Fermi GBM GRBs' multivariate statistics

Rácz, István; Balázs, L. G.; Bagoly, Z.; Horváth, I.; Tóth, L. V.

doi:10.1002/asna.201813504

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…The galactic foreground is not as homogeneous as we had previously thought (Rácz et al ; Toth et al ). It seems to be heavily structured and clumpy.…”

Section: X‐ray Observationmentioning

confidence: 48%

Studying the variability of the X‐ray spectral parameters of high‐redshift GRBs' afterglows

Rácz

Hortobagyi

2018

Astron Nachr

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The Swift satellite has observed more than a thousand γ‐ray bursts (GRBs) with X‐ray data. Almost a third of them have redshift measurement, too. Here we start to investigate the X‐ray spectral fitting of the data considering the low‐energy part where the N(H) absorption takes place. Based on the available more accurate input data, we examine the robustness of previous fittings and test how sensitive the changes of the starting parameters are. We study the change of the intrinsic hydrogen column density during the outburst for a few events. No significant variability of the N(H) column density is identified.

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“…The galactic foreground is not as homogeneous as we had previously thought (Rácz et al ; Toth et al ). It seems to be heavily structured and clumpy.…”

Section: X‐ray Observationmentioning

confidence: 48%

Studying the variability of the X‐ray spectral parameters of high‐redshift GRBs' afterglows

Rácz

Hortobagyi

2018

Astron Nachr

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“…To find the main physical difference between the two emerging clusters on the T eff −FIP bias diagram, we carried out linear discriminant analysis (LDA, see Fisher 1936 for the original paper, McLachlan 2004 for a review of the method, or Rácz et al 2018 for an application) using the LinearDiscriminantAnalysis implementation of sklearn.discriminant_analysis. It finds a linear combination of input features that maximises the separation between some pre-defined clusters, and then uses linear decision boundaries for classification.…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

Extending the FIP bias sample to magnetically active stars

Seli

Oláh

Kriskovics

et al. 2022

A&A

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Context. The different elemental abundances of the photosphere and the corona are striking features of not only the Sun, but of other stars as well. This phenomenon is known as the first ionisation potential (FIP) effect, and its strength can be characterized by the FIP bias, the logarithmic abundance difference between low- and high-FIP elements in the corona, compared to the photosphere. The FIP bias was shown to depend on the surface temperature of the star. Aims. We aim to extend the Teff−FIP bias relationship to a larger stellar sample and analyse the effect of other astrophysical parameters on the relation (e.g. surface gravity, age, activity indicators). Methods. We compiled FIP bias and other parameters for 59 stars for which coronal composition is available, now including evolved stars. Using principal component analysis and linear discriminant analysis, we searched for correlations with other astrophysical parameters within the sample that may influence the stellar FIP bias. Results. Adding stars to the Teff−FIP bias diagram unveiled new features in its structure. In addition to the previously known relationship, there appears to be a second branch: a parallel sequence about 0.5 dex above it. While the Teff remains the main determinant of the FIP bias, other parameters such as stellar activity indicators also have influence. We find three clusters in the FIP bias determinant parameter space. One distinct group is formed by the evolved stars. Two groups contain main sequence stars in continuation separated roughly by the sign change of the FIP-bias value. Conclusions. The new branch of the Teff−FIP bias diagram contains stars with higher activity level, in terms of X-ray flux and rotational velocity. The Rossby number also seems to be important, indicating possible dependence on the type of dynamo operating in these stars influencing their FIP bias. The two main-sequence clusters run from the earliest spectral types of A-F with shallow convection zones through G-K-early-M stars with gradually deeper convection zones, and they end with the fully convective M dwarf stars, depicting the change of the dynamo type with the internal differences of the main sequence stars in connection with the FIP-bias values.

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“…SWIFT BAT was used to estimate T 90 of a burst from 15-150 keV [61], in contrast the FERMI GBM team which computes T 90 in the 50-300 keV range [73]. Earlier, Rácz et al [74] and Racz et al [75] did not find any trace of selection effects in the spectral behaviour of GRBs, using the common GRB observation by both satellites. Pinter et al [76] have shown a small, but measurable, difference between the two satellites' T 90 distribution which appears for the shortest and the very long GRBs.…”

Section: Duration Versus Redshift Distribution Analysesmentioning

confidence: 99%

Does the GRB Duration Depend on Redshift?

et al. 2022

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Several hundred gamma-ray burst (GRB) redshifts have been determined to date. One of the other important properties—besides the distance—of the GRBs is the duration of the burst. In this paper, we analyse these two important quantities of the phenomena. In this paper, we map the two-dimensional distribution and explore some suspicious areas. As it is well known that the short GRBs are closer than the others, we search for parts in the Universe where the GRB duration is different from the others. We also analyse whether there are any ranges in the duration where the redshifts differ. We find some suspicious areas, however, no other significant region was found than the short GRB region.

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Fermi GBM GRBs' multivariate statistics

Cited by 6 publications

References 44 publications

Studying the variability of the X‐ray spectral parameters of high‐redshift GRBs' afterglows

Studying the variability of the X‐ray spectral parameters of high‐redshift GRBs' afterglows

Extending the FIP bias sample to magnetically active stars

Does the GRB Duration Depend on Redshift?

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