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StAR: a simple tool for the statistical comparison of ROC curves

Ismael A Vergara, Tomás Norambuena, Evandro Ferrada, Alex W Slater, Francisco Melo
2008 BMC Bioinformatics  
Conclusion: A new software for the statistical comparison of ROC curves is released here as a web server and also as standalone software for the LINUX operating system.  ...  The software relies on a non-parametric test for the difference of the AUCs that accounts for the correlation of the ROC curves.  ...  Acknowledgements We are truly grateful of the helpful comments and suggestions made by the anonymous reviewers of this manuscript, which in our opinion have substantially improved its clarity of presentation  ... 
doi:10.1186/1471-2105-9-265 pmid:18534022 pmcid:PMC2435548 fatcat:cjb2im23lnf7zhb4av4ego53dy

Morphological Star-Galaxy Separation [article]

Colin T. Slater, Željko Ivezić, Robert H. Lupton
2019 arXiv   pre-print
We discuss the statistical foundations of morphological star-galaxy separation.  ...  We construct a model of the performance of a star-galaxy separator in a realistic survey to understand the impact of observational signal-to-noise ratio (or equivalently, 5-sigma limiting depth) and seeing  ...  The dashed black line is the stellar ROC curve for CBayes, replicated to the other plots to aid comparison.  ... 
arXiv:1912.08210v1 fatcat:owsowgg2ezdwbpz6ktnwlydlqa

Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset [article]

Kyle B Johnston, Hakeem M Oluseyi
2016 arXiv   pre-print
This paper will focus on the construction and application of a supervised pattern classification algorithm for the identification of variable stars.  ...  Given the reduction of a survey of stars into a standard feature space, the problem of using prior patterns to identify new observed patterns can be reduced to time tested classification methodologies  ...  A comparison of the general performance of each classifier can be derived from the generation of AUC for each of the performance curves.  ... 
arXiv:1601.03769v1 fatcat:hqwx5vk2wjdknocikisnuw3lwq

Advanced Astroinformatics for Variable Star Classification [article]

Kyle Burton Johnston
2020 arXiv   pre-print
While our focus will be on the development of machine learning methodologies for the identification of variable star type based on light curve data and associated information, one of the goals of this  ...  This project outlines the complete development of a variable star classification algorithm methodology.  ...  Acknowledgements The author is grateful for the valuable machine learning discussion with S. Wiechecki Vergara and R. Haber. Initial astroinformatics interest was provided by H. Oluseyi.  ... 
arXiv:2008.13775v1 fatcat:7pyngb7qlbfnddilfoe7d4mv5a

Pulsar Star Detection: A Comparative Analysis of Classification Algorithms using SMOTE

Apratim Sadhu
2022 International Journal of Computer and Information Technology(2279-0764)  
A Pulsar is a highly magnetized rotating compact star whose magnetic poles emit beams of radiation. The application of pulsar stars has a great application in the field of astronomical study.  ...  Applications like the existence of gravitational radiation can be indirectly confirmed from the observation of pulsars in a binary neutron star system.  ...  and recall values is maximum for KNN with a value of 97.19.The ROC score for the XGBoosting model is the greatest among the models with a score of 99.62.The ROC score of random forests and bagging is  ... 
doi:10.24203/ijcit.v11i1.193 fatcat:gv363yverjgonjbfnv5qm2xdu4

Development and validation of a mental health screening tool for asylum-seekers and refugees: the STAR-MH

Debbie C. Hocking, Serafino G. Mancuso, Suresh Sundram
2018 BMC Psychiatry  
Using a cut-score of ≥2, the tool provided a sensitivity of 0.93, specificity of 0.75 and predictive accuracy of 80.7%.  ...  Rasch, Differential Item Functioning and ROC analyses evaluated the psychometric properties and tool utility.  ...  Centre for theirassistance throughout the pilot and validation studies.  ... 
doi:10.1186/s12888-018-1660-8 pmid:29548315 pmcid:PMC5857116 fatcat:bydfh7mtfjhzni52uw6fdhikhu

Detecting unresolved binary stars in Euclid VIS images

T. Kuntzer, F. Courbin
2017 Astronomy and Astrophysics  
One method is a simple correlation analysis, while the two others use supervised machine-learning algorithms (random forest and artificial neural network).  ...  Full complexity, in terms of the images and the survey design, is not included, but key aspects of a more mature pipeline are discussed.  ...  We would like to thank Henk Hoekstra and Joana Frontera-Pons for reading a draft of this paper and Vivien Bonvin for useful suggestions.  ... 
doi:10.1051/0004-6361/201730792 fatcat:j6cvs4kb5ffofgkfzfdubbh7cu

Looping Star fMRI in Cognitive Tasks and Resting State

Beatriz Dionisio‐Parra, Florian Wiesinger, Philipp G. Sämann, Michael Czisch, Ana Beatriz Solana
2020 Journal of Magnetic Resonance Imaging  
From the comparison with GE-EPI, further developments of Looping Star fMRI should target increased sensitivity and spatial specificity for both RS and task experiments. 2. 1.  ...  To demonstrate the feasibility of a novel, quiet, T2 *, whole-brain blood oxygenation level-dependent (BOLD)-fMRI method, termed Looping Star, compared to conventional multislice gradient-echo EPI.  ...  Menzel for fruitful scientific discussions about the project. The authors thank Brice Fernandez for valued assistance and helpful comments related to resting state analysis.  ... 
doi:10.1002/jmri.27073 pmid:32073206 fatcat:hf7io43ixnfglmdso2nogcgebq

The PAU survey: star–galaxy classification with multi narrow-band data

L Cabayol, I Sevilla-Noarbe, E Fernández, J Carretero, M Eriksen, S Serrano, A Alarcón, A Amara, R Casas, F J Castander, J de Vicente, M Folger (+9 others)
2018 Monthly notices of the Royal Astronomical Society  
with a completeness of 98.8% for objects brighter than I = 22.5.  ...  Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information.  ...  The PAU data center is hosted by the Port d'Informació Científica (PIC), maintained through a collaboration of CIEMAT and IFAE, with additional support from Universitat Autònoma de Barcelona and ERDF.  ... 
doi:10.1093/mnras/sty3129 fatcat:f3gcaysaener3fadldqkgofohy

ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA

Joseph W. Richards, Dan L. Starr, Nathaniel R. Butler, Joshua S. Bloom, John M. Brewer, Arien Crellin-Quick, Justin Higgins, Rachel Kennedy, Maxime Rischard
2011 Astrophysical Journal  
On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous  ...  In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques.  ...  The authors acknowledge the generous support of a Cyber-Enabled Discovery and Innovation (CDI) grant (No. 0941742) from the National Science Foundation.  ... 
doi:10.1088/0004-637x/733/1/10 fatcat:ntxtwbdmijez3enap26phrynaq

The miniJPAS survey: star-galaxy classification using machine learning [article]

P. O. Baqui, V. Marra, L. Casarini, R. Angulo, L. A. Díaz-García, C. Hernández-Monteagudo, P. A. A. Lopes, C. López-Sanjuan, D. Muniesa, V. M. Placco, M. Quartin, C. Queiroz (+22 others)
2020 arXiv   pre-print
We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses.  ...  We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 1521).  ...  POB thanks, for financial support, the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001.  ... 
arXiv:2007.07622v2 fatcat:mufhnitudrfwtoqcmrfdf4gqyi

The miniJPAS survey: star-galaxy classification using machine learning

P. O. Baqui, V. Marra, L. Casarini, R. Angulo, L. A. Díaz-García, C. Hernández-Monteagudo, P. A. A. Lopes, C. López-Sanjuan, D. Muniesa, V. M. Placco, M. Quartin, C. Queiroz (+7 others)
2020 Astronomy and Astrophysics  
We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g., stars) objects, which is a step required for the subsequent scientific analyses.  ...  When the full magnitude range of 15 ≤ r ≤ 23.5 is analyzed, we find an area under the curve AUC = 0.957 with RF when photometric information alone is used, and AUC = 0.986 with ERT when photometric and  ...  The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), the University  ... 
doi:10.1051/0004-6361/202038986 fatcat:sddmfkdl2re5flxfrwzkjhinfm

A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning [article]

Charles L. Steinhardt, John R. Weaver, Jack Maxfield, Iary Davidzon, Andreas L. Faisst, Dan Masters, Madeline Schemel, Sune Toft
2020 arXiv   pre-print
Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample  ...  Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z>1.  ...  The authors wish to thank Johann Bock Severin, Gabe Brammer, Beryl Hovis-Afflerbach, Adam Jermyn, Christian Kragh Jespersen, Vasily Kokorev, Allison Man, Georgios Magdis, and Jonas Vinther for useful discussions  ... 
arXiv:2002.05729v1 fatcat:j7irv7prhjayjgsh44oof43rti

Cataloging accreted stars within Gaia DR2 using deep learning

B. Ostdiek, L. Necib, T. Cohen, M. Freytsis, M. Lisanti, S. Garrison-Kimmel, A. Wetzel, R. E. Sanderson, P. F. Hopkins
2020 Astronomy and Astrophysics  
The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky  ...  The result is a catalog that identifies ∼767 000 accreted stars within Gaia DR2.  ...  We are grateful to Ben Farr and Graham Kribs for useful discussions. This work utilized the University of Oregon Talapas high performance computing cluster. BO  ... 
doi:10.1051/0004-6361/201936866 fatcat:ngg3ycr3r5cuvgctiwvlyv6u7q

AUTOMATED CLASSIFICATION OF PERIODIC VARIABLE STARS DETECTED BY THEWIDE-FIELD INFRARED SURVEY EXPLORER

Frank J. Masci, Douglas I. Hoffman, Carl J. Grillmair, Roc M. Cutri
2014 Astronomical Journal  
This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful  ...  We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into  ...  Fourier decomposition has been shown to be a powerful tool for variable star classification (Deb & Singh 2009; Rucinski 1993) .  ... 
doi:10.1088/0004-6256/148/1/21 fatcat:bxqriuezbvhqpfi6e4tijbxvne
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