SAG: Stars and Galaxies Vol. 5, id. 5, 2022 December 28
c 2022. Center for Astronomy, University of Hyogo.
⃝

Precise photometry with an improved pipeline for Nayuta/NIC
Tomoki Saito 1
1

Nishi-Harima Astronomical Observatory, Centre for Astronomy, University of Hyogo,407-2 Nishigaichi, Sayo-cho, Sayo-gun, Hyogo 679-5313, Japan
tsaito@nhao.jp(Received 2022 November 16; accepted 2022 December 14)

Abstract
We present development of a new data reduction and analysis pipeline for Nishiharima Infrared Camera on 2 m
Nayuta telescope. The new pipeline includes fundamental reduction procedures, as well as miscellaneous utilities
to prepare the calibration frames such as dark and ﬂatﬁeld. We improved the subtraction procedures of the detectorspeciﬁc background patterns. These together improves the resulting photometric precision by a factor of ∼ 2, whenusing the newly obtained ﬂat frames. We also ﬁnd that the photometric precision can further be improved byintroducing “ubercalibration”, resulting in the rms photometric errors of ∆mag ∼ 0.02−0.03 for 14−15 mag (AB)in H-band, under the very best condition.
Key words: instrumentation — data analysis — near-infrared — photometry
1. Introduction
Recent detection of high-redshift quasars up to z = 7.5 has
opened the new window to study the galaxy/black-hole evolution in the early universe (Sekine et al. 2021). Nishiharima
Infrared Camera (NIC) on 2 m Nayuta Telescope is a unique facility to carry out high-sensitivity observations in near-infrared(NIR) regime with a great ﬂexibility of time allocation. It allows us to detect down to ∼ 19−20 mag (AB) with >∼ 5 σ signiﬁcance of the local background ﬂuctuation, under the reasonable integration time (∼ 1 hrs). However, the photometricprecision is not good enough to perform highly sensitive monitoring observations, especially for faint sources such as distantquasars in the cosmic epoch of reionisation.
NIC data are usually reduced with the procedures reported
in Ishiguro et al. (2011), including dark subtraction, ﬂatﬁelding, bad-pixel correction, hot- and dark-pixel correction, sky
subtraction, detector-speciﬁc background subtraction, imagealignment, and coadding (and astrometry when possible). We
carefully inspected these processes, and found that there aresome rooms of improvements in ﬂatﬁelding, detector-speciﬁcbackground pattern subtraction, and sky subtraction.
In this paper we report the development of a new datareduction/analysis pipeline to improve the precision of photometry, as well as the ﬂexibility of reduction processes. To
this end, we also develop various utilities to ensure easy handling of new calibration data, and incorporate the newly obtained ﬂat frames. The utilities also help us improving the darkframes by performing simple statistics on a set of dark frames.
We then carried out photometry experiments to examine theimprovements by utilising this pipeline. We also performedexperiments to test the possibility to further improve the photometric precision by incorporating “ubercalibration” technique(Padmanabhan et al. 2008).

This paper is organised as follows. We outline the basic procedures of the data reduction for NIC, and describe
which processes we modiﬁed and improved in the new pipeline
in Section 2. We present the ﬂuctuation of the ﬂatﬁeld in
Section 3.1. Section 3.1 also introduce the utility to inspect
the ﬂat frames. The improvement of dark data is presented in
Section 3.2, together with application of the inspection utility
to the dark frames. Section 3.3 presents the new method of
the background- pattern subtraction, including the sky patterns(Section 3.3.1) and the detector-speciﬁc background patterns(Section 3.3.2). Section 4 describes the experiments using thenew pipeline to quantify the photometric precision. We also
describe the possibilities of further improvements by applying
ubercalibration technique. Section 5 summarises this paper,and describes brieﬂy the future prospects to further improve thephotometry. The magnitudes are given in AB magnitude system (Oke 1974; Fukugita et al. 1995), unless otherwise noted.
2. Scheme of data reduction
The procedures of data reduction for NIC are basically thesame as those presented in Ishiguro et al. (2011) with slightly
modiﬁed order: (a) prepare the dark and ﬂat frames, (b) subtract the dark from the object frames, (c) correct the bad pixels,(d) prepare the sky frame, (e) subtract the sky from the object
frames, (f) subtract the detector-speciﬁc background patterns,(g) apply ﬂatﬁelding, (h) align the object frames, (i) medianstack the frames, (j) solve the astrometry to register the WCS.
Our new pipeline also traces the procedures presented in
Ishiguro et al. (2011), and the individual processes are basically built so as to reproduce the same results. We ﬁrst update
the calibration data to correct for the time variation of the ﬂatand dark levels. The step a above includes rather complicatedprocedures, so that we developed the utilities to make it easier2T. Saito

to update the calibration data using new datasets. The second
most major update is to simplify the step f, regardless of thestar density within the ﬁeld of view. In addition to the subtraction algorithm, we apply the step f before ﬂatﬁelding (g) andafter sky subtraction (e). The sky subtraction (e) is updated aswell, in order to obtain proper sky pattern by taking account of
the dark-level ﬂuctuation of each quadrant of the detectors.
These features together are expected to improve the resulting photometric precision. We implemented these features into
the IDL-based pipeline to ensure high portability and ﬂexibility. Each process is implemented as a single IDL procedure,
allowing us to link them together more ﬂexibly according to
the purposes of data analyses. We can also run the individual
procedures according to the same order presented in Ishiguro
et al. (2011).
3. Modiﬁcations in the new pipeline
3.1. Flatﬁeld
Flat frame of NIC includes various systematic patterns, inaddition to the true ﬂat image, i.e., the light equally illuminates
the whole ﬁeld of view (FoV). Such patterns are thought toconsist of the thermal emission from the light path including
the telescope enclosure, the telescope structure, and the optics. In order to subtract these patterns from the ﬂat frames,
we usually obtain pairs of two twilight frames, each having ahigh-count frame and a low-count frame. Then we subtract
the low-count frame from the high-count frame to exclude thesystematics. We take as many such pairs of ﬂat frames as possible, and normalise them to the median counts, and coadd themwith median-stacking. The resulting (coadded) frame is thennormalised so as to make the average count over the FoV to beunity.
Making the master ﬂat frames thus requires a lot of data withfairly good conditions, as we use the twilight sky to obtain theﬂat frames. The reduction procedures are also fairly complicated compared to optical imaging instruments. These together
make it difﬁcult to update frequently the master ﬂat frames.
Fluctuations of the master ﬂat frames does affect the photometry, if we require high precision. The master ﬂat frames usuallyused by the current pipeline is based on the data taken at 2015
June 21. This set of master ﬂats work reasonably good for, e.g.,bright sources (∼ 13 mag) requiring photometric precision of∼ 10 per cent or so. However, the ﬂat level does change intime, so that the old master ﬂats are not good enough when requiring the precision of several per cent for ∼ 20 mag sources.
Fig. 1 shows the time variation of the ﬂat ﬁeld. The ﬂat ﬁeld
pattern changes with time between the two epochs shown here.
The ﬂuctuation levels are mostly within ∼ 2−3 per cent, but,
especially in the regions near the edges, the levels sometimes
change up to several per cent. Caution must be taken when thetargets are located near the edges of the FoV.
NIC has been removed from the telescope several times
since 2015, when the old master ﬂat frames were obtained.
Operation has been stopped at least once every year in the regular power shortage, and once for every 10,000 hours whenthe maintenance of NIC itself takes place. These operation cycles, as well as the long-term variation of the characteristicsof the ditector and optics, may together affect the ﬂat-frameVol. 5,patterns. It is thus recommended to obtain new ﬂat frames toachieve high photometric precision. We then developed theutility to make generating the new ﬂat frames easier. For instance, the pixel counts of twilight ﬂat frames must be monotonically decreasing (when taken around the Sunset) or monotonically increasing in time (when taken around the Sunrise),both being convex downward. Fig. 2 shows the time-series plotof the median counts for the twilight frames taken around theSunset. We can easily ﬁnd and ﬂag out the saturated frames
and frames with unusual counts, and choose high-count andlow-count frames to be used to generate the master ﬂat frame.
3.2. Dark
The detectors of NIC suffer from the ﬂuctuations of the darklevel, especially for the J-band dark frames. Fig. 3 shows
examples of dark frames both for good and bad cases. The
good (usual) case shows rather homogeneous counts among thefour quadrants. On the other hand, the 1st quadrant in the badcase have exceptionally low counts, compared to the remaining
three quadrants. Such an ’outlier’ quadrant gives somewhat irregular counts of the dark level, spoiling the proper estimationof the typical dark levels caused by typical dark current.
In addition to such quadrant-to-quadrant variation, there exists a time variation of the dark level. Fig. 4 shows the mediancounts of the dark frames as a function of the frame ID (i.e.,
time-series plot). We can see some time variation of the darkcounts, as well as the quadrant-to-quadrant variation.
In order to ﬂag out the outlier frames, we utilise the plotting tool used for ﬂat frames. The grey shaded area in Fig. 4
shows the range of deviation from the average over the whole
FoV within the speciﬁed threshold for each frame. The threshold is given by a user, in units of the standard deviation (σ) ofthe median counts of the series of dark frames. The σ in thisexample shown in Fig. 4 is computed from the 20 median values obtained from the 20 frames, taking the median over thewhole FoV (excluding 50 pixel from the edges) of each frame.
If there exist any quadrants (usually one quadrant) with the deviation above the threshold, the frame should be ﬂagged out.
We also need to eliminate the outliers due to the time variation of the dark counts. Fig. 4 also shows the time-series variation of the dark counts. Especially for the frames taken justafter changing the exposure time / read-out mode settings, themedian counts sometimes drastically changes, as seen in theleftmost two frames. In order to ﬂag out such outliers, we set
another threshold of the deviation from the time-average count.
Our new pipeline semi-automatically performs these ﬂagging procedures, making it easier to generate the master ﬂatframe.
3.3. Background subtraction
3.3.1. Sky background patterns
NIC images includes systematic backgrounds caused by various factors. The most major component of such backgroundis called “sky” background. This “sky” consists of not only
the atmospheric sky background, but other backgrounds suchas thermal emission of the light path. These systematics cannot
be easily separated from the true sky background, so that they
are treated altogether as “sky” background.
The sky background is obtained by median-stacking the set

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Improved photometry with NIC

3Fig. 1. Time variation of the ﬂat frames, obtained by dividing the new ﬂat frame taken at 2022 June 1 by the old ﬂat frame (the current master ﬂat as of 2022) taken at 2015 June 21 for each band. The differences between the two epochs are mostly well within ∼ 5 per cent, except for some bad pixels seen as several to ten red/black spots in the FoV for each band. However, the difference becomes close to ∼ 10 per
cent at the very edges of the FoV. Note that these images are smoothed with a Gaussian kernel (FWHM = 10 × 10) to improve the visibility.

Fig. 2. Median J-band counts of the twilight ﬂat as a function of theframe ID. This example shows the case of evening twilight aroundthe Sunset. The input ﬁle names are shown in the ﬁgure title on
the top. The solid lines shows the median counts, with ± 1 σ levels are shown with the dashed lines. The black lines show themedians of the whole FoV, and the red, green, blue and orange
lines show the 1st-, 2nd-, 3rd- and 4th quadrants, respectively. We
can easily ﬁnd useable range of the frame IDs by generating thisplot, avoiding the saturated frames or frames with unusual counts.

of images taken in the same dithering sequence, without performing any alignments, i.e., the self-sky frame. If the target
ﬁeld is very crowded by stars and the self-sky frame is unavailable, e.g., for the case of star clusters or ﬁelds within the galactic plane, the separate sky images must be taken with same exposure time, read-out mode and dithering pattern as the target
ﬁeld. The sky frame for each band thus obtained is then scaled
to match the median level of the object frame before subtraction. If the median level differ from quadrant to quadrant, themedian count is computed after eliminating the “outlier quadrant”. Fig. 5 shows the examples of sky subtraction, summarising the procedures described above.
3.3.2. Detector-speciﬁc background pattern
The second component of the systematic background is thedetector-speciﬁc background, seen as the vertical stripes in theFig. 5. The amplitude of this pattern is much smaller than theFig. 3. Examples of the J-band dark frames of NIC. The left panel
shows the good (usual) case, where the dark counts for the four quadrants are rather homogeneous, e.g., ∼ 0 ± 1 for 20 sec exposure.
The right panel shows the bad case, where the 1st (upper-right) quadrant has exceptionally low counts compared with the remaining three
quadrants. In this case the 1st quadrant has its median count of-10, while the remaining quadrants have median counts of -3 ± 1.

sky component described in Section 3.3.1, so that the subtraction of this pattern must be performed after the sky subtraction.
The detector-speciﬁc background pattern (hereafter columnpattern) is known to be almost constant along the Y -direction
for each quadrant. The column-pattern is estimated in two
methods, according to the star density of the target ﬁeld. If
the ﬁeld contains relatively few stars, the column-pattern is derived by selecting the area wherein no stars are detected, andthe median pixel count is computed for each column withinthat area (sampling method). If the ﬁeld contains many stars,
the column-pattern is estimated by taking the median countof each column (median method). These methods both havedrawbacks: the sampling method requires source extraction,
which complicates the process; the median method suffer frombright stars, which often causes incomplete subtraction.
In order to simplify the process, we implemented the iterative sigma-clipping method in the new pipeline: (1) take themean and standard deviation of the counts for each column,(2) ﬂag out the pixels with deviation exceeding the threhold
value (typically 2 σ), (3) iterate (1) and (2) processes, and (4)take the median count of the remaining pixels for each column.
This method can be applied to the both dense and sparse ﬁelds.
Fig. 6 shows an example of this procedure. We can see in both4T. SaitoFig. 4. Plot of the median counts for dark frames as a function of theframe ID, i.e., the time-series plot. This example shows the case of
H-band dark frames with the exposure time of 20 sec, taken at 2022
May 2. The symbols and lines are color-coded according to the quadrant number, similarly to Fig. 2. The grey shaded area corresponds tothe deviation from the average of the four quadrants within the threshold (1 σ for this example: the σ here is calculated from the distribution of 20 medians of the 20 frames). The dashed lines correspondto ± 1 σ level of the count deviation from the median of each quadrant. The ﬂagged-out frames after applying the threshold of the quadrant-to-quadrant variation and the time variation are shown with thecross marks. The frames ﬁnally used are plotted with the open circles.

Vol. 5,

Fig. 6. Example of column-pattern subtraction. The upper panels show the case of dense ﬁeld, and the lower panels show
the case of sparse ﬁeld. The left panels are sky-subtracted images, and the right panels are column-pattern-subtracted images.
In both cases the column-patterns are sufﬁciently well subtracted.

Fig. 5. Example of sky subtraction. The left panel shows a dark–
subtracted H-band image of a star cluster. The middle panel showsa sky frame generated using a set of sky-ﬁeld image, ∼ 20′ awayfrom the target. The right panel shows the sky-subtracted frame,
generated by subtracting the middle-panel image (scaled) from theleft-panel image. The swell-like pattern seen in the sky image
disappears in the right panel, and the detector-speciﬁc backgroundpattern (vertical stripes) becomes clear after the sky subtraction.

cases that the column-pattern is sufﬁciently well subtracted.
4. Application to the observed data
4.1. Basic improvements
First step to improve the photometric precision is to implement the abovementioned procedures into the new pipeline. In
order to examine the improvements, we carried out test observations targeting on a globular cluster, M53. The observations
were carried out in 2022 June 1, under very good atmosphericconditions with the PSF size of ∼ 0′′ .6 − 0′′ .8. We took 5 setsof 5-point dithering, with a unit exposure time of 20 sec. The
dithering pattern can be seen in Fig. 7. Note that the rotator
position angle (PA) is not changed to save the observing time.
We did try changing the PA, but the results did not change signiﬁcantly.
The scheme of the test observations are as follows: We ﬁrst

Fig. 7. Dithering pattern used for the test observations. The pointing are shown with the cross marks. Each circle shows the trajectory of a single dithering set, with the ﬁrst point located at
its centre, and the remaining four points located along the circuit.

apply our new pipeline to the raw data, as shown in Section 2 up
to (g), and solve the astrometry for each frame. Then we perform photometry with SExtractor software (Bertin & Arnouts
1996) to obtain the ﬂuxes (in counts) of individual stars. The
magnitude is obtained by using a common standard star for all
frame, 2MASS J13125701+1810350, with its 2MASS magnitudes (Vega) of (J, H, Ks ) = (12.527, 11.998, 11.852), andlisted into a single catalogue for each frame. We ﬁnally con-id. 5

Improved photometry with NIC

catenate the catalogues by identifying the identical stars basedon their coordinates, and calculated the magnitude variance.
In calculating the variance, we only use stars observed morethan three times, resulting in ∼ 300−380 stars within the
2′ .7×2′ .7 FoV. We then deﬁne here the photometric error (precision) as the root mean square (rms) deviation of the magnitudes measured for each star, ∆mag . Since the magnitudes
given in the 2MASS catalogue is in Vega system, the magnitudes are converted to AB system by applying the offset terms,
J(AB) = J(Vega) + 0.91, H(AB) = H(Vega) + 1.39 andKs (AB) = Ks (Vega) + 1.85.
For comparison, we reduced the same data with the old (current) pipeline. The major differences are that old pipeline uses
the old master ﬂat frames, and the column-pattern subtraction
is made after ﬂatﬁelding. The magnitudes are obtained withthe same manner with that for the new pipeline, using the same
standard star. As for the old pipeline, some stars used in the
new pipeline cannot be used due to the failure of the source
extraction, sometimes resulting in ∼ 200 stars.
We plot the ∆mag as a function of the magnitude in Fig. 8.
We can see that the new pipeline gives a factor of ∼ 2 improvements of the photometric precision, especially for Ks band. The H-band data also slightly improved, as the ∆magsigniﬁcantly decreases for some stars. The J-band data does
not seem to improve signiﬁcantly by using the new pipeline.
However, even including the J- and H-band results, the error
distribution moves downwards on the plots, suggesting there
is some amount of improvements in the photometric precision.
Globular clusters have very high stellar density around theircentre, leading to the source confusion. Furthermore, such ahigh density results in over-subtraction of the column-pattern,also leading to degradation of the photometric precision. Such
positional dependency (lower precision in the very centre of
the cluster) may result in such ’apparently no signiﬁcant improvements’ in this comparison. The Ks -band has relatively
low sensitivity, so that it may be less-suffered from such confusion effects.
Fig. 8 also shows that there are more than one horizontal sequences at bright-ends of the plots, especially for the H-band.
This may reﬂect the inhomogeneity of the photometric precision over the FoV, i.e., positional ﬂuctuation of the ﬂat level.
The J- and H-band results show that some stars have ∆magsfrom the new pipeline as large as those from the old pipeline,
but some stars have signiﬁcantly reduced ∆mags. The positional ﬂuctuation of the ﬂat levels can explain to some extent
such rather poor improvements of the photometric precisioneven by using the new pipeline with the new master ﬂats.
Another thing to be noted is that the old pipeline (using oldmaster ﬂat) works sufﬁciently well. We can see that the data
reduced with the old pipeline have photometric precision ofseveral per cent, down to ∼ 15 mag. This is quite consistent
with previous measurements presented by Takahashi, Zenno,
and Ishiguro (2013). For observers targeting relatively brightsources (like Nayuta/NIC usually targets on) can use the oldpipeline without any problems.
4.2. Further improvements with “ubercalibration”
In order to further improve the precision, we further madeanother experiments applying “ubercalibration” technique to5

Fig. 8. Comparison of the photometric precision between newand old pipeline.
The rms magnitude error (∆mag) is plotted as a function of the magnitude.
The red circles show
the results from the new pipeline, and the blue diamonds showthat of the old pipeline.
The top, middle and bottom panels show the results of J-, H- and Ks -band, respectively.

reﬁne the master ﬂat frames. This is a simpliﬁed version ofthat applied to Sloan Digital Sky Survey data (Padmanabhan
et al. 2008) or COSMOS data (Capak et al. 2007). The basic
idea is to apply higher-level correction to the ﬂatﬁelded data,by measuring many stars within the FoV and perform polynomial ﬁtting to the ﬂuctuation of the measured magnitudes as afunction of the position within the FoV. Such inhomogeneity
is reported in past study of transit observations of exoplanets(Tabata & Itoh 2020), where highly precise photometry is re-6

T. Saitoquired. Tabata and Itoh 2020 applied the offsets derived by averaging the target object’s ﬂuxes measured at the same dithering positions. This is a simple “self-calibration”, that can beapplied to very bright sources. The ubercalibration is to further
expand the correction to be applied to general cases, where thetargets are very faint.
Let us think about the simplest case of observing a star cluster containing many stars. The ﬂatﬁeld and the atmosphericabsorption are assumed here to be already corrected, and otherfactors such as scattered light and thermal emission of the lightpath are expected to reduce the photometric precision. This is
sufﬁciently good approximation because the atmospheric conditions were very good when the observations were made. The
relation between the true- and the observed magnitudes are:mtrue = mobs + ∆a + f (X, Y ),Vol. 5,The calculation takes up to several to ten minutes even when
the polynomial order is set to 10-th, with an ordinary laptop
machine (MacBook Pro 2020 model with 2.3 GHz Quad-CoreIntel Core i7, 32 GB 3733 MHz LPDDR). The superﬂat frames
thus obtained are shown in Fig. 9, together with the corresponding master ﬂat frames. Among the polynomial orders we examined, the best results are given by 6-th order for H- andKs -bands, and 8-th order for J-band. Note that the superﬂat
deviates by >∼ 10 per cent from the average at the regions close
to the edges of the frames. This is thought to be due to the lackof data points, as we exclude the stars detected very close to
the edges before ubercalibration. We thus do not expect photometric precision for the regions within ∼ 50−100 pixel fromthe edges.

(1)

where mtrue is the true magnitude, mobs is the observed
magnitude. ∆a is the magnitude offset (gain) speciﬁc to thedetector’s quadrant, as the NIC’s detector consists of four quadrants independently read-out the signals. f (X, Y ) is the gain
term dependent on the position on the detector, i.e., the pixel
coordinate. For the i-th star measured in the j-th observations,at the detector position (X, Y ) belonging to α-th quadrant, theobserved magnitude should be in the following form.
mtrue= mobsii,j + ∆aα + f (X, Y ),(2)

where mtrue
is the true magnitude of the i-th star, and mobs
ii,jis the observed magnitude of the i-th star measured in the j-th
observation. ∆aα is the gain term of the α-th quadrant, acting
as a constant term of the polynomial. The rightmost two terms,
i.e., ∆aα + f (X,Y ), is the polynomial model to further correct
the master ﬂat, often called “superﬂat”. The superﬂat can beobtained by minimising the differences between the mtrue
and
i
2
2
mobs
,
i.e.,
χ
.
The
χ
can
be
thus
expressedasfollows.
i,j
(
)2
mtrue
− mobsii,j − ∆aα − f (X, Y )2
χ = Σij,(3)
σjwhere σj is the photometric error of the j-th observation of
the i-th star. The source of this error is estimated by measuring the sky ﬂuctuation around the source, using the SExtractor.
The ﬁtting procedure based on eq. 3 is implemented in C, using
the Gnu Science Library (GSL) ver. 2.6. This is called fromour IDL-based pipeline, but can be used in stand-alone manner.
The scheme of ubercalibration is as follows: (1) take the images of the star cluster with a given pattern of dithering; (2)apply standard reduction processes except coadding, and solve
the astrometry for each frame using Astrometry.net code (Langet al. 2010); (3) perform photometry using SExtractor to generate the source catalogue for each frame; (4) concatenate thecatalogues by identifying the same stars based on the astrometry information, and assign the unique ID for each star; (5) ﬁt
the superﬂat model so as to the same stars are always measured
to have the same magnitudes; (6) generate the superﬂat frame,and divide the ﬂatﬁelded object frames by them.
The (X, Y ) coordinates are normalised to −1 ≤ X ≤ 1 and−1 ≤ Y ≤ 1 to avoid overﬂow, as we try ﬁtting the polynomials with up to 10-th order. The iteration is set to stop when thegradient of the parameter vector becomes smaller than 10−3 .

Fig. 9. Superﬂat images obtained by performing ubercalibration using
M53 images. The panels in the left column show the master ﬂat frames
generated from the data of 2022 June 1 (J-, H- and Ks -bands, from top
to bottom). The panels of the right column show the superﬂat frames
generated with 8-th, 6-th and 6-th order for J-, H- and Ks -bands, respectively. The frames are normalised to unity, excluding the 50 pixel
from the edges. The magenta contour shows the average (unity) level,
and the green (blue) contours show higher (lower)-than-average levels.

Fig. 10 shows the ∆mags as a function of the magnitude,
comparing the results with and without ubercalibration. We
can see signiﬁcant improvements at least for the H- and theKs -bands. The typical photometric precision is estimated to be∼ 2 per cent for J-band, and ∼ 1 per cent for H- and Ks -bands,

id. 5

Improved photometry with NIC

down to ∼ 14 mag. Even for the magnitude range down to ∼15−16, the precision becomes ∼ 4 per cent for J-band, and3 − 4 per cent for H- and Ks -bands. The results shows a factorof ∼ 2 − 3 improvements by ubercailbration, at least for the Hand the Ks -bands. This supports the assumption that the overall ﬂatﬁeld and atmospheric absorption is well corrected during the observations, and other factors such as scattered lightand thermal emission from the light path can be corrected withubercalibration. Although this is the very ideal case, this precision corresponds to ∼ 5−6 per cent for ∼ 20 mag when the
exposure time is ∼ 7200 sec., if we assume the simplest (i.e.,the most optimistic) Poisson statistics.

7

The improvement in the J-band result is, however, not sogood as the other bands. The superﬂat image has rather complicated patterns, and the regions near the edges show rather
catastrophic deviations from the average. J-band (∼ 1.2 µm)
is the closest to the optical wavelength, and there are manylight sources or scattering objects within the telescope enclosure. The contribution from thermal emission is smaller thanthat for the other two bands, so that further investigations of
scattered lights are deﬁnitely needed. Furthermore, the detector patterns and ﬂat patterns of the J-band are the noisiest andlooks the most complicated among three bands. Better ﬂat andbetter detector/optics might be needed to further improve the
J-band photometry.
Another thing that must be noted is that this series of experiments is just a self calibration: we correct the inhomogeneity with the data of a speciﬁc target taken at a speciﬁcdate, and estimating the photometric precision (i.e., consistency). Applying the superﬂat to data taken at other day seems
not straightforward. We did not ﬁnd signiﬁcant improvements
when applying the same superﬂat to the data taken at 2022 May
27. The weather condition of this run was not so good as for theJune 1 run, so that there must remain some causes degrading
the photometric precision. Even in the case for the same day
run, we did not ﬁnd signiﬁcant improvements (Sekine et al.
2022). This discrepancy may caused by elevation- or azimuth
dependence of the ﬂat-pattern ﬂuctuation.
Ideally speaking, the ubercalibration data thus should betaken as close to the object frames as possible, i.e., just before and just after the science frames (close in time series),
and using a star cluster as close to the science target (closein sky position) as possible. However, the current procedure
taking ubercalibration data takes ∼ 30 min, consuming nonnegligible amount of telescope time. In principle, ubercalibration can be carried out with science frames if the ﬁeld contains
certain amount of stars. How often we should take the ubercalibration data, and how many stars we need to perform ubercalibration to achieve precision sufﬁciently high for our sciencetargets, should be inspected in detail in the future works.
5. Summary and future prospect

Fig. 10. Comparison of the photometric precision between the casewith and without ubercalibration. The format is same as Fig. 8. The
red circles show the results after ubercalibration. The results before ubercalibration are plotted with blue diamonds as a comparison.

We develop a new pipeline for data reduction of
Nayuta/NIC. We ﬁrst renew the master ﬂat frames, updating
the basic procedures such as sky/detector-speciﬁc pattern subtraction and ﬂatﬁelding. By implementing these updates, weachieve a factor of ∼ 2 improvements of the photometric precision.
In order to further improve the photometry with NIC, wedevelop ubercalibration code and implement into the new
pipeline. With the simplest case of ubercalibration, we
achieved a factor of ∼ 2−3 improvements of the photometric precision, typically ∼ 2−3 per cent (∆mag ∼ 0.02−0.03)
in rms with an exposure time of 20 sec.
Although we achieve such improvements, there are somedata that cannot be improved so much, especially for the Jband. This is thought to be due to that J-band is the most
likely to suffer from the scattered light. Further investigations
are needed for the light/scatter sources within the telescope enclosure, or perhaps the optics/detectors.

8

T. Saito

The pipeline software is going to be opened for public. We
are willing to open before public release to the readers in arisk-share manner.
Acknowledgements
The author thanks the anonymous referee for his/her valuable comments, that greatly helped improving this manuscript.
This work is supported by the Special Research Grant of
University of Hyogo (兵庫県立大学特別研究助成金 先導研
究 A / 基礎研究支援) in 2020, 2021 and 2022. TS thanks J.
Takahashi for various advices in processing the NIC data. He
also thanks A.K. Inoue and S. Sekine to collaborate in QSO
monitoring survey, which motivated the improvements of the
NIC data. He thanks Y. Itoh for giving advices for proper treatments of NIR data and instruments.
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