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## PLANCKHZSC - Planck High-Redshift Source Candidates Catalog |
HEASARC Archive |

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.

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)

**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 T_{xgal} 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 beta_{xgal} = 1.5
and six different cases of the dust temperature, namely T_{xgal} = 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 T_{xgal} 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 beta_{xgal} = 1.5
and six different cases of the dust temperature, namely T_{xgal} = 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 T_{xgal} 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 beta_{xgal} = 1.5
and six different cases of the dust temperature, namely T_{xgal} = 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 T_{xgal} 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 beta_{xgal} = 1.5
and six different cases of the dust temperature, namely T_{xgal} = 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 T_{xgal} 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 beta_{xgal} = 1.5
and six different cases of the dust temperature, namely T_{xgal} = 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 T_{xgal} 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 beta_{xgal} = 1.5
and six different cases of the dust temperature, namely T_{xgal} = 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 (L_{sun}),
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 (L_{sun}). (LFIR_25K_LOW)

**FIR_Lum_25k_Pos_Err**

The upper 68% confidence uncertainty in the specified quantity, in solar
luminosities (L_{sun}). (LFIR_25K_UP)

**FIR_Lum_30k**

The FIR bolometric luminosity of the source, in solar luminosities (L_{sun}),
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 (L_{sun}). (LFIR_30K_LOW)

**FIR_Lum_30k_Pos_Err**

The upper 68% confidence uncertainty in the specified quantity, in solar
luminosities (L_{sun}). (LFIR_30K_UP)

**FIR_Lum_35k**

The FIR bolometric luminosity of the source, in solar luminosities (L_{sun}),
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 (L_{sun}). (LFIR_35K_LOW)

**FIR_Lum_35k_Pos_Err**

The upper 68% confidence uncertainty in the specified quantity, in solar
luminosities (L_{sun}). (LFIR_35K_UP)

**FIR_Lum_40k**

The FIR bolometric luminosity of the source, in solar luminosities (L_{sun}),
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 (L_{sun}). (LFIR_40K_LOW)

**FIR_Lum_40k_Pos_Err**

The upper 68% confidence uncertainty in the specified quantity, in solar
luminosities (L_{sun}). (LFIR_40K_UP)

**FIR_Lum_45k**

The FIR bolometric luminosity of the source, in solar luminosities (L_{sun}),
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 (L_{sun}). (LFIR_45K_LOW)

**FIR_Lum_45k_Pos_Err**

The upper 68% confidence uncertainty in the specified quantity, in solar
luminosities (L_{sun}). (LFIR_45K_UP)

**FIR_Lum_50k**

The FIR bolometric luminosity of the source, in solar luminosities (L_{sun}),
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 (L_{sun}). (LFIR_50K_LOW)

**FIR_Lum_50k_Pos_Err**

The upper 68% confidence uncertainty in the specified quantity, in solar
luminosities (L_{sun}). (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
[M_{sun} yr^{-1}] = 1.7 x 10^{-10} L_{FIR} [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
[M_{sun} yr^{-1}] = 1.7 x 10^{-10} L_{FIR} [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
[M_{sun} yr^{-1}] = 1.7 x 10^{-10} L_{FIR} [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
[M_{sun} yr^{-1}] = 1.7 x 10^{-10} L_{FIR} [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
[M_{sun} yr^{-1}] = 1.7 x 10^{-10} L_{FIR} [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
[M_{sun} yr^{-1}] = 1.7 x 10^{-10} L_{FIR} [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)