Machine-z: rapid machine-learned redshift indicator forSwiftgamma-ray bursts
نویسندگان
چکیده
منابع مشابه
Testing a new luminosity/redshift indicator for γ-ray bursts
We have tested a relative spectral lag (RSL) method suggested earlier as a luminosity/redshift (or distance) estimator, using the generalized method by Schaefer & Collazzi. We find the derivations from the luminosity/redshift-RSL (L/R-RSL) relation are comparable with the corresponding observations. Applying the luminosityRSL relation to two different GRB samples, we find that there exist no vi...
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— The measure of the distances and luminosities of gamma-ray bursts (GRBs) led to the discovery that many GRB properties are strongly correlated with their intrinsic luminosity, leading to the construction of reliable luminosity indicators. These GRB luminosity indicators have quickly found applications, like the construction of 'pseudo-redshifts', or the measure of luminosity distances, which ...
متن کاملRelative spectral lag: an new redshift indicator to measure the cosmos with gamma-ray bursts
Using 64 ms count data for long gamma-ray bursts ( T90 > 2.6 s), we analyze the quantity, relative spectral lag (RSL), which is defined as τ31/FWHM(1), where τ31 is the spectral lag between energy bands 1 and 3, and FWHM(1) denotes full width at half maximum of the pulse in energy channel 1. To get insights into features of the RSLs, we investigate in detail all the correlations between them an...
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Using 64 ms count data of long gamma-ray bursts (LBs, T90 > 2.6 s), we analyze the quantity named relative spectral lag (RSL), τ31/FWHM (1) =τrel, 31. We investigate in detail the properties of the RSL for a sample of nine LBs, using the general cross-correlation technique that includes the lag between two different energy bands. We find that the distribution of RSLs is normal and has a mean va...
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There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary adaptation and machine learning into one a...
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ژورنال
عنوان ژورنال: Monthly Notices of the Royal Astronomical Society
سال: 2016
ISSN: 0035-8711,1365-2966
DOI: 10.1093/mnras/stw559