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AUSTRALIAN
NATIONAL UNIVERSITY System Design Note 4.01 Created: 4 April 2000 Last modified: 5 April 2000 |
NIFS PERFORMANCE MODEL
Peter J. McGregor
Research School of Astronomy
and Astrophysics
Institute of Advanced
Studies
Australian National
University
Revision History
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Revision No. |
Author & Date |
Approval & Date |
Description |
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Revision 1 |
Peter J. McGregor 21 August 1999 |
Michael S. Bessell 28 January 2000 |
Original document. |
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Revision 2 |
Peter J. McGregor 04 April 2000 |
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Reformatted for Word 2000. |
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Contents
4.1.3 Cryostat
Thermal Emission
4.1.6 Telescope
Thermal Emission
4.1.8 Airglow
Continuum Emission
4.1.9 Scattered
Airglow Emission
4.2.3 Molecular
Hydrogen Emission
5.2 Data
Simulation Program: NIFSSIM
8.4 Extended
Continuum Sources
8.7 Stellar
Velocity Dispersions
Appendix A: Point
Source Sensitivity Data
1 Purpose
This document describes the model used to predict the performance of the Gemini Near-infrared Integral-Field Spectrograph (NIFS). The performance model is implemented as a WWW tool for point sources and as a detailed data simulation program for a variety of observational types. The document describes how these tools should be used and presents NIFS performance predictions.
2 Applicable Documents
|
Document ID |
Source |
Title |
|
RSAA |
NIFS Diffraction Analysis |
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RSAA |
NIFS Optical Throughput |
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RSAA |
NIFS Grating Selection |
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IGPO |
Gemini System Error Budget Plan |
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3 Introduction
The Near-infrared Integral-Field Spectrograph (NIFS) will perform near diffraction-limited near-infrared imaging spectroscopy on Gemini North. It is necessary to predict the instrumental performance in order to realistically assess the scientific potential of the instrument and to optimize its design. The NIFS performance model, including assumptions about the nature of noise sources in the instrument, is described in §4. The use of a WWW-based tool for predicting performance and a detailed data simulation program are discussed in §5. NIFS performance data are summarized in §8 for various scientific applications.
The performance data presented in this document refer to the NIFS design nifs_ful_40.zmx. This uses a Maksutov spectrograph collimator and assumes the spectrograph camera used will be similar to that in nifs_ful_19.zmx.
4 Performance Model
The NIFS performance model estimates the detected currents from various noise sources in the instrument and compares these to the expected signal photo-current. The noise sources are discussed in §4.1 and models of the signal photo-currents are discussed in §4.2. The input parameters used in the model are summarized in Table 1.
Table 1: Model Input Parameters
|
Parameter |
Value |
Unit |
Description |
|
|
|
|
|
|
R |
5 |
e |
Detector read noise |
|
D |
50000 |
e |
Detector well depth |
|
Tmax |
3600 |
s |
Maximum integration time |
|
Tcry |
70 |
K |
Cryostat temperature |
|
lmax |
2.60 |
mm |
Detector sensitivity cutoff |
|
Q |
0.60 |
|
Detector quantum efficiency |
|
Twin |
275 |
K |
Window temperature |
|
ewin |
0.01 |
|
Window emissivity |
|
Taos |
275 |
K |
AOS temperature |
|
eaos |
0.12 |
|
AOS emissivity |
|
taos |
0.88 |
|
AOS transmission |
|
Ttel |
275 |
K |
Telescope temperature |
|
etel |
0.044 |
|
Telescope emissivity |
|
ttel |
0.956 |
|
Telescope transmission |
|
Tsky |
260 |
K |
Sky temperature |
|
Wh |
0.4 |
arcsec |
Seeing FWHM |
|
S |
0.2,0.4,0.6 |
|
Strehl ratio at J, H, K |
4.1 Noise Sources
4.1.1 Detector Read Noise
The NIFS detector will be a Rockwell 2048´2048 HAWAII-2 array with 18 mm pixels. The read noise, R, is expected to be ~ 9 e for a single non-destructive read (NDR). However, it should be possible to reduce this to ~ 5 e using ~ 16 NDRs (i.e., either Fowler sampling or linear fitting). We adopt the lower value. The detector well depth is assumed to be D = 50000 e. This is appropriate for the low detector reverse bias voltage that will be needed to minimize dark current. The maximum integration time will be set by sky variations and cosmic ray events to tmax ~ 3600 s.
4.1.2 Detector Dark Current
The dark current, Idc, for a PACE technology HAWAII-2 array will be similar to that of a HAWAII-1 array. However, the dark current obtained with a typical HAWAII-1 array is poorly documented. At least one device has a mean of ~ 0.0175 e s-1 pix-1 and a standard deviation of ~ 0.0288 e s-1 pix-1 (Rockwell Science Center WWW pages; Figure 1), although various users report higher values. HAWAII-2 arrays based on a CdZnTe substrate and MBE technology are expected to reliably deliver mean dark currents this low (i.e., mean of 0.01 e s‑1 pix‑1). It is not yet clear whether NIFS will use a MBE technology array. We adopt the dark current distribution of Figure 1 and approximate the width of the distribution using random Gaussian deviates with a mean of 0.01 e s-1 pix-1 and a standard deviation of 0.0065 e s-1 pix-1, and resample Gaussian deviates falling below zero. A simulated 3600 s dark exposure is shown in Figure 2. Dark current will be a significant noise source in NIFS in the J and H bands, and the true dark currents may be significantly higher than those modeled if a MBE device is not used in NIFS.
Figure 1: Dark current distribution for a HAWAII-1 array (Rockwell WWW pages).
Figure 2: Simulated 3600 s dark exposure dominated by the adopted fixed dark current pattern.
The standard deviation of the adopted dark current distribution exceeds that expected from measurement errors alone. We interpret this width as due to real pixel-to-pixel dark current variations. This systematic dark current pattern dominates dark current shot noise for typical integration times. This makes it necessary to characterize and remove an average dark current pattern in order to achieve optimal dark current-limited performance. We envisage characterizing the dark current pattern by median combining 5 to 10 dark exposures of 1 hr duration recorded during the day prior to observing. The degree to which this approach will be successful will depend on the stability of the dark current pattern over timescales of ~ 24 hr. We are moderately optimistic that the required stability is achievable based on discussions with Don Hall, but no quantitative assessment has been performed.
4.1.3 Cryostat Thermal Emission
Each pixel views 2p steradians of the cryostat interior which is effectively radiating with unit emissivity as a blackbody at the cryostat temperature, Tcry = 70 K. This emission occurs after the order blocking filter and grating so the detector sees broadband emission extending to the detector cutoff wavelength, lmax = 2.60 mm for a HAWAII-2 array. The detector quantum efficiency, Q, is taken to be that of the HAWAII-1 array published on the WWW pages of the Rockwell Science Center and shown in Figure 3. The detector quantum efficiency is ~ 60% over most of the 1-2.6 mm wavelength range. Cryostat thermal emission produces a photo-current of
e s-1 pix-1
where Apix is the detector pixel size in cm2.
Figure 3: Quantum efficiency for a HAWAII-1 array (Rockwell WWW pages).
Rockwell CdZnTe/MBE technology devices have higher quantum efficiencies due to the better lattice match with HgCdTe compared to PACE devices. Quantum efficiencies of > 85% may be achieved over the 1.0-2.5 mm wavelength range with MBE devices. We do not include this gain here.
There exists a possibility that NIFS will use a 2048´2048 CdZnTe/MBE technology detector with sensitivity to lmax = 5.5 mm. This will dramatically increase the requirement on controlling cryostat thermal emission, principally by requiring that the cryostat temperature, Tcry, be lower than ~ 65 K.
4.1.4 Window Thermal Emission
Dust on the cryostat window is a source of thermal emission. We assume that the window has an emissivity of ewin = 0.01 and a temperature of Twin = 275 K. Thermal emission from the cryostat window generates a photo-current of
e s-1 pix-1
where Atel is the telescope collecting area in cm2, Wpix is the solid angle on the sky subtended by one pixel along the slit, Dl is the spectral resolution per pixel, and tspe is the transmission of the NIFS spectrograph. The reformatted NIFS spectrograph slit has a width of 0.1² and each detector pixel projects to 0.05² on the sky. NIFS spectral resolutions are defined in SDN0005.21 (NIFS Grating Selection). The NIFS spectrograph transmission is described in SDN0005.15 (NIFS Optical Throughput) and is shown in Figure 4. The grating efficiency is the largest throughput uncertainty. All simulations assume a wavelength- independent grating efficiency of 0.65. The high resolution gratings are expected to have efficiencies closer to 80%. The efficiencies of the lower resolution gratings may be as low as 50%.
Figure 4: NIFS spectrograph transmission excluding telescope, AO system, and detector.
4.1.5 ALTAIR Thermal Emission
The ALTAIR science path emissivity budget has been presented in the ALTAIR Preliminary Design Review documentation. We adopt an emissivity of eaos = 0.12, a transmission of taos = 0.88 (ALTAIR PDR Documentation p. 34 minus telescope contribution), and an operating temperature of Taos = 275 K. The emissivity is higher when the ALTAIR atmospheric dispersion corrector is used. This will only be necessary in the J band where thermal emission from the ALTAIR optics is negligible, so the emissivity of the atmospheric dispersion corrector has been ignored. Thermal emission from the ALTAIR science path generates a photo-current of
e s-1 pix-1.
4.1.6 Telescope Thermal Emission
The telescope M1, M2, and M3 mirrors are assumed to use over-coated Silver coatings with a single surface reflectivity of 98.5% as discussed in SDN0005-15 (NIFS Optical Throughput). This gives a combined emissivity of etel = 0.044, a combined transmission of ttel = 0.956, and we adopt a typical night-time operating temperature of Ttel = 275 K. Thermal emission from the M1, M2, and M3 telescope mirrors generates a photo-current of
e s-1 pix-1.
4.1.7 Airglow Line Emission
Sky emission is significant at near-infrared wavelengths. Airglow emission, mainly due to OH molecules, is dominant shortward of ~ 2.2 mm and thermal emission from the sky contributes at longer wavelengths. Airglow line emission data are based on a tabulation of typical line strengths provided by François Piche (to Heath Jones, RSAA) which is based on the data of Maihara et al. (1993). These data have been checked for order of magnitude consistency with the atmospheric emission spectrum provided on the Gemini WWW pages, and extended into the K band with additional line strength and wavelength data chosen to mimic the Mauna Kea emission spectrum. The airglow emission spectrum is calculated using an emission-line profile derived from the NIFS diffraction analysis (NIFS Diffraction Analysis, SDN0005.06). This ignores near-angle scatter at the grating which may prove to be significant. Near-angle scatter at the grating will be dominated by satellite lines produced by grating ruling defects.
4.1.8 Airglow Continuum Emission
A knowledge of the atmospheric continuum emission between strong airglow emission-lines is crucial for accurately predicting NIFS performance in the J and H bands. This is poorly known and difficult to distinguish empirically from instrumental scattered light. McCaughrean (1988) estimated the airglow continuum to be 280 photon/s/m2/arcsec2/mm in the H band. Maihara et al. (1993) measure a continuum of 580 photon/s/m2/arcsec2/mm at 1.665 mm but found higher values on moon lit nights. We adopt the latter value and note that the precise value is not important if other continuum sources dominate. Nevertheless, dark/grey time may be required for sensitive NIFS observations in the J and H bands.
4.1.9 Scattered Airglow Emission
A significant concern is that scattered light within the cryostat will exceed the low natural continuum emission level between strong airglow emission-lines. Scatter or diffraction of line emission into the neighboring continuum will occur at some level. To model this, we adopt the (hopefully) pessimistic assumption that 10% of the total airglow line emission within the spectral band of interest is redistributed uniformly across the detector. This is sufficient to cause scattered airglow emission to rival dark current as a noise source.
4.1.10 Sky Thermal Emission
Thermal emission from the sky will also produce continuum emission with increasing importance towards longer wavelengths. We assume a typical sky temperature of Tsky = 260 K and base the adopted sky emissivity, esky, on Mauna Kea atmospheric transmission data, tsky, available via the Gemini WWW pages. Thermal emission from the sky generates a photo-current of
e s-1 pix-1.
Although thermal emission may be a source of the “airglow” continuum (§4.1.8) in the J and H bands, we prefer to be conservative by including both continuum sources in the NIFS performance model.
4.2 Signal Currents
4.2.1 Point Spread Function
The signal-to-noise ratio achieved by NIFS on point sources depends on the point spread function (PSF), P(q); poorer images spread light over more detector pixels. NIFS will be used with the ALTAIR adaptive optics system which will produce a complex image. We approximate P(q) by a diffraction-limited core, Pc(q), and a seeing-limited halo, Ph(q). The diffraction-limited core is modeled with an Airy function modified to allow for the telescope central obstruction (Schroeder 1987):
where J1 is the Bessel function of order one, e is the ratio of the central obstruction radius to the primary mirror radius and
where RP is the primary mirror radius, q is the angle on the sky, and l is the wavelength. The seeing-halo is modeled by a Moffat function with index of 11/6 as suggested by Racine et al. (1999) based on results from the AO Bonette on CFHT:
where Wh is the seeing FWHM. The contribution of each profile is set by the Strehl ratio, S, such that
and
where Sc and Sh are the total counts in the core and halo templates, respectively. We define baseline observing conditions to correspond to Strehl ratios of 0.2, 0.4, and 0.6 in the J, H, and K bands, respectively, and a seeing FWHM of 0.4².
The quoted Strehl ratios are those derived from the Gemini top-down image quality error budget in median seeing conditions (Gemini System Error Budget Plan, SPE-S-G0041). The Strehl ratio degradation is due to a combination of uncorrected seeing and optical aberrations in the telescope, ALTAIR, and the science instrument. Clearly, only the uncorrected seeing component is likely to be distributed in a manner similar to the seeing PSF. However, in the absence of more detailed information, we assume that all of the halo light is distributed in this way. This is likely to over-estimate the extent of the true halo and over-estimate the smoothness of the halo distribution. In reality, the image halo is likely to be dominated by a number of time-variable discrete peaks superposed on a lower surface brightness continuum.
4.2.2 Point Sources
The source signal current, Isig(q), is derived from the source brightness specified in magnitudes, m, and converted to flux density using the absolute flux calibration of Bersanelli et al. (1991) which is based on a Vega effective temperature of 11200 K:
W cm-2 mm-1
e s-1 pix-1.
The stellar spectral distribution in the K band is interpolated from the FTS spectra of Kleinmann & Hall (1986). These spectra are tabulated at approximately twice the highest resolution of NIFS data.
4.2.3 Molecular Hydrogen Emission
NIFS will perform imaging spectroscopy of extended line emitting regions. Peak 1 in the Orion Molecular cloud is among the highest surface brightness line emitting regions with an H2 1-0 S(1) surface brightness of ~ 10-20 W cm-2 arcsec-2 (Stolovy et al. 1998). Molecular hydrogen line wavelengths and strengths relative to the H2 1-0 S(1) line have been obtained from Oliva & Moorwood (1988) and Brand et al. (1988) for OMC-1 and Oliva, Moorwood, & Danziger (1990) for the supernova remnant RCW 103. All H2 lines are assumed to have a Lorentzian profile with FWHM = 40 km s-1 typical of OMC-1 (Chrysostomou et al. 1997).
4.2.4 Galactic Nuclei
One of the NIFS science drivers is the detection of massive black holes in the nuclei of nearby spiral galaxies. We model the performance of NIFS for this observation using empirical K band surface brightness and velocity dispersion distributions for our Galactic center. Saha, Bicknell, & McGregor (1996) fit the Galactic center cool star and hot star surface brightness distributions with Reynolds-Hubble law profiles and the stellar rotational velocity and velocity dispersion profiles with quartics. We adopt these profiles for our model galactic nuclei after shifting them to distances ranging up to 20 Mpc and convolving with the instrumental point spread function, P(q). Each spatial position in the nuclear profile is assumed to have an M1III type spectrum in the K band, characterized by the Kleinmann & Hall (1986) spectrum of 75 Cyg, before convolution with a Gaussian velocity dispersion profile.
4.2.5 Disk Galaxies
NIFS may be able to spatially and spectrally resolve redshifted Ha in disk galaxies at z ~ 1 to determine if they experience ordered rotation and, if so, how their luminosities and rotational velocities compare with those of present day disk galaxies as defined by the Tully-Fisher relation. We adopt the “universal” disk galaxy rotation curve of Persic & Salucci (1991). The Ha luminosity in z ~ 1 galaxies is uncertain. We adopt the relation
between the AB absolute blue magnitude, M(BAB), and Ha luminosity in erg s-1, L(Ha), of z < 0.3 disk galaxies in the Canada-France redshift survey (Tresse & Maddox 1998) and assume that the Ha emission is distributed in an exponential disk with the same scale length as the continuum light. As a test case, we consider the z = 0.9877 Sc galaxy 064-4412 for which a rotation curve has been measured in redshifted [O II] l3727 using the Keck telescope (Vogt et al. 1996). This galaxy has a disk scale length of 4.1 kpc, and inclination of 68°, B band extinction AB = 0.61 mag, absolute blue magnitude MB = -21.4 mag and a terminal rotational velocity of 265±30 km s-1.
4.2.6 Quasars
Quasars are treated as point sources using the composite QSO optical spectrum of Francis et al. (1991). This spectrum extends redward to 0.600 mm. Ha has been included in this spectrum by the addition of data for the QSO Q1831+731 from Wills, Netzer, & Wills (1985) to a rest wavelength of 0.750 mm. The QSO host galaxy light profile is currently not modeled.
5 Performance Tools
5.1 Performance Web Tool
A simplistic web-based tool is available at http://www.mso.anu.edu.au/nifs/predictions/performance.html for making approximate signal-to-noise ratio calculations for point sources only (see Figure 5). Default parameters should be used in most cases. The source brightness in magnitudes and the grating selection must be specified. Click on the Calculate button to display contributing photocurrents and the approximate signal-to-noise ratio achieved in a 0.1²´0.1² aperture with a Strehl ratio of 0.2 in 0.4² FWHM seeing. The signal photo-current is quoted as 17% of the total stellar signal in order to approximate aperture effects on the PSF (see §8.2). The calculation is performed at the central wavelength of the selected grating. OH line emission is not included in detail. However, a numerical line flux can be specified. The calculated signal-to-noise ratio includes a factor of Ö2 degradation for sky subtraction during data reduction. No averaging is performed in the spectral direction. No attempt is made to treat wavelength dependent parameters in detail. In short, this tool has limited application.
Figure 5: NIFS web-based performance calculator.
5.2 Data Simulation Program: NIFSSIM
Detailed assessment of NIFS performance
requires full simulation of NIFS data frames including all spatial and spectral
dependencies and data reduction steps. The program NIFSSIM has been written to
perform full 3D simulations of NIFS data sets. The program simulates all the
NIFS noise sources discussed in §4.1 and can include signals from single stars, binary
stars, galactic nuclei, disk galaxies, and QSOs as discussed in §4.2. The total detected signal, Stot, is given by
e pix-1
where T is the integration time. To this is added statistical noise which is derived from random Gaussian deviates with standard deviation, N, given by
for each pixel.
NIFSSIM produces bias frames, dark frames, arc frames, sky frames, and object frames which can then be processed to simulate a complete data analysis. In its simplest form, NIFSSIM can be run in “Observe” mode to generate typical observational data using default parameters. Default parameters can be changed by editing the NIFSSIM.DAT file. NIFSSIM produces a 2048´2048 pixel image in FITS format containing spectra of the 31 NIFS slitlets which each contain 66 pixels in the spatial direction and 2048 pixels in the spectral direction. Figure 6 shows a simulated 1800 s frame of a K = 15 mag star in 0.4² FWHM seeing with a Strehl ratio of 0.2. Spectral dispersion is in the horizontal direction. Due to the PSF, several spectra of the star are obtained through adjacent NIFS slitlets. Airglow emission lines are seen as vertical bright lines; the “staircase” slit of real data is not modeled. The brightening to the red (i.e., to the right) is due to increasing thermal background contributions.
Figure 6: Simulated 1800 s integration with the K grating on a K = 15.0 mag star in 0.4² seeing with a Strehl of 0.2.
A NIFS package of IRAF tasks is used to process these images. Data processing consists of first subtracting a sky frame with IMARITH, then reformatting the 2D image into a data cube with the NIFS package task ENCUBE. A sky subtracted version of the Figure 6 image is shown in Figure 7. ENCUBE forms a cube with 2048 pixels in the spectral direction and 62´66 pixels in the spatial directions so that images of each cube plane have the correct aspect ratio. Images of subsections of the data cube can be made by averaging in the spectral direction using the NIFS package task IEXTRACT (Figure 8). Similarly, spectra of subsections of the image can be extracted with the NIFS package task SEXTRACT (Figure 9). These tasks have been used to derived the NIFS performance characteristics described in §8 below.
Figure 7: Sky subtracted version of the frame in Figure 6.
Figure 8: Spatial image of the K = 15 mag star shown in Figure 7. Note the diffraction-limited core and the 0.1²´0.05² pixels produced by the NIFS IFU. The field-of-view is 3.1²´3.3². The Strehl ratio is a modest 0.2.
Figure 9: K band spectrum extracted from the central 0.1²´0.1² of the data cube shown in Figure 8. Atmospheric CO2 absorption is seen at left. Stellar CO Dv = 2 absorption bands are seen at right.
6 Dominant noise sources
The various noise sources in NIFS are wavelength dependent and so are not parameterized simply (hence the need for NIFSSIM). The web-based performance tool provides a means of identifying the dominant noise sources at the central wavelengths of each grating. These are listed in Table 2 which demonstrates that observations with NIFS will be limited by dark current and read noise in the J and H bands, and limited primarily by thermal emission from ALTAIR in the K band. K band observations that do not use ALTAIR will be limited in approximately equal proportions by read noise, dark current noise and thermal emission from the telescope mirrors, and perhaps the uncertain sky continuum.
Table 2: Dominant Noise Sources in 3600 s NIFS Exposure
|
Grating |
RN2 |
Sky Thermal |
OH Cont. |
Tel. Thermal |
ALTAIR Thermal |
Cryostat Thermal |
Dark Current |
|
|
(e) |
(e/pix) |
(e/pix) |
(e/pix) |
(e/pix) |
(e/pix) |
(e/pix) |
|
|
|
|
|
|
|
|
|
|
J1 |
25 |
0 |
8 |
0 |
0 |
0 |
36 |
|
J2 |
25 |
0 |
9 |
0 |
0 |
0 |
36 |
|
H |
25 |
0 |
14 |
0 |
0 |
0 |
36 |
|
K |
25 |
4 |
17 |
17 |
116 |
0 |
36 |
|
|
|
|
|
|
|
|
|
|
J |
25 |
0 |
18 |
0 |
0 |
0 |
36 |
|
HK |
25 |
9 |
38 |
37 |
254 |
0 |
36 |
7 Grating Selection
NIFS will use four high resolution gratings
with two-pixel resolving powers of ~ 5340 to provide velocity resolutions of ~
55 km s-1 which will significantly separate OH airglow
emission-lines, especially in the J
and H bands. The combination of high
spatial and high spectral resolution means that observations with the J1, J2,
and H gratings are likely to be
limited by dark current and readout noise in regions between strong OH airglow
emission-lines (§6). It is desirable to also provide lower resolution
gratings that will deliver wider wavelength coverage for observations not
requiring such high velocity resolution. Observations with these gratings will
be less dominated by instrumental noise sources but will be contaminated to a
greater degree by OH airglow emission. Parameters for the range of gratings
considered are listed in Table
3. NIFSSIM has been used to determine the percentage of
the wavelength range for each grating that is occupied by OH airglow
emission-lines (excluding the OH-free long wavelength end of the K band). These percentages are also
listed in Table 3. Two criteria dictate the inclusion of only the J and HK gratings in NIFS, in addition to the four high resolution
gratings; 1) it is desirable to limit the percentage of pixels contaminated by
OH airglow emission to less than ~ 20%, and 2) the JH and JHK gratings in Table 3, with ~ 33% OH contamination, deliver similar
resolving powers to the grisms provided in NIRI. There is no specific need to
duplicate NIRI capability, although operation in the NIFS IFU at this
resolution may be desirable in some applications.
Table 3: OH Airglow Contamination for Possible NIFS Gratings
|
|
lc |
l/mm |
R |
Dv |
lbegin |
lend |
Percentage OH Lines |
||
|
|
(mm) |
|
|
(km s-1) |
(mm) |
(mm) |
J |
H |
K |
|
|
|
|
|
|
|
|
|
|
|
|
J1 |
1.05 |
600 |
5090 |
59.0 |
0.94 |
1.16 |
6.9% |
|
|
|
J2 |
1.25 |
600 |
6100 |
49.2 |
1.14 |
1.35 |
10.6% |
|
|
|
H |
1.65 |
400 |
5340 |
56.2 |
1.49 |
1.80 |
|
13.3% |
|
|
K |
2.20 |
300 |
5340 |
56.2 |
2.00 |
2.42 |
|
|
9.8% |
|
|
|
|
|
|
|
|
|
|
|
|
J |
1.15 |
300 |
2790 |
107.6 |
0.94 |
1.36 |
18.1% |
|
|
|
HK |
2.08 |
150 |
2526 |
118.7 |
1.66 |
2.50 |
|
31.1% |
19.6% |
|
|
|
|
|
|
|
|
|
|
|
|
JH |
1.38 |
150 |
1687 |
177.8 |
0.96 |
1.80 |
32.8% |
33.9% |
|
|
JHK |
2.02 |
85 |
1404 |
213.6 |
1.29 |
2.76 |
|
51.5% |
33.4% |
8 Performance Results
8.1 ALTAIR Performance
We first consider the effect of the performance of ALTAIR as parameterized by the Strehl ratio on the fraction of light contained within the central 0.1²´0.1² of a stellar profile. This is shown in Figure 10 for our adopted PSF and a seeing FWHM of 0.4² typical of Mauna Kea. Figure 10 demonstrates several effects of significance to the scientific performance of NIFS: 1) Only ~ 17% of the total stellar flux is contained within the central diffraction core for a Strehl ratio of 0.2. This is the case modeled by the web–based performance calculator and is appropriate to J band observations with ALTAIR in median seeing conditions. 2) Higher Strehl ratios lead to significantly higher sensitivities to point sources. The highest Strehl ratios will be obtained in the K band where Strehl ratios of ~ 0.6 are expected in median seeing conditions. Even then, only ~ 0.5 of the light from a point source will be contained in the central 0.1²´0.1² of the PSF. 3) High Strehl ratios will also be required to separate the spectra of spatially distinct sources. This is particularly true of K band stellar velocity dispersion measurements of galactic nuclei. Approximately 50% of the central high velocity dispersion light will contaminate spectra at larger radii. The effect of this light will need to be modeled based on knowledge of the actual PSF. This will have to be determined by frequent measurements of a nearby star, derived from the OIWFS output, or modeled from knowledge of the ALTAIR control loop output.
Figure 10: Fraction of PSF contained within a 0.1²´0.1² square aperture for 0.4² FWHM seeing.
8.2 Aperture Effects
We now consider the signal-to-noise ratio obtained with NIFS on point sources with different extraction aperture sizes. This is shown in Figure 11 for the K grating and in Figure 12 for the H grating with 1800 s integration times, 0.4² FWHM seeing, and a Strehl ratio of 0.2. The aperture radii are quoted in units of 0.05² pixels. These plots show that the best signal-to-noise ratio is achieved, in general, with an extraction aperture matched to the 0.1²´0.1² diffraction core. Only for extremely bright point sources is the seeing halo sufficiently bright to warrant the use of a larger extraction aperture. The halo will be less important in observation achieving higher Strehl ratios. Unless otherwise stated, we always adopt a 0.1²´0.1² extraction aperture in what follows.
Figure 11: Signal-to-noise ratio in K band integrations of 1800 s for different extraction aperture sizes. The seeing FWHM is 0.4² and the Strehl ratio is 0.2.
Figure 12: Signal-to-noise ratio in H band integrations of 1800 s for different extraction aperture sizes. The seeing FWHM is 0.4² and the Strehl ratio is 0.2.
8.3 Point Source Performance
We now determine the point source performance of NIFS. We do this for 1800 s integration times in 0.4² seeing with each of the four high resolution gratings for a range of source magnitudes and Strehl ratios. Signal-to-noise ratios per spectral pixel for the J1, J2, H, and K gratings are shown in Figure 13 to Figure 16, and for the J and HK gratings are shown in Figure 17 and Figure 18. The data are tabulated in an Appendix. These plots were obtained by extracting a simulated spectrum from the central 0.1²´0.1² stellar core, dividing by a normalized extracted spectrum of an identical 0.0 mag star, and determining the mean and standard deviation of the pixel values in the resulting featureless spectrum.
Figure 13: SNR per spectral pixel obtained with the J1 grating in 1800 s and 0.4² seeing on point sources with different Strehl ratios.
Figure 14: SNR per spectral pixel obtained with the J2 grating in 1800 s and 0.4² seeing on point sources with different Strehl ratios.
Figure 15: SNR per spectral pixel obtained with the H grating in 1800 s and 0.4² seeing on point sources with different Strehl ratios.
Figure 16: SNR per spectral pixel obtained with the K grating in 1800 s and 0.4² seeing on point sources with different Strehl ratios.
Figure 17: SNR per spectral pixel obtained with the J grating in 1800 s and 0.4² seeing on point sources with different Strehl ratios.
Figure 18: SNR per spectral pixel obtained with the HK grating in 1800 s and 0.4² seeing on point sources with different Strehl ratios.
Based on these predictions, NIFS should achieve a signal-to-noise ratio of ~ 10:1 per spectral pixel in a 0.1²´0.1² aperture with median seeing and the expected ALTAIR Strehl ratios of 0.2 at J, 0.4 at H, and 0.6 at K in a single 1800 s exposure on point sources with J = 18.6, J = 18.0, H = 18.0, and K = 17.8 mag using the J1, J2, H, and K gratings, respectively, and J = 18.8 and K = 17.1 mag using the J and HK gratings, respectively.
8.4 Extended Continuum Sources
The signal-to-noise ratio achieved on a uniform extended continuum source can also be estimated from Figure 13 to Figure 18. The detected signal from such a source is not reduced by incomplete AO-correction as it is for a point source. Rather, the detected signal is contaminated by light from adjacent regions. The signal-to-noise ratio achieved in this situation can be estimated from the results shown in Figure 13 to Figure 18 for a Strehl ratio of 1.0. The signal-to-noise ratios in these figures were measured with a 0.1²´0.1² square aperture. Therefore the stellar magnitudes can be converted to equivalent surface brightnesses in mag arcsec-2 by subtracting 5 mag from the magnitudes in the figures (in fact, only ~ 0.8 of the light from a point source is included in a 0.1²´0.1² aperture for a Strehl ratio of 1.0 [Figure 10], but we ignore this loss).
Based on these surface brightness predictions, NIFS should achieve a signal-to-noise ratio of ~ 10:1 per spectral pixel in a 0.1²´0.1² aperture with median seeing for a single 1800 s exposure on uniform, extended, continuum sources with surface brightnesses of J = 15.0, J = 14.1, H = 14.0, and K = 13.3 mag arcsec-2 using the J1, J2, H, and K gratings, respectively, and 15.3 and 13.7 mag arcsec-2 using the J and HK gratings, respectively.
8.5 Binary Star Detection
A potential NIFS science driver is the detection of brown dwarfs and possibly planets near bright stars. The full NIFS spectral resolution is not required for observation of broad molecular absorption features in such objects. We assess the performance of NIFS in this task by determining the signal-to-noise ratio that will be obtained in K band spectra smoothed to a two-pixel resolving power of R ~ 1000 for a faint point source at a radius of 0.5² from a bright star (Figure 19 and Figure 20). The extent to which the halo of the bright star can be subtracted will be a limiting factor in detecting faint companions. Conventional PSF fitting routines, such as currently in IRAF, do not cope well with the AO-corrected image profiles NIFS will produce. We also expect that there will be residual speckle structure which will not be well fitted by analytic PSF profiles. These complex issues have not been address in the present analysis; we simply subtract a spectrum extracted from the diametrically opposite position in the primary star image.
Figure 19: Compressed image showing a K=18 mag star 0.5² to the left of a K=12 mag star in 0.4² FWHM seeing with a Strehl ratio of 0.6. The exposure time with the K grating is 1800 s which just saturates the K=12 mag star.
Figure 20: Spectrum of the K=18 mag star in Figure 19 smoothed to a two-pixel resolving power R ~ 1000. Atmospheric absorption features have been removed by division with a flat spectra star.
Our simulations show that the NIFS detector will just saturate using the K grating in 1800 s on a K = 12 mag star in 0.4² FWHM seeing with a Strehl ratio of 0.6. The R = 1000 signal-to-noise ratios achieved on 0.5² offset companion stars in this case are listed in Table 4. The minimum full frame integration time will be limited by the detector readout speed to ~ 10 s. A K = 6.4 mag star will saturate in this time under the above seeing and AO correction conditions. Consequently, it will be necessary to occult stars brighter than K ~ 6.5 in order to search for companions around them. Table 4 also lists R = 1000 signal-to-noise ratios for companions to a K = 7 mag primary recorded with an integration time of 15 s. These estimates are scaled to a total integration time of 1800 s assuming SNR µ Öt. It can be seen from Table 4 that there is a 1.5-2.0 mag penalty in detecting companions around the brighter star.
Table 4: Binary Companion SNR, R = 1000.
|
K = 12 mag Primary Star |
K = 7 mag Primary Star |
||||
|
Secondary Mag |
SNR in 1800 s |
Secondary Mag |
SNR in 15 s |
SNR in 1800 s |
|
|
14.0 |
342.3 |
14.0 |
13.5 |
147.5 |
|
|
15.0 |
180.6 |
15.0 |
6.0 |
65.2 |
|
|
16.0 |
107.1 |
16.0 |
2.3 |
25.2 |
|
|
17.0 |
43.4 |
17.0 |
1.0 |
10.5 |
|
|
18.0 |
17.6 |
18.0 |
0.4 |
3.9 |
|
|
19.0 |
8.6 |
|
|
|
|
|
20.0 |
3.3 |
|
|
|
|
8.6 Emission-Line Detection
Molecular hydrogen produces a typical emission-line spectrum that NIFS will be required to detect. A simulation of a 1800 s K grating spectrum of a 1.0² diameter circular “bullet” in OMC-1 (Stolovy et al. 1998) is shown in Figure 21. Analysis of this image indicates that the 5s flux limit for detecting H2 1-0 S(1) line emission spread over 3 spectral pixels in 1800 s with 0.1²´0.1² spatial resolution will be ~ 2´10‑23 W cm-2 arcsec-2; ~ 500 times fainter than OMC-1. Flux limits appropriate to other lines depend on their proximity to terrestrial OH emission-lines and to the thermal emission beyond 2.3 mm. Figure 22 shows the simulated H2 1-0 S(1) line spectrum of the 0.1²´0.1² square region of a source having a surface brightness of 2´10-23 W cm-2 arcsec-2.
Figure 21: Molecular hydrogen spectrum in the K band for a uniform 1.0² diameter circular region with a H2 1-0 S(1) line surface brightness appropriate for OMC Peak 1.
Figure 22: Simulated spectrum of the H2 1-0 S(1) line with surface brightness 2´10-23 W cm-2 arcsec‑2 (at pixel 597) extracted from a 0.1²´0.1² square aperture from an 1800 s exposure. Other apparently significant features are residual noise from subtracted OH lines.
8.7 Stellar Velocity Dispersions
One of the main science drivers for NIFS is the detection of massive black holes in the nuclei of spiral galaxies to distances of ~ 20 Mpc. The presence of a mass concentration is indicated by a rising stellar velocity dispersion profile near the nucleus. Techniques for measuring stellar velocity dispersion from the broadening of the stellar CO Dv=2 absorption bands at 2.3 mm have been discussed by Gaffney, Lester, & Doppmann (1995). Spectra with continuum signal-to-noise-ratios of ~ 30 and high spatial resolution are required to measure stellar velocity dispersions in to small radii. The same techniques can be used for measuring mass-to-light ratios for the late-type stellar populations in normal galactic nuclei and starburst galaxies.
Figure 23 shows a simulated 3600 s K grating exposure on the nucleus of a spiral galaxy at 10 Mpc having a velocity dispersion profile identical to that of the Milky Way with no interstellar extinction. The simulated spatial image for this galaxy (compressed in the spectral direction) is shown in Figure 24. The peak K band surface brightness in the central 0.1²´0.1² region is 10.53 mag arcsec-2. The K band surface brightness at a fiducial radius of 1.0² (~ 50 pc) is 12.8 mag arcsec-2. Figure 25 shows the K grating spectrum extracted from the central 0.1²´0.1² region of the simulated exposure in Figure 23 after subtraction of a 3600 s sky exposure, transformation for 2D wavelength calibration, and division by a flat spectrum star. The stellar velocity dispersion has been estimated from this frame using a least-square fit to a Gaussian-smoothed template spectrum which is identical in spectral type to the simulated galaxy spectrum. Various subtleties complicate the analysis of real data including interstellar extinction, smearing due to the PSF, incomplete sky subtraction and correction for terrestrial atmospheric absorption, and systematic differences between the galaxy and template spectra. None of these is considered here. However, this simple analysis is sufficient to demonstrate the potential of this type of observation. The stellar velocity dispersions measured for each 0.1²´0.05² spatial pixel are shown in Figure 26. The green dots are azimuthal averages of the individual velocity dispersion measurements, and the red curve is the velocity dispersion profile from which the data were simulated. The formal velocity dispersion errors are more than adequate for the purpose of detecting a central massive black hole, but these are likely to be under-estimates of the uncertainties in real data. The K band surface brightness profile in the Galactic center declines as r-0.8 so the signal-to-noise ratios for the annular azimuthal averages should be proportional to ~ r0.2, thus accounting for the approximate constancy of the signal-to-noise ratios with radius in the simulation. This highlights the benefits of using an integral field unit for these types of observation.
Figure 23: Simulated 3600 s K grating exposure of the nucleus of a galaxy at 10 Mpc. The CO Dv=2 absorption bands in the galaxy spectrum are apparent at right.
Figure 24: Spatial image of the galaxy nucleus shown in Figure 23. The K band surface brightness in the central 0.1²´0.1² region is ~ 10.53 mag arcsec-2. Each vertical slitlet is 0.1² wide. The field of view is 3.1²´3.3².
Figure 25: Extracted spectrum of the central 0.1²´0.1² region of the simulated galaxy at 10 Mpc shown in Figure 23.
Figure 26: Radial stellar velocity dispersion profile derived from the spectra in Figure 23. The black points indicate the velocity dispersion measurements for each 0.1²´0.05² spatial pixel. The green dots are azimuthal averages of these data. The red curve shows the velocity dispersion profile from which the simulated data were derived.
The radial velocity dispersion profile inferred from a simulated 3600 s exposure with the K grating of the same simulated galaxy now shifted to 20 Mpc is shown in Figure 27. Two effects are seen here; the linear spatial resolution is degraded, and the signal-to-noise ratios of the measurements are worse. The poorer spatial resolution is expected to be the limiting factor in detecting massive black holes at distances beyond ~ 20 Mpc. On the other hand, the velocity dispersion uncertainties are expected to limit attempts to infer total stellar masses and hence mass-to-light ratios in objects such as ultra-luminous IRAS galaxies which are typically at distances between 20 and 100 Mpc. The K band surface brightness (with no extinction) in the 20 Mpc simulation at a fiducial radius of 1² (~ 100 pc) is 13.4 mag arcsec-2.
The 55 km s-1 two-pixel resolution of the K grating severely over-samples all velocity dispersions measured for the simulated galaxy (s = 70 km s-1 in Figure 26 and Figure 27 corresponds to a Gaussian FWHM of 165 km s-1). It is illustrative to consider the velocity dispersion uncertainties that would result from using the HK grating to measure the same 20 Mpc galaxy. This grating offers wider wavelength coverage with a factor of ~ 2 lower spectral resolution. The velocity dispersion profile obtained from a simulated 3600 s exposure with the HK grating on a spiral galaxy at 20 Mpc is shown in Figure 28. The velocity dispersion uncertainties are not significantly different from those obtained with the K grating data. This is of course because the simulations with both gratings are background-limited, and the least-square fitting procedure sums over all available data. Clearly, integration times longer than 3600 s will be required to reduce the velocity dispersion uncertainties.
Figure 27: Radial stellar velocity dispersion profile for a simulated 3600 s observation of a spiral galaxy at 20 Mpc using the K grating. The black points indicate the velocity dispersion measurements for each 0.1²´0.05² spatial pixel. The green dots are azimuthal averages of these data. The red curve shows the velocity dispersion profile from which the simulated data were derived.
Figure 28: Radial stellar velocity dispersion profile for a simulated 3600 s observation of a spiral galaxy at 20 Mpc using the HK grating. The black points indicate the velocity dispersion measurements for each 0.1²´0.05² spatial pixel. The green dots are azimuthal averages of these data. The red curve shows the velocity dispersion profile from which the simulated data were derived.
The finite spatial resolution achieved with NIFS will be the main factor limiting our ability to detect massive black holes. The enclosed stellar mass at any radius is derived from the velocity dispersion profile and the light distribution via the collision-less Boltzmann equation. Under the reasonable assumptions that the velocity dispersion is constant in the inner region of the galaxy and that the volume mass density is proportional to ~ r-2, the mass enclosed within radius r is given very approximately by
where D is the distance to the galaxy, s is the velocity dispersion at radius r, and Menc is in units of Mʘ. Figure 26 indicates that a galaxy like the Milky Way at a distance of 10 Mpc will have a velocity dispersion of ~ 70 km s-1 at r ~ 0.1² (~ 5 pc), the innermost measurement. This corresponds to an enclosed mass of ~ 1.2 ´ 107 Mʘ according to the above equation. The K band luminosity of the Milky Way within a radius of 5 pc is ~ 3.5´107 Lʘ, so the mass-to-light ratio of our simulated galaxy within the region probed by NIFS is M/LK ~ 0.34. Normal stellar populations can have M/LK in the range 0.0-2.5 (Figure 29), so we conservatively require mass-to-light ratios in excess of ~ 5.0 to unambiguously indicate the presence of a massive black hole. This will be the case if the mass enclosed within r ~ 0.1² is > 1.8´108 Mʘ due to the presence of a > 1.7´108 Mʘ black hole. If this were the case, the velocity dispersion at r = 0.1² would be ~ 270 km s-1. We therefore expect that NIFS will unambiguously detect black holes with masses > 2´108 Mʘ in spiral galaxies out to distances of ~ 10 Mpc. The 2´106 Mʘ black hole in the Milky Way nucleus is uncharacteristically small; the best estimate of the mean relation between black hole mass and bulge mass (Magorrian et al. 1998) predicts that the 2´1010 Mʘ Milky Way bulge would typically be associated with a 1.2´108 Mʘ nuclear black hole mass. If this mean relation proves to be appropriate for late-type spiral galaxies, our simulations suggest that it should be possible to detect only the more massive of the black holes that may be present in late-type spiral galaxies to distances of at least 10 Mpc with NIFS.
Figure 29: M/LK versus cluster age for a exponentially declining star formation rate with 109 yr time constant from Thatte et al. (1997).
The signal-to-noise ratios simulated here are much larger than those predicted in the NIFS proposal. This is due to the central concentration of galaxy light which was ignored previously. The predictions in the NIFS proposal were based on estimated central surface brightnesses averaged over the 3² diameter NIFS aperture. The simulations presented here show that the central 0.1²´0.1² region has a surface brightness brighter than this average by ~ 2.6 mag arcsec-2 for distances ³ 1Mpc, if a Milky Way light distribution is appropriate.
8.8 Nearby Seyfert Nuclei
NIFS will be used to 1) constrain black hole masses in Seyfert nuclei, 2) determine mass-to-light ratios for the nuclear stellar populations, and 3) probe the structure and excitation of the inner narrow-line regions (NLRs). The feasibility of measuring the stellar velocity dispersions needed to achieve the first two goals depends in the K band surface brightness due to the near-nuclear stellar light in Seyfert galaxies, as well as the concentration of AGN core light achieved with the AO-corrected PSF. Peletier et al. (1999) have compiled a database of ground-based subarcsecond resolution J, H, and K surface photometry of a sample of CfA Seyfert galaxies (see http://www.pa.uky.edu/~shlosman/gals/SEYFERT/). The K band surface brightnesses of this representative sample of Seyfert galaxies at a fiducial radius of 1² (which largely avoids the non-stellar core emission) are shown in Figure 30 as a function of redshift. The brightest of these Seyfert galaxies have surface brightnesses at 1² radius brighter than the value of 13.4 mag arcsec-2 simulated for the 20 Mpc galaxy in Figure 27 and Figure 28. However, most of the sample have K surface brightnesses at 1² extending to ~ 15.0 mag arcsec-2. This surface brightness is reached at 1² radius if our simulated galaxy is moved to a distance of 120 Mpc. Figure 31 shows the velocity dispersion profile inferred from a simulated 3600 s exposure using the K grating with our simulated galaxy at a distance of 120 Mpc. Reliable velocity dispersions cannot be inferred for individual spatial pixels. However, random errors in the annular averages shown in Figure 31 are low enough to be useful if systematic errors can be controlled. Most CfA Seyfert galaxies in the Peletier et al. (1999) sample should therefore be accessible to NIFS in this way. However, velocity dispersion cannot be separated from rotational velocity in annular averages so it remains unclear how scientifically useful such data will prove to be.
Figure 30: Measured K surface brightness at a radius of 1² for CfA Seyfert galaxies (Peletier et al. 1999).
Figure 31: Radial stellar velocity dispersion profile for a simulated 3600 s observation with the K grating of a spiral galaxy at 120 Mpc having a K band surface brightness at 1² radius of 15 mag arcsec-2. The green dots are the values inferred from azimuthally averaged spectra. The red curve shows the velocity dispersion profile from which the simulated data were derived.
Near-infrared emission-lines from the NLR are seen against stellar emission from the galaxy and thermal dust emission from the Seyfert core. We use the signal-to-noise ratio predictions for point sources observed with a Strehl ratio of 1.0 (§8.3) to infer the signal-to-noise ratios per 0.1²´0.1² aperture obtained in 3600 s for different continuum surface brightnesses, and from these estimate 3s detection limits for emission-lines seen against these continua. We adopt an emission-line FWHM of 300 km s-1 typical of H2 1-0 S(1) emission in Seyfert galaxies (Veilleux, Goodrich, & Hill 1997, Fig. 14). [Fe II] emission in Seyfert galaxies typically has a FWHM of 500 km s-1, so our detection limits should be increased by 5/3 for typical [Fe II] 1.257 mm and 1.644 mm lines. The 3s emission-line detection limit predictions for a 3600 s integration time are listed in Table 1 for each of the NIFS gratings at their full resolving power and for a two-pixel resolving power of R = 1000 matched to the adopted emission-line width. The latter is better suited to detecting NLR emission-lines, but provides little profile information.
Table 5: 3s emission-line detection limits in 3600 s integrations for 300 km s-1 line width.
|
J1
grating |
J2
grating |
||||
|
mJ |
R =
5090 |
R =
1000 |
mJ |
R =
6100 |
R =
1000 |
|
(mag arcsec-2) |
(W cm‑2 arcsec‑2) |
(W cm‑2 arcsec‑2) |
(mag arcsec-2) |
(W cm‑2 arcsec‑2) |
(W cm‑2 arcsec‑2) |
|
9.0 |
1.1´10-21 |
5.0´10-22 |
9.0 |
8.9´10-22 |
3.6´10-22 |
|
10.0 |
7.2´10-22 |
3.2´10-22 |
10.0 |
5.8´10-22 |
2.4´10-22 |
|
11.0 |
4.5´10-22 |
2.0´10-22 |
11.0 |
3.8´10-22 |
1.4´10-22 |
|
12.0 |
3.1´10-22 |
1.4´10-22 |
12.0 |
2.6´10-22 |
1.1´10-22 |
|
13.0 |
2.0´10-22 |
8.9´10-23 |
13.0 |
1.8´10-22 |
7.4´10-23 |
|
14.0 |
1.5´10-22 |
6.7´10-23 |
14.0 |
1.4´10-22 |
5.8´10-23 |
|
15.0 |
1.2´10-22 |
5.2´10-23 |
15.0 |
1.3´10-22 |
5.2´10-23 |
|
|
|
||||
|
H
grating |
K
grating |
||||
|
mH |
R =
5340 |
R =
1000 |
mK |
R =
5340 |
R =
1000 |
|
(mag arcsec-2) |
(W cm‑2 arcsec‑2) |
(W cm‑2 arcsec‑2) |
(mag arcsec-2) |
(W cm‑2 arcsec‑2) |
(W cm‑2 arcsec‑2) |
|
9.0 |
5.2´10-22 |
2.2´10-22 |
9.0 |
3.1´10-22 |
1.4´10-22 |
|
10.0 |
3.4´10-22 |
1.5´10-22 |
10.0 |
2.1´10-22 |
9.2´10-23 |
|
11.0 |
2.2´10-22 |
9.5´10-23 |
11.0 |
1.4´10-22 |
6.0´10-23 |
|
12.0 |
1.6´10-22 |
6.9´10-23 |
12.0 |
1.1´10-22 |
4.9´10-23 |
|
13.0 |
1.2´10-22 |
5.2´10-23 |
13.0 |
8.8´10-23 |
3.8 |