Probing the sparse tails of redshift distributions with Voronoi tessellations

Granett, B. R.

doi:10.1016/j.ascom.2016.12.001

Cited by 1 publication

(1 citation statement)

References 29 publications

(19 reference statements)

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“…Other related works are Liu et al (2017) and Barnes et al (2016). Furthermore, three recent applications of RF in astrophysics are by (Vilalta, Gupta, and Macri, 2013;Schuh, Angryk, and Martens, 2015;Granett, 2017).…”

Section: Random Forestsmentioning

confidence: 99%

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

et al. 2018

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We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Spaceweather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), K. Florios cflorios@aueb.gr I. Kontogiannis K. Florios et al.taken over a five-year interval (2012 -2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several Machine Learning (ML) and Conventional Statistics techniques to predict flares of peak magnitude >M1 and >C1, within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM) and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02) and Heidke skill score HSS=0.49(0.01) for >M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01) and HSS=0.59(0.01) for >C1 flare prediction with probability threshold 35%.

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