Predicting MSATA Reference Star Magnitudes
The NIRSpec MSA target acquisition (MSATA) observes a set of stars (typically 5–8 objects) through the open micro-shutter assembly (MSA), in order to correct for JWST's pointing and roll uncertainty.
Observers using MSATA will need to include the magnitudes of reference stars in the NIRSpec TA bands (F110W, F140X, and CLEAR) in their MPT catalogs during the program update phase. This article presents strategies for predicting the magnitudes of reference stars in these bands.
On this page
Required MSATA reference star magnitudes
Words in bold are GUI menus/
panels or data software packages;
bold italics are buttons in GUI
tools or package parameters.
NIRSpec's MSATA is carried out in one of the 3 TA bands (F110W, F140X, and CLEAR), with the option to choose from a number of readout modes and patterns. The MSATA algorithm will take 2 exposures, with a half-shutter dither in both dispersion and cross-dispersion directions, in order to mitigate the effect of the MSA bars. Each exposure uses 3 groups and one integration, leading to a fixed sensitivity for each filter/readout pattern. Table 1 shows the ranges of magnitudes in the NIRSpec TA bands that will achieve S/N > 20, while also avoiding saturation. Observers can specify MSATA at the visit level in APT, where a pull-down menu labeled Reference Star Bin will offer the choice of suitable reference stars for these magnitude range. This pull-down menu will be available if the catalog contains columns specifying the magnitudes of reference objects in the TA bands (column headers NRS_F110W, NRS_F140X, NRS_CLEAR), and if there are at least 5 suitable objects in one of the magnitude bins.
Table 1. Brightness ranges for NIRSpec MSATA filter and readout pattern options
|Readout mode||S/N = 20||Saturation|
Table note: Limiting bright and faint magnitudes for target acquisition reference stars. All values are given in AB magnitudes; subtract 0.9 for approximate J-band magnitude values (Vega magnitudes). Readout patterns NRSRAPIDD1 and NRSRAPIDD2 are only used with the CLEAR filter for TA exposures. All TA exposures are acquired with Ngroups = 3. NRSRAPIDD6 replaced NRS for MSATA for improved cosmic ray mitigation.
It is important to consider that observed magnitudes in the NIRSpec TA bands are not, and will not, be available in advance. In many cases, it is sufficient to use other near-infrared magnitudes, under the assumption that transformation into the NIRSpec TA band magnitudes is negligible. The large magnitude ranges between S/N = 20 and saturation in Table 1 implies that the accuracy of the NIRSpec magnitude estimates does not have to be particularly high. As long as the inferred magnitudes are not near the edges of the magnitude bins, there is room for uncertainty in their estimates. However, in cases where near-infrared imaging is unavailable, it is still often possible to estimate the TA-band magnitudes of stars. The remainder of this article presents strategies for carrying out this estimation.
Predicting the NIRSpec TA-band magnitudes of stars from optical imaging
The colors of field stars with low or constant extinction form a tight sequence, with minimal scatter, as illustrated by Figure 1. This figure uses stars from the 5 fields of the Cosmic Assembly Near Infrared Deep Extragalactic Legacy Survey (CANDELS; Koekemoer et al. 2011, Grogin et al. 2011), demonstrating that it is possible to predict the NIRSpec TA-band magnitudes with precision that is sufficient for MSATA when the Galactic foreground extinction is low. As a result, with some analysis, it is possible to avoid the requirement for NIRCam pre-imaging in fields that lack near-infrared photometry (provided that there are sufficient numbers of stars). Here, we provide some guidance on making these predictions, as well as empirical and theoretical tables of stellar colors, and an iPython notebook to aid in this analysis.
Strategy for predicting NIRSpec TA-band magnitudes from optical colors
Both empirical and theoretical tables of stellar colors can be used to select samples of stars with optical colors similar to a star that is being considered for MSATA. A given sample of stars will show a distribution in their optical-to-infrared colors, which gives a statistical measurement of the likelihood that the reference star will fall in an appropriate MSATA magnitude range (Table 1). For example, a reference star might have HST/ACS F606W = 23.1 ± 0.05 and F814W = 22.0 ± 0.05. From the CANDELS catalog of stars shown in Figure 1, we can select objects that match this color, having F606W-F814W = 1.1 ± 0.07. There are 89 such stars, with a mean color in ACS-NIRSpec, F814W-F140X = 0.51, and a scatter of 0.06 mag. Therefore, we infer that the NIRSpec F140X magnitude of this particular TA reference star is 21.5 ± 0.07. Comparing to the saturation limits in Table 1, we see that this star would be suitable for TA using NRSRAPID with F110W and F140X, but would likely saturate in other cases. It is straightforward to extend this technique to predict the NIRSpec TA-band magnitudes from multiple colors instead of just one. After inferring these magnitudes, adding them to the catalog with column headers NRS_F110W, NRS_F140X, NRS_CLEAR will allow APT to identify a Reference Star Bin that can be selected at the visit level.
The advantage to this empirical approach is that the stars in CANDELS reach similar magnitudes to what is used for MSATA. At these magnitudes, even small area surveys are likely to uncover cool red stars (Ryan & Reid 2016), which are few in number at shallower depths, even in the area of the entire SDSS (e.g., Metchev et al. 2008). Hence, the colors of stars used for MSATA should be reasonably well matched to the colors of stars seen in CANDELS. However, the disadvantage to using stars in high Galactic latitude survey fields like CANDELS is that—with relatively dust-free sight lines—this database is not applicable to regions of higher Galactic foreground extinction. Therefore, in order to include Galactic foreground extinction, we are also providing theoretical distributions of stars in the Milky Way, with Galactic foreground extinction applied as a function of their simulated distance. These models, given below, are calculated using TRILEGAL 1.6 (Girardi et al. 2012).
Tables of stellar colors and example calculations
Tables of stellar colors using an empirically based derivation (CANDELS) and a theoretical calculations (TRILEGAL) are given here, along with an example iPython Notebook. Details regarding these tables are given below.
Empirical method: stars in CANDELS
In order to compute an empirical database of stellar colors, we take stars from the CANDELS photometric tables that were compiled by Skelton et al. (2014), which are available from the 3D-HST project. These stars reach a depth of 25th magnitude (AB) in WFC3/F160W, so they are well-matched in brightness to the MSATA reference star magnitude bins. In order to predict the magnitudes of the stars in bands that are not used by CANDELS, we fit Phoenix model spectra (Allard et al. 2016) to these stars, using the spectral energy distribution (SED) fitting tool Chorizos (Maíz-Apellániz 2004). We then calculated synthetic magnitudes using filter bandpasses and the best fitting SED, including the NIRSpec TA bands and NIRCam imaging bands (in some cases, extrapolating the best-fit model). Since the imaging coverage of the CANDELS fields is inhomogeneous, we also used these best-fitting SEDs to calculate synthetic magnitudes predicted by the fit for the full set of optical and near-infrared bands in the various CANDELS observations. As such, the ACS F606W and F814W photometry in Figure 1 is a mixture of observed and predicted magnitudes, since these bands were not observed in all 5 of the CANDELS fields. In cases where there are both observed and predicted photometric measurements, the difference (residual) can be used to assess the reliability of the model fits.
The lookup table that we have derived for the stars in CANDELS is provided in the first bullet of the box above. The table includes:
- The ID of the source, including the field name and the ID from Skelton et al. (2014)
- Parameters from the Chorizos SED fits; stellar effective temperature (Temperature), stellar surface gravity (log_g), stellar metallicity (Z), and foreground extinction (EBV; constrained to be < 0.1). Errors are also included (sigma_Temperature, sigma_log_g, sigma_Z, sigma_EBV).
- Residuals from the fit for the 3 worst photometric points for each object (color_uncert, color_resid, and color_normerr). The quantities color_uncert are uncertainties in colors used by the fit, color_resid are the residuals from the fit, and color_normerr is the ratio of the two. This information can also be calculated (for all bands) from the observed and predicted photometry that is included in the table.
- The χ2 and number of degrees of freedom in the fit (chisq, ndf) from Chorizos
- Photometry in more than sixty bands, in AB magnitudes, including the observed magnitudes, the measured error, and a prediction from integrating the best-fitting SED under the desired bandpass (e.g., acs_f435w, acs_f435w_err, acs_f435w_pred). When a desired photometric band is not observed in a particular field, the measurement and the error are set to -1, but predicted magnitude is still included. Note that for NIRSpec and NIRCam magnitudes, only the predicted magnitude columns are included. At this time, real NIRCam data has not been included in this analysis.
We assessed the accuracy of the NIRSpec TA-band predictions by comparing the observed and SED-fit predicted magnitudes in WFC3/IR F125W, F140W, and F160W, as well as the IRAC 1 (3.6 μm) and IRAC 2 (4.5 μm) channels. For the WFC3/IR bands, which cover similar wavelengths as NIRSpec's F110W and F140X, we find no systematic difference between the observed and predicted magnitudes, and an RMS of 0.05–0.06 magnitudes. We take this to imply a similar level of accuracy for NIRSpec's F110W and F140X TA bands. The IRAC bands, on the other hand, show worse residuals, with systematic offsets around 0.1–0.2 magnitudes, and scatter of a similar amount. Hence, we expect that the prediction of the flux in NIRSpec's CLEAR band is likely 0.1–0.3 magnitudes too bright for the wavelength range ~2.5–5 μm. Since this inaccuracy only impacts around half of the wavelengths covered by the CLEAR band, we infer that the CLEAR magnitude predictions are somewhat worse than the WFC3/IR bands but not as bad as the IRAC magnitudes. These uncertainties should be considered when using CLEAR magnitudes that are predicted to be near the edges of the MSATA reference star magnitude ranges in Table 1. Note that the lookup table for CANDELS stars also includes predicted magnitudes for all medium-band and broadband filters for NIRCam. The longer wavelength NIRCam predictions are subject to the same uncertainties as NIRSpec's CLEAR band.
Theoretical Method: Stellar population models from TRILEGAL
The same strategy can be applied using a simulated database of stellar colors. In this way, it is possible to select a simulation with a non-zero Galactic foreground extinction, in order to match a chosen MOS observation field. A set of simulated stellar colors, created with TRILEGAL 1.6 (Girardi et al. 2012), can be downloaded above. Each simulation predicts the magnitudes of around 1,400 stars in an area of 0.1 square degrees, to limiting magnitude of J = 27.5 mag, in the SDSS ugriz + 2MASS photometric bands. Stellar binaries are included in the simulated stellar population. These models presently include foreground extinction values of Av = 0.0, 0.1, ...5.0, 6.0, ...10.0; additional extinction values can be simulated using the TRILEGAL web interface. An iPython notebook, showing how to predict the Ks-band magnitude of an individual star from its SDSS r- and i-band magnitudes is also provided above. Transformations between J, H, and Ks bands and NIRSpec's TA bands should be small relative to the magnitude ranges in Table 1, and rough estimates can be made from the empirical CANDELS database given above.
The TRILEGAL models can be used to determine how increasing Galactic foreground extinction increases the uncertainty in the predicted near-infrared magnitudes. Figure 2 shows an example for Galactic extinction of AV = 1.0 (gold points), comparing to the case with no foreground extinction (blue points). In the former case, the extinction seen by the stars varies between AV = 0.54–1.0, depending on the distance to the star. For the simulation with higher Galactic extinction, when r - i = 1.6 ± 0.07, the 95% confidence interval on the inferred Ks-magnitude spans 0.63 mags. This uncertainty is larger than the uncertainty for the case with no extinction. In cases like this, with larger uncertainties on the inferred infrared magnitudes, candidate MSATA reference stars should be excluded if their predicted magnitude is close to the edge of the magnitude bins listed in Table 1. As with the empirical approach, multiple optical colors, if available, can be used to select a sample most closely matched to the MSATA reference star under consideration.
Allard, F. 2016, SF2A-2016: Proceedings of the Annual Meeting of the French Society of Astronomy and Astrophysics, 223
The Phoenix Model Atmosphere Grid for Stars
Girardi, L., et al. 2012, Astrophysics and Space Science Proceedings, 26, 165
TRILEGAL, a TRIdimensional modeL of thE GALaxy: Status and Future
Grogin, N. A., Kocevski, D. D., Faber, S. M. 2011, ApJS, 197, 35
CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey
Koekemoer, A., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36
CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey—The Hubble Space Telescope Observations, Imaging Data Products, and Mosaics
Maíz-Apellániz, J. 2004, PASP, 116, 859
CHORIZOS: A χ2 Code for Paremterized Modeling and Characterization of Photometry and Spectrophotometry
Metchev, S. A., Kirkpatrick, J. D., Berriman, G. B., & Looper, D., 2008, ApJ, 676, 1281
A Cross-Match of 2MASS and SDSS: Newly Found L and T Dwarfs and an estimate of the Space Density of T-Dwarfs
Ryan, R. E., Jr. & Reid, I. N. 2016, AJ, 151, 92
The Surface Densities of Disk Brown Dwarfs in JWST Surveys
Skelton, R., et al. 2014, ApJS, 214, 24
3D-HST WFC3-selected Photometric Catalogs in the Five CANDELS/3D-HST Fields: Photometry, Photometric Redshifts, and Stellar Masses