Are GRB optical afterglows relatively brighter at high z?

📝 Abstract

The redshift distribution of gamma-ray bursts (GRBs) is strongly biased by selection effects. We investigate, via Monte Carlo simulations, one possible selection effect that may be modifying the Swift GRB redshift distribution. We show how telescope response times to acquire a GRB redshift may, via the Malmquist effect and GRB optical afterglow brightness distribution, introduce a bias into the average of the observed redshift distribution. It is difficult to reconcile a recently reported correlated trend between telescope response time and average redshifts unless we employ a redshift-dependent optical afterglow distribution. Simulations of this selection effect suggest that GRB optical afterglows may have been either intrinsically brighter early in the Universe or suffered less local host galaxy extinction.

💡 Analysis

The redshift distribution of gamma-ray bursts (GRBs) is strongly biased by selection effects. We investigate, via Monte Carlo simulations, one possible selection effect that may be modifying the Swift GRB redshift distribution. We show how telescope response times to acquire a GRB redshift may, via the Malmquist effect and GRB optical afterglow brightness distribution, introduce a bias into the average of the observed redshift distribution. It is difficult to reconcile a recently reported correlated trend between telescope response time and average redshifts unless we employ a redshift-dependent optical afterglow distribution. Simulations of this selection effect suggest that GRB optical afterglows may have been either intrinsically brighter early in the Universe or suffered less local host galaxy extinction.

📄 Content

Mon. Not. R. Astron. Soc. 000, 1–5 (2009) Printed 23 March 2022 (MN LATEX style file v2.2) Are GRB optical afterglows relatively brighter at high z? A. Imerito1⋆, D. M. Coward1†, R. R. Burman1 and D. G Blair1 1School of Physics, University of Western Australia, M013, Crawley WA 6009, Australia Accepted xxx. Received yyy; in original form zzz; this draft: 23 March 2022 Rev 38 ABSTRACT The redshift distribution of gamma-ray bursts (GRBs) is strongly biased by selec- tion effects. We investigate, via Monte Carlo simulations, one possible selection effect that may be modifying the Swift GRB redshift distribution. We show how telescope response times to acquire a GRB redshift may, via the Malmquist effect and GRB opti- cal afterglow brightness distribution, introduce a bias into the average of the observed redshift distribution. It is difficult to reconcile a recently reported correlated trend between telescope response time and average redshifts unless we employ a redshift- dependent optical afterglow distribution. Simulations of this selection effect suggest that GRB optical afterglows may have been either intrinsically brighter early in the Universe or suffered less local host galaxy extinction. Key words: gamma-rays: bursts 1 INTRODUCTION The NASA Swift satellite, launched in 2004 November, her- alded a new era of rapid GRB localization. X-ray and UV telescopes on board Swift provided the means to localize GRBs with small error boxes, so that dedicated ground- based telescopes could image the fading optical afterglow (OA). Interestingly, only about 50% of localized GRBs were identified with an optical afterglow prior to Swift. Swift’s sensitivity, combined with the growing number of rapid response ground-based telescopes capable of spectroscopy, promised to fill the gaps of GRB redshifts. Surprisingly, this did not happen: optical/NIR afterglows have been found for nearly 80% of GRBs but only 40–50% have measured redshifts (Tanvir & Jakobsson 2007). The synergy between Swift’s sensitivity and localization capabilities and the grow- ing number of rapid response ground-based telescopes has greatly improved the number of GRB redshifts that could be determined. 2 GRB REDSHIFT SELECTION EFFECTS The probability of GRB redshift measurement is propor- tional to the signal-to-noise ratio of the absorption or emis- sion lines of the OA. Usually, multiple prominent lines are required, but this condition is hampered because GRB OA brightness decays roughly as ∼1/t. In addition, many GRB host galaxies are too faint for redshifts to be obtained, so that the time taken to image and acquire spectra becomes ⋆E-mail: alan@physics.uwa.edu.au † E-mail: coward@physics.uwa.edu.au critical. This was first pointed out by Fiore et al. (2007) for the observed discrepancy between the HETE and Bep- poSAX redshift distributions compared to Swift. Coward (2009) showed that the time taken to acquire a redshift mea- surement has led to a selection effect that is biasing the Swift redshift distribution. Coward (2007) and Coward et al. (2008) showed that for z ∼0 −1, the GRB redshift distribution should increase rapidly because of increasing differential volume sizes and strong SFR evolution. Until mid-2007, this characteristic in the Swift redshift distribution was not apparent. To account for this discrepancy, they argue that other biases, indepen- dent of the Swift sensitivity, are required. The lack of mea- sured redshifts for z ∼1 −2 up to mid-2007, discussed by Coward et al. (2008), is at least partially explained by selec- tion effects from ground-based optical telescopes. 2.1 GRB redshifts and their statistics A shift of the mean of the GRB redshift distribution was observed in the early part of the Swift mission (Berger et al. 2005). This was attributed to the improved sensitivity and more accurate localization by Swift, resulting in a bias for fainter and higher redshift bursts. Jakobsson et al. (2006) showed that within the first year of Swift the mean redshift for a subset of 28 bursts had increased to approximately 2.8, about double that of the pre-Swift average redshift. Assuming that satellite sensitivity is the dominant fac- tor for determining redshift statistics, one could assume that the high mean redshift observed in the early part of the mis- sion would remain fairly constant. If other factors impact on the statistics, such as the time taken to acquire high signal- to-noise spectra, the statistics may well reflect this. Assum- arXiv:0908.0410v1 [astro-ph.HE] 4 Aug 2009 2 A. Imerito, D. Coward, R. Burman and D. Blair 1.5 2 2.5 3 3.5 10 4 10 5 Average Redshift, Average Response Time, [s] Figure 1. The correlation of average response times, ⟨Tz⟩, to acquire a spectroscopic redshift with the mean of the redshift dis- tribution, ⟨z⟩(adapted from fig. 3 of Coward (2009)). The average redshifts were obtained from a moving average filter (sliding z- window) applied to the z data. The average response times are calculated for each

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