The Palomar Transient Factory Photometric Calibration
Author(s): E. O. Ofek, R. Laher, N. Law, J. Surace, D. Levitan, B. Sesar, A. Horesh, D.
Poznanski, J. C. van Eyken, S. R. Kulkarni, P. Nugent, J. Zolkower, R. Walters, M. Sullivan,
M. Agüeros, L. Bildsten, J. Bloom, S. B. Cenko, A. Gal-Yam, C. Grillmair, G. Helou, M. M.
Kasliwal and R. Quimby
Source:
Publications of the Astronomical Society of the Pacific,
Vol. 124, No. 911 (January
2012), pp. 62-73
Published by: The University of Chicago Press on behalf of the Astronomical Society of the Pacific
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The Palomar Transient Factory Photometric Calibration
E. O. O
FEK
,
1,2,3
R. L
AHER
,
4
N. L
AW
,
5
J. S
URACE
,
4
D. L
EVITAN
,
1
B. S
ESAR
,
1
A. H
ORESH
,
1
D. P
OZNANSKI
,
2,6,7
J. C.
VAN
E
YKEN
,
8
S. R. K
ULKARNI
,
1
P. N
UGENT
,
6
J. Z
OLKOWER
,
9
R. W
ALTERS
,
9
M. S
ULLIVAN
,
10
M. A
GÜEROS
,
11
L. B
ILDSTEN
,
12,13
J. B
LOOM
,
7
S. B. C
ENKO
,
7
A. G
AL
-Y
AM
,
3
C. G
RILLMAIR
,
4
G. H
ELOU
,
4
M. M. K
ASLIWAL
,
1
AND
R. Q
UIMBY
1
Received 2011 August 21; accepted 2011 December 5; published 2012 January 30
ABSTRACT. The Palomar Transient Factory (PTF) provides multiple epoch imaging for a large fraction of the
celestial sphere. Here, we describe the photometric calibration of the PTF data products that allows the PTF mag-
nitudes to be related to other magnitude systems. The calibration process utilizes Sloan Digital Sky Survey (SDSS)
r ∼ 16 mag point-source objects as photometric standards. During photometric conditions, this allows us to solve
for the extinction coefficients and color terms and to estimate the camera illumination correction. This also enables
the calibration of fields that are outside the SDSS footprint. We test the precision and repeatability of the PTF
photometric calibration. Given that PTF is observing in a single filter each night, we define a PTF calibrated mag-
nitude system for the R band and g band. We show that, in this system, ≈59% (47%) of the photometrically cali-
brated PTF R-band (g-band) data achieve a photometric precision of 0.02–0.04 mag and have color terms and
extinction coefficients that are close to their average values. Given the objects’ color, the PTF m agnitude syst em
can be converted to other systems. Moreover, a night-by-night comparison of the calibrated magnitudes of indi-
vidual stars observed on multiple nights shows that they are consistent to a level of ≈0:02 mag. Most of the data that
were taken under nonphotometric conditions can be calibrated relative to other epochs of the same sky footprint
obtained during photometric conditions. We provide a concise guide describing how to use the PTF photometric-
calibration data products, as well as the transformations between the PTF magnitude system and the SDSS and
Johnson-Cousins systems.
Online material: color figures
1. INTRODUCTION
The Palomar Transient Factory
14
(PTF; Law et al. 2009; Rau
et al. 2009) is a synoptic survey designed to explore the transient
sky. The project utilizes th e 48″ Samuel Oschin Schmidt Tele-
scope on Mount Palomar. The telescope has a digital camera
equipped with 11 active CCDs, each 2K×4K pixels (Rahmer
et al. 2008). Each PTF image covers 7:26 deg
2
with a pixel
scale of 1:01″ pixel
1
. From the beginning of the PTF survey
in 2009 March until 2011 January, most of the images were
taken with the Mould R filter. Starting 2011 January, we per-
formed the PTF main survey in the g band during dark time and
in the R band during bright time. In addition, a few nights
around times of full Moon are used for taking images with nar-
rowband Hα filters. A PTF system overview and review of the
first year’s performance are given in Law et al. (2010).
Accurate photometric calibration is a nontrivial task, since
one must know both the atmospheric transmission (e.g.,
Padmanabhan et al. 2008; Burke et al. 2010) and the optical/
detector system response as a function of wavelength, time,
1
Division of Physics, Mathematics and Astronomy, California Institute of
Technology, Pasadena, CA 91125.
2
Einstein Fellow.
3
Benoziyo Center for Astrophysics, Weizmann Institute of Science, 76100
Rehovot, Israel.
4
Spitzer Science Center, MS 314-6, California Institute of Technology, Jet
Propulsion Laboratory, Pasadena, CA 91125.
5
Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50
St. George Street, Toronto, Ontario M5S 3H4, Canada.
6
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA
94720.
7
Department of Astronomy, University of California, Berkeley, Berkeley, CA
94720-3411.
8
NASA Exoplanet Science Institute, California Institute of Technology,
Pasadena, CA 91125.
9
Caltech Optical Observatories, California Institute of Technology, Pasadena,
CA 91125.
10
Department of Physics, University of Oxford, Denys Wilkinson Building,
Keble Road, Oxford OX1 3RH, UK.
11
Columbia University, Department of Astronomy, 550 West 120th street,
New York, NY 10027.
12
Department of Physics, Broida Hall, University of California, Santa
Barbara, CA 93106.
13
Kavli Institute for Theoretical Physics, Kohn Hall, University of California,
Santa Barbara, CA 93106.
14
See http://www.astro.caltech.edu/ptf/.
62
P
UBLICATIONS OF THE
A
STRONOMICAL
S
OCIETY OF THE
P
ACIFIC
, 124:62–73, 2012 January
© 2012. The Astronomical Society of the Pacific. All rights reserved. Printed in U.S.A.
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and sky position. Moreover, observations of flux standards are
required, and the true spectral energy distribution of these stan-
dards needs to be known. In practice, simplifying assumptions
are made in order to achieve solutions that are good to the
few-percent level. For example, in many cases, it is customary
to use the calibrated magnitudes of the standard stars instead
of their spectral energy distribution and to assume that the atmo-
spheric transmission is a smooth function of air mass. We note
that relative-photometry approaches (e.g., Gilliland & Brown
1988; Gilliland et al. 1991; Honeycutt 1992; Everett & Howell
2001; Ofek et al. 2011) can achieve better accuracy from the
ground, but are limited by scintillation noise (e.g., Young 1967;
Gilliland & Brown 1988), Poisson noise, and flat-fielding errors.
There are several approaches to photometric calibration. For
example, the Sloan Digital Sky Survey (SDSS; York et al. 2000)
images are calibrated using an auxiliary 20 inch telescope,
which determines the photometric condition on a nightly
basis (Hogg et al. 2001). This telescope measures the extinction
and photometric zero point using a network of standard stars
(Smith et al. 2002), which, in turn, are tied to the standard star
BD þ 17:4708. Furthermore, the photometric uniformity in the
SDSS is achieved by the algorithm described in Padmanabhan
et al. (2008). The SDSS photometry is uniform to better than 2%
in all bands (Adelman-McCarthy et al. 2008).
Ofek (2008; see also Pickles & Depagne 2010) has suggested
using Tycho-2 (Hog et al. 2000) stars to photometrically
calibrate astronomical images. The SDSS griz magnitudes of
such stars are based on synthetic magnitudes of stellar spectral
templates fitted with the Hipparcos B
T
and V
T
magnitudes and
the 2MASS (Skrutskie et al. 2006) JHK magnitudes. However,
this strategy requires images containing unsaturated Tycho
calibration stars, which lie in the ≈9–12 mag range. The satura-
tion limit for the PTF g- and R-band survey is typically
15
around 14 mag.
Herein, we describe the method we have developed for the
photometric calibration of PTF data taken in the g and R bands.
The photometric calibration of the PTF Hα survey will be
described elsewhere. We note that the relative photometric
calibration of PTF currently
16
achieves precision as good as
3 mmag, in given fields, at magnitude 15 (e.g., van Eyken et al.
2011; Agüeros et al. 2011; Law et al. 2011; Levitan et al. 2011;
Polishook et al. 2011). The PTF relative-photometry pipeline
will be described in Levitan et al. (2012, in preparation).
Our method of photometric calibration for PTF data is simi-
lar to the “classical” method of observing a small number of
calibration stars (e.g., Landolt 1992) through various air masses
and assuming photometric conditions—i.e., the atmosphere
transmission properties are constant in time and are a continu-
ous function of air mass. The main difference is that we are
using SDSS stars as standard standards and we typically observe
∼10
5
SDSS stars with high signal-to-noise ratio in each CCD
per night. Another important difference is that PTF observations
are done in a single filter each night. Therefore, in order to relate
the PTF calibrated magnitudes to one of the common absolute
systems, one need to apply color terms. The article is organized
as follows. In § 2, we describe the PTF photometric-calibration
method. In § 3, we discuss the illumination correction, while § 4
describes a related problem that plagues early PTF data. In § 5
we describe the data products and what calibration informat ion
is stored with the image-product data. The performance of the
PTF magnitude calibration is given in § 6, and the derived
photometric parameter statistics are discussed in § 7. We pro-
vide the color transformation between the PTF magnitude sys-
tem and other systems in § 8. Finally, we conclude the article in
§ 9. Unless specified otherwise, the statistics given here are
based on all PTF data obtained from 2009 March to 2011 July.
2. PHOTOMETRIC-CALIBRATION METHOD
PTF has two main data-reduction pipelines. The first is for
real-time (≈30 min; e.g., Gal-Yam et al. 2011) image subtrac-
tion and transient detection, hosted by the Lawrence Berkeley
National Laboratory (Nugent et al. 2012, in preparation). This
article will make no further mention of this pipeline, as only the
second pipeline is relevant here, as discussed subsequently.
The photometric calibration described in this article is
implemented in the second pipeline. This pipeline, hosted by the
Infrared Processing and Analysis Center (IPAC), performs final
image reduction and extracts the source catalogs. The proces-
sing includes splitting the multiextension FITS images, debias-
ing, flat-fielding, astrometric calibration, generation of mask
images, source extraction, and photometric calibration (the sub-
ject of this article). This pipeline is described in Grillmair et al.
(2010) and Laher et al. (2012, in preparation ).
Our photometric-calibration process runs on PTF data sepa-
rately for each night, filter, and CCD. It first attempts to match
the sources extracted from the PTF images taken during a given
night with SDSS-DR7 PhotoPrimary
17
point sources (i.e., SDSS
type ¼ 6). In order to ensure good photometric quality, only
SDSS stellar objects with photometric errors smaller than 0.05
mag in r and i bands and that are fainter than 15 mag (to avoid
saturated stars) in the g, r and i bands are used. The photometric
solutions are calculated only if more than 30 science images
were taken during the night and only if we were able to select
more than 1000 SDSS stars in all the images taken with a given
CCD and filter during the entire night.
15
The PTF camera electronics were modified on 2009 October 22 to increase
the dynamic range. Before this date, the saturation limit was around the 15th
magnitude.
16
For relative photometry, we use a scheme similar to that proposed by
Honeycutt (1992) with some modifications outlined in Ofek et al. (2011).
17
PhotoPrimary is an SDSS table. See definitions in the SDSS Schema
Browser: http://cas.sdss.org/dr7/en/help/browser/browser.asp.
PALOMAR TRANSIENT FACTORY PHOTOMETRIC CALIBRATION 63
2012 PASP, 124:62–73
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We use the SDSS-matched stars as a set of standard stars and
solve for the photometric zero points, air-mass terms, and color
terms in a given night. The fitting process is done separately for
each one of the 11 active CCDs and the g- and R-band filters.
This is required, since the CCDs are not identical and some of
them have a different spectral response (see Law et al. 2009 for
details). For observations taken using the R-band
18
filter, we fit
the following model:
r
SDSS
R
inst
PTF
¼ ZP
R
þ α
c;R
ðr
SDSS
i
SDSS
Þþα
a;R
AM
þ α
ac;R
AMðr
SDSS
i
SDSS
Þþα
t;R
ðt t
m
Þ
þ α
t2;R
ðt t
m
Þ
2
2:5 log
10
ðδtÞ; (1)
while for g-band observations we fit
g
SDSS
g
inst
PTF
¼ ZP
g
þ α
c;g
ðg
SDSS
r
SDSS
Þþα
a;g
AM
þ α
ac;g
AMðg
SDSS
r
SDSS
Þþα
t;g
ðt t
m
Þ
þ α
t2;g
ðt t
m
Þ
2
2:5 log
10
ðδtÞ: (2)
Here, f
inst
PTF
is the PTF instrumental magnitude s in band f (either
R or g); f
SDSS
is the SDSS magnitude in band f (either g, r,or
i); ZP
f
is the photometric zero point for filter f; α
c;f
is the color
term for filter f; α
a;f
is the extinction coefficient (air-mass term)
for filter f; α
ac;f
is the air-mass/color term for filter f; AM is the
air mass; α
t;f
and α
t2;f
are the polynomial coefficients for the
change in the zero point of filter f as a function of time, t,in
days, during the night, where t
m
in days is the middle of the
night; and δt is the exposure time in seconds.
The instrumental magnitudes used in the photometric-
calibration process are based on the SExtractor (Bertin &
Arnouts 1996) MAG_AUTO magnitude
19
(see also § 6.1), with
an internal SExtractor zero point of 0. The preceding set of
equations is solved using linear least-squares fitting. The errors
in r
SDSS
R
inst
PTF
are taken as ðΔ r
2
SDSS
þ ΔR
inst2
PTF
þ 0:015
2
Þ
1=2
,
where Δr
SDSS
is the SDSS magnitude error, ΔR
inst
PTF
is the PTF
magnitude errors, and 0.015 is the assumed internal accuracy of
the SDSS photometric calibration (Adelman-McCarthy et al.
2008). The fit is performed iteratively, up to 3 times, with sigma
clipping of 3σ. The sigma clipping ensures removal of stars with
bad photometry (e.g., influenced by cosmic rays; saturated
pixels).
Next, in order to be able to correct for zero-point variations
across a given CCD (see § 3), the residuals from the best fit of
equations (1) or (2) are binned in cells of 256 × 256 pixel
2
along the X and Y dimensions of each CCD. In each cell,
we take the mean of the residuals and subtract from it the mean
of the residuals in the center-of-image cell,
20
in order to render
the residuals relative to the residual at the image center. The
resulting coarse image of the mean of the residuals is linearly
interpolated to generate an image at the resolution of PTF
images of pixel-to-pixel zero-point variations. This image
product is equivalent to an illumination correction and herein
is also called the zero-point variation map (ZPVM; see § 3).
The uncertainty in the ZPVM is estimated by calculating the
standard deviation (StD) of the residuals in each cell.
21
We note that the PTF illumination-correction images are
usually smooth on large scales (comparable with the CCD
image size). Therefore, in most cases the ZPVM can be repre-
sented by low-order polynomials. In order to provide users with
a simpler version of the illumination correction, we also fit
versions of equations (1) and (2) that consist of a low-order-
polynomial representation of the ZPVM, e.g., for the R band:
r
SDSS
R
inst
PTF
¼ ZP
R
þ α
c;R
ðr
SDSS
i
SDSS
Þþα
a;R
AM
þ α
ac;R
AMðr
SDSS
i
SDSS
Þþα
t;R
ðt t
m
Þ
þ α
t2;R
ðt t
m
Þ
2
2:5 log
10
ðδtÞ
þ α
x1;R
ðX
1
2
X
size
Þ=X
size
þ α
y1;R
ðY
1
2
Y
size
Þ=Y
size
þ α
y2;R
½ðY
1
2
Y
size
Þ=Y
size
2
þ α
y3;R
½ðY
1
2
Y
size
Þ=Y
size
3
þ α
xy;R
ðX
1
2
X
size
Þ
× ðY
1
2
Y
size
Þ=ðX
size
Y
size
Þ; (3)
where X and Y are the positions on the CCD; X
size
is the size of
the CCD, in pixels, in the X dimension (2048 pixels); and Y
size
corresponds to the Y dimension (4096 pixels). A similar
equation is fitted for the g-band observations (see eq. [2] for
an analogy). Here, the illumination correction is represented
by polynomials with coefficients α
x1;f
, α
y1;f
, α
y2;f
, α
y3;f
,
and α
xy;f
. This representation usually provides a good estimate
of the illumination correction over the image. We note that for
each solution we store a variety of information regarding the
quality of the fit (see § 5).
3. ILLUMINATION CORRECTION
The ZPVM and its polynomial representation amount to an
illumination correction that describes the variations in the
photometric zero point spatially across the image (e.g., Regnault
et al. 2009). Naively, such zero-point variations should be re-
moved by the flat-fielding process. However, for example, if
the fraction of flux in the stars’ point-spread function (PSF)
wings, relative to the flux integrated to infinity in the PSF, is
variable as a function of position on the CCD, then this may
induce variations that are not removed by the flat-fielding
18
The PTF Mould R filter is similar in shape to the SDSS r-band filter, but
shifted 27 Å redward.
19
Defined with kron
fact
¼ 1:5 and min
radius
¼ 2:5.
20
The position of the center of this central cell is x ¼ 1025 and y ¼ 2049.
21
The StD is not divided by the square root of number of data points in each
cell. Therefore, it represents the scatter rather than the error in the mean.
64 OFEK ET AL.
2012 PASP, 124:62–73
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process. Such CCD position-dependent variations are indeed
detected in PTF images (e.g., § 4). Figure 1 presents two exam-
ples of illumination-correction images. The left panel shows a
typical case, in which the illumination corrections have a low
range of variations. The right panel shows one of the worst cases
we encountered thus far, with larger amplitude variations (see
§ 4). We note that the median peak-to-peak variations in the zero
point across the image over all photometric nights, filters, and
CCDs is about 0.06 mag.
In the first year of the PTF project following first light, we
detected a problem with the PTF image quality. This issue is
referred to as the fogging problem, and it manifested itself as
a diffuse halo around bright stars and a prominent large-scale
nonuniformity in the illumination corrections. The fogging pro-
blem and our tes ting and subsequent apparatus modifications
for ameliorating its effects are described in § 4.
4. THE FOGGING PROBLEM
The PTF camera window, which also acts as a field flattener,
is fairly large (≈320 cm
2
). The window surface temperature is
≈16°C below the ambient temperature. To prevent condensa-
tion from forming on the window, a constant stream of dry air
or nitrogen is blown across the window’s outer surface. In the
original installation of the camera, new nylon tubing was used to
convey the gas to the window. This tubing was selected for its
reported high durability, flexibility, and low outgassing proper-
ties. Once the window fogging problem was discovered, a test
chamber was assembled, which simulated the cold window
environment in which dry nitrogen could be conveyed across
a cold test surface.
The outgassing properties of various tubing materials were
evaluated in the test chamber. The nylon tubing used in the orig-
inal camera installation was found to have a moderate amount of
outgassing, which produced volatiles that would condense on
the cold test surface. Tubing made from Teflon FEP (fluorinated
ethylene propy lene) was found to have low outgassing with no
detectable volatile condensation on the cold test surface. The
camera window was cleaned, and all tubing between the dry
nitrogen supply and the camera window was replaced with this
material on 2010 September 2. No window fogging issues have
been observed since the tubing materia l was replaced.
We note that the camera window was cleaned several times
during PTF operations, and after each cleaning an immediate
reduction in ZPVM deviations from zero is expected. The
cleaning dates are listed in Table 1.
F
IG
.1.—Two examples of ZPVM images. The shading represents the varia-
tions in photometric zero point with respect to the center of image. Left: a good
case with peak-to-peak range of 0.016 mag in the zero-point variations across the
image. This ZPVM image was constructed based on data obtained on 2011 July
13 with CCDID ¼ 8 and the R-band filter. Right: One of the worst examples
with a peak-to-peak range of 0.22 mag in the zero point across the image. This
ZPVM image was constructed based on data obtained on 2010 May 7 with
CCDID ¼ 5 and the R-band filter. In such bad cases the polynomial representa-
tion may deliver poor-quality results. We note that this bad example is not re-
presentative of PTF data and that typical images obtained using this system have
good image quality. See the electronic edition of the PASP for a color version of
this figure.
TABLE 1
PTF C
AMERA
W
INDOW
C
LEANING
D
ATES
Cleaning date
Year Month Day Comments
2009 .......... 07 17
2009 .......... 07 29
2009 .......... 08 11
2009 .......... 08 20
2009 .......... 09 15
2009 .......... 09 29
2009 .......... 10 21
2009 .......... 11 10
2010 .......... 01 11
2010 .......... 01 27
2010 .......... 02 04
2010 .......... 02 18
2010 .......... 03 04
2010 .......... 04 06
2010 .......... 04 21
2010 .......... 05 12
2010 .......... 06 02
2010 .......... 06 17
2010 .......... 07 01
2010 .......... 08 04
2010 .......... 08 16
2010 .......... 09 02 New tubing
N
OTE
—.Local dates that the camera window was cleaned
(e.g., the last cleaning date is 2010 September 2, so all the data
taken on UTC 2010 September 3 and afterward are affected by
this cleaning). At these dates, the illumination correction may
change markedly.
PALOMAR TRANSIENT FACTORY PHOTOMETRIC CALIBRATION 65
2012 PASP, 124:62–73
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