$<$ $None$. Together with stringent requirements this led to highly
specialised manufacturing specifications. Further challenges arose from the
grisms’ operating conditions in space, as the NISP detectors (Sect. 3.5)
require the optics to operate at a temperature of $\sim$ 135 K to reduce
thermal background.333A second reason is that 135 K is the maximum temperature
for the whole structure of NISP, which drives the minimal temperature the NISP
detectors can reach. Higher detector temperatures would mean a worse
performance due to for example higher dark current and read noise. To minimise
intrinsic thermo-mechanical stresses, the grisms were built each from a single
fused-silica block with the grating directly engraved onto the hypotenuse
surface of the prism, possible through the cumulative etching technology of
SILIOS Technologies SA444https://www.silios.com (Caillat et al. 2017).

Another design driver was the required redshift precision of
$\sigma(z)<0.001(1+z)$ for $\text{H}\alpha$ emission-line galaxies with a FWHM
of $None$ and a $3.5\sigma$ detection limit at $2\times 10^{-16}$ erg s-1 cm-2
(Euclid Collaboration: Scaramella et al. 2022). The red grisms were built with
a prism angle of $A=$$$ and a groove frequency of $13.7$ grooves mm-1. The
blue grism has $A=$$$ and $15.1$ grooves mm-1. The grooves were engraved onto
the prism surface following complex curves. This allows provides a wavefront
correction that compensates for the tilted NISP FPA and the aberrations of the
optical design, resulting in a nearly constant dispersion law and uniform
image quality (Costille et al. 2019a).

The grism raw material Suprasil 3001 (Heraeus LLC 2020) is a water-free
synthetic fused silica with OH and metallic impurities lower than 1 ppm, for
maximum transmission in the NIR. The grating blaze angle maximises
transmission of the $1^{\text{st}}$ order, with a peak transmission at the
central wavelength of the grisms passband, which is at $\sim 1.5$
$\mathrm{\SIUnitSymbolMicro m}$ for the red grisms and at $\sim 1.1$
$\mathrm{\SIUnitSymbolMicro m}$ for the blue grism. This minimises the flux
losses near the passbands cut-on and cut-off wavelengths. With $85\%$ in the
$1^{\text{st}}$-order, the measured transmission of the finalised grisms is
considerably larger than the requirement of $>65\%$. The out-of-band
transmission is below 2% (Costille et al. 2018, 2019a). Transmission in the
$0^{\text{th}}$ order was measured to be $2\%$ on average; it is important for
wavelength calibration to have enough flux in this order, as it is a reference
for wavelength calibration. The mean total spectral response of the
$1^{\text{st}}$-order as shown in the bottom of Fig. 7, as well as for the
$0^{\text{th}}$-order, is available
online.555https://euclid.esac.esa.int/msp/refdata/nisp/NISP-SPECTRO-
PASSBANDS-V1 The data include the contributions from the mirrors, the
dichroic, the grisms, the NI-OA, and the mean detector quantum efficiency
(QE).

The NISP spectroscopic performance at instrument level was assessed during
ground test campaigns in 2019 and 2020. A LED light source combined with a
Fabry-Perot etalon with $\simeq$ 34 transmission peaks in each grism passband
– its spectral energy distribution represented in grey in the bottom of Fig. 7
– was used to determine the spectroscopic dispersion of each grism. The
measured NISP image quality is nearly diffraction-limited. The spectral
resolution of the grisms was measured to be $1.239$ nm pixel-1 for the blue
grism BGS000, $1.372$ nm pixel-1 for the red grisms RGS000, and $-1.372$ nm
pixel-1 for RGS180. Here, the negative sign shows that the the RGS180
disperses in the opposite direction as the RGS000. The NISP resolving power
has a minimal value along each spectrum of
$\mathcal{R}_{BG_{\scriptscriptstyle\rm E}}>400$ and
$\mathcal{R}_{RG_{\scriptscriptstyle\rm E}}>480$, enabling redshift
measurements with an error $\sigma(z)<0.001(1+z)$. These numbers for
$\mathcal{R}$ are based on ground measurements, exact values in flight will
depend on the details of data reduction and spectral extraction. A detailed
analysis of the dependency on wavelength, object size, etc. based on in-orbit
data will be presented in a future paper, an initial assessment based on
simulations is made in Euclid Collaboration: Gabarra et al. (2023).

Further tests of NISP were repeated at payload-module level, with all of
Euclid’s telescope mirrors and the dichroic in the light path. These tests
demonstrated an excellent telescope and that the presence of these additional
components – specifically the dichroic beamsplitter – do not alter the
excellent spectroscopic performance.

That said, please note that NISP carries three red grisms (Table 2). Owing to
a coordinate system misunderstanding during integration, the grisms were
erroneously glued into their holding structures rotated by 180∘. While for the
RGS000 and RGS180 grisms this only creates an inversion of the dispersion
direction, the tilted focal plane along RGS270’s dispersion direction means
that observations with the RGS270 are out of focus outside its central
wavelength. The RGS270 can therefore not be used for spectrum overlap
decontamination as was its original main purpose. At the source this issue
could only have been corrected by producing a new set of grisms and grism
holders. This would have induced an estimated mission-delay of 1–2 years and
posed extra risk from dismantling the instrument as well as degrading the
initial integration that was outperforming expectations. Instead a procedure
was developed without the RGS270, using the RGS000 and RGS180 grisms also with
a 4∘ rotation of the GWA, which was shown to be a functioning approach to
decontaminate spectral overlaps in the Euclid Wide Survey. Hence while along
with the other grisms the RG270 has been launched to maintain spacecraft
balance, it will not be used in the survey.

NISP-S blue grism is used to extend the lower redshift limit for H$\alpha$ to
$z\,{=}\,0.41$. It will solely be used for observations of the Euclid Deep and
Auxiliary fields (Euclid Collaboration: Scaramella et al. 2022), where
multiple visits over time will observe these field at different orientation
angles for overlap decontamination. The main purpose of the blue grism is to
provide a large reference sample of galaxies with 99% redshift completeness
and 99% purity required to characterise the typical Euclid galaxy population,
and to maximise the legacy value of these fields (Euclid Collaboration:
Mellier et al. 2024).

As for the filters above (Sect. 3.3) the NISP grisms are selected by rotating
the GWA, and the same wheel positioning uncertainties apply. This has the
consequence of inducing slight angles of the dispersion direction with respect
to the nominal orientation. This angle can vary by a few
0$\aas@@fstack{\circ}$1 between observations with the same grism, if the GWA
has been moved in between.

###  3.5 Detector system and cold electronics

The 16 H2RGs in the NISP Detector System (DS) are shown to the right in Fig.
1. Each detector is part of a sensor chip system (SCS), delivered by NASA JPL,
and is composed of three items (Fig. 11, left panel).

First, the SCA is a Mercury Cadmium Telluride (MCT) 2048$\times$2048
photodiode array hybridised to a H2RG readout-integrated chip. The SCAs are
designed and manufactured by Teledyne Imaging Sensors. The long-wavelength
cut-off was set to 2.3 $\mathrm{\SIUnitSymbolMicro m}$ by tuning the ratio of
mercury to cadmium in the MCT. This choice of cutoff reduces the total dark
current to a negligible level $<$0.01 e- s-1 at the operating temperature of
100 K and maintains the highest QE across the NISP wavelength range. A total
of 34 working SCAs were made and through a rigorous test programme
downselected to 16 flight models and 4 flight spares (Bai et al. 2018;
Waczynski et al. 2016).

Second, the SCE is a cold electronics package used to provide timing, biases,
communications, and data conversion for the operation of the SCA, as well as
serving as an interface to the NISP warm electronics.

The SCEs include an application-specific integrated circuit (ASIC) termed the
‘SIDECAR’ (Loose et al. 2007) that was developed by Teledyne Imaging Systems.
The design, construction, acceptance testing, and performance testing of the
SCEs were performed through a collaboration among NASA JPL, NASA GSFC, and the
larger NISP team, and are described in Holmes et al. (2022). The flight units
and flight spares were tested in combination with non-flight SCAs. The units
incorporated in the NISP flight detector system were selected from a larger
collection of constructed units based on their noise performance and other
characteristics.

Third, the cryo-flex cable (CFC) connects the SCA to the SCE while keeping the
thermal conductance between the 95 K of the FPA and the 135 K of the cold
electronics as low as possible. The measured thermal conductance is 0.85 mW
K-1 over the range 135 K to 95 K, well below the specification of 1.5 mW K-1
Holmes et al. (2019).

Figure 11: Left: SCS cold electronics triplet delivered by NASA, composed of
the H2RG detector arrays (SCA), flex cable (CFC) and SCE SIDECAR ASIC (Holmes
et al. 2022). Right: Metrological measurements of the detectors mounted in
their baffle.

A detailed description of the detector chain as well as detector effects such
as nonlinearity and persistence seen in the NISP flight SCS triplets can be
found in Barbier et al. (2018). The latest results on inter-pixel capacitance,
which is of the order of 0.6%, is reported for one flight SCS in Le Graët et
al. (2022).

The H2RGs are arranged in a 4$\times$4 mosaic as described and displayed in
Fig. 22 in Appendix A. The SCEs are placed at the back of the FPA, in each two
rows of four at the top and bottom, respectively. A picture of a single SCS is
shown in Fig. 11 before (left) and the 4$\times$4 array after integration into
the DS (right). The array position numbering is indicated in Fig. 9.

The SCE flight firmware was delivered by Markury Scientific and validated by
NASA (Loose et al. 2003). The firmware implements the science MACC modes and
calibration modes, such as the single-pixel reset along the pixel grid, while
keeping the power consumption within specifications.

To first order, the primary driver of the detector-chain sensitivity
performance for faint objects is the SNR. It is mainly driven by the noise in
the science acquisition modes, that is the noise of the flux estimator – the
combination of read-out hardware and algorithm estimating the flux in each
pixel from multiple reads – in spectro- and photo-readout modes, and by the QE
across each passband. Table 3 summarises the main features of the FPA. Noise
properties for both photometry and spectroscopy modes (Table 4) were measured
during both testing of each SCA in a joint campaign of the Centre de Physique
des Particules de Marseille (CPPM) and the Institut de Physique des 2 Infinis
de Lyon (IP2I) as well as the first thermal vacuum test at instrument level at
LAM. These extensive campaigns – each one month per detector, and 1.5 months
for the instrument as a whole – had the overall goal to create a general
characterisation of all detector components, that would both allow to develop
and test models, e.g. for non-linearity and persistence effects, as well as to
establish ground reference maps for dark current, read noise, dead pixels,
gain, and other parameters. These were being used for initial work by the SGS
as well as are providing a reference for comparison of in-flight behaviour.
During these measurement campaigns also the implemented standard MACC readout
modes for photometry and spectroscopy were used, providing reference
observations under various conditions for later comparison, as well as for
preparation of final operation parameters, testing of the on-board data
processing, and calibration activities.

Table 3: Detector chain performance of the Flight Model NISP focal plane, for all 16 SCS, identified by their SCA number. ‘Position’ refers to position in the 4$\times$4 array as defined in Fig. 22, ‘QE’ is the quantum efficiency in the respective photometry passbands, with an absolute accuracy of 5% (Waczynski et al. 2016). ‘Noise’ is the read noise in the standard survey MACC modes for photometry and spectroscopy. ‘Disconnected pixels’ gives the number of pixels per detector that do not produce a signal due to a missing or faulty indium bump. If not specified then values given are medians over the array. Mosaic | Position | 11 | 12 | 13 | 14 | 21 | 22 | 23 | 24 | 31 | 32 | 33 | 34 | 41 | 42 | 43 | 44
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
SCA 18XXX | 453 | 272 | 632 | 267 | 268 | 285 | 548 | 452 | 280 | 284 | 278 | 269 | 458 | 249 | 221 | 628
QE [%] |  $Y_{\scriptscriptstyle\rm E}$-band | 93 | 96 | 88 | 95 | 92 | 94 | 94 | 93 | 91 | 92 | 94 | 94 | 92 | 93 | 96 | 92
$J_{\scriptscriptstyle\rm E}$-band | 95 | 97 | 95 | 96 | 93 | 96 | 95 | 95 | 93 | 94 | 95 | 95 | 96 | 94 | 98 | 95
$H_{\scriptscriptstyle\rm E}$-band | 96 | 98 | 95 | 97 | 94 | 96 | 96 | 95 | 93 | 94 | 95 | 96 | 95 | 94 | 99 | 95
Noise [e-] | Spectro | 8.2 | 9.0 | 7.8 | 9.2 | 8.2 | 8.9 | 8.3 | 8.4 | 8.1 | 7.9 | 8.0 | 8.7 | 7.7 | 7.9 | 8.1 | 8.7
Photo | 6.0 | 6.2 | 5.9 | 6.4 | 6.3 | 6.7 | 6.4 | 6.4 | 5.9 | 5.8 | 5.6 | 6.4 | 5.7 | 5.6 | 6.1 | 6.1
Disconnected pixels [#] | 135 | 3223 | 589 | 252 | 313 | 2580 | 1512 | 146 | 1420 | 269 | 262 | 970 | 198 | 334 | 222 | 255

Figure 12 shows the measured noise properties as well as the wavelength-
dependent QE, measured at NASA JPL as part of a special characterisation
campaign. The top panel shows the excellent QE spectral response against a
cold calibrated photodiode, reported on the level reached by 95% of pixels.
The centre and bottom panel show the cumulative distribution of the spectro
and photo noise across all detector pixels for the 16 selected flight SCS,
obtained during ground testing at LAM.

Figure 12: Top: Wavelength-dependent QE of the 16 flight SCA, 5-percentile
values measured at NASA (95% of pixels have at least this QE). Centre+bottom:
Noise cumulative distribution of the 16 flight triplet SCS. Black points are
the 5, 50, and 95 percentiles of the cumulative distribution. SCS and SCS are
identified by their SCA number 18XXX as in Table 3.

With respect to other space missions the 2.3 $\mathrm{\SIUnitSymbolMicro m}$
sensitivity cutoff reduces some of the thermal background that would be
incurred with the more common 2.5 $\mathrm{\SIUnitSymbolMicro m}$ cutoff SCA
used for example in the JWST NIRCam. Aside from the near-optimal QE – at 1
$\mathrm{\SIUnitSymbolMicro m}$ slightly better than NIRCam – and low noise
properties, what stands out about the NISP FPA is the sheer number of
detectors needed to populate the NISP focal plane. The 16 flight detectors,
and the batch of 29 detector they were selected from, were drawn from a much
larger set of H2RG that were actually manufactured, but where many did not
pass the rigorous requirements set for Euclid by ESA, NASA, and the NISP
development team.

The overall downselection and subsequent characterisation were only possible
by a substantial and automated unattended testing effort with streamlined data
analysis. The characterisation campaigns performed at the SCS level are
described in Secroun et al. (2016) – as mentioned above, they alone were a
considerable effort with three months of testing in a row with two cryostats,
each cryostat testing two SCAs at a time. Nearly 1 PB of ground
characterisation data were taken for the 16 flight and four spare SCS, which
facilitates a deep understanding of various detector effects.

###  3.6 Calibration source

The NISP calibration concept (see Sect. 4) requires the ability to measure the
detector response and linearity pixel-by-pixel both in the integrated
instrument on ground as well as in-flight. For this purpose the instrument has
the NISP calibration unit (NI-CU), a calibration lamp that directly
illuminates the FPA with one out of five custom-manufactured LEDs. Their peak-
emission wavelengths are distributed between 940 and 1870 nm (at an operating
temperature of 135 K; full description in Euclid Collaboration: Hormuth et al.
2024), with their emission characteristics shown in the upper panel of Fig. 7.

Each of the LEDs has a comparatively narrow emission spectrum over a well-
defined wavelength range. Their advantage over traditional incandescent
tungsten lamps is that that their spectral temperature-dependence is extremely
small: for example, NISP’s nonlinearity calibration requires the capability to
shine light at different fluences onto the detector. To use a single tungsten
lamp for various brightness levels, one would vary the current flowing through
the filament, which would dissipate more heat, change its temperature, and
therefore would have shifted the central black-body wavelength. This is not
the case with LEDs, where the wavelength is solely given by the semiconductor
band gap, as opposed to blackbody emission. There is a negligibly small
temperature dependence of the emitted spectrum, much smaller than for a
tungsten element. At the same time operating the LEDs leads to minimal energy
dissipation facilitating a stable thermal equilibrium.

Inside NI-CU, at each point in time any one of the five LED can be operated,
by the same power source in the warm electronics (Sect. 3.7), but not several
LEDs simultaneously. When operated, an LED illuminates a common central 3 mm
$\times$ 3.5 mm oval reflective patch of space-grade Spectralon, a porous
polytetrafluoroethylene (PTFE) plastic. Spectralon is a commonly used, near-
perfect diffusive Lambertian reflector, owed to multiple internal scattering
of light inside its several millimetre high body. The reflector patch in turn
directly illuminates the FPA with a field footprint shaped by five consecutive
baffles along NI-CU (Fig. 13).

Figure 13: Flight model of the calibration source NI-CU before integration
into NISP. Two supply harnesses can be seen, feeding the nominal and redundant
side, respectively, of NI-CU with each 5 LEDs emitting at different
wavelengths. Inside the base any LED illuminates the same PTFE reflector patch
that in turn directly illuminates the FPA, with a field shaped by a set of
five baffles, the last one visible at the top. The full NI-CU height is
approximately 150 mm.

NI-CU is mounted off-axis, next to CoLA (see Fig. 1), that is it illuminates
the FPA directly, bypassing all optical elements including the filters. Hence
NI-CU’s purpose is not to calibrate the NISP $Y_{\scriptscriptstyle\rm E}$,
$J_{\scriptscriptstyle\rm E}$, and $H_{\scriptscriptstyle\rm E}$ passbands,
but solely the detector response. A mapping to detector properties within a
specific passband takes place by combining calibration images from one or
several LEDs with the QE maps obtained on ground (top panel of Fig. 12).

The combination of Euclid’s off-axis optical system and NI-CU’s placement in
the instrument results in an angle between the axis from NI-CU to the FPA and
the FPA surface normal of $\sim$ $None$. To improve uniformity in the
illumination of the FPA, the Spectralon patch is tilted by $\sim$ $None$ with
respect to NI-CU’s central axis, tilting the Lambertian emission cone. The
resulting flux homogeneity across the FPA is $\sim$ 12%, providing similar
flux and thus SNR for all pixels. Any large-scale variations that might be
imprinted on the data by NI-CU or the telescope optics itself are captured by
a photometric flat calculated from dithered on-sky data (Sect. 4).

NI-CU contains two sets of LEDs, one nominal, one redundant, connected to the
corresponding sides of the NISP warm electronics power supply and control
units (Sect. 3.7). They are operated in a pulsed mode with a constant
frequency but variable pulse width (pulse-width modulation, pulse-width
modulation (PWM)), and variable current. The pulse width can be varied between
5% and 50% duty cycle, the current between typically 10 mA and maximally 100
mA, providing a dynamic range of $>$ 100 in fluence yet safely away from the
upper operational limits that might reduce the LEDs lifetime, and away from
very small currents and duty cycles that are difficult to drive while
guaranteeing stability. As a result NI-CU can provide very stable illumination
levels over typical exposures of at least $\sim$ 100 s, at fluence rates
between $\sim$ 15 ph-1 s-1 pixel-1 and $>$ 1500 ph-1 s-1 pixel-1, for all
LEDs. All but one LED channel has a stability rating for drive currents $\geq$
10 mA for linearity calibration to less than 0.2% linear drift and remaining
$\sim$ 0.1% RMS after removal of the linear drift component over 1200 s. NI-CU
can and is being used with a current as low as 1 mA, but at the price of lower
stability, below a current of 10 mA the temporal drift of the power supply and
hence LED fluence can increase proportionally.

Even though NI-CU’s illumination levels and illumination pattern will be quite
stable over time, due to the mechanical and optical design, a temporally
perfectly fixed illumination pattern on the FPA and a fixed absolute flux per
given PWM and current setting is not guaranteed, and illumination patterns can
in principle show small differences between the five LEDs. NISP’s calibration
approach is designed correspondingly, assuming varying photon fluences and
variable illumination patterns.666Absolute calibration lamps with perfectly
known fluence levels at each time from internal information are a chicken-and-
egg problem, requiring calibration of the fluence levels with an extra device
like a photodiode, which itself needs a calibration mechanism. NISP has
therefore decoupled small-scale spatial calibration (LED flatfields), large-
scale calibration (on-sky data), and absolute calibration (celestial reference
source).

###  3.7 Warm electronics

The NISP warm electronics is a system composed of the Instrument Control Unit
(ICU) and two identical Data Processing Units, operating at $\sim$ 293 K. The
DPUs handle the raw-data acquisition of the NISP FPA – the exposure start
times of the 16 detectors are synchronised to better than 10 ns – the onboard
processing, the compression, and the transmission to the spacecraft’s Mass
Memory Unit (MMU). The warm electronics units in their flight configuration
are shown in Fig. 14.

Each DPU hosts eight DCUs that pre-process independently and in parallel the
SCEs’ live data stream; three radiation-tolerant class-S CPU boards (MaxwellTM
3$\times$SCS750F®-PPC) with a 400 MHz clock error detection running the
VxWorks 5.1 real-time operative system; memory banks that consist of error-
logging SDRAM of 256 MB (Reed-Solomon protected), non-volatile E2PROM of 8 MB
(Error Correction Code protected), a transit data buffer board of 6 GB, and a
127 MB Space Wire router board; and a power-supply board.

All boards inside each DPU – besides the DCUs – have a ‘cold’-redundant
counterpart, and three central processing unit boards. The latter work jointly
but in case of a failure the DPU could also work with only two of three. Each
DPU is equipped with two communication interfaces: a 1553 MILBUS I/F for
receiving telecommands with a maximum rate of 1 Hz ($\sim$ 512 bit) and
transmitting telemetry with a maximum rate of 40 Hz ($\sim$ 18 kbit) with the
ICU, and a Space Wire I/F for the science data stream, internally connecting
with the DCUs, and externally with Euclid’s MMU (Medinaceli et al. 2020).

Figure 14: NISP Warm Electronics: the identical DPUs can be seen on the top of
the picture, while the ICU is located at the centre (right) of the so-called
Spacecraft Y panel. Image courtesy Airbus Defence and Space

####  3.7.1 Command structure

The NISP warm electronics comprises two different systems that are located in
the warm environment of the SVM – the ICU for main control, and the DPUs for
data handling. The ICU together with its on-board application software
orchestrates the NISP operations, controls the NISP wheels and the
calibration-unit electronics (Sect. 3.6), and regulates the heating elements
within NISP based on the readings of various thermal sensors (Sect. 3.7.3).
The ICU interfaces with the two DPUs that each synchronously command half of
the focal plane. It also provides the main commanding interface with the
satellite, receiving telecommands from and sending telemetry data to the
spacecraft. Data from the FPAs flows to the DCUs, then to the DPUs for
processing and compression, and from there to the MMU. The ICU has two
identical sections, one nominal and one redundant, connected with the
corresponding versions of the wheel motors, NI-CU LEDs, and NISP thermal
control. Either one can be operated at a time, following a full ‘cold’
redundancy scheme where always one section is fully ‘off’ and has to be
explicitly activated when needed, by switching off one side and starting the
other.

####  3.7.2 Detector read-out and processing

NISP detectors have a nominal frame rate of 1.45408 s. While Euclid already
has a large data-downlink budget, it still does not permit to downlink every
observed frame. NISP’s standard reference observing sequence (ROS), that is
used in both the Wide and Deep Surveys, consists of the repetition of four
dithers, each made of one spectroscopic exposure of about 549.6 s of
integration time followed by three consecutive photometric exposures of 87.2 s
integration time each (Table 4; full description in Euclid Collaboration:
Scaramella et al. 2022). For NISP transmitting each read frame would represent
an accumulated data volume of $\simeq$ 20 Tbit per day – factors of many
beyond NISP’s available downlink budget of 290 Gbit/day for NISP.

To reduce the amount of data to an acceptable volume while minimising the
exposure read-noise, the DPUs perform on-board data processing to estimate
photons – or rather charge – accumulated in the pixels during each exposure
(see Bonoli et al. 2016). For this purpose NISP detectors acquire data through
a MACC sampling scheme, using non-destructive readout. A number of groups $g$
each consists of several consecutively read-out frames $r$ that are averaged
by the DCU. Between groups, a number $d$ of frames is ‘dropped’ – that is not
read out – while integration of photons continues. The MACC parameters used in
the ROS for the spectroscopy and photometry channels are shown in Table 4.

Table 4: MACC($g$,$r$,$d$) mode parameters for spectroscopy and photometry in the reference observing sequence (ROS). Here $g$ is the number of groups in the slope fit, $r$ the number of reads per group, and $d$ the number of drops after each but the last group. Both grisms use the same MACC settings, as do all three photometry bands. For both MACC modes the integration time $t_{\mathrm{int}}$ (effective time accumulating light; the time relevant for science) and exposure time $t_{\mathrm{exp}}$ (total duration of frame including one reset frame before start of charge integration; the time relevant for exposure planning) are given, using the frame duration of 1.45408 s. | $g$ | $r$ | $d$ |  $t_{\mathrm{int}}$ [s] |  $t_{\mathrm{exp}}$ [s]
---|---|---|---|---|---
Spectroscopy | 15 | 16 | 11 | 549.6 | 574.4
Photometry | 4 | 16 | 4 | 87.2 | 112.0

At the end of the exposure, the DPU corrects each averaged group for the
detector baseline offset (Sect. 4.1.1) based on the group average of the un-
illuminated reference pixels. It then estimates for each pixel a signal slope
through a straight-line least-square fit to the averaged group differences,
that is the signal between consecutive groups, which optimally reduces the
read-out and shot noise (Kubik et al. 2016). This value – with a fixed digital
offset of 1024 ADU added – is downlinked to ground.

In addition to estimating the accumulated signal, the DPU evaluates a
statistical quality factor of the fit (see Kubik et al. 2016). In case of the
spectroscopic mode this quality factor is an 8-bit $\chi^{2}$ integer value,
and for photometry mode it is a 1-bit flag. It provides feedback whether a
pixel had a truly linear response during the exposure, enabling the on-ground
processing pipeline to detect spurious events like cosmic-ray hits or random
telegraph noise (Kohley et al. 2018), as well as pixels often characterised by
high non-linearity.

The processing times for photometric and spectroscopic exposures were
evaluated from ground test campaigns to be of the order of 7 s and 14 s per
detector, respectively (Medinaceli et al. 2022), where each DPU processes 8
detectors in sequence.

After onboard processing, both the science data and quality-factor data are
compressed with a loss-less Rice compression algorithm777The RiceComp routine
from NASA’s CFITSIO library(5) is available at
https://heasarc.gsfc.nasa.gov/fitsio/fitsio.html (Rice & Plaunt 1971; Pence et
al. 2009) adapted to the DPU application software by enforcing the required
coding standards and test procedures. Data are then packaged with headers and
detector telemetry to be sent to the MMU. The compression factor for the
science images varies from 2.8 to 3.4 depending on the exposure mode. The mean
size of a nominal NISP dither with one NISP-S and three NISP-P exposures is
about 378 MB, or about 31 $\pm$ 3 GB/day for 20 ROS pointings of four dithers
each. These 248 Gbit/day fit with some margin into the NISP’s allocated
downlink data volume of 290 Gbit/day.

####  3.7.3 NISP Thermal Control

The thermal design of NISP is based on a double passive radiator system to
provide the main temperature references for the NISP opto-mechanical system
and detector system. The first radiator provides a $\sim$ 130 K heat-sink for
the structure, optics, cryo-motors, and the SCE cold electronics. The second
colder radiator sets the reference temperature for the infrared detectors at
about 95 K. The thermal interfaces have been designed to achieve a thermal
stability of $\pm$2 K throughout the mission, allowing to stabilise the optics
to much better than that. Specifically for the detector radiator interface, an
even stricter requirement is in place, mandating a temperature fluctuation of
less than $\pm$0.1 K over a period of 3 hours, and less than $\pm$4 mK over
1210 s, about the duration of a nominal dither. From a radiative perspective,
NISP is situated within the Euclid instrument’s cavity, where temperatures
range from 130 K to 135 K. This environment is characterised by surfaces all
having an emissivity greater than 0.9, a specification driven by optical
requirements.

To achieve the designated thermal performance, the NISP thermal management
system is made up of a set of thermal sensors, a heating system, and an multi-
layer insulation (MLI) blanket. The thermal sensors use 12 PT100 thermistors,
six located on the mechanical structure, and each two on the cryo-motors, the
SCE thermal straps, and on the FPA. The thermistors are read by the ICU, and
their value reported in the NISP housekeeping telemetry. In addition to
thermal sensors the system contains two heaters, installed near the optics on
the NISP structure, functioning in an open-loop configuration to supply the
necessary power to maintain the target temperature. Finally, the instrument is
wrapped in a six-layer, black-coated MLI blanket to isolate the inner
instrument from the external radiative environment.

Preliminary data, collected during the Euclid in-flight performance
verification phase, indicate that NISP temperatures can be stable at the level
of 2 mK (RMS) for the optics and 1 mK for the NISP focal plane during steady-
state operations.

####  3.7.4 Contingency and recovery

NISP has multiple levels of fault detection encoded in its warm electronics,
to take safety measures autonomously when needed in order to protect the
instrument at all times and in all situations. The ICU continuously monitors
instrument telemetry data including telemetry from the detectors sent by the
DPU. In case of deviations with respect to expectations it triggers the
instrument-level Fault Detection, Isolation and Recovery (FDIR), changing
NISP’s operation state depending on the severity and location of the incident.

The DPU application software again monitors any potential DPU anomalies,
including all DPU subsystems and the NISP FPA. In case of a detected anomaly
it can change the instrument operational mode to SAFE, powering off the FPA,
or PARKED, disabling the FPA commanding capability but leaving it powered on.
These operations are done autonomously and independently by each DPU, while
signalling the anomalies to the ICU through alarms and housekeeping
parameters. The ICU transmits the anomaly status to the spacecraft and sets
NISP’s resulting operation mode according to the criticality of the anomaly.

The DPUs have all of the hardware failure modes, effects, and the
specification of the criticality-analysis for all its subsystem-hardware
encoded in its FDIR system, and are able to consider each detector chain
independently. The event signalling and isolation is performed by the
application software. The FDIR system covers anomalies in temperature,
currents and voltages exceeding thresholds of all hardware components, as well
as memory upsets. Also the main processes managed by the DPU software
implement several FDIR checks based on programmable watchdog systems, used for
the monitoring of the correct operations and especially timing of the
dynamical processing chain. This means it constantly tests if the relative
timing of image exposure, science data collection, science data processing,
and science data product transmission to the MMU work out. Additionally, as
mentioned above, both the ICU and DPU have cold-redundancy counterparts. Thus
we can independently switch from the nominal to the redundant side of the ICU
and DPU chain in case anomalies are detected, at full functionality.

##  4 Calibration approach

The instrumental and data-processing capabilities of NISP flow down from the
required FOV and depth. Together, they facilitate an efficient coverage of the
targeted 14 000 $\deg^{2}$ down to 24.0 AB mag – which led to the instrument
design as just described – but at the same time set requirements on accuracy.
At a higher level these requirements differ for the two NISP channels, but
also affect the same properties, for example in the common NISP detector
system.

For NISP-P the main requirement is a relative photometric accuracy of 1.5% in
the fully processed and calibrated data. NISP-S must achieve a relative
spectrophotometric accuracy of 0.7%, and a wavelength accuracy of 5 Å or 0.3
NISP pixel anywhere in the FPA. Both channels need accurate distortion
correction on the order of $None$: first, for the unambiguous matching with
the VIS astrometric reference system, which in turn is based on Gaia (Gaia
Collaboration: Prusti et al. 2016); second, for the decontamination of the
overlapping spectra orders in the slitless spectroscopy exposures.

A total of 31 calibration products are defined for NISP that are maintained
during flight, in addition to further calibration and characterisation files –
such as QE – that could only be generated on ground. Calibrating the VIS and
NISP instruments and the telescope is a complex task with a substantial number
of dependencies and limitations set by (i) the spacecraft, (ii) the
instruments’ hardware, (iii) the instruments’ on-board processing
capabilities, (iv) the data-downlink volume, and (v) timescales of spacecraft
degradation by space weathering and molecular outgassing (Euclid
Collaboration: Schirmer et al. 2023).

Within the scope of this paper we provide an overview of the principal effects
and the calibration strategies. The associated calibration products can be
divided into three categories: those that are common to both channels, and
those that are individual to NISP-P and NISP-S. Calibration products were
updated during the initial performance verification (PV) phase that followed
commissioning and extended over three months. Those calibration products that
we consider to likely evolve over time are monitored with appropriate
cadences. The stability of the other calibration products can be assessed with
regular in-flight science and calibration data, and corrective actions taken
if necessary. Concrete results from the PV phase will be reported elsewhere.

###  4.1 Calibration products common to NISP-P and NISP-S

Common calibration products apply mostly to the detector level and the read-
out electronics. They are independent of the illumination provided by the NISP
optics. The following calibration products are either used to monitor
evolution of detector properties, or are applied to data in the Euclid SGS
data pipeline.

####  4.1.1 Detector baseline

The baseline is a bias value set for each pixel after reset. Reference and
science pixels have respective pedestals of about 5.5 and 10 kADU, to ensure
that the pixels operate in the linear range of the analogue-digital chain and
to encompass the large variation across pixels. The available dynamic range is
reduced accordingly. The baseline has a temperature dependence of about $-120$
ADU K-1, was redetermined for each pixel during commissioning once the
detectors stabilised to their actual in-flight temperatures, and once more
during PV. The baseline is estimated from 512 reference pixels per read-out
channel for every MACC exposure and automatically removed by the onboard
processing before the ramp-fitting process. Baseline instabilities are
monitored using regular dark and flat-field calibration frames.

####  4.1.2 Dark current

The dark current for the NISP detectors is very low with $\sim$ 0.02 e- s-1
pixel-1, requiring hundreds of exposures for a high-SNR measurement. The goal
of the dark maps is to characterise dark current levels and to identify warm
and hot pixels that need to be masked. Dark maps were obtained during PV at a
time when persistence charges (Sect. 4.1.5) had decayed, and are subtracted
from the science data in the pipeline.

Monitoring of hot pixels is provided by so-called ‘slew darks’, one of which
is taken every 1.5 h during the slews from one survey field to the next. Slew
darks are affected by persistence charge from the previous exposure history.

####  4.1.3 Detector cross-talk

Cross-talk is caused within the detector as well as the detector electronics.
The amplitudes inferred on ground are on the order of 0.001%, meaning that
cross-talk is detectable only for saturated field stars. Intra-chip cross-talk
between readout-channels, as well as cross-talk between detectors, are
determined from in-flight observations of bright stars. Victim pixels are
masked if the cross-talk flux violates the relative photometric accuracy
requirement.

####  4.1.4 Interpixel capacitance (IPC)

IPC is a localised form of electronic cross-talk that is independent of
wavelength. Prior to read-out, it spreads charge from the pixel in which it
was collected to the adjacent pixels because of the capacitive coupling
between pixels. inter-pixel capacitance (IPC) broadens the PSF and smooths the
Poisson noise, and hence must be corrected before determining for example the
gain. The IPC is expected to change very little over time. It is temperature-
dependent, though, and was therefore determined once the detectors reached
their stable in-flight temperature (for more details see Le Graët et al.
2022). It can be evaluated by measuring the cross-talk when charge is
artificially injected in a pre-defined pattern of pixels, enabled by a special
calibration feature implemented in the NISP detectors.

####  4.1.5 Persistence

Persistence is an achromatic effect caused by charge traps in the detector.
The traps remove photo-electrons from the readout of the current exposure and
release them over time in an exponentially decaying manner, leading to ghost
images in subsequent exposures (Smith et al. 2008). NISP ground and in-flight
data show visible persistence signals from previous observations. This is
applies most visibly to spectroscopy traces in subsequent photometry images,
but also vice versa. The amount of persistence depends on the number density
and types of charge traps, its illumination history, and it is also
considerably different below and above full-well saturation. We adopt a simple
persistence model where each pixel is characterised by two time constants
representative for short- and long-duration traps, and a persistence amplitude
that scales with the local trap density. Based on the fluence of a pixel in an
exposure the model then determines for how long – that is for how many
subsequent exposures – that pixel needs to be masked; a time that can easily
exceed several hours.

A more complex persistence model would account for the illumination history,
that is bright objects falling on the same pixel in several exposures. Whether
that pixel would be masked in a future exposure could then also depend on
whether it falls on a bright or a faint source, the latter being relatively
more affected. This will be considered at a later time when more experience is
available with the in-flight system. We might consider actual pixel-level
correction rather than masking, depending on whether high-quality persistence
models can be built.

We assume that the time constants and amplitudes are stable over time as the
detectors are radiation-hard and receive a total dose of only 2.5 krad during
the mission. Therefore, persistence is characterised during the PV phase by
flat-field illumination followed by several hours of short- and long dark
exposures, capturing both time constants as well as amplitudes. The
persistence model can be validated with every single survey exposure.

####  4.1.6 LED flats

The NISP detectors can be illuminated directly with the five LEDs in the NI-CU
calibration source, bypassing all optical elements. These LED images – or lamp
flats – provide a nearly monochromatic illumination that is uniform to better
than 0.2% on spatial scales of $\leq$ 100 pixel. They are taken on a monthly
basis to compute effective flat fields for the NISP-P and NISP-S modes (Sects.
4.2.3 and 4.3.5).

####  4.1.7 Brighter-fatter effect

The brighter-fatter effect (BFE) is caused by the increasing electrostatic
field in a pixel as it accumulates photo-electrons. The changing field
strength shifts the effective pixel boundaries in the sense that newly created
photo-electrons are pushed into neighbouring pixels. The FWHM of stars, which
would be independent of source flux for a perfect detector and only determined
by the optics, therefore increases with brightness. The BFE is measurable over
large parts of the dynamic range, and it is not fully flux-conserving. The BFE
also affects the pixel variance in flat fields, and is characterised from LED
flats with different fluences. A more in-depth description can be found in
Plazas et al. (2018) and Hirata & Choi (2019).

####  4.1.8 Nonlinearity

The measured signal in a H2RG pixel is increasingly nonlinear with increasing
charge-filling level of that pixel, independent of wavelength. To calibrate
this nonlinearity, we illuminate the detectors with the calibration lamp while
obtaining MACC ramps; the filter wheel is closed during these observations, so
that only flux from the lamps is observed. Contrary to nominal science
observations, where the slope-fit to the individual MACC group images is
obtained on-board and downlinked (Sect. 3.7), we downlink the individual group
images. Nonlinearity manifests as a deviation from the expected linear trend
in the increasing fluence of the group images as they approach saturation. The
data volume exceeds the nominal telemetry budget, so that this calibration has
to be obtained for two detectors at a time instead of all 16 detectors
simultaneously.

The MACC(4,16,4) and MACC(15,16,11) modes for standard photometry and
spectroscopy observations (Table 4) provide four and 15 group images,
respectively, meaning that the dynamic range would be sparsely sampled with
just a single LED flux level. Therefore we observe five different LED flux
levels, and obtain independent nonlinearity calibrations for both photometry
and spectroscopy readout modes for every pixel. Nonlinearity calibrations are
obtained on a monthly basis, with a full set achieved every four and six
months for MACC(4,16,4) and MACC(15,16,11), respectively. We note that BFE
(Sect. 4.1.7), IPC (Sect. 4.1.4), persistence (Sect. 4.1.5), and reciprocity
failure (Sect. 4.1.9) are also forms of nonlinearity, calibrated separately.

####  4.1.9 Reciprocity failure

Reciprocity failure is a flux-dependent nonlinearity caused by charge
trapping; as such it is related to persistence (Sect. 4.1.5). A brighter
source will fill – and thus passivate – charge traps in a pixel more quickly
than a fainter source, therefore resulting in earlier linear pixel response.
We expect this flux nonlinearity to be independent of wavelength, an
assumption to be verified with the PV data. There, we observed the same
stellar field in different filters first without and then with increasingly
higher additional flux from the calibration lamps. The measured fluxes of the
same sources without and with additional LED background then carry the
reciprocity-failure signal. This means we can only sparsely sample the
detectors, and only an average correction can be achieved instead of a pixel-
based correction such as in Sect. 4.1.8. During the nominal mission,
reciprocity failure will be calibrated on a yearly basis as we expect only a
slow evolution of the charge-trap density due to radiation damage (Sect.
4.1.5).

###  4.2 Calibration products specific to NISP-P

####  4.2.1 Absolute flux calibration

For absolute flux calibration we observed the white dwarf (WD) standard star
J175318+644502 ($J_{\scriptscriptstyle\rm E}=18.56$ AB mag) in the self-
calibration field. This star was identified by us using available multi-colour
photometry and long-term photometric monitoring, using among others Pan-
STARRS, Gaia, and the Zwicky Transient Factory (ZTF). The star was
spectroscopically confirmed as a WD by us using the Gemini Multi-Object
Spectrograph (GMOS). We excluded considerable short-term variability on time-
scales of about 100 s–1000 s using fast photometry with GMOS. Accordingly,
this star is stable to better than 1% from time scales of 45 s to several
years. The data we have collected for various Euclid spectrophotometric WD
standard stars will be discussed in a different paper.

The WD was placed on 16 positions per detector. To establish the absolute flux
scale we obtained accurate Hubble Space Telescope (HST) imaging and
spectroscopic flux measurements of this star – and several others which will
be observed periodically in the Euclid Deep Fields as validation – with the
Wide-Field Camera 3 (WFC3) and Space Telescope Imaging Spectrograph (STIS) at
optical and near-infrared wavelengths (Proposal 16702, cycle 29: PI. P.
Appleton, and Proposal 17442, cycle 31, PI: S. Deustua). Hence the NISP-P
absolute photometry is based on a very well-understood photometric reference
system.888The VIS instrument in Euclid uses the same standard star.

####  4.2.2 Astrometry and optical distortions

The astrometric solution is based on a VIS astrometric reference catalogue for
every survey field, which in turn is based on highly reliable Gaia (Gaia
Collaboration: Prusti et al. 2016) data. The absolute positions of NISP-P
sources must be known to better than $None$, which is easily achievable using
the VIS/Gaia reference catalogue for every single exposure. The latter is
important, as the filters carry optical power and the filter wheel has an
intrinsic positioning RMS of about $None$, leading to a nontrivial change in
optical field distortion between exposures.

####  4.2.3 Small-scale flats

To correct for pixel-response non-uniformity (PRNU) in the
$Y_{\scriptscriptstyle\rm E}$, $J_{\scriptscriptstyle\rm E}$ and
$H_{\scriptscriptstyle\rm E}$ images we use a weighted combination of the
various LED flats (Sect. 4.1.6). The weighting factors are determined by the
spectral energy distribution (SED) of the zodiacal background and the
detectors’ QE maps. These flats will correct any PRNU effects to better than
0.2%. However, since the LED flats have a Lambertian profile, the FPA corners
receive about 10% less flux than the centre of the FPA. The zero point (ZP) in
the flat-fielded images varies accordingly, which is corrected by additional
photometric flats (Sect. 4.2.4).

####  4.2.4 Photometric flat

While Euclid’s optics are essentially free of vignetting, apodisation may
cause additional spatial variations of several percent in the illumination.
These will result in a change of the ZP across the FPA. By observing the self-
calibration field with a special pattern of 60 dither positions we can measure
how the fluxes of individual sources vary as they are imaged onto different
parts of the FPA. From this information we then compute a photometric flat
(see e.g. Holmes et al. 2012) to correct any large-scale illumination non-
uniformities to better than 0.5%.

####  4.2.5 Straylight and ghosting

During PV phase we validated the straylight models by placing very bright
stars ($J_{\scriptscriptstyle\rm E}$ $\sim$ 0–2 AB mag) at increasingly
smaller distances outside the FPA corners and edges. We also measured the
positions of ghost images caused by internal double reflections in the
dichroic element and the filters, and built suitable models for masking. Ghost
images are masked once they exceed a certain surface brightness.

###  4.3 Calibration products specific to NISP-S

####  4.3.1 Absolute flux calibration

For absolute flux calibration during the PV phase, we observed the WD standard
star GRW+70 ($J_{\scriptscriptstyle\rm E}$ =14.33 AB mag) from the HST CALSPEC
database on 5 positions per detector and per grism. Mean sensitivity functions
– one for the blue and four for each red grism/grism tilt combination – are
calculated based on the observations. The density of field sources around this
star is fairly low so that clean slitless spectra that are not contaminated by
other sources could be observed. Once the absolute flux scale is established,
no more observations are required as any throughput variations are monitored
by the monthly self-calibration observations. The absolute flux scale will be
further validated by observations of other spectrophotometric standards over
the course of the mission, as well as monitoring relative flux changes using
the self-calibration field.

####  4.3.2 Dichroic ghost model

As for NISP-P, the dichroic-ghost model must be validated for NISP-S, so that
affected pixels can be masked. The mapping from bright source positions to
ghost positions is given by a ray-tracing model and can be validated with
every on-sky exposure.

####  4.3.3 0-th order SED dependence

The 0th order in the spectra is an important reference point for the
wavelength solution, and it is double-peaked for two reasons. First, the prism
in the grism has some dispersive power even in the 0th order, resulting in a
10 pixel long spectrum of the 0th order. Second, the grating is blazed for a
specific wavelength, maximising the number of photons for that wavelength in
the 1st order and minimising it in the 0th order. The double-peaked shape also
has some field dependence.

The reference 0th-order position for wavelength calibration is given by the
mean of the two peak positions, which depend on the SED of the source. The
main impact of the source SED is to change the ratio of the 2 peaks, and
therefore the inferred mean value. The effect is expected to be at the level
of one pixel and below. The calibration will be built up as the Euclid Wide
Survey grows and sources with very different SEDs are mapped at different
positions in the focal plane.

Figure 15: NISP-S grism slitless spectroscopy mode. Left: NISP-P colour-
composite image of the planetary nebula (PN) SMC-SMP-20 (Euclid Collaboration:
Paterson et al. 2023), observed during the PV phase for wavelength
calibration. The PN is located at the image centre; with a diameter of $None$
it appears unresolved. Right: Raw NISP-S 2D-spectrogram of the same area, the
photometry field is sketched again with a green box. Each horizontal stripes
is a spectrogram of a bright object. Small dots between these spectrograms are
mostly cosmic rays hitting the detector. The PN can easily be identified in
the 2D-spectrogram by the distinct emission lines above a weak continuum; it
has been colourised for emphasis. The zoom below repeats the 2D-spectrum
together with the extracted 1d-spectrum. Identified emission lines are marked.
We note that the direction of the wavelength axis will differ on the sky
depending on the specific grism used. The visible strong Pa$\beta$ emission in
$Y_{\scriptscriptstyle\rm E}$-band and the Pa$\alpha$ line in the
$H_{\scriptscriptstyle\rm E}$-band lend a distinct purple colour to the PN and
its prominent diffraction ring in the image on the left.

####  4.3.4 Wavelength calibration

Determining the dispersion law of NISP-S involves a complex calibration during
the PV phase consisting of several steps: (i) a mapping from the astrometric
sky as defined by VIS to the 0th order position in the NISP-S dispersed
images; (ii) an offset from the 0th order position to a reference wavelength
in the 1st order; (iii) the spatial curvature of the spectral trace; and (iv)
the nonlinear wavelength dispersion within the 1st order. The calibrated
spectral distortion laws consist of fourth-degree Chebyshev polynomials to
each of these mappings as a function of field position. The wavelength
dispersion itself is measured using the emission lines of a point-like
planetary nebula (PN; Euclid Collaboration: Paterson et al. 2023, see also
Fig. 15). We expect the wavelength calibration to be stable and will check it
after one year into the survey by observing the PN once more. Furthermore, we
transfer the PN-based wavelength solution to stellar absorption-line systems
in the self-calibration field, so monthly consistency checks are possible.

####  4.3.5 Flat-fielding

Like NISP-P, the slitless spectroscopy channel must also be corrected for PRNU
(Sect. 4.2.3) and large-scale illumination effects (Sect. 4.2.4). The
corresponding pixel-correction maps will be built in an analogous fashion. For
each grism’s transmission band a flatfield map will be built for three
different wavelengths, to be subsequently combined.

##  5 NISP performance

Euclid was launched on 1 July 2023 and during the initial commissioning and
performance verification phases a first assessment of the instrument’s
performance was carried out. Overall the pre-launch predictions and
expectations are met or exceeded. NISP is producing excellent data in both
channels, as can be seen for NISP-S spectroscopy in Fig. 15 and for NISP-P
imaging in Fig. 16.

Figure 16:  Quality of the NISP imaging channel. Shown is a $$$\times$$$ wide
area in the self-calibration field. The depth corresponds to that of the
Euclid Deep Survey, that is about 26.3 AB mag for $5\sigma$ point sources,
achieved in 3.7 h integration time per filter. For this composite we mapped
the $H_{\scriptscriptstyle\rm E}$, $J_{\scriptscriptstyle\rm E}$, and
$Y_{\scriptscriptstyle\rm E}$-passbands to RGB colour channels. (Near)
saturated stars appear with characteristic spikes and diffraction rings
differing in size as a function of wavelength. The Euclid Deep Survey will in
total cover about 53 deg2, more than 20 000$\times$ the area shown here.

###  5.1 PSF

The NISP PSF as measured on-sky is very compact, with initial estimates of the
50% and 80% encircled-energy radii rEE50 and rEE80 of
0$\aas@@fstack{\prime\prime}$21–0$\aas@@fstack{\prime\prime}$27 and
0$\aas@@fstack{\prime\prime}$37–0$\aas@@fstack{\prime\prime}$55, respectively,
as shown in Fig. 17 (for photometry) and tabulated for both channels in Table
5. As expected, both rEE50 and rEE80 are larger after projection onto the
detector than the pure optical component at 960 nm measured in the lab (Fig.
6): (i) The $Y_{\scriptscriptstyle\rm E}$-band has a central wavelength of
1081 nm (see Table 1 and Euclid Collaboration: Schirmer et al. 2022), $\sim$
13% longer than the lab reference, and hence produces a PSF also 13% wider,
given that the optical PSF is practically diffraction limited (Sect. 3.2.7).
(ii) In flight the pointing jitter by the spacecraft amounts to $\sim$ 35 mas,
which directly broadens every image. (iii) On top come the effects of IPC
(Sect. 4.1.4), which additionally broadens the PSF upon readout, most
pronounced for the steep centre, and (iv) BFE (Sect. 4.1.7), which broadens
the PSF more for brighter stars. Overall the PSF measured on the FPA is
consistent with a diffraction-limited image delivered by the NISP optics.

Figure 17:  Modelled encircled energy (EE) of the Euclid NISP PSF in the three photometry channels, from data observed in the self-calibration field during the PV phase, and based on simplified PSF models. Shown is the cumulative EE of point sources as a function of radius from the objects’ centre. The three colours are curves for the $Y_{\scriptscriptstyle\rm E}$-band (green), $J_{\scriptscriptstyle\rm E}$-band (orange), and $H_{\scriptscriptstyle\rm E}$-band (blue). For each passband there is one line for each of the 16 detectors in the FPA, in each case describing the mean detector EE curve – which is very similar for all detectors – and an overall average. Vertical lines mark the radius encircling 50% (rEE50) and 80% (rEE80) of the the total flux.  Table 5: Initial in-flight estimates of PSF 50% and 80% energy radii, PSF FWHM, AB-mag zeropoints, and limiting sensitivities for the three photometric and two spectroscopic passbands of NISP, where available. Photometry sensitivity is given in AB-mag for 5$\sigma$-detections of point-sources using a model fit. For red grism spectroscopy this is a 3.5$\sigma$-detection of a point-source line with a putative width of two resolution elements of each 13.4 Å, given in erg s-1 cm-1. For the blue grism a limiting sensitivity was not yet available. Both throughputs and limiting sensitivities are snapshots at the time of writing and might change over time as both the instruments and image reduction pipeline evolve.  Filter | rEE50 | rEE80 | FWHM | ZP | Lim. sens.
---|---|---|---|---|---
$Y_{\scriptscriptstyle\rm E}$ | 0$\aas@@fstack{\prime\prime}$22 | 0$\aas@@fstack{\prime\prime}$37 | 0$\aas@@fstack{\prime\prime}$35 | 24.95 | 24.6
$J_{\scriptscriptstyle\rm E}$ | 0$\aas@@fstack{\prime\prime}$24 | 0$\aas@@fstack{\prime\prime}$45 | 0$\aas@@fstack{\prime\prime}$34 | 25.19 | 24.6
$H_{\scriptscriptstyle\rm E}$ | 0$\aas@@fstack{\prime\prime}$27 | 0$\aas@@fstack{\prime\prime}$55 | 0$\aas@@fstack{\prime\prime}$35 | 25.11 | 24.5
$BG_{\scriptscriptstyle\rm E}$ | 0$\aas@@fstack{\prime\prime}$21 | – | – | – | N.A.
$RG_{\scriptscriptstyle\rm E}$ | 0$\aas@@fstack{\prime\prime}$24 | – | – | – |  $\sim$ 2$\times$10-16

We note that the NISP pixels ($None$) strongly undersample the PSF. However, a
reference NISP-P PSF (Fig. 18) and encircled-energy curves in full resolution
have been derived by modelling $\sim$ 1400 unsaturated stars from 76 different
exposures with Gauss-Laguerre shapelet vectors (Massey & Refregier 2005),
using 6-fold subsampling per pixel. The PSF has a small trefoil component
stemming from the Euclid primary mirror, resulting in the visible slightly
triangular shape.

Figure 18:  NISP-P example PSF in the $H_{\scriptscriptstyle\rm E}$-passband.
This shows an initial shapelet reconstruction basen $\sim$ 1400 unsaturated
point sources, on a 6$\times$ oversampled grid
(0$\aas@@fstack{\prime\prime}$05) compared to the native NISP pixel sampling.
Clearly visible is the trefoil component that is present in all NISP images as
well as VIS. A 6-fold spike pattern becomes visible for brighter point sources
towards or beyond saturation.

###  5.2 Zero point and sensitivity

A first assessment of the in-flight ZPs is given in Table 5. For photometry
they are within 0.1 mag from the pre-launch zeropoints derived in Euclid
Collaboration: Schirmer et al. (2022). Preliminary ${\rm\acs{SNR}}=5$ limiting
sensitivities for point sources in the three passbands were calculated using
PV data and are listed in Table 5. They are averages over a ROS pointing
footprint, where the majority of area has a depth of either 3 ($\sim$ 40%), 4
($\sim$ 42%), or even $>$ 4 ($\sim$ 8%) individual dither exposures, using the
standard S-dither pattern (see Sect. 4 of Euclid Collaboration: Scaramella et
al. 2022). These are 0.1 mag to 0.3 mag fainter than the earlier predictions
for limiting sensitivities listed in (Euclid Collaboration: Scaramella et al.
2022), and these initial in-flight ZP and depth values demonstrate that NISP’s
throughput and sensitivity are as expected.

For spectroscopy, the initial estimates for the red grism also conform with
expectations and requirements. The 3.5 $\sigma$ point-source sensitivity for
emission lines lies around 2$\times$10-16 erg s-1 cm-1, for integration of a
line width of two resolution elements of each 13.4 Å.

We note that both throughputs and limiting sensitivities are snapshots of the
system at the time of writing based on data taken during the PV phase and will
change with both an evolving spacecraft and SGS data reduction pipeline.

###  5.3 Optical ghosting

As for all optical systems with refractive elements, NISP shows ‘ghost’ images
from multiple reflections in different parts, as well as reflections from
outside the nominal light path towards the FPA. These arise in different
situations, and depending on their nature require different treatment in data
reduction.

####  5.3.1 Dichroic ghost

The dichroic ghost images are caused by a double reflection inside the
dichroic beamsplitter. They are doughnut-shaped, showing the central
obstruction by the secondary mirror (Fig. 19). The images are very weak, with
a ghost ratio – that is the ratio $f_{\rm g}$ between the total flux of a
bright star and the ghost – of $3\times 10^{-8}$. This has the consequence
that dichroic ghosts are only apparent for very bright saturated stars. The
strength of the ghosts decreases with wavelength, that is they are brightest
in $H_{\scriptscriptstyle\rm E}$ and faintest in $Y_{\scriptscriptstyle\rm
E}$.

We model the position of the ghosts with respect to their stars with a third-
degree 2D-polynomial. The angular size of the ghosts is constant as a function
of position, but increases with the brightness of the star. The diameter is
$\sim$5″. The surface brightness of the ghosts is highly variable across the
FOV and thus they cannot be subtracted; we mask them when they violate the
relative photometry requirements of faint galaxies.

####  5.3.2 Filter ghosts

Filter ghosts are considerably brighter than the dichroic ghost images. They
are caused by a double-reflection between the planar and curved side of the
NISP filters. Their images are approximately circular, with a characteristic
cusp caustic in their centre (Fig. 19) that could be mistaken for an
extragalactic source. The size of the filter ghosts is $\sim$ 7–12″and depends
on the position in the FOV. As for the dichroic, their relative position with
respect to the bright stars is modelled with a third-degree 2D-polynomial as a
function of star position. Separate models are required for each NISP filter.
The ghosts must be masked as for dichroic ghost images.

Any other reflections between optical surfaces inside NISP become very large
in area once they reach the focal plane. Their surface brightness is therefore
so low that they are not recognisable even for the brightest stars (4 mag AB)
allowed inside the FOV.

####  5.3.3 Reflections on mechanical parts

Ghost images also occur when short streaks or glints are created by bright
stars falling onto reflective spots just outside the detectors. In case of
baffle edges, bright streaks of about 1 arcmin appear in the corners or along
the edges of the FOV (Fig. 20). They are considerably brighter than the
dichroic and filter ghosts and must be masked in all cases.

####  5.3.4 Reflections on NI-OA

Large and complex caustic-shaped reflections occur for very bright stars due
to multiple reflections within NI-OA (Fig. 21). The optical surfaces involved
have not yet been identified. The star and its ghosts are diametrically
opposite the centre of the FPA. The ghost’s morphology and strength are highly
dependent on the star’s position, necessitating ray-tracing for the masking
models. At this point we can likely rule out an involvement of the detector
surfaces, as these ghosts also appear when the star moves into the gap between
detectors.

####  5.3.5 Persistence ghosts from wheel actuations

The last characteristic features in NISP images are bright arcs with a
curvature radius of approximately 1.7 arcmin, running through the centres of
very bright stars (Fig. 19). These are caused when NISP switches between
filter and grism observations. Since NISP does not have a separate shutter,
bright-star images fall on the detector while the wheels are moving. Since
both filters and grisms carry optical power, the images move on the detector
plane while the wheels rotate. The ghosts are either focused and of high
surface brightness, or defocused and diffuse and of low surface brightness.
They also need to be masked. A model for them still is still in development.

Figure 19: Appearance of the common optical ghosts in a NISP
$Y_{\scriptscriptstyle\rm E}$-band image. The bright star has about 8.7 AB mag
in the 2MASS $J$-band. Most other linear features – vertical or near-vertical
– are persistence effects from preceding spectroscopic exposures. Figure 20:
Glints in NISP images, when a bright star falls onto the edge (top panel) or
near the corner (bottom) of the baffle just outside the FPA. The red line
marks the edges of the detectors. Figure 21: The centre two detector rows,
showing examples of large point-source caustics. The bright star at the
bottom-left edge causes a pair of concentric arcs in the upper-right detector,
diametrically opposite the centre of the FOV. Characteristically, the
caustics’ symmetry axes do not point to the star, but at a $None$ angle to it.

##  6 Operational flexibility and limits

Euclid is not a classical observatory such as HST or JWST, but by design of
its operation closer to an experiment with a very constrained but often-
repeated set of observations. The Euclid Wide and Deep Surveys will consist of
basically 50 000 identical observations, the ROS, just at different positions
on the sky. Executed once per field on the sky for the Wide Survey, many times
for the Deep Fields.

This repetitive nature of survey activities allowed the design of two more
robust and accurate instruments, but led to a limited flexibility in
commanding NISP. In the following we discuss the commanding and hardware
limits – mainly to manage expectations and options for what kind of other
programmes could be possible with NISP in the future, in case that time-slots
were to become available during idle periods of survey operations, or after
the surveys are completed. Neither is guaranteed, and specific calls would
provide much more detailed options and constraints in due time.

Lead times: Overall, planning cycles are long and observations have a long
lead time, of at least 4 weeks, and are limited by the need to design,
approve, and upload commanding sequences. Any non-standard observations would
in addition require substantial individual vetting and testing in order to not
run into any instrumental limits of onboard processing, storage capacity, data
rate, or interference with the VIS instrument’s operations. Euclid is best for
surveys and not suited for example for rapid-response target-of-opportunity
observations.

Standard and non-standard operation modes: Euclid science operation and
calibration has been optimised for the standard ROS observing modes. For NISP
this is the MACC readout mode with several groups of read images that are
averaged, and then a slope is fit to these values to derive a count-rate of
signal in each pixel. Effectively only the slope value and a value for the
quality of the slope fit is linked down.

As mentioned already in Sect. 4 there are also other modes that are not used
for science, but regularly for calibration or were used for diagnostics during
initial commissioning. These include a mode downlinking not the fitted slope
but the value of each group. For a standard spectroscopy observation using
MACC(15,16,11) this would provide 15 datapoints instead of one.

Even more information can be obtained with a ‘engineering raw mode’, which
could read and downlink all group-average frames of a MACC observation, not
just the fitted slope. For the ROS there is even a routine downlink of up to
five individual rows for each read of each detector, as a diagnostic if non-
nominal behaviour is observed for an exposure, and for monitoring of the FPA
properties over time.

Using non-ROS or non-standard modes for science is in principle feasible, but
requires substantial work beforehand: For each non-ROS configuration the
timing of commanding, timing of data processing and internal transfer, data
storage buffer sizes, data downlink rates, and many other dependencies have to
be simulated on ground, and be planned in conjunction with VIS and overall
Euclid operations and commanding. For example engineering raw-mode
observations can usually only be done for single detectors at a time instead
of 16 in the FPA due to sheer data volume. MACC observations with fewer than
16 reads per group could bring on-board data processing to its limits, for
example by violating timing constraints.

Aside from this, corresponding calibration data are routinely only created for
ROS data. This means any non-ROS MACC or other non-standard observing modes
need their own plan to create required calibration data and concepts. It is
unlikely that a simple scaling from ROS calibration to other observing modes
can be achieved beyond a rather low level of accuracy.

So while other than ROS MACC-mode options exist, they can be quite complex to
implement. They also depend on resources on ground that are not always
guaranteed.

Bright and faint limits: Further core constraints interesting beyond Euclid’s
surveys are NISP’s observational bright and faint limits. An imaging ROS
MACC(4,16,14) will start saturating for $\sim$16.0 AB mag point sources,
differing by $\sim$ 0.3 mag between passbands due to different levels of
undersampling, and with some variation across the FOV. Something that is
ultimately fixed for NISP is the frame time: each read will take a fixed
1.45408 s, set by the read-out clocks. For shorter exposures one will
therefore have to consider other MACC parameters. From the architecture of the
warm electronics, the shortest integration possible with NISP is a MACC(2,1,1)
exposure, that is two groups of one frame each, with one dropped frame in the
middle. This corresponds to an integration time of $t_{\mathrm{int}}=2.91$ s.
For photometry the point-source saturation limit would then be $\sim$3.6 mag
brighter than for a standard MACC(4,16,4), around 12.0 mag–12.5 mag. This is a
hard bright limit for NISP photometry for the full FOV and using standard
observing modes. Corresponding limits apply when using a grism.

Long exposures are constrained by the maximal allowed number of 15 groups and
16 reads per group. Even though there is no limit on the number of dropped
frames, there is a maximal duration of a spacecraft pointing of 50 min. At
some point it will be more efficient to simply obtain several subsequent
exposures at the same location, especially given non-zero backgrounds that are
adding up over time. If the telescope is slightly dithered in between then
this will also aid cosmic ray rejection. This is the approach of the Euclid
Deep Survey with up to $\sim$ 50 ROS observations per sky location.

Time-resolution: The second interesting parameter is the potential time-
resolution of NISP data. As just stated, the shortest regular observation is
$t_{\mathrm{int}}=2.91$ s. At this exposure time up to seven exposures can be
recorded in direct succession – with an extra reset frame before each of these
exposures – and then after a buffer time of 3 s the next block of seven. With
the engineering raw mode a MACC(15,1,1) can be programmed, that downlinks each
of the 15 single ‘group’ frames, each followed by a dropped frame. Here every
other frame is read and downlinked – but, as noted, owing to the data volume
only for a single detector instead of 16. Hence this in principle also
provides an option for shorter exposures of 1.45408 s and cadence of 2.91 s,
for 15 frames, but for a much smaller area on the sky. At this point it seems
unlikely that Euclid and NISP with their explicit survey focus will provide
substantially improved capabilities over other facilities.

##  7 Summary and outlook

Euclid has launched and NISP is working at L2 as envisioned. NISP’s
performance is excellent, in both spectroscopy and imaging channels, and the
instrument will create two unprecedented datasets that will be the new
standard for NIR data for decades to come. Post-launch expectations for Euclid
are an operational lifetime of substantially longer than the nominal 6-year
survey, possibly up to ten years in total.

As Euclid was launched not too long from the Solar maximum, NISP will
experience most of its exposure to Solar cosmic rays rather early in its
operational lifetime, but the impact will be small. All of the optics are
resistant to radiation, as are the detectors, which are expected to degrade
very little. A formal 5% loss in QE is budgeted for NISP’s performance, but
the actual impact is expected to be substantially less.

NISP operations cover both nominal survey operations as well as regular
calibration observations to update and improve calibration products, and to
monitor any evolutionary effects. In addition there will be options for
‘decontamination’ periods, in case too much water outgassing from the various
Euclid components deposits onto mirrors, or NISP’s lenses or FPA. The latter
is the coldest part of the instrument and specifically prone to ice
deposition. Plans for a brief partial warmup of Euclid are in place and have
already been executed, which can be repeated when needed.

All this provides the prospect of excellent data during Euclid’s wide and deep
surveys, and potentially beyond. Despite its undersampling of the PSF, NISP is
an enormous advance from any previous near-infrared instrument capability
currently available. Only the combination of field area and very high
throughput of telescope and instrument make it possible to carry out the
planned surveys, for which HST or JWST would take more than 100 years. All of
NISP’s data will be released to the public in regular intervals along the
mission timeline (see Euclid Collaboration: Mellier et al. 2024, and
https://www.euclid-ec.org), calibrated and ready to use for any astrophysical
project – and for anyone in the world.

###### Acknowledgements.

The Euclid Consortium acknowledges the European Space Agency and a number of
agencies and institutes that have supported the development of Euclid, in
particular the Agenzia Spaziale Italiana, the Austrian
Forschungsförderungsgesellschaft funded through BMK, the Belgian Science
Policy, the Canadian Euclid Consortium, the Deutsches Zentrum für Luft- und
Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark, the French
Centre National d’Etudes Spatiales, the Fundação para a Ciência e a
Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia,
Innovación y Universidades, the National Aeronautics and Space Administration,
the National Astronomical Observatory of Japan, the Netherlandse
Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research
Council of Finland, the Romanian Space Agency, the State Secretariat for
Education, Research, and Innovation (SERI) at the Swiss Space Office (SSO),
and the United Kingdom Space Agency. A complete and detailed list is available
on the Euclid web site (http://www.euclid-ec.org).

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##  Appendix A NISP focal plane layout

NISP’s focal plane array (FPA) and detector system (DS) comprises the 16
sensor chip systems (SCS) in a 4$\times$4 array, each being a triplet of
sensor chip array (SCA), sensor chip electronics (SCE), connected by a cryo
flex cable (CFC). In order to associate the positions of these SCS, with their
properties listed in Table 3 and shown in Fig. 12, and with data products
created by NISP, we show two versions of the physical layout of the FPA: Fig.
22 is the layout as looking from the sky or NISP optics towards the FPA, Fig.
23 is the reverse view, the FPA as it would appear projected onto the sky.

Both versions show the same information, the SCA positions from 11 to 44 in
the FPA detector mosaic, which can also be found in headers of all FITS files
associated with detector-based data products created by NISP. Also indicated
are the frame orientations for each SCA in the physical ‘R-MOSAIC’ coordinate
system, with units in mm. Each SCA has its own pixel coordinate system: light-
sensitive pixels range from 4 to 2043 in each of the two dimensions
($x_{\mathrm{SCA}}$,$y_{\mathrm{SCA}}$), with count starting at 0.
Rows/columns 0–3 and 2044–2047 contain reference pixels that are not sensitive
to light. Note that the SCA-coordinate system orientation is rotated by 180∘
between the upper and lower 8 detectors. The black square for each detector
marks the physical location of pixel (4,4), the black circle of pixel (2043,4)
in the ‘R-MOSAIC’ coordinate system.

On the side the R-MOSAIC physical coordinate system
($y_{\mathrm{MOSAIC}}$,$z_{\mathrm{MOSAIC}})$, using units of mm, is shown for
reference.

Figure 22: Layout of the NISP FPA, orientated as looking towards the FPA from
the sky and NISP optics. Shown are the positions 11 to 44 of the SCS in the
focal plane mosaic (see Table 3), the origin point and orientation of the
physical R-MOSAIC coordinate system, as well as the pixel coordinate system
for each of the SCA detectors. For the latter please note the 180∘ rotation
between the upper and lower half of detectors. For a view towards the sky see
Fig. 23. Figure 23: Layout of the NISP FPA, orientated as looking towards the
sky. Shown are the positions 11 to 44 of the SCS in the focal plane mosaic
(see Table 3), the origin point and orientation of the physical R-MOSAIC
coordinate system, as well as the pixel coordinate system for each of the SCA
detectors. For the latter please note the 180∘ rotation between the upper and
lower half of detectors. For a view from the sky towards the FPA see Fig. 22.
An internal definition of a position angle value of zero would rotate this
view on the sky so that North is upwards and East is to the left.

  *[H2RG]: HAWAII-2RG
  *[SNR]: signal-to-noise ratio
  *[ESA]: European Space Agency
  *[NISP]: Near-Infrared Spectrometer and Photometer
  *[VIS]: visible imager
  *[FOV]: field of view
  *[SGS]: science ground segment
  *[LAM]: Laboratoire d’Astrophysique de Marseille
  *[SVM]: service module
  *[FPA]: focal plane array
  *[PSF]: point spread function
  *[DCU]: Detector Control Unit
  *[MACC]: Multiple Accumulated
  *[LED]: light-emitting diode
  *[SiC]: silicon carbide
  *[NI-OA]: near-infrared optical assembly
  *[CoLA]: corrector-lens assembly
  *[CaLA]: camera-lens assembly
  *[CTE]: coefficient of thermal expansion
  *[MZ-CGHs]: multi-zonal computer-generated hologram
  *[CTEs]: coefficient of thermal expansion
  *[PSFs]: point spread function
  *[SHS]: Shack-Hartmann sensor
  *[CCD]: charge-coupled device
  *[EE]: encircled energy
  *[FWA]: filter-wheel assembly
  *[GWA]: grism-wheel assembly
  *[PARMS]: plasma-assisted reactive magnetron sputtering
  *[SCA]: sensor chip array
  *[FWHM]: full width at half maximum
  *[CNES]: Centre National d’Etude Spacial
  *[QE]: quantum efficiency
  *[DS]: Detector System
  *[SCS]: sensor chip system
  *[MCT]: Mercury Cadmium Telluride
  *[SCAs]: sensor chip array
  *[SCE]: sensor chip electronic
  *[CFC]: cryo-flex cable
  *[CPPM]: Centre de Physique des Particules de Marseille
  *[IP2I]: Institut de Physique des 2 Infinis de Lyon
  *[JWST]: James Webb Space Telescope
  *[NI-CU]: NISP calibration unit
  *[LEDs]: light-emitting diode
  *[PTFE]: polytetrafluoroethylene
  *[PWM]: pulse-width modulation
  *[ICU]: Instrument Control Unit
  *[DPUs]: Data Processing Unit
  *[MMU]: Mass Memory Unit
  *[DPU]: Data Processing Unit
  *[DCUs]: Detector Control Unit
  *[SCEs]: sensor chip electronic
  *[FPAs]: focal plane array
  *[ROS]: reference observing sequence
  *[MLI]: multi-layer insulation
  *[FDIR]: Fault Detection, Isolation and Recovery
  *[PV]: performance verification
  *[IPC]: inter-pixel capacitance
  *[BFE]: brighter-fatter effect
  *[WD]: white dwarf
  *[SED]: spectral energy distribution
  *[ZP]: zero point
  *[SEDs]: spectral energy distribution
  *[ZPs]: zero point