Multiwavelength Energy Distributions and Bolometric Luminosities of the 12 Micron Galaxy Sample

Spinoglio, L.; Malkan, Matthew A.; Rush, Brian; Carrasco, L.; Recillas-Cruz, E.

doi:10.1086/176425

Cited by 180 publications

(250 citation statements)

References 30 publications

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“…2 http://www.cfht.hawaii.edu/~arnouts/lephare.html nosity dependence similar to the SEDs of the libraries proposed by Chary & Elbaz (2001) and Lagache et al (2004). In this scheme, the monochromatic luminosity at any given IR wavelength is linked to L IR by a monotonic relation, similar to the correlations that have been observed between the mid-IR emission and the total IR luminosity of galaxies in the local Universe (e.g., Spinoglio et al 1995;Chary & Elbaz 2001;Takeuchi et al 2003;Treyer et al 2010;Goto et al 2011). In this way, a 24 µm flux density observed at a given redshift is associated to a unique L IR .…”

Section: The Infrared Luminositysupporting

confidence: 67%

Encoding of the infrared excess in theNUVrKcolor diagram for star-forming galaxies

Arnouts

Floc’h

Chevallard

et al. 2013

A&A

186

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We present an empirical method of assessing the star formation rate (SFR) of star-forming galaxies based on their locations in the rest-frame color-color diagram (NUV − r) vs. (r − K). By using the Spitzer 24 µm sample in the COSMOS field (∼16 400 galaxies with 0.2 ≤ z ≤ 1.3) and a local GALEX-SDSS-SWIRE sample (∼700 galaxies with z ≤ 0.2), we show that the mean infrared excess IRX = L IR /L UV can be described by a single vector, NRK, that combines the two colors. The calibration between IRX and NRK allows us to recover the IR luminosity, L IR , with an accuracy of σ ∼ 0.21 for the COSMOS sample and 0.27 dex for the local one. The SFRs derived with this method agree with the ones based on the observed (UV+IR) luminosities and on the spectral energy distribution (SED) fitting for the vast majority (∼85%) of the star-forming population. Thanks to a library of model galaxy SEDs with realistic prescriptions for the star formation history, we show that we need to include a two-component dust model (i.e., birth clouds and diffuse ISM) and a full distribution of galaxy inclinations in order to reproduce the behavior of the IRX stripes in the NUVrK diagram. In conclusion, the NRK method, based only on the rest-frame UV/optical colors available in most of the extragalactic fields, offers a simple alternative of assessing the SFR of star-forming galaxies in the absence of far-IR or spectral diagnostic observations.

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Section: The Infrared Luminositysupporting

confidence: 67%

Encoding of the infrared excess in theNUVrKcolor diagram for star-forming galaxies

Arnouts

Floc’h

Chevallard

et al. 2013

A&A

186

250

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“…This means that the selection in the MIR affects the result only very slightly, and the sample properties are controlled by the FIR. It is a clear contrast to the sample of Spinoglio et al (1995) which was 12-µm selected: the present sample consists of more quiescent, normal galaxies than theirs. A full treatment including the upper-limit sample will be presented elsewhere (Takeuchi et al 2004, in preparation).…”

Section: Iras Samplecontrasting

confidence: 58%

“…Based on their 12-µm sample of galaxies, Spinoglio et al (1995) made a pioneering study to examine various correlations between flux densities from near-IR (NIR) to far-IR (FIR), and presented useful diagnostics for Seyferts and normal galaxies on color-color diagrams. They also found that the 12-µm Appendices are only available in electronic form at http://www.edpsciences.org…”

Section: Introductionmentioning

confidence: 99%

Mid-infrared luminosity as an indicator of the total infrared luminosity of galaxies

Takeuchi

Buat

Iglésias-Páramo

et al. 2005

A&A

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Abstract. The infrared (IR) emission plays a crucial role in understanding the star formation in galaxies hidden by dust. We first examined four estimators of the IR luminosity of galaxies, L FIR (Helou et al. 1988 (Sanders & Mirabel 1996) by using the observed SEDs of wellknown galaxies. We found that L IR provides excellent estimates of the total IR luminosity for a variety of galaxy SEDs. The performance of L TIR2 was also found to be very good. Using L IR , we then statistically analyzed the IRAS PSCz galaxy sample (Saunders et al. 2000) and found useful formulae relating the MIR monochromatic luminosities [L(12 µm) and L(25 µm)] and L IR . For this purpose we constructed a subsample of 1420 galaxies with all four IRAS band (12, 25, 60, and 100 µm) flux densities. We found linear relations between L IR and MIR luminosities, L(12 µm) and L(25 µm). The prediction error with a 95% confidence level is a factor of 4-5. Hence, these formulae are useful for the estimation of the total IR luminosity only from 12 µm or 25 µm observations. We further tried to make an "interpolation" formula for galaxies at 0 < z < 1. For this purpose we construct the formula of the relation between 15-µm luminosity and the total IR luminosity. We conclude that the 15-µm formula can be used as an estimator of the total IR luminosity from 24 µm observation of galaxies at z 0.6.

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“…The majority of the 24 μm-selected sources lies at z < 1 and the distribution of 350 μm-selected sources peaks at z ∼ 1.5. Another possible explanation is that fainter sources lies near z = 1 and are thus selected at the 12 μm rest-frame, which is a very good estimator of the infrared bolometric luminosity according to Spinoglio et al (1995).…”

Section: Accuracy Of the Stacking Counts Methods On Blastmentioning

confidence: 99%

Submillimeter number counts at 250 μm, 350 μm and 500 μm in BLAST data

Béthermin

Dole

Cousin

et al. 2010

A&A

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Context. The instrument BLAST (Balloon-borne Large-Aperture Submillimeter Telescope) performed the first deep and wide extragalactic survey at 250, 350 and 500 μm. The extragalactic number counts at these wavelengths are important constraints for modeling the evolution of infrared galaxies. Aims. We estimate the extragalactic number counts in the BLAST data, which allow a comparison with the results of the P(D) analysis of Patanchon et al. (2009). Methods. We use three methods to identify the submillimeter sources. 1) Blind extraction using an algorithm when the observed field is confusion-limited and another one when the observed field is instrumental-noise-limited. The photometry is computed with a new simple and quick point spread function (PSF) fitting routine (FASTPHOT). We use Monte-Carlo simulations (addition of artificial sources) to characterize the efficiency of this extraction, and correct the flux boosting and the Eddington bias. 2) Extraction using a prior. We use the Spitzer 24 μm galaxies as a prior to probe slightly fainter submillimeter flux densities. 3) A stacking analysis of the Spitzer 24 μm galaxies in the BLAST data to probe the peak of the differential submillimeter counts. Results. With the blind extraction, we reach 97 mJy, 83 mJy and 76 mJy at 250 μm, 350 μm and 500 μm respectively with a 95% completeness. With the prior extraction, we reach 76 mJy, 63 mJy, 49 mJy at 250 μm, 350 μm and 500 μm respectively. With the stacking analysis, we reach 6.2 mJy, 5.2 mJy and 3.5 mJy at 250 μm, 350 μm and 500 μm respectively. The differential submillimeter number counts are derived, and start showing a turnover at flux densities decreasing with increasing wavelength. (2009) models slightly overestimate the observed counts, but the data agree very well near the peak of the differential number counts. Models predict that the galaxy populations probed at the peak are likely z ∼ 1.8 ultra-luminous infrared galaxies.

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Multiwavelength Energy Distributions and Bolometric Luminosities of the 12 Micron Galaxy Sample

Cited by 180 publications

References 30 publications

Encoding of the infrared excess in theNUVrKcolor diagram for star-forming galaxies

Encoding of the infrared excess in theNUVrKcolor diagram for star-forming galaxies

Mid-infrared luminosity as an indicator of the total infrared luminosity of galaxies

Submillimeter number counts at 250 μm, 350 μm and 500 μm in BLAST data

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