# Scientific CMOS sensors in Astronomy: IMX455 and IMX411

Miguel R. Alarcon Instituto de Astrofísica de Canarias (IAC), C/ Vía Láctea,
s/n, E-38205, La Laguna, Spain  Departamento de Astrofísica, Universidad de La
Laguna (ULL), E-38206 La Laguna, Canarias, Spain  Javier Licandro Instituto de
Astrofísica de Canarias (IAC), C/ Vía Láctea, s/n, E-38205, La Laguna, Spain
Departamento de Astrofísica, Universidad de La Laguna (ULL), E-38206 La
Laguna, Canarias, Spain  Miquel Serra-Ricart Instituto de Astrofísica de
Canarias (IAC), C/ Vía Láctea, s/n, E-38205, La Laguna, Spain  Departamento de
Astrofísica, Universidad de La Laguna (ULL), E-38206 La Laguna, Canarias,
Spain  Enrique Joven Instituto de Astrofísica de Canarias (IAC), C/ Vía
Láctea, s/n, E-38205, La Laguna, Spain  Vicens Gaitan Aplicaciones en
Informática Avanzada (AIA), E-08172 Sant Cugat del Vallès, Catalonia, Spain
Rebeca de Sousa  Departamento de Historia y Filosofía de la Ciencia, la
Educación y el Lenguaje, Universidad de La Laguna (ULL), E-38206 La Laguna,
Canarias, Spain

(Received March 29, 2023; Accepted April 26, 2023)

###### Abstract

Scientific complementary metal-oxide-semiconductor (CMOS) detectors have
developed quickly in recent years thanks to their low cost and high
availability. They also have some advantages over charge-coupled devices
(CCDs), such as high frame rate or typically lower readout noise. These
sensors started to be used in astronomy following the development of the first
back-illuminated models. Therefore, it is worth studying their
characteristics, advantages, and weaknesses. One of the most widespread CMOS
sensors are those from the Sony IMX series, which are included in large
astronomical survey projects based on small and fast telescopes because of
their low cost, and capability for wide-field and high-cadence surveys. In
this paper, we aim to characterize the IMX455M and IMX411M sensors, which are
integrated into the QHY600 and QHY411 cameras, respectively, for use in
astronomical observations. These are large (36 $\times$ 24 and 54 $\times$ 40
mm) native 16 bit sensors with 3.76 $\mu$m pixels and are sensitive in the
optical range. We present the results of the laboratory characterization of
both cameras. They showed a very low dark current of 0.011 and 0.007 e- px-1
s-1 @–10°C for the QHY600 and QHY411 cameras, respectively. They also show the
presence of warm pixels, $\sim$0.024% in the QHY600 and 0.005% in the QHY411.
Warm pixels proved to be stable and linear with exposure time, and are
therefore easily corrected using dark frames. Pixels affected by the Salt &
Pepper noise are $\sim$2% of the total and a method to correct for this effect
is presented. Both cameras were attached to night telescopes and several on-
sky tests were performed to prove their capabilities. On-sky tests demonstrate
that these CMOS behave as well as CCDs of similar characteristics and (for
example) they can attain photometric accuracies of a few milli-magnitudes.

Astronomical detectors (84) — Astronomical instrumentation (799)

PASP

##  1 Introduction

Historically, observational astronomy has been determined by the development
of imaging technology. In the early twentieth century, the introduction of the
photographic plate marked an unprecedented revolution, but their very limited
quantum efficiency (QE) of $<$0.1% made it imperative to develop more
effective alternatives. The turning point came in 1969 with the appearance of
charge-coupled devices (CCDs, Boyle & Smith (1970)), which are silicon-based
sensors with two-dimensional arrays of photosensitive units (pixels). These
devices produced improved linearity and quantum efficiency over photographic
plates and simplified data analysis thanks to their analog-to-digital
conversion (ADC). The first astronomical image that was taken with a CCD
appeared in 1975 when scientists from NASA’s Jet Propulsion Laboratory
obtained an image of the planet Uranus at a wavelength of 8900 Å (Janesick et
al., 1987). The development and refinement of CCDs have increasingly made
these sensors the most widely used option for the manufacture of astronomical
instruments.

Over the last two decades, the rise of alternative technologies has undermined
the prevalence of CCD sensors. Complementary metal-oxide-semiconductor (CMOS)
image sensors started to be developed in the 1990s (Fossum, 1997). However,
they still had a number of disadvantages when compared with CCDs, such as
lower dynamic range (DR), and poorer linearity and sensitivity (Bigas et al.,
2006). Although CMOS sensors soon established themselves in the consumer
market, their inherent constraints restricted their application in certain
fields, especially those related to science. To overcome the typical
limitations of CMOS, the so-called scientific CMOS (sCMOS) were introduced in
2009 as a result of a collaboration between Andor Technology, Fairchild
Imaging (BAE Systems) and PCO Imaging (Coates et al., 2009). This new
generation of sensors combined high frame rates, reasonable pixel and sensor
sizes, and quantum efficiencies comparable to CCDs—especially back-illuminated
(BI) sCMOS (Princeton Instruments, 2016)—and a considerable reduction in the
noise levels that are traditionally associated with CMOS.

Figure 1: Diagram of the optical test bench set-up, with the (1) Newport 68945
digital power supply, (2) Newport M-66881 QTH lamp, (3) Newport 76994 shutter,
(4) Newport Oriel Cornerstone monochromator, (5) Hamamatsu S1336-5B1
photodiode, (6) Labsphere SC6000, (7) Labsphere US-080-SF/SL integrating
sphere, (9) Hamamatsu S2281 photodiode, and (10) QHY600M Pro / QHY411M

Scientific CMOS are beginning to be accepted in astronomy. The first analyses
in this respect mainly involve two sensors: CIS2051 (later rebranded as
CIS2521), which was developed by Andor Technology and integrated into the Neo
series detectors; and GSENSE, which was developed by Gpixel and integrated by
several companies, e.g., Finger Lakes Instrumentation, QHYCCD, and Andor
Technology. Experiments performed with Andor Neo showed low readout noise
($\sim$1 e-) and linearity deviations up to the saturation point within the
expected range of $\pm$1%. However, two features were noticed: the transfer
curve was observed to bend, even at low signal levels, which was possibly
caused by the non-linearity of an amplifier stage; and the signal shows
certain irregularities with high variance in the transition region between
high and low gain modes (Schildknecht et al., 2013). This, along with a
limited QE due to being front-illuminated and a lower fill factor (Qiu et al.,
2013), means that this sensor is not the most suitable for regular
observations, and its usefulness is mostly constrained to bright objects and
high frame rates. The back-illuminated GSENSE2020 sensor, integrated in the
Andor Marana, was recently tested for its application in astronomy by Qiu et
al. (2021) and Karpov et al. (2021). This sensor showed a low readout noise
(1.6 e-), good linearity ($99.7\%$), and a stable bias. This sensor family has
a dual-amplifier structure in which two 12 bit images are taken simultaneously
and are then merged into a single 16 bit high dynamic range image. However,
this mechanism is susceptible to jumps and instabilities around the transition
region between high and low gain. In addition, edge glowing and charge
persistence over long periods of time is also present. The capabilities of
these sensors decrease considerably with exposure times longer than several
seconds, which is a consequence of these effects and the increase of dark
current; however, they are still suitable instruments for high frame rate
observations.

Many leading manufacturers are now developing instrumentation based on next-
generation sCMOS sensors, but their suitability for general use in astronomy
is still largely unexplored. One of the most widespread sensors are those from
the Sony IMX series, which are being included in large projects such as the
Argus Optical Array (Law et al., 2022), Large Array Survey Telescope (Ofek et
al., 2023) or the next generation of telescopes for the ATLAS project (Tonry
et al., 2018) , such as the one that will be installed at Teide Observatory,
ATLAS-Teide (Licandro et al., 2023a), because of their low cost and capability
for wide-field and high-cadence surveys.

In this paper, we present the results of the laboratory characterization of
the sCMOS BI Sony IMX455M and IMX411M sensors when integrated into the QHY600M
and QHY411M cameras. In Section 2, the two devices are tested, and the
laboratory set-up and the telescopes that we used are presented. Sections 3.1
and 3.2 describe the tests performed under dark conditions: spatio-temporal
variation of bias and dark and contaminating effects, such as random telegraph
noise and the presence of warm pixels. In Section 3.3, the main operating
features such as the gain fix pattern noise and linearity are verified using
the photon transfer curve. The quantum efficiency measures are shown in
Section 3.4 and the charge persistence effect reported in other sCMOS sensors
is reviewed in Section 3.5. Finally, several approaches to processing
telescope data and on-sky results based on this analysis are discussed in
Section 5.

Table 1: Technical data of QHY600M Pro and QHY411M sCMOS cameras provided by the manufacturer. Feature | QHY600M Pro | QHY411M
---|---|---
Sensor | Sony BI IMX455M | Sony BI IMX411M
Sensor size (diagonal) | 43.3 mm | 66.7 mm
Pixel size | 3.76 $\times$ 3.76 $\mu$m
Pixel area | 9600 $\times$ 6422 | 14304 $\times$ 10748
Effective pixels | 61.17 Mpx | 151 Mpx
Max full frame rate |  |
(USB 3.0 port, full frame |  |
and 16-bit output) | 2.5 fps | 1 fps
A/D sample depth |  |
(1$\times$1 binning) | 16–bit
Shutter type | Rolling shutter
Cooling system |  |
(temperatures below ambient) | Air cooling (-30C) | Air cooling (-35C)
|  | Water cooling (-45C)

##  2 Methods

###  2.1 Instruments

The QHY600M111https://www.qhyccd.com/scientific-camera-qhy600pro-imx455/
camera is based on the back-illuminated IMX455 monochrome sensor that is
manufactured by SONY, which is a full-frame (35 mm format) sensor with
$9576\times 6388$, 3.76 $\mu$m square pixels. The
QHY411M222https://www.qhyccd.com/scientific-camera-qhy411-qhy461/ camera is
based on SONY’s IMX411 monochrome sensor, which is also backilluminated but
with a larger sensor size, $14304\times 10748$ (equivalent to medium-format
cameras, $54\times 40$ mm). Both of these sensors include an overscan region
of 33 and 91 rows, respectively, and they are native ADC sampled at 16 bit,
which is a significant change from previous generations of sCMOS sensors that
were based on 12 bit image merging (see Karpov et al. (2021)). The main
features of the cameras are listed in Table 1. We used the QHY600M Pro
version, which allows a faster $2\times 10$ Gbps fiber connection to a frame
grabber with an additional 4 GB of DDR3 memory—although in this paper all
tests were done via USB 3.0 connection—and triggering the rolling shutter via
an external GPS with an accuracy of more than a microsecond. It is worth
mentioning that both sensors, especially the IMX455, are used in cameras from
other manufactures, e.g., Atik Apx60, ZWO ASI6200MM Pro.

These cameras can operate in several modes and gain settings, which
essentially change their gain, readout noise (RON), and full-well capacity
(FWC). In this paper, we have focused on those that are considered most
appropriate for use in astronomy because they maintain a good balance between
these characteristics, which are Mode#1 (High Gain Mode) and gain setting 0
(hereafter, #1@0) on the QHY600M Pro and Mode #4, and gain setting 0
(hereafter, #4@0) on the QHY411M, although some results for other modes are
also shown.

###  2.2 Optical test bench

The evaluation of the sensors was performed using existing experimental
equipment, which are available at the Laboratory of Imaging and Sensors for
Astronomy (LISA), at the Instituto de Astrofísica de Canarias (Tenerife,
Spain). The set-up is schematically shown in Figure 1. A Newport M-66881 QTH
lamp was connected to A Newport 68945 digital power supply, with intensity
stabilization. In the Newport Oriel Cornerstone monochromator, the option to
apply an order sorting filter was selected. This filter blocked higher
diffraction orders from interfering with the selected wavelengths. A Newport
76994 shutter was placed between the lamp and the monochromator to control the
light beam before taking each dark frame. A Labsphere US-080-SF/SL integrating
sphere, placed next to the monochromator, had incorporated a Hamamatsu
S1336-5B1 photodiode, which was in turn connected to a Keysight B2980A
picoammeter. Next, a dark box was located, followed by the test of the camera.
A second photodiode (Hamamatsu S2281) was connected to the exit port of the
dark box. The conversion factors between the intensity measured by the
picoammeter on the first photodiode and the radiant power received on the
second photodiode at the exit of the dark box were characterized, in addition
to the correction to the distance between the photodiode and the sensor back
focus. During the tests, the laboratory temperature was about $23\degree$C and
the humidity was 40–50%. Both cameras were air-cooled, with operating
temperatures of $-5\degree$C for the QHY600M Pro and $0\degree$C for the
QHY411M.

###  2.3 Telescopes

The sky tests were performed with two of the robotic telescopes (Telescopios
Abiertos Robticos, TAR) of Teide Observatory (Tenerife, Canary Islands,
Spain). The QHY600M Pro was installed on the prime focus of TAR03, 0.46-m
$f$/2.8 C18 reflector telescope on a Planewave L500 altaz mount. An ROI of
$4096\times 4096$ was used, giving a FOV of $41^{\prime}.1\times
41^{\prime}.1$ with $0^{\prime\prime}.6$ px-1. The QHY411M was mounted on a
Meade LX200-ACF 16′′ 0.406-m $f$/10, on an APM GE-300 Direct Drive equatorial
mount, with UV/IR-Cut/L, SDSS g′, r′ and i′ 50 mm filterswhich were
manufactured by Baader Planetarium GmbH. The sensor was trimmed to $40\times
40$ mm, giving a FOV of $34^{\prime}.2\times 34^{\prime}.2$ arcmin with 0.40′′
px-1. Both cameras were connected via USB 3.0 and operated with air cooling at
$-10\degree$C. After the first tests, the QHY411M was installed in one of the
80 cm telescopes of the Two-meter Twin Telescope (TTT) project, which is a
0.80-m $f$/6.85 Ritchey–Chrétien altaz telescope that was manufactured by ASA
Astrosysteme GmbH, with a total FOV of $33^{\prime}.4\times 25^{\prime}.0$ and
a plate scale of $0^{\prime\prime}.14$ px-1.

##  3 Results

Figure 2: Full-frame master bias of the QHY600M Pro (top) and QHY411M (bottom)
obtained by 3$\sigma$-clipping median stacking of 21 bias frames taken
consecutively.

Figure 3: Left-hand panel: Standard deviation vs. mean signal of each QHY411M
pixel (warm colors) in 21 consecutive bias frames. Color indicates data
density on a logarithmic scale. The histogram of the distribution of these two
variables is included below and to the right-hand side. For comparison, the
same information is shown in blue tones for a CCD, the ANDOR iKon-L 936
BEX2DD. The QHY411M pixels have been grouped according to their standard
deviation over the 21 frames and, for each of them, the signal measured in
these frames was taken individually. The distributions of each group are given
in the right-hand plot.

###  3.1 Bias stability and ”Salt & Pepper” effect

The bias frame stability in both cameras was tested in the laboratory under
conditions of complete darkness. In all of the tests that are presented here,
including the telescope runs, the offset setting was fixed at 10. First, 21
continuous unbinned full frames bias were obtained and stacked with a
3$\sigma$-clipping median to obtain the master bias. The result is shown in
Figure 2. Both cameras show dark signal non-uniformity (DSNU), with a notable
column-to-column pattern and pixel-to-pixel variations. Gradients can be
observed in the QHY600M Pro toward the upper region, with variations of less
than 1 ADU, especially in the QHY411M, with more than 2 ADU of difference
between the upper left-hand and the lower right-hand corner.

In the readout process of typical CCDs, the charge is transferred to one—or
several in the case of large sensors—output channels, each with a high-quality
amplifier and an analog-to-digital converter (ADC). In contrast, in CMOS, each
pixel has its own built-in electronics, which contain an amplifier and ADC.
This allows a very high frame rate by parallelizing the readout process but
exposes the sensor to these kinds of pixel-to-pixel variations, both in
darkness (with different readout or thermal noises) and illuminated (with
variations in gain or quantum efficiency). In sCMOS, each pixel must be
understood as an independent sensor, so all corrections over them, such as
subtraction of the bias level, must be done while bearing in mind that there
is no single value that characterizes all of the pixels simultaneously. For
this reason, although the QHY600M Pro and QHY411M have an overscan region, it
is more accurate to use a full frame master bias or dark that takes into
consideration these non-uniformities between pixels.

Once the spatial behavior of each pixel in darkness has been revised, the same
21 bias frames of the QHY411M are used to see its temporal performance in
detail. Now, instead of taking the three-clipped median, the unclipped
standard deviation and mean signal are plotted against each other, for every
single pixel, in the left-hand plot of Figure 3. This shows that most of the
pixels are found in a circular zone with a well-determined average signal
between 170 and 175 ADU, corresponding to the median bias value, and with a
standard deviation below 4 ADU. However, about 11% of the pixels show a
temporal standard deviation that is higher than this value and also present
deviations in the mean signal, with a kite-shaped dispersion. If this scatter
were caused by pixels exhibiting higher values of read noise only, then the
points would be distributed vertically, like a gradated column. If, however,
the pixels had a different threshold voltage, i.e., bias level, but maintained
the RON, then the points would be distributed horizontally. Instead, the kite-
shape indicates that either both occur at the same time for these pixels or
that there is an additional anomalous behavior at play.

For comparison, the same number of frames was taken with a CCD, the ANDOR
iKon-L 936 BEX2-DD333https://andor.oxinst.com/assets/uploads/
products/andor/documents/andor-ikon-l-936-specifications.pdf, and the standard
deviation and temporal average of each pixel was also obtained. The kite-
shaped dispersion is not visible in this case. Furthermore, the histograms
along both axes show the expected Gaussian shape: on the standard axis, a peak
centered on the RON value; and on the mean signal axis, a peak centered on the
average bias level and a width equal to the RON/$\sqrt{21}$. In the case of
the QHY411M—the same behavior is observed in the QHY600M Pro—the histograms
show a narrow peak and tails toward high standard deviation. This implies that
most of the pixels show a low RON, but some others have a skewed signal on
both sides of the average bias level.

With this distribution, defining a nominal value for RON is not
straightforward. Looking at the standard deviation histogram, the peak is
located at 3.0 ADU (3.1 e-), and the rms at 3.74 ADU (3.83 e-), close to the
3.72 e- reported by the manufacturer. From here on, the rms value will be used
as a reference for the RON. For the QHY600M Pro, an rms value of 3.48 e- was
obtained, compared to the 3.67 e- reported by the manufacturer. The values for
the other modes of operation are included in Table 3.

Figure 4: Top: temporal mean signal (left-hand) and standard deviation n terms
of readout noise (right-hand) obtained in a $20\times 20$ pixel central region
of 1000 bias frames taken consecutively with the QHY600M Pro. Bottom: signal
vs. frame number for some relevant pixels identified in the images above.
Points within the average value of the master bias $\pm 3\times$ RON are shown
in black, with outliers identified in red. The signal distribution of these
pixels is displayed on the right-hand side.

Figure 5: Dark current distributions for the QHY600M Pro (left-hand panel) and
the QHY411M (right-hand panel). They were obtained as the median stacked set
of five dark frames with an exposure time of 1000 s.

Returning to the standard versus average plot for the sCMOS in Figure 5, the
pixels are now grouped in ranges of std. $\sigma_{\text{ADU}}$ and the signal
of each obtained in all of the 21 frames is retrieved. The distribution of
each of these groups is shown in the right-hand plot of Figure 5. For pixels
showing a temporal deviation of the order of the RON
($\sigma_{\text{ADU}}\leq$7.5), the distribution of the measured signal is
Gaussian, similar to that of a conventional CCD. The large majority of the
pixels are in these first two ranges (45% of the pixels with
$\sigma_{\text{ADU}}\leq$3 and 98% with $\sigma_{\text{ADU}}\leq$7.5). For the
other ranges (with larger deviations), the pixels begin to show a triple
Gaussian distribution, with two smaller peaks that are centered on both sides
of the central peak and with similar widths. For larger values of
$\sigma_{\text{ADU}}$ the separation of the peaks is greater. In these
anomalous pixels, the pixel distribution is indicative of the following
behavior: while most of the time these pixels return values that are around
the mean value of the bias level with a normal noise equivalent to readout,
their returned values occasionally jump toward larger or smaller values with
defined separations and different probabilities for each pixel.

The symmetry in the three Gaussian distributions does not necessarily mean
that all of the anomalous pixels jump between higher and lower levels with the
same probability. In that case, the standard versus average plot would not
show a kite-shaped scatter but a column, given that the average of the signal
would always lie in the central range. To show a kite-shaped dispersion, there
must be some pixels that tend to exhibit more deviations toward one of the
Gaussians, either the upper or the lower Gaussian, thus skewing the average to
either side and resulting in the widening of the tails of the mean signal
distribution, which is shown in yellow in the lower left-hand plot of Figure
3.

For a more detailed insight into the behavior of these pixels with high
standard deviation, 1000 consecutive bias frames were obtained with the
QHY600M Pro. The temporal average and standard deviation of all the frames has
been taken in a small central region of $20\times 20$, which is shown in the
upper part of Figure 4. Most of the pixels have an average signal around 171
ADU and a dispersion below the RON, as expected. However, some of them show
anomalous patterns in the average value, the standard deviation, or both at
the same time. Several pixels have been selected as samples, showing the time
evolution of the signal in the lower plots. As a reference, a pixel with
normal values of average signal and deviation has been taken, following a
normal distribution over the 1000 frames, with mean 170.4 ADU and standard
deviation 3.69 ADU. The pixel labeled S&P 1 also has an average signal similar
to the others, with a slightly higher standard deviation of 6.42 ADU but still
close to the RON obtained in the previous section. However, the temporal
distribution reveals how some points, 5.5% of the total, appear both above and
below the average signal, with a gap of about 20 ADU, which is more than three
times the RON (see the red dots in the plots below). Other pixels show the
same effect more often, e.g., S&P 3, where 19.3% of the 1000 frames show an
anomalous value of $\pm 60$ ADU. This is revealed by a high standard deviation
of 23.6 ADU. Although this can be understood as a high RON in its electronics,
it should be noted that its distribution is not a wide Gaussian but a set of
three normal distributions—the main distribution is centered on 171 ADU, and
two smaller ones corresponding to these random leaps to higher and lower
values at 110 and 230 ADU. The S&P 2 pixel shows jumps at the same levels but
with a higher proportion of values in the upper level (10.6% of the total)
than in the lower level, 3%. Consequently, the signal that was obtained in the
average frame, 174.5 ADU, deviates from the other pixels, as shown in the
image.

These jumps between above- and below-average signal values are observed when
blinking between images, regardless of their exposure time and temperature. A
pattern of bright and dark pixels that appear and disappear from one frame to
the next is observed. This effect, which is sometimes referred as Salt &
Pepper, is random telegraph noise (RTN). RTN is the fluctuation of the signal
between discrete levels as a consequence of the capture and emission of
charges by defects or traps located very close to the Si–SiO2 interface (Uren
et al., 1985). In scaled CMOS detectors, this trapping process causes a shift
in the relationship between the drain current of the MOS transistor and the
gate voltage, discretely increasing or decreasing the offset level, which
fluctuates as random trapping and de-trapping of charges, either electrons or
holes (Martin-Martinez et al., 2020).

This random noise must be addressed when processing images taken with these
detectors. Unusually low or high pixel values can cause deviations in
photometric measurements of sources in low light levels. Simply averaging
images is not an optimal method because it does not avoid, but may reduce,
RTN. For instance, the pixel “Salt” in the Figure 4 shows fluctuations only
toward higher, not lower, values, and therefore any averaging that is done by
including any of the anomalous points (25.5%) will be inaccurate. The way to
deal with these outliers is to take several images and stack them using a more
robust statistic. This technique will be discussed further in Section 5. It is
also possible to identify some pixels with higher than average signals but
with low standard deviation. Some of them are independent of exposure time,
such as the pixel “High” in the figure, which can be corrected by subtracting
the master bias. However, others depend on exposure time and are treated as
warm pixels in the next section.

###  3.2 Dark current and warm pixels

Dark current (DC) refers to the unwanted leakage current that is generated in
photosensitive devices in the absence of incoming light, which is mainly due
to the thermal generation of charges in the silicon layer, and is strongly
dependent on the temperature and exposure time. To characterize the DC, five
dark frames of 1000 s exposure time were taken, a master bias taken just
before was subtracted, and they were median stacked. This was done for various
temperatures: from $5\degree$C to $-10\degree$C on the QHY600M Pro and from
$-5\degree$C to $-20\degree$C on the QHY411M, on which water cooling was
installed for these tests. The DC distribution in electrons per pixel and
seconds of exposure is shown in Figure 5. The distribution curves have similar
shapes, with a shift toward higher DC with increasing temperature. When the
peak is reached, the DC drops rapidly toward a smooth hump, from which it
drops back down again. This hump corresponds to a set of pixels with
exceptionally higher DC, which are seen in the images as pixels with a higher
signal that is steady from frame to frame, unlike the Salt & Pepper effect
described in the previous section, which increases with exposure time. At
$-10\degree$C, only 0.024% of the QHY600M Pro pixels and 0.005% of the QHY411M
have a DC greater than RON. These warm pixels have signals lower than the
saturation level, and therefore they can be corrected with an appropriate
master dark. The median value of the dark current for each camera and
temperature is shown in Table 2. It is worth mentioning here that no glow has
been seen in any of the darks taken in either of the cameras, as reported for
other sCMOS sensors (Karpov et al., 2021).

Figure 6: Signal of two warm and two normal pixels of the QHY411M as a function of exposure time, obtained as the median value of these pixels over sets of 21 frames taken consecutively, with the standard deviation included as its uncertainty. The parameters of the least squares fitted lines are shown at the top. Figure 7: Signal of two warm and two normal pixels of the QHY411M obtained daily for more than three weeks, obtained as the median of the value of these pixels in sets of 21 frames of 30 s exposure taken consecutively, including the standard deviation as its uncertainty. Table 2: Median dark current (DC) for different cooling temperatures for each camera. T ($\degree$C) | DC (e- px-1 s-1)
---|---
| QHY600M Pro | QHY411M
5 | 0.025 |
0 | 0.018 |
-5 | 0.014 | 0.009
-10 | 0.011 | 0.007
-15 |  | 0.005
-20 |  | 0.004

Some warm pixels in a central region of the QHY411M have been identified and
revised in more detail. First, sets of 21 dark frames taken consecutively with
different exposure times have been stacked. The median signal of two warm and
two normal pixels is shown in Figure 6, where the standard deviation is
included as uncertainty bars. It is clear that the signal of these warm pixels
scales linearly with the exposure time in the range of 1–300 s. The result of
the linear least squares fit is shown at the top of the plot, all of them with
$R^{2}>0.998$. Note that in this case a master bias has not been subtracted,
and therefore the value of the offset is different from 0. This result implies
that it is possible to correct the warm pixels in images of a given exposure
time if the slope of this line for each pixel is known, which can be obtained
with other exposure times (e.g., at the beginning of the night). Although a
scaled master dark is intended to be subtracted from the science images, it
should be noted that warm pixels, having a higher thermal signal, will be
noisier than those with a lower DC.

For more than 3 weeks, sets of 21 darks of 30 s exposure were taken every
night. The median value in these four pixels as a function of time is shown in
Figure 7, where the error bars again show their standard deviation. With this
test, we have tried to see if there is a substantial variation in the values
of these pixels over longer periods of time. Taking into account that the
noise in these pixels is a combination of the RON and the thermally generated
electrons, which follow a Poissonian distribution, it can be seen that the
possible variations in the pixel dark signal over at least these three weeks
is not significant with respect to the total noise. Therefore, meaningful
night-to-night variations of the master dark or bias are not expected.

Figure 8: Photon transfer curves obtained from sets of three bias-subtracted
images, median stacked, and taken under uniform illumination with increasing
exposure times. The QHY600M Pro Mode #1@0 (left-hand panel) and the QHY411M
Mode #4@0 (right-hand panel). The principal components of the noise have been
distinguished as colored lines, whose characteristic values are also included.
The gain and FPN has been obtained from the fitting to the total noise
function.

Figure 9: Average signal in sets of three images taken with increasing
exposure time (top) for the QHY600M Pro Mode #1@0 (left-hand panel) and the
QHY411M Mode #4@0 (right-hand panel). The saturation level is indicated by a
horizontal-dashed line. A straight line was fitted to points with values
between 100 and 60,000 ADU. The relative rms of the differences between the
measurements and the fitted line is shown in the lower plots.

###  3.3 Photon Transfer Curve and linearity

The photon transfer curve (PTC) is used to characterize the response of the
sensors to homogeneous illumination, obtaining main features such as the
gain—conversion factor between electrons and digital counts (ADU), the full-
well capacity (FWC), or the contribution of the different sources to the total
noise. The laboratory set-up that is described in Figure 1 was modified by
removing the monochromator, so that the light coming from the QTH lamp could
directly enter the integrating sphere. The power supply was kept stable. Sets
of three images of uniformly illuminated exposures were then taken while
increasing the exposure time until the saturation turn-off point was reached.
Three bias frames were taken before and after each series, stacked by median
3$\sigma$-clipping and subtracted from all of the illuminated images. A
central region of interest (ROI) of $4096\times 4096$ unbiased pixels was used
for both cameras. The signal was obtained as the mean of the three images
stacked. The mean of the standard deviation across the three frames was taken
to obtain the noise value.

The total noise under illumination is given by the quadrature sum of three
main components: RON, shot noise, and fixed pattern noise (FPN):

$\sigma_{\text{TOTAL}}(\text{ADU})=\left[\sigma_{\text{RON}}^{2}+\frac{S}{G}+\left(P_{\text{FPN}}S\right)^{2}\right]^{1/2}$
(1)

where $\sigma_{\text{RON}}$ (ADU) is the readout noise, $S$ (ADU) the signal,
$G$ (e${}^{-}/$ADU) the gain and $P_{\text{FPN}}$ the FPN factor (Janesick,
2007).

The relationship between the noise and signal for the two sensors is shown in
Figure 8. The RON was fixed with the value defined in the previous section,
while the gain and FPN parameters were obtained by fitting the expression (1)
on a logarithmic scale. This procedure was repeated in windows of $200\times
200$ pixels along the ROI to obtaining a curve for each of them, 360 in total.
The rms of the parameter distributions was used to estimate their uncertainty.
In both plots, three regions can be distinguished. At low illumination, below
10 ADU, RON, which does not depend on the signal, is the dominating noise
source. From then on, shot noise starts to become important, and is
essentially the main component between 100 and 10,000 ADU. Thereafter, the
contribution of the FPN becomes significant and a deviation from Poissonian
behavior is observed. In the QHY600M Pro, this source is the main contributor
above 50,000 ADU, with a factor of $0.55\%\pm 0.02\%$ , being lower,
$0.31\%\pm 0.02\%$, in the QHY411M, where the shot noise is dominant almost up
to the saturation point. A PTC has also been obtained for other operating
modes, whose resulting values are included in Table 3.

The linearity of the sensors is evaluated by plotting the signal (adding the
bias level) against the exposure time, as is done for the standard operating
modes in Figure 9. A straight line was fitted to the points between 100 and
60,000 ADU (top plot). The relative rms difference between the points and the
line is shown below. In both cases, the deviation from linearity is less than
2% up to the saturation point, which was determined for all of the operating
modes and included in Table 3.

Table 3: Main features of the different operating modes of the QHY600M Pro (top) and QHY411M (bottom), obtained in laboratory tests. including the gain, readout noise (RON), fixed pattern noise (FPN) and full-well capacity (FWC). Mode | Gain | RON | FPN | FWC
---|---|---|---|---
| (e-/ADU) | (ADU) | (%) | (ke-)
QHY600M Pro
0@26 | 0.405$\pm$0.003 | 6.28 | 0.521$\pm$0.005 | 25.3$\pm$0.2
1@0 | 0.763$\pm$0.003 | 4.56 | 0.55$\pm$0.07 | 50.0$\pm$0.2
QHY411M
1@0 | 0.979$\pm$0.008 | 3.03 | 0.29$\pm$0.02 | 27.2$\pm$0.2
4@0 | 1.024$\pm$0.010 | 3.74 | 0.31$\pm$0.02 | 67.1$\pm$0.7
5@0 | 0.332$\pm$0.002 | 4.65 | 0.32$\pm$0.02 | 22.8$\pm$0.1
6@0 | 0.776$\pm$0.003 | 4.48 | 0.32$\pm$0.02 | 50.6$\pm$0.2
7@0 | 0.251$\pm$0.001 | 5.69 | 0.34$\pm$0.02 | 16.50$\pm$0.05

###  3.4 Quantum efficiency

Using the optical setting described in Figure 1, central wavelengths in the
range between 350 and 1100 nm with 25 nm steps were selected in the
monochromator. The grating configuration was set to have an outcoming light
with $\pm 1$ nm bandwidth. The QHY600M Pro was placed at 13.3 mm from the exit
of the dark box, which is separated by 30 mm from the Hamamatsu S2281
photodiode. The total distance between the IMX455 sensor and the photodiode,
considering the back focus distance of the camera, was 66.8 mm. In the case of
the QHY411M, it could be placed in contact with the box, so the total distance
between the photodiode and the IMX411 sensor was 58.5 mm. Both cameras were
binned $4\times 4$ to have enough signal with exposure times shorter than 10 s
at those wavelengths where they are less efficient.

Three images were taken at each wavelength step, a master bias that was
created at the beginning of the series was subtracted from all of them and
they were stacked with the 3$\sigma$-clipped median. Simultaneously, the
output intensity of the photodiode placed at the secondary port of the
integrating sphere was measured with the picoammeter for approximately 30 s,
taking an average value. The observed fluctuations in this value were always
less than 2%. The systematic uncertainty of the method is estimated to be
around 2%. To avoid vignetting effects at the edges of the dark box-camera
junction, a central ROI of $1000\times 1000$ binned pixels was used to obtain
the median signal $S$, after checking that there were no inhomogeneities in
the sensor illumination in that area. The exposure time $t_{\text{exp}}$ had
to be varied throughout wavelength steps to keep all of the measurements in
the shot noise dominated region between 1000 and 40,000 ADUs. The absolute
quantum efficiency for each wavelength is given by the expression:

$\text{QE}(\%)=\frac{S\text{(ADU)}\cdot
G(\text{e}^{-}/\text{ADU})}{t_{\text{exp}}\text{(s)}\cdot
F(\text{e}^{-}/\text{s})}\cdot 100$ (2)

Figure 10: Absolute quantum efficiency curves of the QHY600M Pro (blue-open
circles) and the QHY411M (yellow dots). The systematic uncertainty is
estimated to be around 2% Figure 11: Etaloning pattern observed when
illuminating the QHY600M Pro in a central ROI of $1000\times 1000$ pixels
$4\times 4$ binned with $\pm 1$ nm bandwidth light at different wavelengths.
Similar behaviour is observed in the QHY411M.

The QE curves for the sensors are shown in Figure 10. Both show very similar
behavior, with a peak efficiency of 80% at 475 nm, a steep drop at shorter
wavelengths, and a gradual decrease toward the redder ones, with a QE of 40%
at 700 nm and 10% at 900 nm. Back-illuminated sCMOS sensors with reduced pixel
size, such as the IMX455 and IMX411, have a typical silicon substrate
thickness of around 3 $\mu$m (Yokogawa et al., 2017). This optimizes the
photon absorption in the visible range but makes the less energetic photons,
which have a higher penetration capability, less likely to be detected.
Without any additional red enhancement technology, improving efficiency in the
red and near-infrared requires a thicker substrate that, with such small
pixels, would lead to image degradation owing to crosstalk between adjacent
pixels. This is the reason for the poor performance at longer wavelengths.

It should be noted that the curves that are obtained here represent an overall
reduction of 9% over the full absolute QE curve published by QHYCCD, which has
a peak of 92% at 450 nm, 46% at 700 nm, and 15% at 900 nm. Nonetheless, the
conditions and configuration used by them, as well as the uncertainties in
their measurements, are unknown and the calibration method to obtain the QE
curves is different from the one used here, which has been used to calibrate
multiple astronomical instruments before. The same procedure and test bench
have been used, for instance, to calculate the QE in sCMOS cameras such as
Andor Marana, FLI-Kepler, and ORCA-Hamamatsu. They have also been used in
several deep depletion CCDs, such as the well-known Teledyne e2v 4482 and
231-84 BI. In all cases, QE fits rather well with data supplied by the
manufacturers. Gill et al. (2022) have very recently presented a low-cost
method to obtain, among other features, the absolute QE of detectors being
applied to the IMX455 sensor. Their results also show a lower performance than
that presented by QHYCCD, especially in the red part, although they had a
similar peak efficiency, with 93% at 480 nm, 42% at 700 nm, and 10% at 900 nm,
making an overall deviation from our results of 5%. Finally, Betoule et al.
(2023) have studied the quantum efficiency of the IMX411 sensor in great
detail and obtained a result that is very similar to Figure 10, with a peak of
83% at 490 nm, 37% at 700 nm and 6% at 900 nm.

During the tests, optical etaloning was observed at longer wavelengths, as
shown in Figure 11 for the QHY600M Pro. Similar behavior was observed also for
the QHY411M and reported by Betoule et al. (2023). The maximum variation over
the average frame mean value is around 1% at 650 nm, and reaches 10% at 850 nm
and above. This is a known effect in thinned back-illuminated devices,
resulting from multiple reflections produced inside the depletion region by a
mismatch between its refractive indices and the adjacent layers. This effect
has not been observed in sky images taken with TAR04 using a wider bandwidth,
such as SDSS i′ (690–850 nm).

###  3.5 Charge persistence

Charge persistence is an effect that occurs when a portion of the signal
remains in the detector element after the sensor has been read out. It is a
consequence of the creation of traps at the interface between the photodiode
and the transfer gates, which capture free electrons and gradually release
them, resulting in a decay of the residual signal in the subsequent images
after the illuminating source has been removed. This effect has been observed
with tests performed in the laboratory with the FLI Kepler KL400 and Andor
Marana cameras, both with GSENSE400BSI sensors, showing a behavior similar to
that reported by Karpov et al. (2021). Although they did not observe any
effect in sky images, previous tests performed with these cameras on the same
telescopes used in our work did show a smearing effect in pixels that had
previously been exposed close to the FWC, which remained visible up to tens of
minutes later.

Figure 12: Average signal (black dots) in 1 second frames taken continuously
during sensor flooding (central shaded area). Images before (left-hand),
during (center), and after (right-hand) are shown at the top. The region in
which the average signal has been calculated is indicated by dashed
rectangles.

This effect has been tested in the laboratory for both sensors. To do so,
following the same set-up used in Section §3.3, i.e., removing the
monochromator and exposing the cameras to the QTH lamp light, a pinhole was
placed inside the black box, just in front of the sensor. Consecutive 1 s
images were taken with the shutter closed. The shutter was then opened for
about 10 s, saturating the illuminated pixels, and closed again. This sequence
is shown in Figure 12. On the top are the images taken with the QHY600M Pro
before the shutter was opened (left-hand), during the exposure to the light
coming from the pinhole (center), and just after the shutter was closed again
(right-hand). In the central region of the illuminated spot area, indicated by
a dashed square, the mean signal has been measured and is shown in black dots
below. The signal before and after the flooding shows a stable trend, with
very small oscillations, not a sharp increase in the signal right after the
exposition followed by a slow decay to the original level, as shown in Figure
10 of Karpov et al. (2021). Hence, we conclude that the IMX455M and IMX411M
sensors do not exhibit charge persistence.

##  4 On-sky tests

Figure 13: Phased light curve of the asteroid (3200) Phaethon, obtained with
the QHY600M mounted at the prime focus of a 0.46-m $f$/2.2 telescope, no
filter installed. Figure 14: Transit of the exoplanet TOI-1135 observed with
QHY411M mounted on a Nasmyth focus of the TTT-2 telescope at 0.80-m and
$f$/6.85, SDSS g′ filter.

Both cameras have been extensively tested on images taken with telescopes,
obtaining photometric accuracies as expected for the characteristics described
above. The QHY600M Pro was installed at the prime focus of a 0.46-m $f$/2.2
telescope. Figure 13 shows the phased light curve of asteroid (3200) Phaethon,
which was observed for 6 consecutive hours. At the time of observation, the
object had an apparent magnitude of $V=17$ and was moving at a speed of 1′′.6
minute${-1}$. The photometric uncertainties that we obtained are in the order
of hundredths of a magnitude and the rotation of the asteroid, with an
amplitude of 0.13 mag, is clearly detectable. This camera is a very suitable
choice for very fast telescopes with primary focus because its small size and
compact shape drastically reduce obscuration. With a pixel size of 3.76
$\mu$m, it also allows us to obtain a plate scale that is very suitable for
sites with excellent seeing, such as the Teide Observatory. Furthermore, given
its low readout noise and negligible readout time, it is possible to take
continuous short frames and combine them by aligning with the object or the
stars, thus allowing fainter objects to be reached with very little time lost.
This also allows the study of, for instance, very fast rotating objects with
good temporal sampling (Licandro et al., 2023b) or using “shift-and-add”
techniques, such as synthetic tracking (Shao et al., 2014), to improve the
detection performance of faint fast-moving objects.

Figure 14 shows a transit of the exoplanet TOI-1135 that was observed with the
QHY411M mounted on one of the Nasmyth foci of the TTT-2 telescope, 0.80-m
$f$/6.85, SDSS g′ filter, with a plate scale of 0′′.14 px-1. Groups of four
images of 5 s exposure were stacked to improve the SNR. The standard deviation
of the differential aperture photometry measurements in pre-transit, during,
and post-transit is 2.2, 3.3, and 2.0 mmag, respectively (M. Mallorquin et
al., in preparation). With such a small plate scale, the psf was oversampled,
with approximately 8 px of FWHM, spreading the flux of the star over a larger
number of pixels. For very bright targets, such as this $V=9.6$ mag star, this
allows slightly longer exposures to be taken without reaching the saturation
point, thus reducing the random and flat-fielding errors of the telescope
that, with a larger scale, would perhaps need some defocusing (Southworth et
al., 2009). In addition, by taking short exposures, the time lost on a CCD
could be equivalent to, or even longer than, the exposure time, which makes it
very inefficient. With the QHY411M, the exposure time could be extended, thus
improving photometric accuracy, and the readout time is almost non-existent.
Although such small plate scales are generally undesirable, they allow these
cameras to be used in other scientific applications (e.g., fast-moving object
astrometry or lucky imaging).

Figure 15: (a) Image of M33 taken with the QHY411M at the TAR04 telescope with
an exposure time of 60 s. (b) Zoomed region $100\times 100$ pixels where the
Salt & Pepper effect is visible. (c) From top to bottom: $3\times 20$ s
sequence of the same zoomed region; convolution with a $3\times 3$ median
filter; difference between the frames in the previous rows, showing the S&P
contaminated pixels in red; and raw frames with those pixels masked in red.
(d) Result of stacking the three frames with an average 3$\sigma$-sigma
clipping (top) and with an average after masking the S&P affected pixels.

##  5 Discussion

The two instruments with sCMOS sensors that are analyzed here present
characteristics that are compatible with their use in astronomy: they are
linear over the whole dynamic range, have a high full-well capacity, and are
slightly affected by dark current, despite being able to work at higher
temperatures than CCDs. Regarding the quantum efficiency, although the curve
obtained here is slightly lower than that reported by the manufacturer, 80% at
500 nm is an acceptable performance for many scientific programmes and is in
general similar or better than other CCD sensors in the same cost range. An
improvement in efficiency toward redder wavelengths should be achieved in the
next few years, so that sCMOS sensors can be used on a wider variety of
observational targets.

The small pixel size means that these sensors are generally not the best
solution for slow focal length systems, except for dedicated programmes such
as high spatial resolution or lucky imaging. Binning in sCMOS sensors is done
after exposure, so it does not improve the readout noise or frame rate. In
general, in case it is needed, it is better to do this by software after the
exposure, so that the statistics can be preserved and the 16 bit limit is not
reached. Nevertheless, they can be very valuable in fast telescopes with
larger fields and higher plate scales, which allows better sampling of the
PSF. In addition, their manageability, and small size and weight are very
interesting, e.g., for prime focus telescopes.

The S&P effect is one of the main issues that affect the use of these IMX455
and IMX411 sensors for photometric measurements. Figure 15(a) shows an image
of the galaxy M33 taken with the QHY411M on the TAR04 telescope, with a UV/IR-
Cut/L filter, in a single exposure of 60 s. When zooming in on a small area of
100$\times$100 pixels (b), several pixels are observed with values that are
clearly higher than those of their neighbors, as described in Section 3.1.
This effect could influence the photometry of faint objects because the
amplitude of this random fluctuation may be comparable to the source signal in
the pixel.

SSeveral strategies can be pursued to mitigate this problem, especially by
using several consecutive frames. Figure 15(c) shows a sequence of $3\times
20$s, where the S&P is clearly visible in each of them. If simple averaging
were performed, then the outliers would skew the signal obtained. Algorithms
such as 3$\sigma$ clipping that use outlier-sensitive dispersion measures
generally do not work either because these metrics are biased by these
fluctuations and define a very wide clipping range. This is seen in Figure
15(d) (top plot), where the three frames have been combined with an average
after 3$\sigma$-clipping. Some of the pixels that exhibited S&P also show
deviating values in the stacked frame.

It is common to use spatial filters for this kind of localized noise. The most
typical for S&P is a median filter, where the value of each pixel is replaced
by the median of the values of this and its close neighbors. This is done in
the second row of Figure 15(c). Note that the image has been smoothed and the
anomalous pixels may have disappeared. However, this has been done at the
expense of: (1) changing the signal and noise pattern of the image and (2)
correlating the nearby pixels. Although this is a very useful filter for
improving the cosmetics of the images, the photometry measurement in the
resulting image may be highly biased because the fluxes of each pixel have
been altered by its neighbors.

In this work, a solution based on convolutions and these two previous ideas is
proposed to try to mitigate the S&P effect. First, it should be considered
that the S&P effect mostly impacts low noise areas, such as the sky background
or faint sources. For bright sources, the shot noise becomes higher than the
random telegraph noise and dominates all of the other fluctuations. The median
filter can be used to obtain a reference frame to identify outliers because,
in well-sampled fields, they are a good approximation to a smoothed frame.
This can be seen in Figure 15(c), where the third row shows the difference
between the raw frame and the one filtered with a $3\times 3$ median kernel.
The residual pattern is generally homogeneous, both in the sky area and in the
vicinity of the sources. To identify the S&P, a threshold of 12 ADU has been
set because it is at this point that the distribution of three Gaussians in
Figure 3 (right-hand) starts to be revealed. Hence, all of the pixels whose
absolute difference between the raw value and that resulting from the
convolution with the median filter is greater than 12 ADU are masked. In the
bottom row of Figure 15(c), the raw frames are shown with the pixels masked in
red. By having a sequence of frames, the average of the unmasked values of
each pixel can be taken to get a stacked image, which is shown on the bottom
right-hand of the figure, where the S&P contamination has been highly reduced.
In cases where a pixel shows S&P in all of the frames of the sequence and is
therefore masked, the astronomer has to decide, for instance, either not to
take that pixel into account in the photometry or to replace its value by an
approximation, such as an interpolation of the neighboring pixels or their
median. In the example included here, this happens in only two pixels out of
$10^{4}$. It should be noted that this method may be less accurate in fields
with critically sampled sources. A further review is currently underway for
future work.

Developing new algorithms or even using those already available in common
packages such as IRAF (Tody, 1986) or Astropy (Astropy Collaboration et al.,
2013) may not be trivial when working with these cameras. The size of the raw
16 bit images of the QHY600M Pro are about 120 MB, while those of the QHY411M
are 300 MB. If a sequence is taken at a high frame rate, then the data set may
not be manageable with commonly available CPU capacities. The advantage of
convolution-based approaches and simple arithmetical operations, such as
median filtering or frame differencing, is that they are easily deployable on
GPUs, which allows the data to be processed more efficiently and faster. The
development of GPU algorithms for astronomical image processing is essential
for further progress in the use of large sensors, such as these sCMOS.

##  6 Conclusion

In the previous sections, the key features of the QHY600M Pro and QHY411M
cameras as scientific instruments have been discussed in detail. For
astronomy, they have characteristics that make them very suitable for general
use, although certain issues need to be addressed. Our main conclusions are
that:

  1. 1.

The built-in electronics in the pixels of sCMOS sensors require that each
pixel should be considered as an individual detector and this should be taken
into account when performing processes such as bias or dark subtraction.
Spatial inhomogeneities in darkness are detectable all over the frame.

  2. 2.

Their dark current is very low, as is the number of warm pixels. They are
stable for at least several weeks and their signal scales linearly with
exposure time, so they may be quite easily removed with dark subtraction. This
is an improvement from previous sCMOS sensors and makes it possible to take
images with longer exposure times without being affected by dark current.

  3. 3.

Its quantum efficiency peaks at 80% at 475 nm and then drops rapidly at longer
wavelengths, with 40% at 700 nm and 10% at 900 nm.

  4. 4.

They do not exhibit charge persistence or edge glow.

  5. 5.

These sensors are affected by random telegraph noise, which can introduce non-
negligible deviations in photometric measurements of low-brightness sources.
Simple frame averaging, even with algorithms such as $\sigma$-clipping, is not
enough to mitigate its effect.

  6. 6.

They show promising performance on photometric observations done with both
fast and slow telescopes.

Their low cost, power consumption, and replicability make both cameras a very
suitable solution for astronomical applications, especially with regard to
their high frame range, near-zero readout time, and low readout noise. These
sensors are very good options for fast small telescopes with large fields
because of their small pixel size and large formats. The combination of such
telescopes and cameras permit very large field-of-view images with plate
scales with reasonably good sampling of the PSF. For instance, a 11′′ $f$/2.2
telescope such as the Celestron RASA11 with a QHY600 camera can produce images
covering a FOV of 7.5 deg2 with a plate scale of 1′′.27 px-1, which are
excellent options for surveys such as ATLAS-Teide (Licandro et al., 2023a).
Even so, having the necessary computational tools to process the data,
especially GPU developments, is essential to take advantage of the full
performance of these cameras.

The authors declare no conflict of interest or relationship with the
manufacturers of the cameras tested. M.R.A, M.S-R and J.L. acknowledge support
from the ACIISI, Consejería de Economía, Conocimiento y Empleo del Gobierno de
Canarias and the European Regional Development Fund (ERDF) under grant with
reference ProID2021010134 and from the Agencia Estatal de Investigacion del
Ministerio de Ciencia e Ińnovacion (AEI-MCINN) under grant ”Hydrated Minerals
and Organic Compounds in Primitive Asteroids” with reference
PID2020-120464GB-100. This research has been partially funded by Light
Bridges, SL. which provided the QHY411M and Andor iKon-L 936 cameras for the
tests presented here. This article includes observations made in the Two meter
Twin Telescope (TTT) at the IAC’s Teide Observatory that Light Bridges, SL,
operates on the Island of Tenerife, Canary Islands (Spain).

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