Kernel density estimators applied to fast timing hard X-ray observations of the crab pulsar

Carstairs, I. R.; Bazzano, A.; Court, A.; Dean, Anthony J.; Dipper, N. A.; Gorrod, M. J.; Lewis, Robert A.; Maggioli, P.; Perotti, F.; Quadrini, E.; Stephen, J. B.; Ubertini, P.

doi:10.1016/0273-1177(91)90156-e

Cited by 2 publications

(3 citation statements)

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“…KDEs have been used before in astronomy for estimating the pdf from a discrete or noisy data set (Fadda, Slezak & Bijaoui 1998;Bissantz et al 2007;Ascasibar 2010), identifying groups (Balogh et al 2004) and clusters (Valtchanov et al 2004) in galaxy surveys and determining the timings of millisecond pulsars (Carstairs et al 1991) and gamma-ray bursts (de Jager, Raubenheimer & Swanepoel 1986), to name a few examples.…”

Section: Applicationmentioning

confidence: 99%

“…Kernel Density Estimators have been used before in astronomy for estimating the probability density function from a discrete or noisy data set (Fadda et al 1998;Bissantz et al 2007;Ascasibar 2010), identifying groups (Balogh et al 2004) and clusters (Valtchanov et al 2004) in galaxy surveys, and determining the timings of millisecond pulsars (Carstairs et al 1991) and gamma-ray bursts (de Jager et al 1986), to name a few examples. In Section 2.2.1 we described how we fit the SN lightcurves in each of the 4 bands using the parameterised function (1), resulting in twenty lightcurve parameters.…”

Section: Applicationmentioning

confidence: 99%

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Statistical classification techniques for photometric supernova typing

Newling

Varughese

Bassett

et al. 2011

Monthly Notices of the Royal Astronomical Society

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Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on light curves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20 000 simulated Dark Energy Survey light curves. We demonstrate that these methods perform very well provided a representative sample of the full population is used for training. Interestingly, we find that they do not require the redshift of the host galaxy or candidate supernova. However, training on the types of spectroscopic subsamples currently produced by supernova surveys leads to poor performance due to the resulting bias in training, and we recommend that special attention be given to the creation of representative training samples. We show that given a typical non‐representative training sample, S, one can expect to pull out a representative subsample of about 10 per cent of the size of S, which is large enough to outperform the methods trained on all of S.

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Section: Applicationmentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

Statistical classification techniques for photometric supernova typing

Newling

Varughese

Bassett

et al. 2011

Monthly Notices of the Royal Astronomical Society

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“…X and γ‐ray telescopes record the individual arrival times of high‐energy photons which then form a time series of events. This time series is searched for periodicities in the data using well‐known Fourier techniques or lesser‐known, but more powerful, kernel density estimator (KDE) techniques (de Jager 1987, Carstairs 1990, 1992). Figure 4 shows a periodicity search on hard X‐ray data from the Crab Pulsar.…”

Section: Seti and Pulsar Time Seriesmentioning

confidence: 99%

Spreading the net

Carstairs¹

2002

Astronomy &Amp; Geophysics

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Ian R Carstairs reports on efforts to extend the search for extraterrestrial intelligence to X and γ‐ray regions. Abstract Traditional Search for Extra‐Terrestrial Intelligence (SETI) strategies have used radio, microwave and, to a limited extent, optical searches. But this ignores the higher energy X and γ‐ray regions that a technologically advanced extraterrestrial civilization might use to attract our attention – using messages encoded in discrete photon‐counting exchange, much like the signals seen from pulsars. Here, the methods used in high‐energy pulsar detection and analysis are reviewed and applied to this new SETI initiative.

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Kernel density estimators applied to fast timing hard X-ray observations of the crab pulsar

Cited by 2 publications

References 10 publications

Statistical classification techniques for photometric supernova typing

Statistical classification techniques for photometric supernova typing

Spreading the net

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