaxy power spectrum and source identification in radio data. Finally, I will look forward to new research on an automated Bayes Network anomaly detector and the possible use of the Locally Linear Embedding algorithm (LLE; Roweis & Saul 2000) for spectral classification of SDSS spectra.

AB - I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics2 (PiCA) group. This group is dedicated to developing fast and efficient statistical algorithms for analysing huge astronomical data sources. I begin with a short review of multi-resolutional kd-trees which are the building blocks for many of our algorithms. For example, quick range queries and fast N-point correlation functions. I will present new results from the use of Mixture Models (Connolly et al. 2000) in density estimation of multi-color data from the Sloan Digital Sky Survey (SDSS). Specifically, the selection of quasars and the automated identification of X-ray sources. I will also present a brief overview of the False Discovery Rate (FDR) procedure (Miller et al. 2001a) and show how it has been used in the detection of “Baryon Wiggles” in the local galaxy power spectrum and source identification in radio data. Finally, I will look forward to new research on an automated Bayes Network anomaly detector and the possible use of the Locally Linear Embedding algorithm (LLE; Roweis & Saul 2000) for spectral classification of SDSS spectra.

KW - False Discovery Rate

KW - Mixture Model

KW - Projection Pursuit

KW - Point Correlation Function

KW - Virtual Observatory

U2 - 10.1007/0-387-21529-8_18

DO - 10.1007/0-387-21529-8_18

M3 - Conference proceeding contribution

SN - 9780387955469

BT - Statistical challenges in astronomy

A2 - Feigelson, Eric D.

A2 - Babu, G. Jogesh

PB - Springer, Springer Nature

Y2 - 18 July 2021 through 21 July 2021

ER -

Computational AstroStatistics: Fast and efficient tools for analysing huge astronomical data sources

Abstract

Conference

Keywords

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Cite this