4.6 Article

The star-formation rates of 1.5 < z < 2.5 massive galaxies

期刊

ASTRONOMY & ASTROPHYSICS
卷 518, 期 -, 页码 -

出版社

EDP SCIENCES S A
DOI: 10.1051/0004-6361/201014621

关键词

galaxies: evolution; galaxies: starburst; galaxies: fundamental parameters; cosmology: observations; infrared: galaxies

资金

  1. BMVIT (Austria)
  2. ESA-PRODEX (Belgium)
  3. CEA (France)
  4. CNES (France)
  5. DLR (Germany)
  6. ASI (Italy)
  7. INAF (Italy)
  8. CICYT (Spain)
  9. MCYT (Spain)

向作者/读者索取更多资源

The star formation rate (SFR) is a key parameter in the study of galaxy evolution. The accuracy of SFR measurements at z similar to 2 has been questioned following a disagreement between observations and theoretical models. The latter predict SFRs at this redshift that are typically a factor 4 or more lower than the measurements. We present star-formation rates based on calorimetric measurements of the far-infrared (FIR) luminosities for massive 1.5 < z < 2.5, normal star-forming galaxies (SFGs), which do not depend on extinction corrections and/or extrapolations of spectral energy distributions. The measurements are based on observations in GOODS-N with the Photodetector Array Camera and Spectrometer (PACS) onboard Herschel, as part of the PACS evolutionary probe (PEP) project, that resolve for the first time individual SFGs at these redshifts at FIR wavelengths. We compare FIR-based SFRs to the more commonly used 24 mu m and UV SFRs. We find that SFRs from 24 mu m alone are higher by a factor of similar to 4-7.5 than the true SFRs. This overestimation depends on luminosity: gradually increasing for log L(24 mu m) > 12.2 L-circle dot. The SFGs and AGNs tend to exhibit the same 24 mu m excess. The UV SFRs are in closer agreement with the FIR-based SFRs. Using a Calzetti UV extinction correction results in a mean excess of up to 0.3 dex and a scatter of 0.35 dex from the FIR SFRs. The previous UV SFRs are thus confirmed and the mean excess, while narrowing the gap, is insufficient to explain the discrepancy between the observed SFRs and simulation predictions.

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