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Register a new userAn online repository of Swift/XRT light curves of GRBs
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Authors
P.A. Evans
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Context. Swift data are revolutionising our understanding of Gamma Ray Bursts. Since bursts fade rapidly, it is desirable to create and disseminate accurate light curves rapidly. Aims. To provide the community with an online repository of X-ray light curves obtained with Swift. The light curves should be of the quality expected of published data, but automatically created and updated so as to be self-consistent and rapidly available. Methods. We have produced a suite of programs which automatically generates Swift/XRT light curves of GRBs. Effects of the damage to the CCD, automatic readout-mode switching and pile-up are appropriately handled, and the data are binned with variable bin durations, as necessary for a fading source. Results. The light curve repository website (http://www.swift.ac.uk/xrt_curves) contains light curves, hardness ratios and deep images for every GRB which Swifts XRT has observed. When new GRBs are detected, light curves are created and updated within minutes of the data arriving at the UK Swift Science Data Centre.
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We have computed the luminosity rest frame light curves of the first 40 Gamma-ray bursts (GRBs) detected by Swift with well established redshift. We studied average properties of the light curves in the four subsamples of bursts given by z<1, 1<z<2, 2<z<=4, and z>=4. We conclude that all the last three subsamples share the same morphology and the same luminosity range. Very high redshift (z>=4) GRBs detected up to now are not intrinsically longer than lower redshift long GRBs. Nearby long GRBs (z<1) are fainter than average. Possible selection effect are under investigation.
Since GRBs fade rapidly, it is important to publish accurate, precise positions at early times. For Swift-detected bursts, the best promptly available position is most commonly the X-ray Telescope (XRT) position. We present two processes, developed by the Swift team at Leicester, which are now routinely used to improve the precision and accuracy of the XRT positions reported by the Swift team. Both methods, which are fully automated, make use of a PSF-fitting approach which accounts for the bad columns on the CCD. The first method yields positions with 90% error radii <4.4 90% of the time, within 10--20 minutes of the trigger. The second method astrometrically corrects the position using UVOT field stars and the known mapping between the XRT and UVOT detectors, yielding enhanced positions with 90% error radii of <2.8 90% of the time, usually ~2 hours after the trigger.
We present ultravioliet (UV) observations of supernovae (SNe) obtained with the UltraViolet/Optical Telescope (UVOT) on board the Swift spacecraft. This is the largest sample of UV light curves from any single instrument and covers all major SN types and most subtypes. The UV light curves of SNe Ia are fairly homogenous while SNe Ib/c and IIP show more variety in their light curve shapes. The UV-optical colors clearly differentiate SNe Ia and IIP, particularly at early times. The color evolution of SNe IIP, however, makes their colors similar to SNe Ia at about 20 days after explosion. SNe Ib/c are shown to have varied UV-optical colors. The use of UV colors to help type SNe will be important for high redshift SNe discovered in optical observations. These data can be added to ground based optical and near infrared data to create bolometric light curves of individual objects and as checks on generic bolometric corrections used in the absence of UV data. This sample can also be compared with rest-frame UV observations of high redshift SNe observed at optical wavelengths.
During the pre-Swift era, a clustering of light curves was observed in the X-ray, optical and infrared afterglow of gamma-ray bursts. We used a sample of 254 GRB X-ray afterglows to check this fact in the Swift era. We corrected fluxes for distance, time dilation and losses of energy due to cosmological effects. With all our data in hand, we faced with a problem: our data were scattered. We investigated 3 possibilities to explain this, namely: the clustering does not exist, there are problems during calibration of data, and there are instrumental problems. We finally confirm that our sample is consistent with Dainotti correlation.
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