اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدPrecursors in Swift Gamma Ray Bursts with redshift
58
0
0.0
(
0
)
تأليف
D. Burlon
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We study a sample of Gamma-Ray Bursts detected by the Swift satellite with known redshift which show a precursor in the Swift-BAT light curve. We analyze the spectra of the precursors and compare them with the time integrated spectra of the prompt emission. We find neither a correlation between the two slopes nor a tendency for the precursors spectra to be systematically harder or softer than the prompt ones. The energetics of the precursors are large: on average, they are just a factor of a few less energetic (in the source rest frame energy range 15-150 keV) than the entire bursts. These properties do not depend upon the quiescent time between the end of the precursor and the start of the main event. These results suggest that what has been called a precursor is not a phenomenon distinct from the main event, but is tightly connected with it, even if, in some case, the quiescent time intervals can be longer than 100 seconds.
قيم البحث
اقرأ أيضاً
115 -
F. Daigne Institutn dAstrophysique de Paris - UMR 7095 CNRS et Universite Pierre et Marie Curie
2006
We predict the redshift distribution of long Gamma-Ray Bursts (GRBs) with Monte Carlo simulations. Our improved analysis constrains free parameters with three kinds of observation: (i) the log(N)-log(P) diagram of BATSE bursts; (ii) the peak energy d
istribution of bright BATSE bursts; (iii) the HETE2 fraction of X-ray rich GRBs and X-ray flashes. The statistical analysis of the Monte Carlo simulation results allow us to carefully study the impact of the uncertainties in the GRB intrinsic properties on the redshift distribution. The comparison with SWIFT data then leads to the following conclusions. The Amati relation should be intrinsic, if observationally confirmed by SWIFT. The progenitor and/or the GRB properties have to evolve to reproduce the high mean redshift of SWIFT bursts. Our results favor an evolution of the efficiency of GRB production by massive stars, that would be ~6-7 times higher at z~7 than at z~2. We finally predict around 10 GRBs detected by SWIFT at redshift z>6 for a three year mission. These may be sufficient to open a new observational window over the high redshift Universe.
The fireshell model for Gamma-Ray Bursts (GRBs) naturally leads to a canonical GRB composed of a proper-GRB (P-GRB) and an afterglow. P-GRBs, introduced by us in 2001, are sometimes considered precursors of the main GRB event in the current literatur
e. We show in this paper how the fireshell model leads to the understanding of the structure of GRBs, with precise estimates of the time sequence and intensities of the P-GRB and the of the afterglow. It leads as well to a natural classification of the canonical GRBs which overcomes the traditional one in short and long GRBs.
Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble ex
pansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here we introduce machine-z, a redshift prediction algorithm and a high-z classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time our high-z classifier can achieve 80% recall of true high-redshift bursts, while incurring a false positive rate of 20%. With 40% false positive rate the classifier can achieve ~100% recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.
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 l
uminosity indicators have quickly found applications, like the construction of pseudo-redshifts, or the measure of luminosity distances, which can be computed independently of the measure of the redshift. In this contribution I discuss various issues connected with the construction of luminosity-redshift indicators for gamma-ray bursts.
Decades ago two classes of gamma-ray bursts were identified and delineated as having durations shorter and longer than about 2 s. Subsequently indications also supported the existence of a third class. Using maximum likelihood estimation we analyze t
he duration distribution of 888 Swift BAT bursts observed before October 2015. Fitting three log-normal functions to the duration distribution of the bursts provides a better fit than two log-normal distributions, with 99.9999% significance. Similarly to earlier results, we found that a fourth component is not needed. The relative frequencies of the distribution of the groups are 8% for short, 35% for intermediate and 57% for long bursts which correspond to our previous results. We analyse the redshift distribution for the 269 GRBs of the 888 GRBs with known redshift. We find no evidence for the previously suggested difference between the long and intermediate GRBs redshift distribution. The observed redshift distribution of the 20 short GRBs differs with high significance from the distributions of the other groups.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
