Abstract

Supernova (SN) rates are potentially powerful diagnostics of metal enrichment and SN physics, particularly in galaxy clusters with their deep, metal-retaining potentials and relatively simple star-formation histories. We have carried out a survey for supernovae (SNe) in galaxy clusters, at a redshift range 0.5<z<0.9, using the Advanced Camera for Surveys (ACS) on the Hubble Space Telescope. We reimaged a sample of 15 clusters that were previously imaged by ACS, thus obtaining two to three epochs per cluster, in which we discovered five likely cluster SNe, six possible cluster SNe Ia, two hostless SN candidates, and several background and foreground events. Keck spectra of the host galaxies were obtained to establish cluster membership. We conducted detailed efficiency simulations, and measured the stellar luminosities of the clusters using Subaru images. We derive a cluster SN rate of 0.35 SNuB +0.17/-0.12 (statistical) \pm0.13 (classification) \pm0.01 (systematic) [where SNuB = SNe (100 yr 10^10 L_B_sun)^-1] and 0.112 SNuM +0.055/-0.039 (statistical) \pm0.042 (classification) \pm0.005 (systematic) [where SNuM = SNe (100 yr 10^10 M_sun)^-1]. As in previous measurements of cluster SN rates, the uncertainties are dominated by small-number statistics. The SN rate in this redshift bin is consistent with the SN rate in clusters at lower redshifts (to within the uncertainties), and shows that there is, at most, only a slight increase of cluster SN rate with increasing redshift. The low and fairly constant SN Ia rate out to z~1 implies that the bulk of the iron mass in clusters was already in place by z~1. The recently observed doubling of iron abundances in the intracluster medium between z=1 and 0, if real, is likely the result of redistribution of existing iron, rather than new production of iron.

Highlights

  • Quantifying the rates and properties of supernovae (SNe) in high-redshift galaxy clusters is important for several applications

  • In terms of cosmic metal-enrichment history, SNe are the sources of iron and other heavy elements that can be observed in the intracluster medium (ICM) and are detectable through X-ray observations (e.g., Balestra et al 2007; Maughan et al 2008; de Plaa et al 2007)

  • Since gamma-ray burst (GRB) are often associated with some CC SNe (Galama et al 1998; Hjorth et al 2003; Stanek et al 2003; Malesani et al 2004; Pian et al 2006; see Woosley & Bloom 2006 for a review), we argue that in addition to being unlikely, they would have affected only our classification as SNe Ia or CC SNe, not the identification as SNe

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Summary

INTRODUCTION

Quantifying the rates and properties of supernovae (SNe) in high-redshift galaxy clusters is important for several applications. In terms of cosmic metal-enrichment history, SNe are the sources of iron and other heavy elements that can be observed in the intracluster medium (ICM) and are detectable through X-ray observations (e.g., Balestra et al 2007; Maughan et al 2008; de Plaa et al 2007) The abundances of these elements in the ICM depend on the integrated history of SN explosions (e.g., Maoz & Gal-Yam 2004), as all of the elements produced during all stages of cluster formation and evolution must remain in the cluster due to its deep potential well. In recent years, constraining the DTD has been attempted by comparing cosmic star formation history (SFH) to redshift-dependent rates of SNe Ia in the field (e.g., Gal-Yam & Maoz 2004; Dahlen et al 2004, 2008; Cappellaro et al 2005; Neill et al 2006; Botticella et al 2008; Poznanski et al 2007; Kuznetsova et al 2008). Magnitudes are reported in the Vegabased system unless stated otherwise

HST OBSERVATIONS AND REDUCTIONS
SUBARU IMAGES
HOST-GALAXY SPECTROSCOPY
CANDIDATE CLASSIFICATION
CLASSIFICATION OF CLUSTER SN CANDIDATES
Background
Candidates with Cluster-Member Hosts
Candidates with Ambiguous Hosts
Hostless Candidates
Candidates Without Measured Host Redshift
Classification Summary
SN RATE CALCULATION
Visibility Time
Detection Efficiency Estimate
Light Curves
Cluster Stellar Luminosity
Error Budget
Statistical Errors
Systematic Errors
Results
DISCUSSION AND SUMMARY
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