1. The HerMES SPIRE Submillimeter Luminosity Function Mattia Vaccari & Lucia Marchetti & Alberto Franceschini (University of Padova) Isaac Roseboom (University of Sussex) & the HerMES Consortium - http://hermes.sussex.ac.uk Mattia Vaccari mattia@mattiavaccari.netwww.mattiavaccari.net mattia@mattiavaccari.netwww.mattiavaccari.net A) Summary We present the first measurement of the Submillimeter Local Luminosity Function based on HerMES data. Herschel SPIRE observations are combined with Spitzer, 2MASS & SDSS datasets in the SWIRE Lockman Hole (LH) and Spitzer Extragalactic First Look Survey (XFLS) fields (totaling 14.7 deg 2 ) to optimally detect and identify Herschel sources. We compute the 250, 350, 500 µm as well as the IR bolometric (8-1000 µm) local (0<z<0.2) luminosity function and thus derive local benchmarks for models of the formation and evolution of submillimeter galaxies at a very early stage of the Herschel mission. A) Summary We present the first measurement of the Submillimeter Local Luminosity Function based on HerMES data. Herschel SPIRE observations are combined with Spitzer, 2MASS & SDSS datasets in the SWIRE Lockman Hole (LH) and Spitzer Extragalactic First Look Survey (XFLS) fields (totaling 14.7 deg 2 ) to optimally detect and identify Herschel sources. We compute the 250, 350, 500 µm as well as the IR bolometric (8-1000 µm) local (0<z<0.2) luminosity function and thus derive local benchmarks for models of the formation and evolution of submillimeter galaxies at a very early stage of the Herschel mission. B) HerMES SPIRE Source Extraction and Cross-Identification (Roseboom et al., MNRAS, in preparation) At the depth of our SDP imaging there are ~1-10 beams/src, significantly below the traditional confusion limit of 30 beams/src (see Figure 1). Thus we need to take account of source blending in performing source photometry and cross-identification At the depth of our SDP imaging there are ~1-10 beams/src, significantly below the traditional confusion limit of 30 beams/src (see Figure 1). Thus we need to take account of source blending in performing source photometry and cross-identification We achieve this by making use of the strong correspondence between 24 μm imaging from Spitzer and the SPIRE bands. The SPIRE fluxes of 24 μm sources are estimated using a linear inversion method which finds the best fit set of fluxes considering the 24 μm positions and the SPIRE maps (see mat). Similar methods have been used previously on Spitzer and BLAST data (Magnelli et al. 2009, Bethermin et al. 2010, Chapin et al. 2010) We achieve this by making use of the strong correspondence between 24 μm imaging from Spitzer and the SPIRE bands. The SPIRE fluxes of 24 μm sources are estimated using a linear inversion method which finds the best fit set of fluxes considering the 24 μm positions and the SPIRE maps (see mat). Similar methods have been used previously on Spitzer and BLAST data (Magnelli et al. 2009, Bethermin et al. 2010, Chapin et al. 2010) Testing on simulations (see Figures 2/3 & 4) shows that our method is both reliable and returns high completeness for even the faintest SPIRE sources Testing on simulations (see Figures 2/3 & 4) shows that our method is both reliable and returns high completeness for even the faintest SPIRE sources While our method requires 24 μm detections, we estimate that in our deepest fields we are missing at most 15% of the faint SPIRE population with extreme S 250 /S 24 flux ratios While our method requires 24 μm detections, we estimate that in our deepest fields we are missing at most 15% of the faint SPIRE population with extreme S 250 /S 24 flux ratios For local (0<z<0.2) sources we estimate to be complete down to the adopted flux limits For local (0<z<0.2) sources we estimate to be complete down to the adopted flux limits B) HerMES SPIRE Source Extraction and Cross-Identification (Roseboom et al., MNRAS, in preparation) At the depth of our SDP imaging there are ~1-10 beams/src, significantly below the traditional confusion limit of 30 beams/src (see Figure 1). Thus we need to take account of source blending in performing source photometry and cross-identification At the depth of our SDP imaging there are ~1-10 beams/src, significantly below the traditional confusion limit of 30 beams/src (see Figure 1). Thus we need to take account of source blending in performing source photometry and cross-identification We achieve this by making use of the strong correspondence between 24 μm imaging from Spitzer and the SPIRE bands. The SPIRE fluxes of 24 μm sources are estimated using a linear inversion method which finds the best fit set of fluxes considering the 24 μm positions and the SPIRE maps (see mat). Similar methods have been used previously on Spitzer and BLAST data (Magnelli et al. 2009, Bethermin et al. 2010, Chapin et al. 2010) We achieve this by making use of the strong correspondence between 24 μm imaging from Spitzer and the SPIRE bands. The SPIRE fluxes of 24 μm sources are estimated using a linear inversion method which finds the best fit set of fluxes considering the 24 μm positions and the SPIRE maps (see mat). Similar methods have been used previously on Spitzer and BLAST data (Magnelli et al. 2009, Bethermin et al. 2010, Chapin et al. 2010) Testing on simulations (see Figures 2/3 & 4) shows that our method is both reliable and returns high completeness for even the faintest SPIRE sources Testing on simulations (see Figures 2/3 & 4) shows that our method is both reliable and returns high completeness for even the faintest SPIRE sources While our method requires 24 μm detections, we estimate that in our deepest fields we are missing at most 15% of the faint SPIRE population with extreme S 250 /S 24 flux ratios While our method requires 24 μm detections, we estimate that in our deepest fields we are missing at most 15% of the faint SPIRE population with extreme S 250 /S 24 flux ratios For local (0<z<0.2) sources we estimate to be complete down to the adopted flux limits For local (0<z<0.2) sources we estimate to be complete down to the adopted flux limits HerMES ~70 deg 2 C) Optical/NIR Counterparts & SED Fitting HerMES/Spitzer Large-Area SDP Fields LH & XFLS were used in this work NameArea (deg 2) S lim (mJy) 0<z<0.2 Sources Total (Spec/Phot) LH ~ 10 40 478 (369/109) XFLS ~ 5 30 275 (222/53) Source #s are for the 250 μm sample Flux limits are the same at 350 & 500 μm D) SPIRE 250, 350, 500 µm & IR Bolometric (8-1000 µm) LLF The accurate 24 μm positions used for Herschel source extraction allows us to reliably determine the SDSS and 2MASS counterparts of all our 0<z<0.2 sources. SDSS, NED and SWIRE follow-up spectroscopic redshifts are used along with SDSS photometric redshifts to obtain a complete redshift information. Archival multi-wavelength spectrophotometry (including IRAC and MIPS) then allows us to determine accurate photometric redshifts and IR luminosities for all 0<z<0.2 sources. E) Conclusions and future work We provide useful local (0<z<0.2) benchmarks for submillimeter galaxy formation and evolution studies at a very early stage of the Herschel mission and thus pave the way for wider-area data soon to be provided by Herschel surveys such as HerMES and H-ATLAS We provide useful local (0<z<0.2) benchmarks for submillimeter galaxy formation and evolution studies at a very early stage of the Herschel mission and thus pave the way for wider-area data soon to be provided by Herschel surveys such as HerMES and H-ATLAS Using 24 positions and combining linear inversion and model selection techniques we reliably detect Herschel sources and identify them in multi-wavelength images Using 24 μm positions and combining linear inversion and model selection techniques we reliably detect Herschel sources and identify them in multi-wavelength images We find a slightly more abundant local submillimeter population than predicted by most models in recent literature and an IR bolometric (8-1000 μm) LLD of 1.3 10 8 L  at z~0.1 We find a slightly more abundant local submillimeter population than predicted by most models in recent literature and an IR bolometric (8-1000 μm) LLD of 1.3 10 8 L  at z~0.1 The continuation of the Herschel mission will yield larger samples and improved SED templates, providing better IR bolometric luminosity estimates and stronger constraints on models for galaxy evolution and dust emission from the local to the distant Universe The continuation of the Herschel mission will yield larger samples and improved SED templates, providing better IR bolometric luminosity estimates and stronger constraints on models for galaxy evolution and dust emission from the local to the distant Universe E) Conclusions and future work We provide useful local (0<z<0.2) benchmarks for submillimeter galaxy formation and evolution studies at a very early stage of the Herschel mission and thus pave the way for wider-area data soon to be provided by Herschel surveys such as HerMES and H-ATLAS We provide useful local (0<z<0.2) benchmarks for submillimeter galaxy formation and evolution studies at a very early stage of the Herschel mission and thus pave the way for wider-area data soon to be provided by Herschel surveys such as HerMES and H-ATLAS Using 24 positions and combining linear inversion and model selection techniques we reliably detect Herschel sources and identify them in multi-wavelength images Using 24 μm positions and combining linear inversion and model selection techniques we reliably detect Herschel sources and identify them in multi-wavelength images We find a slightly more abundant local submillimeter population than predicted by most models in recent literature and an IR bolometric (8-1000 μm) LLD of 1.3 10 8 L  at z~0.1 We find a slightly more abundant local submillimeter population than predicted by most models in recent literature and an IR bolometric (8-1000 μm) LLD of 1.3 10 8 L  at z~0.1 The continuation of the Herschel mission will yield larger samples and improved SED templates, providing better IR bolometric luminosity estimates and stronger constraints on models for galaxy evolution and dust emission from the local to the distant Universe The continuation of the Herschel mission will yield larger samples and improved SED templates, providing better IR bolometric luminosity estimates and stronger constraints on models for galaxy evolution and dust emission from the local to the distant Universe We evaluate the Monochromatic & IR Bolometric LLFs using the 1/V max estimator We evaluate the Monochromatic & IR Bolometric LLFs using the 1/V max estimator We compare our estimates with models and measurements from recent literature We compare our estimates with models and measurements from recent literature (Poisson) errors are estimated in each field and a weighted mean is then computed (Poisson) errors are estimated in each field and a weighted mean is then computed A&A Herschel Special Issue Figures 2/3. Simulations used for XID: GOODS-N 250 μm simulated map (left) & real map (right) Figures 2/3. Simulations used for XID: GOODS-N 250 μm simulated map (left) & real map (right) Figure 1. GOODS-N 250 μm image (left) & R-band optical image (right). Green squares are 24 μm sources. Circle is beam at 250 μm. It is clear that even if we consider only 24 μm sources there are still ~1 beam/src. The strong correspondence between 250 μm and 24 μm can be seen in the left image. Testing was performed on two simulated datasets by taking the mock catalogs of Fernandez-Conde et al. (2008) and producing maps which match the observed properties (i.e. noise, PRF) of our GOODS-N (deep) and LH (shallow) data Testing was performed on two simulated datasets by taking the mock catalogs of Fernandez-Conde et al. (2008) and producing maps which match the observed properties (i.e. noise, PRF) of our GOODS-N (deep) and LH (shallow) data Comparisons are performed for the shallow data between our XID method and two existing techniques; the ubiquitous p-statistic coupled with Sussextractor derived source catalogues, and a simpler map-based approach based on Bethermin et al. (2010) Comparisons are performed for the shallow data between our XID method and two existing techniques; the ubiquitous p-statistic coupled with Sussextractor derived source catalogues, and a simpler map-based approach based on Bethermin et al. (2010) At 250 μm the HerMES XID method is seen to clearly outperform the others At 250 μm the HerMES XID method is seen to clearly outperform the others At the longer wavelengths the advantage currently offered by our method is limited At the longer wavelengths the advantage currently offered by our method is limited Testing was performed on two simulated datasets by taking the mock catalogs of Fernandez-Conde et al. (2008) and producing maps which match the observed properties (i.e. noise, PRF) of our GOODS-N (deep) and LH (shallow) data Testing was performed on two simulated datasets by taking the mock catalogs of Fernandez-Conde et al. (2008) and producing maps which match the observed properties (i.e. noise, PRF) of our GOODS-N (deep) and LH (shallow) data Comparisons are performed for the shallow data between our XID method and two existing techniques; the ubiquitous p-statistic coupled with Sussextractor derived source catalogues, and a simpler map-based approach based on Bethermin et al. (2010) Comparisons are performed for the shallow data between our XID method and two existing techniques; the ubiquitous p-statistic coupled with Sussextractor derived source catalogues, and a simpler map-based approach based on Bethermin et al. (2010) At 250 μm the HerMES XID method is seen to clearly outperform the others At 250 μm the HerMES XID method is seen to clearly outperform the others At the longer wavelengths the advantage currently offered by our method is limited At the longer wavelengths the advantage currently offered by our method is limited Φ 250,350,500 plots : black solid line is total model and color lines are different populations of Franceschini et al. (2010), dashed black line is model of Xu et al. (2001), dot-dot-dot- dashed black line is model of Negrello et al. (2007) Φ 250,350,500 plots : black solid line is total model and color lines are different populations of Franceschini et al. (2010), dashed black line is model of Xu et al. (2001), dot-dot-dot- dashed black line is model of Negrello et al. (2007) Φ IR plot : dashed, dashed red line is modified Schechter (double exponential) best fit Φ IR plot : dashed, dot-dashed, dot-dot-dot-dashed and dotted black lines indicate predictions by Xu et al. (2001), Lagache et al. (2004), Negrello et al. (2007) and Valiante et al. (2009), dashed red line is modified Schechter (double exponential) best fit http://hermes.sussex.ac.uk Empty Diamonds Sanders et al. 2003