Spitzer 70μm confusion measurements are presented based on ultra-deep MIPS-70μm observations of GOODS Hubble Deep Field North (HDFN). The instrument noise for the MIPS-70μm band integrates down with nearly t^(−0.5) for the low background HDF-N field. The estimated confusion level is σ_c = 0.30 ± 0.15mJy for a limiting flux density of 1.5mJy (q = 5)

Frayer, D. T.

Huynh, M. T.

English

Caltech Authors - Main

The 2nd Spitzer Conference: Infrared Diagnostics of Galaxy Evolution
ASP Conference Series, Vol. 381, c©2008
Ranga-Ram Chary, Harry I. Teplitz, Kartik Sheth, eds.
Spitzer 70-micron Confusion Level
D. T. Frayer and M. T. Huynh
220-6 Spitzer Science Center, Caltech, Pasadena, CA 91125
Abstract. Spitzer 70µm confusion measurements are presented based on
ultra-deep MIPS-70µm observations of GOODS Hubble Deep Field North (HDF-
N). The instrument noise for the MIPS-70µm band integrates down with nearly
t−0.5 for the low background HDF-N field. The estimated confusion level is
σc = 0.30± 0.15 mJy for a limiting flux density of 1.5 mJy (q = 5).
1. Introduction
The interpretations of the deep 24µm surveys (e.g., Yan et al. 2004; Le Floćh
et al. 2005; Daddi et al. 2005; Papovich et al. 2006) are limited by possible
strong emission and absorption features redshifted into the 24µm band and
the expected wide range of the mid-infrared and far-infrared spectral energy
distributions (SEDs) (e.g., Dale et al. 2005). Observations at 70µm are crucial
for constraining the infrared luminosities and SFRs of the high-redshift Spitzer-
selected galaxies.
The previous deep 70µm Multiband Imaging Photometer for Spitzer (MIPS,
Rieke et al. 2004) Guaranteed Time Observer (GTO) surveys did not achieve
sufficient sensitivity to detect individual sources which make up the majority of
the Cosmic Infrared Background (CIB) and were unable to measure the confu-
sion level at 70µm. The MIPS GTO deep surveys had integration times of only
10 minutes at 70µm and provided source counts to a shallow depth of 15 mJy
(Dole et al. 2004a). In these proceedings, we report on the measured confusion
level at 70µm based on ultra-deep (10.6 ks) observations of the GOODS Hubble
Deep Field North (HDF-N).
2. Observations and Reduction
The MIPS 70µm observations of the GOODS HDF-N were carried in Cycle-1 of
the General Observer program (Spitzer program 3325). The data were embar-
goed until after the MIPS GTO propriety period and were released to our team
in 2005 August. The raw data were downloaded from the SSC archive (ver-
sion S11) and were processed using the offline Germanium Reprocessing Tools
(GeRT, S13 version 1.0), following the algorithms derived by the MIPS Instru-
ment and Instrument Support Teams (Gordon et al. 2005). The instrumental
artifacts in the basic calibrated data (BCD) products were removed adopting
the filtering techniques used for the reduction of the extragalactic First Look
Survey (xFLS, Frayer et al. 2006). The BCD pipeline processing and filter-
ing procedures were optimized for these deep photometry data. We improved
260
70µm Confusion Level 261
the sensitivity by 20% with respect to the on-line (version S11) filtered BCDs
(fBCDs). We corrected the data for the updated calibration of MIPS (S13.2),
assuming an absolute flux calibration factor of 702 MJy/sr per MIPS-70µm unit
for stellar SEDs, and applied the appropriate color correction for galaxy SEDs.
In comparison, the total calibration correction, including color corrections, is a
factor of 1.20 times the pre-S13.2 versions of the on-line data products and is
3.4% larger than the calibration adopted for the xFLS analysis.
The data were combined with the Spitzer Science Center (SSC) mosaicking
and source extraction software (MOPEX, version 112505) and were projected
onto a sky grid with 4′′ pixels, correcting for array distortions. The average
noise level of the image was derived empirically by making multiple aperture
measurements at random locations throughout the residual mosaic after source
extraction. The standard deviation of the random aperture measurements mul-
tiplied by the appropriate aperture correction was adopted for the average point
source noise level.
3. Results
Previous surveys with Spitzer are not deep enough to measure the confusion level
at 70µm. The published confusion estimates are based on predicted number
counts from models of galaxy evolution (e.g., Dole et al. 2003, 2004b). With the
ultra-deep data of HDF-N, we can directly measure the confusion level at 70µm.
We define the instrument noise (including photon noise, detector noise, and noise
associated with the data processing) as σI . The total noise (σT ) represents the
noise in the mosaic after the extraction of sources above a limiting flux density
(Slim), and the confusion noise (σc) represents fluctuations due to sources with
flux densities below Slim. As defined here, σc is the “photometric” confusion
noise, following the terminology of Dole et al. (2003). In the direction of HDF-
N, the contribution of Galactic cirrus to the confusion noise is negligible; the
confusion noise is due to distant galaxies.
The instrument noise was estimated empirically by subtracting pairs of data
with the same integration time and covering the exact same region on the sky
to remove sources and any remaining residuals from the sky after filtering. The
error bars for the instrument noise were derived by combining different inde-
pendent pairs of data with the same integration time. We found a repeatability
on the noise measurements of about 5% for long observations. The measured
instrument noise integrates down nearly with t−0.5 (Fig. 1). With the current
processing and at the low background level of HDF-N, σ2I ∝ t−1(1 + βt0.5),
where β ≃ 0.04 for integration time t in units of ks. The functional form of this
relationship is based on empirical results from several different data sets taken
over a range backgrounds and integration times. The β parameter was derived
from a general weighted least-squares fit and depends on the background level
and the quality of the data reduction. We used the above function to extrapo-
late the instrument noise from half the data to the entire data set and derived
σI = 0.0399± 0.0036 MJy/sr.
After source extraction, we measure σT . Since the total noise image and
the instrument noise image have approximately Gaussian distributions, the con-
fusion noise is also consistent with a Gaussian and is approximated by σc =
262 Frayer and Huynh
Figure 1. Measured rms noise for 4′′ pixels as a function of integration
time (1σ). The corresponding point source noise level in mJy is shown at the
right. Instrument noise measurements are shown by the open circles and are
represented by the solid line. For comparison, the dotted line shows a t−0.5
function. The derived confusion level (σc) is shown by the dashed-dotted line,
and the total noise after the extraction of sources with S70 > 5σc is shown by
the dashed line. The total noise and instrument noise for the HDF-N mosaic
is shown by the solid square and solid circle respectively.
(σ2T − σ2I )0.5. We iterate between source extraction at different limiting flux
densities and confusion measurements until we converge to a solution with
q ≡ Slim/σc = 5. For the q = 5 solution, we derive σT = 0.0485± 0.0034 MJy/sr
and σc = 0.0276±0.0079 MJy/sr for a limiting source flux density of S = 1.5 mJy.
Including the systematic uncertainties of the absolute calibration scale and the
conversion between point source noise and surface brightness noise, we derive a
point source confusion noise of σc = 0.30± 0.15 mJy (q = 5).
In comparison, Dole et al. (2003) predict a q = 5 photometric confusion
level of σc = 0.224 mJy and based on the updated predictions of Dole et al.
(2004a), one would expect σc ≃ 0.28 mJy (q = 5), depending on the exact shape
of the differential source counts. The measured confusion level of 0.3 mJy from
the ultra-deep HDF-N data agree well with the predicted photometric confusion
levels (q = 5). The 3.2 mJy confusion limit based on the source density criterion
(SDC) adopted by Dole et al. is about a factor of two higher than the limiting
flux density derived here. The Dole et al. SDC limit corresponds to q ≃ 7 and
a high completeness level of > 90%.
70µm Confusion Level 263
With these ultra-deep GOODS HDF-N data, we have derived faint source
counts down to a flux density of 1.2 mJy, directly resolving the majority of
the CIB due to galaxies at 70µm (D. Frayer et al. in preparation). Studies
of the counterparts of the faint 70µm population are ongoing (M. Huynh et
al., in preparation) and will help to constrain the infrared luminosities and the
relative fraction of AGN versus starburst-dominated galaxies in the high-redshift
Spitzer-selected surveys.
Acknowledgments. This work is based on observations made with the
Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory,
California Institute of Technology under NASA contract 1407.
References
Daddi, E., et al. 2005, ApJ, 631, L13
Dale, D. A., et al. 2005, ApJ, 633, 857
Dole, H., et al. 2003, ApJ, 585, 617
Dole, H., et al. 2004a, ApJS, 154, 87
Dole, H., et al. 2004b, ApJS, 154, 93
Frayer, D. T., et al. 2006, AJ, 131, 250
Gordon, K. D., et al. 2005, PASP, 117, 503
Le Floćh, E., et al. 2005, ApJ, 632, 169
Papovich, C., et al. 2006, ApJ, 640, 92
Rieke, G. H., et al. 2004, ApJS, 154, 25
Yan, L., et al. 2004, ApJS, 154, 60


Spitzer 70-micron Confusion Level

The 2nd Spitzer Conference: Infrared Diagnostics of Galaxy Evolution
ASP Conference Series, Vol. 381, c©2008
Ranga-Ram Chary, Harry I. Teplitz, Kartik Sheth, eds.
Spitzer 70-micron Confusion Level
D. T. Frayer and M. T. Huynh
220-6 Spitzer Science Center, Caltech, Pasadena, CA 91125
Abstract. Spitzer 70µm confusion measurements are presented based on
ultra-deep MIPS-70µm observations of GOODS Hubble Deep Field North (HDF-
N). The instrument noise for the MIPS-70µm band integrates down with nearly
t−0.5 for the low background HDF-N field. The estimated confusion level is
σc = 0.30± 0.15mJy for a limiting flux density of 1.5mJy (q = 5).
1. Introduction
The interpretations of the deep 24µm surveys (e.g., Yan et al. 2004; Le Floc´h
et al. 2005; Daddi et al. 2005; Papovich et al. 2006) are limited by possible
strong emission and absorption features redshifted into the 24µm band and
the expected wide range of the mid-infrared and far-infrared spectral energy
distributions (SEDs) (e.g., Dale et al. 2005). Observations at 70µm are crucial
for constraining the infrared luminosities and SFRs of the high-redshift Spitzer-
selected galaxies.
The previous deep 70µmMultiband Imaging Photometer for Spitzer (MIPS,
Rieke et al. 2004) Guaranteed Time Observer (GTO) surveys did not achieve
sufficient sensitivity to detect individual sources which make up the majority of
the Cosmic Infrared Background (CIB) and were unable to measure the confu-
sion level at 70µm. The MIPS GTO deep surveys had integration times of only
10minutes at 70µm and provided source counts to a shallow depth of 15mJy
(Dole et al. 2004a). In these proceedings, we report on the measured confusion
level at 70µm based on ultra-deep (10.6 ks) observations of the GOODS Hubble
Deep Field North (HDF-N).
2. Observations and Reduction
The MIPS 70µm observations of the GOODS HDF-N were carried in Cycle-1 of
the General Observer program (Spitzer program 3325). The data were embar-
goed until after the MIPS GTO propriety period and were released to our team
in 2005 August. The raw data were downloaded from the SSC archive (ver-
sion S11) and were processed using the oﬄine Germanium Reprocessing Tools
(GeRT, S13 version 1.0), following the algorithms derived by the MIPS Instru-
ment and Instrument Support Teams (Gordon et al. 2005). The instrumental
artifacts in the basic calibrated data (BCD) products were removed adopting
the filtering techniques used for the reduction of the extragalactic First Look
Survey (xFLS, Frayer et al. 2006). The BCD pipeline processing and filter-
ing procedures were optimized for these deep photometry data. We improved
260
70µm Confusion Level 261
the sensitivity by 20% with respect to the on-line (version S11) filtered BCDs
(fBCDs). We corrected the data for the updated calibration of MIPS (S13.2),
assuming an absolute flux calibration factor of 702MJy/sr per MIPS-70µm unit
for stellar SEDs, and applied the appropriate color correction for galaxy SEDs.
In comparison, the total calibration correction, including color corrections, is a
factor of 1.20 times the pre-S13.2 versions of the on-line data products and is
3.4% larger than the calibration adopted for the xFLS analysis.
The data were combined with the Spitzer Science Center (SSC) mosaicking
and source extraction software (MOPEX, version 112505) and were projected
onto a sky grid with 4′′ pixels, correcting for array distortions. The average
noise level of the image was derived empirically by making multiple aperture
measurements at random locations throughout the residual mosaic after source
extraction. The standard deviation of the random aperture measurements mul-
tiplied by the appropriate aperture correction was adopted for the average point
source noise level.
3. Results
Previous surveys with Spitzer are not deep enough to measure the confusion level
at 70µm. The published confusion estimates are based on predicted number
counts from models of galaxy evolution (e.g., Dole et al. 2003, 2004b). With the
ultra-deep data of HDF-N, we can directly measure the confusion level at 70µm.
We define the instrument noise (including photon noise, detector noise, and noise
associated with the data processing) as σI . The total noise (σT ) represents the
noise in the mosaic after the extraction of sources above a limiting flux density
(Slim), and the confusion noise (σc) represents fluctuations due to sources with
flux densities below Slim. As defined here, σc is the “photometric” confusion
noise, following the terminology of Dole et al. (2003). In the direction of HDF-
N, the contribution of Galactic cirrus to the confusion noise is negligible; the
confusion noise is due to distant galaxies.
The instrument noise was estimated empirically by subtracting pairs of data
with the same integration time and covering the exact same region on the sky
to remove sources and any remaining residuals from the sky after filtering. The
error bars for the instrument noise were derived by combining different inde-
pendent pairs of data with the same integration time. We found a repeatability
on the noise measurements of about 5% for long observations. The measured
instrument noise integrates down nearly with t−0.5 (Fig. 1). With the current
processing and at the low background level of HDF-N, σ2I ∝ t−1(1 + βt0.5),
where β ≃ 0.04 for integration time t in units of ks. The functional form of this
relationship is based on empirical results from several different data sets taken
over a range backgrounds and integration times. The β parameter was derived
from a general weighted least-squares fit and depends on the background level
and the quality of the data reduction. We used the above function to extrapo-
late the instrument noise from half the data to the entire data set and derived
σI = 0.0399± 0.0036MJy/sr.
After source extraction, we measure σT . Since the total noise image and
the instrument noise image have approximately Gaussian distributions, the con-
fusion noise is also consistent with a Gaussian and is approximated by σc =
262 Frayer and Huynh
Figure 1. Measured rms noise for 4′′ pixels as a function of integration
time (1σ). The corresponding point source noise level in mJy is shown at the
right. Instrument noise measurements are shown by the open circles and are
represented by the solid line. For comparison, the dotted line shows a t−0.5
function. The derived confusion level (σc) is shown by the dashed-dotted line,
and the total noise after the extraction of sources with S70 > 5σc is shown by
the dashed line. The total noise and instrument noise for the HDF-N mosaic
is shown by the solid square and solid circle respectively.
(σ2T − σ2I )0.5. We iterate between source extraction at different limiting flux
densities and confusion measurements until we converge to a solution with
q ≡ Slim/σc = 5. For the q = 5 solution, we derive σT = 0.0485± 0.0034MJy/sr
and σc = 0.0276±0.0079MJy/sr for a limiting source flux density of S = 1.5mJy.
Including the systematic uncertainties of the absolute calibration scale and the
conversion between point source noise and surface brightness noise, we derive a
point source confusion noise of σc = 0.30± 0.15mJy (q = 5).
In comparison, Dole et al. (2003) predict a q = 5 photometric confusion
level of σc = 0.224mJy and based on the updated predictions of Dole et al.
(2004a), one would expect σc ≃ 0.28mJy (q = 5), depending on the exact shape
of the differential source counts. The measured confusion level of 0.3mJy from
the ultra-deep HDF-N data agree well with the predicted photometric confusion
levels (q = 5). The 3.2mJy confusion limit based on the source density criterion
(SDC) adopted by Dole et al. is about a factor of two higher than the limiting
flux density derived here. The Dole et al. SDC limit corresponds to q ≃ 7 and
a high completeness level of > 90%.
70µm Confusion Level 263
With these ultra-deep GOODS HDF-N data, we have derived faint source
counts down to a flux density of 1.2mJy, directly resolving the majority of
the CIB due to galaxies at 70µm (D. Frayer et al. in preparation). Studies
of the counterparts of the faint 70µm population are ongoing (M. Huynh et
al., in preparation) and will help to constrain the infrared luminosities and the
relative fraction of AGN versus starburst-dominated galaxies in the high-redshift
Spitzer-selected surveys.
Acknowledgments. This work is based on observations made with the
Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory,
California Institute of Technology under NASA contract 1407.
References
Daddi, E., et al. 2005, ApJ, 631, L13
Dale, D. A., et al. 2005, ApJ, 633, 857
Dole, H., et al. 2003, ApJ, 585, 617
Dole, H., et al. 2004a, ApJS, 154, 87
Dole, H., et al. 2004b, ApJS, 154, 93
Frayer, D. T., et al. 2006, AJ, 131, 250
Gordon, K. D., et al. 2005, PASP, 117, 503
Le Floc´h, E., et al. 2005, ApJ, 632, 169
Papovich, C., et al. 2006, ApJ, 640, 92
Rieke, G. H., et al. 2004, ApJS, 154, 25
Yan, L., et al. 2004, ApJS, 154, 60


http://authors.library.caltech.edu/19425/1/Frayer2008p8405Second_Annual_Spitzer_Science_Center_Conference_Infrared_Diagnostics_Of_Galaxy_Evolution.pdf

Spitzer 70-micron Confusion Level

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