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PLANCKHZSC - Planck High-Redshift Source Candidates Catalog

HEASARC
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Overview

The Planck mission, thanks to its large frequency range and all-sky coverage, has a unique potential for systematically detecting the brightest, and rarest, sub-millimeter sources on the sky, including distant objects in the high-redshift Universe traced by their dust emission. A novel method, based on a component-separation procedure using a combination of Planck and IRAS data, has been validated and characterized on numerous simulations, and applied to select the most luminous cold sub-millimeter sources with spectral energy distributions peaking between 353 and 857GHz at 5-arcminute resolution. A total of 2,151 Planck high-z source candidates (the PHZ list) have been detected in the cleanest 26% of the sky, with flux density at 545 GHz above 500 mJy. Embedded in the cosmic infrared background close to the confusion limit, these high-z candidates exhibit colder colors than their surroundings, consistent with redshifts z greater than 2, assuming a dust temperature of Txgal = 35 K and a spectral index of betaxgal = 1.5. Exhibiting extremely high luminosities, larger than 1014 Lsun, the PHZ objects may be made of multiple galaxies or clumps at high redshift, as suggested by a first statistical analysis based on a comparison with number count models. Furthermore, first follow-up observations obtained from optical to sub-millimeter wavelengths, which can be found in companion papers, have confirmed that this list consists of two distinct populations. A small fraction (around 3%) of the sources have been identified as strongly gravitationally lensed star-forming galaxies at redshift 2 to 4, while the vast majority of the PHZ sources appear as overdensities of dusty star-forming galaxies, having colors consistent with being at z > 2, and may be considered as proto-cluster candidates. The PHZ provides an original sample, which is complementary to the Planck Sunyaev-Zeldovich Catalog (PSZ2); by extending the population of virialized massive galaxy clusters detected below z < 1.5 through their SZ signal to a population of sources at z > 1.5, the PHZ may contain the progenitors of today's clusters. Hence the Planck list of high-redshift source candidates opens a new window on the study of the early stages of structure formation, particularly understanding the intensively star-forming phase at high-z.

The compact source detection algorithm used herein requires positive detections simultaneously within a 5-arcminute radius in the 545-GHz excess map, and the 857-, 545-, and 353-GHz cleaned maps. It also requires a non-detection in the 100-GHz cleaned maps, which traces emission from synchrotron sources. A detection is then defined as a local maximum of the signal-to-noise ratio (S/N) above a given threshold in each map, with a spatial separation of at least 5 arcminutes being required between two local maxima. A threshold of S/N > 5 is adopted for detections in the 545-GHz excess map, while this is slightly relaxed to S/N > 3 for detections in the cleaned maps because the constraint imposed by the spatial consistency between detections in all three bands is expected to reinforce the robustness of a simultaneous detection. Concerning the 100-GHz band, the authors adopt a similar threshold by requiring the absence of any local maximum with S/N > 3 within a radius of 5 arcminutes.

The HEASARC has changed the names of many of the parameters from those given in the original table. In such cases we have listed the original names in parentheses at the end of the parameter descriptions given below.


Catalog Bibcode

2016A&A...596A.100P

References

Planck intermediate results. XXXIX.
The Planck list of high-redshift source candidates.
    Planck Collaboration:
    Ade P.A.R., Aghanim N., Arnaud M., Aumont J., Baccigalupi C., Banday A.J.,
    Barreiro R.B., Bartolo N., Battaner E., Benabed K., Benoit-Levy A.,
    Bernard J.-P., Bersanelli M., Bielewicz P., Bonaldi A., Bonavera L.,
    Bond J.R., Borrill J., Bouchet F.R., Boulanger F., Burigana C.,
    Butler R.C., Calabrese E., Catalano A., Chiang H.C., Christensen P.R.,
    Clements D.L., Colombo L.P.L., Couchot F., Coulais A., Crill B.P.,
    Curto A., Cuttaia F., Danese L., Davies R.D., Davis R.J., de Bernardis P.,
    de Rosa A., de Zotti G., Delabrouille J., Dickinson C., Diego J.M.,
    Dole H., Dore O., Douspis M., Ducout A., Dupac X., Elsner F., Ensslin T.A.,
    Eriksen H.K., Falgarone E., Finelli F., Flores-Cacho I., Frailis M.,
    Fraisse A.A., Franceschi E., Galeotta S., Galli S., Ganga K., Giard M.,
    Giraud-Heraud Y., Gjerlow E., Gonzalez-Nuevo J., Gorski K.M., Gregorio A.,
    Gruppuso A., Gudmundsson J.E., Hansen F.K., Harrison D.L., Helou G.,
    Hernandez-Monteagudo C., Herranz D., Hildebrandt S.R., Hivon E., Hobson M.,
    Hornstrup A., Hovest W., Huffenberger K.M., Hurier G., Jaffe A.H.,
    Jaffe T.R., Keihanen E., Keskitalo R., Kisner T.S., Kneissl R., Knoche J.,
    Kunz M., Kurki-Suonio H., Lagache G., Lamarre J.-M., Lasenby A.,
    Lattanzi M., Lawrence C.R., Leonardi R., Levrier F., Liguori M.,
    Lilje P.B., Linden-Vornle M., Lopez-Caniego M., Lubin P.M.,
    Macias-Perez J.F., Maffei B., Maggio G., Maino D., Mandolesi N.,
    Mangilli A., Maris M., Martin P.G., Martinez-Gonzalez E., Masi S.,
    Matarrese S., Melchiorri A., Mennella A., Migliaccio M., Mitra S.,
    Miville-Deschenes M.-A., Moneti A., Montier L., Morgante G., Mortlock D.,
    Munshi D., Murphy J.A., Nati F., Natoli P., Nesvadba N.P.H., Noviello F.,
    Novikov D., Novikov I., Oxborrow C.A., Pagano L., Pajot F., Paoletti D.,
    Partridge B., Pasian F., Pearson T.J., Perdereau O., Perotto L.,
    Pettorino V., Piacentini F., Piat M., Plaszczynski S., Pointecouteau E.,
    Polenta G., Pratt G.W., Prunet S., Puget J.-L., Rachen J.P., Reinecke M.,
    Remazeilles M., Renault C., Renzi A., Ristorcelli I., Rocha G., Rosset C.,
    Rossetti M., Roudier G., Rubino-Martin J.A., Rusholme B., Sandri M.,
    Santos D., Savelainen M., Savini G., Scott D., Spencer L.D., Stolyarov V.,
    Stompor R., Sudiwala R., Sunyaev R., Suur-Uski A.-S., Sygnet J.-F.,
    Tauber J.A., Terenzi L., Toffolatti L., Tomasi M., Tristram M., Tucci M.,
    Turler M., Umana G., Valenziano L., Valiviita J., Van Tent F., Vielva P.,
    Villa F., Wade L.A., Wandelt B.D., Wehus I.K., Welikala N., Yvon D.,
    Zacchei A., Zonca A.
    <Astron. Astrophys. 596, A100 (2016)>
    =2016A&A...596A.100P        (SIMBAD/NED BibCode)

Provenance

This table was created by the HEASARC in May 2017 based upon the CDS Catalog J/A+A/596/A100 file phz.dat.

Parameters

Name
The Planck high-redshift (PHZ) source candidate designation based on its position in Galactic coordinates, viz., 'PHZ GLLL.ll+BB.bb'.

RA
The Right Ascension of the Planck high-redshift (PHZ) source candidate in the selected equinox. This was given in decimal degrees to a precision of 10-6 degrees. The maps used in this study were smoothed to a common FWHM of 5 arcminutes. The authors discuss the positional accuracy of the detected sources in Section 5.2 of the reference paper and estimate it to be of the order of a few arcminutes.

Dec
The Declination of the Planck high-redshift (PHZ) source candidate in the selected equinox. This was given in decimal degrees to a precision of 10-6 degrees. The maps used in this study were smoothed to a common FWHM of 5 arcminutes. The authors discuss the positional accuracy of the detected sources in Section 5.2 of the reference paper and estimate it to be of the order of a few arcminutes.

LII
The Galactic Longitude of the Planck high-redshift (PHZ) source candidate.

BII
The Galactic Latitude of the Planck high-redshift (PHZ) source candidate.

SNR_Excess_545_GHz
The signal-to-noise ratio of the Planck high-redshift (PHZ) source candidate in the 545-GHz excess map. The SEDs of sources located at high redshift will exhibit an excess of power at lower frequencies, located at their dust emission peak. In order to enhance this effect, the authors built the excess map at 545 GHz by subtracting from the cleaned map at 545 GHz a linear interpolation between the two surrounding bands, i.e., the 857- and 353-GHz maps. See Section 3.4 of the reference paper for further details. (SNR_X545)

SNR_857_GHz
The signal-to-noise ratio of the Planck high-redshift (PHZ) source candidate in the 857-GHz cleaned map. (SNR_D857)

SNR_545_GHz
The signal-to-noise ratio of the Planck high-redshift (PHZ) source candidate in the 545-GHz cleaned map. (SNR_D545)

SNR_353_GHz
The signal-to-noise ratio of the Planck high-redshift (PHZ) source candidate in the 353-GHz cleaned map. (SNR_D353)

Major_Axis
The FWHM along the major axis of the elliptical Gaussian fit in the 545-GHz cleaned map at the location of the source, in arcminutes.(GAU_MAJOR_AXIS)

Major_Axis_Error
The RMS uncertainty of the FWHM along the major axis of the elliptical Gaussian fit in the 545-GHz cleaned map, in arcminutes. (GAU_MAJOR_AXIS_SIG)

Minor_Axis
The FWHM along the minor axis of the elliptical Gaussian fit in the 545-GHz cleaned map at the location of the source, in arcminutes.(GAU_MINOR_AXIS)

Minor_Axis_Error
The RMS uncertainty of the FWHM along the minor axis of the elliptical Gaussian fit in the 545-GHz cleaned map, in arcminutes. (GAU_MINOR_AXIS_SIG)

Position_Angle
The position angle of the elliptical Gaussian fit to the source in the 545-GHz cleaned map, in degrees (converted by the HEASARC from the radian units used in the original table). (GAU_POSITION_ANGLE)

Position_Angle_Error
The RMS uncertainty of the position angle of the elliptical Gaussian fit in the 545-GHz cleaned map, in degrees (converted by the HEASARC from the radian units used in the original table). (GAU_POSITION_ANGLE_SIG)

Flux_857_GHz
The source flux density at 857 GHz, in mJy (converted by the HEASARC from the Jansky units used in the original table). (Flux_CLEAN_857)

Flux_857_GHz_Sky_Err
The RMS uncertainty at 857 GHz due to sky confusion, in mJy (converted by the HEASARC from the Jansky units used in the original table). This represents the level of the local cosmic infrared background (CIB) fluctuations that dominate the signal at high latitude. (Flux_CLEAN_857_SIG_SKY)

Flux_857_GHz_Meas_Err
The RMS uncertainty at 857 GHz due to measurement error, in mJy (converted by the HEASARC from the Jansky units used in the original table). This uncertainty is due to the noise measurement of the Planck data and is estimated using half-ring maps (see Section 4.2 of the reference paper for further details). (Flux_CLEAN_857_SIG_DATA)

Flux_857_GHz_Fit_Err
The RMS uncertainty at 857 GHz due to the uncertainty of the elliptical Gaussian fit, in mJy (converted by the HEASARC from the Jansky units used in the original table). The uncertainty in the aperture photometry induced by the quality of the elliptical Gaussian fit on the cleaned frequency maps includes uncertainties on all elliptical Gaussian parameters, i.e., the coordinates of the centroid, and also the major and minor axes. It has been obtained by repeating the aperture photometry in 1000 Monte Carlo simulations, where the elliptical Gaussian parameters are allowed to vary within a normal distribution centered on the best-fit parameters and a sigma-dispersion provided by the fit. The uncertainty is defined as the mean absolute deviation over the 1000 flux density estimates. (Flux_CLEAN_857_SIG_GEOM)

Flux_545_GHz
The source flux density at 545 GHz, in mJy (converted by the HEASARC from the Jansky units used in the original table). (Flux_CLEAN_545)

Flux_545_GHz_Sky_Err
The RMS uncertainty at 545 GHz due to sky confusion, in mJy (converted by the HEASARC from the Jansky units used in the original table). This represents the level of the local cosmic infrared background (CIB) fluctuations that dominate the signal at high latitude. (Flux_CLEAN_545_SIG_SKY)

Flux_545_GHz_Meas_Err
The RMS uncertainty at 545 GHz due to measurement error, in mJy (converted by the HEASARC from the Jansky units used in the original table). This uncertainty is due to the noise measurement of the Planck data and is estimated using half-ring maps (see Section 4.2 of the reference paper for further details). (Flux_CLEAN_545_SIG_DATA)

Flux_545_GHz_Fit_Err
The RMS uncertainty at 545 GHz due to the uncertainty of the elliptical Gaussian fit, in mJy (converted by the HEASARC from the Jansky units used in the original table). The uncertainty in the aperture photometry induced by the quality of the elliptical Gaussian fit on the cleaned frequency maps includes uncertainties on all elliptical Gaussian parameters, i.e., the coordinates of the centroid, and also the major and minor axes. It has been obtained by repeating the aperture photometry in 1000 Monte Carlo simulations, where the elliptical Gaussian parameters are allowed to vary within a normal distribution centered on the best-fit parameters and a sigma-dispersion provided by the fit. The uncertainty is defined as the mean absolute deviation over the 1000 flux density estimates. (Flux_CLEAN_545_SIG_GEOM)

Flux_353_GHz
The source flux density at 353 GHz, in mJy (converted by the HEASARC from the Jansky units used in the original table). (Flux_CLEAN_353)

Flux_353_GHz_Sky_Err
The RMS uncertainty at 353 GHz due to sky confusion, in mJy (converted by the HEASARC from the Jansky units used in the original table). This represents the level of the local cosmic infrared background (CIB) fluctuations that dominate the signal at high latitude. (Flux_CLEAN_353_SIG_SKY)

Flux_353_GHz_Meas_Err
The RMS uncertainty at 353 GHz due to measurement error in mJy (converted by the HEASARC from the Jansky units used in the original table). This uncertainty is due to the noise measurement of the Planck data and is estimated using half-ring maps (see Section 4.2 of the reference paper for further details). (Flux_CLEAN_353_SIG_DATA)

Flux_353_GHz_Fit_Err
The RMS uncertainty at 353 GHz due to the uncertainty of the elliptical Gaussian fit, in mJy (converted by the HEASARC from the Jansky units used in the original table). The uncertainty in the aperture photometry induced by the quality of the elliptical Gaussian fit on the cleaned frequency maps includes uncertainties on all elliptical Gaussian parameters, i.e., the coordinates of the centroid, and also the major and minor axes. It has been obtained by repeating the aperture photometry in 1000 Monte Carlo simulations, where the elliptical Gaussian parameters are allowed to vary within a normal distribution centered on the best-fit parameters and a sigma-dispersion provided by the fit. The uncertainty is defined as the mean absolute deviation over the 1000 flux density estimates. (Flux_CLEAN_353_SIG_GEOM)

Flux_217_GHz
The source flux density at 217 GHz, in mJy (converted by the HEASARC from the Jansky units used in the original table). (Flux_CLEAN_217)

Flux_217_GHz_Sky_Err
The RMS uncertainty at 217 GHz due to sky confusion, in mJy (converted by the HEASARC from the Jansky units used in the original table). This represents the level of the local cosmic infrared background (CIB) fluctuations that dominate the signal at high latitude. (Flux_CLEAN_217_SIG_SKY)

Flux_217_GHz_Meas_Err
The RMS uncertainty at 217 GHz due to measurement error, in mJy (converted by the HEASARC from the Jansky units used in the original table). This uncertainty is due to the noise measurement of the Planck data and is estimated using half-ring maps (see Section 4.2 of the reference paper for further details). (Flux_CLEAN_217_SIG_DATA)

Flux_217_GHz_Fit_Err
The RMS uncertainty at 217 GHz due to the uncertainty of the elliptical Gaussian fit, in mJy (converted by the HEASARC from the Jansky units used in the original table). The uncertainty in the aperture photometry induced by the quality of the elliptical Gaussian fit on the cleaned frequency maps includes uncertainties on all elliptical Gaussian parameters, i.e., the coordinates of the centroid, and also the major and minor axes. It has been obtained by repeating the aperture photometry in 1000 Monte Carlo simulations, where the elliptical Gaussian parameters are allowed to vary within a normal distribution centered on the best-fit parameters and a sigma-dispersion provided by the fit. The uncertainty is defined as the mean absolute deviation over the 1000 flux density estimates. (Flux_CLEAN_217_SIG_GEOM)

Cc_Sel_Prob
The color-color selection probability of the source candidate. The authors use the ratios of the flux densities at 545 GHz and 857 GHz, and of the flux densities at 353 GHz and 545 GHz for this purpose. They estimate the probability for each source for the two color ratios to lie within the high-z domain, given the 1-sigma error bars associated with the flux densities, as is discussed in detail in Section 4.3 of the reference paper. (PROB_COLCOL)

E_BV_Mean
The mean extinction, E(B-V)xgal, within the source PSF, in magnitudes, obtained from the map released in 2013 in the Planck Legacy Archive. (EBV_MEAN)

E_BV_Aperture
The aperture estimate of the extinction, E(B-V)xgal, within the source PSF, in magnitudes. (EBV_APER)

E_BV_Aperture_Error
The RMS uncertainty in the aperture estimate of the extinction, E(B-V)xgal, within the source PSF, in magnitudes. (EBV_APER_SIG)

Phot_Redshift_25
The sub-millimeter photometric redshift estimate for the source, assuming a dust temperature Txgal of 25 K. The authors performed a photometric redshift determination for each source, assuming simple SED modeling given by a modified black-body emission with a dust spectral index betaxgal = 1.5 and six different cases of the dust temperature, namely Txgal = 25, 30, 35, 40, 45, and 50 K. In order to take into account the impact of the cleaning algorithm introduced in Sect. 3.5 of the reference paper, they built a grid of attenuated flux densities modeled for each value of the redshift (0 < z < 8) and the dust temperature. A chi-squared 2 analysis based on this grid yielded the best fit of the redshift together with 1-sigma lower and upper limits. The accuracy of the redshift estimate processing has been analyzed on Monte Carlo simulations (see Appendix B of the reference paper). The average uncertainties associated with these photometric redshift estimates are about 0.5, given a specific dust temperature. The degeneracy between the redshift and the dust temperature may induce much larger uncertainties on those sources without spectroscopic data. (ZPHOT_25K)

Phot_Redshift_25_Neg_Err
The lower 68% confidence uncertainty in the specified quantity. (ZPHOT_25K_LOW)

Phot_Redshift_25_Pos_Err
The upper 68% confidence uncertainty in the specified quantity. (ZPHOT_25K_UP)

Phot_Redshift_25_Chi2
The reduced chi-squared value for the best-fit model assuming a dust temperature of 25 K. (ZPHOT_25K_CHI2)

Phot_Redshift_30
The sub-millimeter photometric redshift estimate for the source, assuming a dust temperature Txgal of 30 K. The authors performed a photometric redshift determination for each source, assuming simple SED modeling given by a modified black-body emission with a dust spectral index betaxgal = 1.5 and six different cases of the dust temperature, namely Txgal = 25, 30, 35, 40, 45, and 50 K. In order to take into account the impact of the cleaning algorithm introduced in Sect. 3.5 of the reference paper, they built a grid of attenuated flux densities modeled for each value of the redshift (0 < z < 8) and the dust temperature. A chi-squared 2 analysis based on this grid yielded the best fit of the redshift together with 1-sigma lower and upper limits. The accuracy of the redshift estimate processing has been analyzed on Monte Carlo simulations (see Appendix B of the reference paper). The average uncertainties associated with these photometric redshift estimates are about 0.5, given a specific dust temperature. The degeneracy between the redshift and the dust temperature may induce much larger uncertainties on those sources without spectroscopic data. (ZPHOT_30K)

Phot_Redshift_30_Neg_Err
The lower 68% confidence uncertainty in the specified quantity. (ZPHOT_30K_LOW)

Phot_Redshift_30_Pos_Err
The upper 68% confidence uncertainty in the specified quantity. (ZPHOT_30K_UP)

Phot_Redshift_30_Chi2
The reduced chi-squared value for the best-fit model assuming a dust temperature of 30 K. (ZPHOT_30K_CHI2)

Phot_Redshift_35
The sub-millimeter photometric redshift estimate for the source, assuming a dust temperature Txgal of 35 K. The authors performed a photometric redshift determination for each source, assuming simple SED modeling given by a modified black-body emission with a dust spectral index betaxgal = 1.5 and six different cases of the dust temperature, namely Txgal = 25, 30, 35, 40, 45, and 50 K. In order to take into account the impact of the cleaning algorithm introduced in Sect. 3.5 of the reference paper, they built a grid of attenuated flux densities modeled for each value of the redshift (0 < z < 8) and the dust temperature. A chi-squared 2 analysis based on this grid yielded the best fit of the redshift together with 1-sigma lower and upper limits. The accuracy of the redshift estimate processing has been analyzed on Monte Carlo simulations (see Appendix B of the reference paper). The average uncertainties associated with these photometric redshift estimates are about 0.5, given a specific dust temperature. The degeneracy between the redshift and the dust temperature may induce much larger uncertainties on those sources without spectroscopic data. (ZPHOT_35K)

Phot_Redshift_35_Neg_Err
The lower 68% confidence uncertainty in the specified quantity. (ZPHOT_35K_LOW)

Phot_Redshift_35_Pos_Err
The upper 68% confidence uncertainty in the specified quantity. (ZPHOT_35K_UP)

Phot_Redshift_35_Chi2
The reduced chi-squared value for the best-fit model assuming a dust temperature of 35 K. (ZPHOT_35K_CHI2)

Phot_Redshift_40
The sub-millimeter photometric redshift estimate for the source, assuming a dust temperature Txgal of 40 K. The authors performed a photometric redshift determination for each source, assuming simple SED modeling given by a modified black-body emission with a dust spectral index betaxgal = 1.5 and six different cases of the dust temperature, namely Txgal = 25, 30, 35, 40, 45, and 50 K. In order to take into account the impact of the cleaning algorithm introduced in Sect. 3.5 of the reference paper, they built a grid of attenuated flux densities modeled for each value of the redshift (0 < z < 8) and the dust temperature. A chi-squared 2 analysis based on this grid yielded the best fit of the redshift together with 1-sigma lower and upper limits. The accuracy of the redshift estimate processing has been analyzed on Monte Carlo simulations (see Appendix B of the reference paper). The average uncertainties associated with these photometric redshift estimates are about 0.5, given a specific dust temperature. The degeneracy between the redshift and the dust temperature may induce much larger uncertainties on those sources without spectroscopic data. (ZPHOT_40K)

Phot_Redshift_40_Neg_Err
The lower 68% confidence uncertainty in the specified quantity. (ZPHOT_40K_LOW)

Phot_Redshift_40_Pos_Err
The upper 68% confidence uncertainty in the specified quantity. (ZPHOT_40K_UP)

Phot_Redshift_40_Chi2
The reduced chi-squared value for the best-fit model assuming a dust temperature of 40 K. (ZPHOT_40K_CHI2)

Phot_Redshift_45
The sub-millimeter photometric redshift estimate for the source, assuming a dust temperature Txgal of 45 K. The authors performed a photometric redshift determination for each source, assuming simple SED modeling given by a modified black-body emission with a dust spectral index betaxgal = 1.5 and six different cases of the dust temperature, namely Txgal = 25, 30, 35, 40, 45, and 50 K. In order to take into account the impact of the cleaning algorithm introduced in Sect. 3.5 of the reference paper, they built a grid of attenuated flux densities modeled for each value of the redshift (0 < z < 8) and the dust temperature. A chi-squared 2 analysis based on this grid yielded the best fit of the redshift together with 1-sigma lower and upper limits. The accuracy of the redshift estimate processing has been analyzed on Monte Carlo simulations (see Appendix B of the reference paper). The average uncertainties associated with these photometric redshift estimates are about 0.5, given a specific dust temperature. The degeneracy between the redshift and the dust temperature may induce much larger uncertainties on those sources without spectroscopic data. (ZPHOT_45K)

Phot_Redshift_45_Neg_Err
The lower 68% confidence uncertainty in the specified quantity. (ZPHOT_45K_LOW)

Phot_Redshift_45_Pos_Err
The upper 68% confidence uncertainty in the specified quantity. (ZPHOT_45K_UP)

Phot_Redshift_45_Chi2
The reduced chi-squared value for the best-fit model assuming a dust temperature of_45 K. (ZPHOT_45K_CHI2)

Phot_Redshift_50
The sub-millimeter photometric redshift estimate for the source, assuming a dust temperature Txgal of 50 K. The authors performed a photometric redshift determination for each source, assuming simple SED modeling given by a modified black-body emission with a dust spectral index betaxgal = 1.5 and six different cases of the dust temperature, namely Txgal = 25, 30, 35, 40, 45, and 50 K. In order to take into account the impact of the cleaning algorithm introduced in Sect. 3.5 of the reference paper, they built a grid of attenuated flux densities modeled for each value of the redshift (0 < z < 8) and the dust temperature. A chi-squared 2 analysis based on this grid yielded the best fit of the redshift together with 1-sigma lower and upper limits. The accuracy of the redshift estimate processing has been analyzed on Monte Carlo simulations (see Appendix B of the reference paper). The average uncertainties associated with these photometric redshift estimates are about 0.5, given a specific dust temperature. The degeneracy between the redshift and the dust temperature may induce much larger uncertainties on those sources without spectroscopic data. (ZPHOT_50K)

Phot_Redshift_50_Neg_Err
The lower 68% confidence uncertainty in the specified quantity. (ZPHOT_50K_LOW)

Phot_Redshift_50_Pos_Err
The upper 68% confidence uncertainty in the specified quantity. (ZPHOT_50K_UP)

Phot_Redshift_50_Chi2
The reduced chi-squared value for the best-fit model assuming a dust temperature of 50 K. (ZPHOT_50K_CHI2)

FIR_Lum_25k
The FIR bolometric luminosity of the source, in solar luminosities (Lsun), assuming a dust temperature of 25 K. This is computed as the integral of the redshifted modified black-body emission between 300 GHz and 37.5 THz. (LFIR_25K)

FIR_Lum_25k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_25K_LOW)

FIR_Lum_25k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_25K_UP)

FIR_Lum_30k
The FIR bolometric luminosity of the source, in solar luminosities (Lsun), assuming a dust temperature of 30 K. This is computed as the integral of the redshifted modified black-body emission between 300 GHz and 37.5 THz. (LFIR_30K)

FIR_Lum_30k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_30K_LOW)

FIR_Lum_30k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_30K_UP)

FIR_Lum_35k
The FIR bolometric luminosity of the source, in solar luminosities (Lsun), assuming a dust temperature of 35 K. This is computed as the integral of the redshifted modified black-body emission between 300 GHz and 37.5 THz. (LFIR_35K)

FIR_Lum_35k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_35K_LOW)

FIR_Lum_35k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_35K_UP)

FIR_Lum_40k
The FIR bolometric luminosity of the source, in solar luminosities (Lsun), assuming a dust temperature of 40 K. This is computed as the integral of the redshifted modified black-body emission between 300 GHz and 37.5 THz. (LFIR_40K)

FIR_Lum_40k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_40K_LOW)

FIR_Lum_40k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_40K_UP)

FIR_Lum_45k
The FIR bolometric luminosity of the source, in solar luminosities (Lsun), assuming a dust temperature of 45 K. This is computed as the integral of the redshifted modified black-body emission between 300 GHz and 37.5 THz. (LFIR_45K)

FIR_Lum_45k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_45K_LOW)

FIR_Lum_45k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_45K_UP)

FIR_Lum_50k
The FIR bolometric luminosity of the source, in solar luminosities (Lsun), assuming a dust temperature of 50 K. This is computed as the integral of the redshifted modified black-body emission between 300 GHz and 37.5 THz. (LFIR_50K)

FIR_Lum_50k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_50K_LOW)

FIR_Lum_50k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar luminosities (Lsun). (LFIR_50K_UP)

SFR_25k
The star formation rate estimate, SFR, in solar masses per year, assuming a dust temperature of 25 K. Following the prescription of Kennicutt (1998, ARA&A, 36, 189) and assuming that the contribution from the AGN is negligible for these objects, the authors estimate the star formation rate as SFR [Msun yr-1] = 1.7 x 10-10 LFIR [L_sun_]. (SFR_25K)

SFR_25k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_25K_LOW)

SFR_25k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_25K_UP)

SFR_30k
The star formation rate estimate, SFR, in solar masses per year, assuming a dust temperature of 30 K. Following the prescription of Kennicutt (1998, ARA&A, 36, 189) and assuming that the contribution from the AGN is negligible for these objects, the authors estimate the star formation rate as SFR [Msun yr-1] = 1.7 x 10-10 LFIR [L_sun_]. (SFR_30K)

SFR_30k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_30K_LOW)

SFR_30k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_30K_UP)

SFR_35k
The star formation rate estimate, SFR, in solar masses per year, assuming a dust temperature of 35 K. Following the prescription of Kennicutt (1998, ARA&A, 36, 189) and assuming that the contribution from the AGN is negligible for these objects, the authors estimate the star formation rate as SFR [Msun yr-1] = 1.7 x 10-10 LFIR [L_sun_]. (SFR_35K)

SFR_35k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_35K_LOW)

SFR_35k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_35K_UP)

SFR_40k
The star formation rate estimate, SFR, in solar masses per year, assuming a dust temperature of 40 K. Following the prescription of Kennicutt (1998, ARA&A, 36, 189) and assuming that the contribution from the AGN is negligible for these objects, the authors estimate the star formation rate as SFR [Msun yr-1] = 1.7 x 10-10 LFIR [L_sun_]. (SFR_40K)

SFR_40k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_40K_LOW)

SFR_40k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_40K_UP)

SFR_45k
The star formation rate estimate, SFR, in solar masses per year, assuming a dust temperature of 45 K. Following the prescription of Kennicutt (1998, ARA&A, 36, 189) and assuming that the contribution from the AGN is negligible for these objects, the authors estimate the star formation rate as SFR [Msun yr-1] = 1.7 x 10-10 LFIR [L_sun_]. (SFR_45K)

SFR_45k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_45K_LOW)

SFR_45k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_45K_UP)

SFR_50k
The star formation rate estimate, SFR, in solar masses per year, assuming a dust temperature of 50 K. Following the prescription of Kennicutt (1998, ARA&A, 36, 189) and assuming that the contribution from the AGN is negligible for these objects, the authors estimate the star formation rate as SFR [Msun yr-1] = 1.7 x 10-10 LFIR [L_sun_]. (SFR_50K)

SFR_50k_Neg_Err
The lower 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_50K_LOW)

SFR_50k_Pos_Err
The upper 68% confidence uncertainty in the specified quantity, in solar masses per year. (SFR_50K_UP)

Matching_Planck_Catalogs
The list of Planck catalogs that contain matches to the source, using the following acronyms: (XFLAG_PLANCK)

  Acronym     Full Name                                Reference

  PCCS2 nnn   Planck Catalogue of Compact Sources      Planck Collab. XXVI 2016
              nnn GHz Source Catalogue
  PSZ2        Planck Catalogue of SZ Sources           Planck Collab. XXVII 2016
  PGCC        Planck Catalogue of Galactic Cold Clumps Planck Collab. XXVIII 2016
  

Herschel_Flag
This flag parameter is set to 1 to indicates the presence of the source in the Herschel Follow-up Program, else is set to 0. (XFLAG_HERSCHEL)


Contact Person

Questions regarding the PLANCKHZSC database table can be addressed to the HEASARC User Hotline.
Page Author: Browse Software Development Team
Last Modified: Monday, 22-May-2017 10:39:10 EDT