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Register a new userThe redshift distribution of SWIFT Gamma-Ray Bursts: evidence for evolution
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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 distribution 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.
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We compute the luminosity function (LF) and the formation rate of long gamma ray bursts (GRBs) by fitting the observed differential peak flux distribution obtained by the BATSE experiment in two different scenarios: i) the GRB luminosity evolves with redshift and ii) GRBs form preferentially in low-metallicity environments. In both cases, model predictions are consistent with the Swift number counts and with the number of detections at z>2.5 and z>3.5. To discriminate between the two evolutionary scenarios, we compare the model results with the number of luminous bursts (i.e. with isotropic peak luminosity in excess of 10^53 erg s^-1) detected by Swift in its first three years of mission. Our sample conservatively contains only bursts with good redshift determination and measured peak energy. We find that pure luminosity evolution models can account for the number of sure identifications. In the case of a pure density evolution scenario, models with Z_th>0.3 Zsun are ruled out with high confidence. For lower metallicity thresholds, the model results are still statistically consistent with available lower limits. However, many factors can increase the discrepancy between model results and data, indicating that some luminosity evolution in the GRB LF may be needed also for such low values of Z_th. Finally, using these new constraints, we derive robust upper limits on the bright-end of the GRB LF, showing that this cannot be steeper than ~2.6.
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 the 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.
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.
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 expansion. 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 luminosity 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.
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