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Register a new userRedshift indicators for gamma-ray bursts
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J-L. Atteia
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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 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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The desire to extend the Hubble Diagram to higher redshifts than the range of current Type Ia Supernovae observations has prompted investigation into spectral correlations in Gamma Ray Bursts, in the hope that standard candle-like properties can be identified. In this paper we discuss the potential of these new `cosmic rulers and highlight their limitations by investigating the constraints that current data can place on an alternative Cosmological model in the form of Conformal Gravity. By fitting current Type 1a Supernovae and Gamma Ray Burst (GRB) data to the predicted luminosity distance redshift relation of both the standard Concordance Model and Conformal Gravity, we show that currently emph{neither} model is strongly favoured at high redshift. The scatter in the current GRB data testifies to the further work required if GRBs are to cement their place as effective probes of the cosmological distance scale.
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.
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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 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.
Gamma Ray Bursts (GRBs) are a powerful probe of the high redshift Universe. We present a tool to estimate the detection rate of high-z GRBs by a generic detector with defined energy band and sensitivity. We base this on a population model that reproduces the observed properties of GRBs detected by Swift, Fermi and CGRO in the hard X-ray and gamma-ray bands. We provide the expected cumulative distributions of the flux and fluence of simulated GRBs in different energy bands. We show that scintillator detectors, operating at relatively high energies (e.g. tens of keV to the MeV), can detect only the most luminous GRBs at high redshifts due to the link between the peak spectral energy and the luminosity (Ep-Liso) of GRBs. We show that the best strategy for catching the largest number of high-z bursts is to go softer (e.g. in the soft X-ray band) but with a very high sensitivity. For instance, an imaging soft X-ray detector operating in the 0.2-5 keV energy band reaching a sensitivity, corresponding to a fluence of ~10^-8 erg cm^-2, is expected to detect ~40 GRBs yr^-1 sr^-1 at z>5 (~3 GRBs yr^-1 sr^-1 at z>10). Once high-z GRBs are detected the principal issue is to secure their redshift. To this aim we estimate their NIR afterglow flux at relatively early times and evaluate the effectiveness of following them up and construct usable samples of events with any forthcoming GRB mission dedicated to explore the high-z Universe.
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.
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