PandExo: A Community Tool for Transiting Exoplanet Science with JWST & HST

TL;DR

PandExo is an open-source Python tool that simulates JWST and HST noise for exoplanet spectroscopy, aiding community planning and optimizing observations before launch.

Contribution

It introduces PandExo, a community-accessible noise simulator for JWST and HST, validated against existing tools and observations, to support exoplanet atmospheric studies.

Findings

PandExo's noise estimates agree within 10% with existing simulators.

PandExo effectively models JWST and HST instrument noise for exoplanet spectroscopy.

The tool facilitates planning and optimizing future exoplanet observations.

Abstract

As we approach the James Webb Space Telescope (JWST) era, several studies have emerged that aim to: 1) characterize how the instruments will perform and 2) determine what atmospheric spectral features could theoretically be detected using transmission and emission spectroscopy. To some degree, all these studies have relied on modeling of JWST's theoretical instrument noise. With under two years left until launch, it is imperative that the exoplanet community begins to digest and integrate these studies into their observing plans, as well as think about how to leverage the Hubble Space Telescope (HST) to optimize JWST observations. In order to encourage this and to allow all members of the community access to JWST & HST noise simulations, we present here an open-source Python package and online interface for creating observation simulations of all observatory-supported time-series spectroscopy modes. This noise simulator, called PandExo, relies on some aspects of Space Telescope Science Institute's Exposure Time Calculator, Pandeia. We describe PandExo and the formalism for computing noise sources for JWST. Then, we benchmark PandExo's performance against each instrument team's independently written noise simulator for JWST, and previous observations for HST. We find that \texttt{PandExo} is within 10% agreement for HST/WFC3 and for all JWST instruments.

Tables

Table 1. Table 1 Instrument modes

Instrument	Filter	Wavelength Range	Resolving Power	RN
		($\mu$m)		e-/frame
NIRISS SOSS	–	0.6–2.8	700	11.55
NIRSpec Prism	Clear	0.7–5	100	16.8
NIRSpec G140M/H	F070LP	0.7–1.27	1000/2700	16.8
NIRSpec G140M/H	F100LP	0.97–1.89	1000/2700	16.8
NIRSpec G235M/H	F170LP	1.70–3.0	1000/2700	16.8
NIRSpec G395M/H	F290LP	2.9–5	1000/2700	16.8
NIRCam Grism	F322W2	2.5–4.0	1500	10.96
NIRCam Grism	F444W	3.9–5.0	1650	10.96
MIRI LRS	–	5.0–14	100	32.6
WFC3 G102	–	0.84–1.13	210	20.0
WFC3 G141	–	1.12–1.65	130	20.0

Equations

$$\begin{array}{rl}{\sigma }_{\text{tot}}^2=\frac{12(n-1)}{mn(n+1)}{\sigma }_{\text{read}}^2& +\frac{6(n^2+1)}{5n(n+1)}(n-1)t_{g}f\\ & -\frac{2(m^2-1)(n-1)}{mn(n+1)}{t}_{f}f\end{array}$$

$$\text{eff}=\frac{n-1}{n+1}$$

$${\sigma }_{shot}^2=F_{in,\lambda }{t}_g(n-1)n_{i,in}+F_{out,\lambda }{t}_g(n-1)n_{i,out}$$

$${\sigma }_{read}^2=2{\text{RN}}^2{n}_{pix}(n_{i,in}+n_{i,out})$$

$${\sigma }_{tot}^2={\sigma }_{shot}^2+{\sigma }_{bkg}^2+{\sigma }_{read}^2.$$

$${\sigma }_{tot,MULTI}^2\approx t_g(F_{in}{n}_{i,in}+F_{out}{n}_{i,out})$$

$${\sigma }_{tot,MULTI}^2\approx \frac{6}{5}{t}_{g}n(F_{in}{n}_{i,in}+F_{out}{n}_{i,out}).$$

$$z_{\lambda }=1\pm \frac{N_{in,\lambda }}{N_{out,\lambda }}+\sqrt{{\sigma }_{prop}^2}\times N(0,1)$$

$${\sigma }_{prop}^2={\sigma }_{in}^2{\left(\frac{n_{i,out}}{n_{i,in}{N}_{out,\lambda }}\right)}^2+{\sigma }_{out}^2{\left(\frac{n_{i,out}{N}_{in,\lambda }}{n_{i,in}{N}_{out,\lambda }^2}\right)}^2$$

Full text

1. Introduction

JWST is equipped with a 6.5-meter primary mirror and four visible to mid-IR instruments (NIRCam, NIRISS, NIRSpec, MIRI) that span 0.6-28 $\micron$ with low and medium resolution modes, which has the potential for ground-breaking exoplanet science. This led to several studies focused on characterizing the observatory’s expected performance and estimating the planetary properties that could be constrained.

Greene et al. (2007) were among the first to baseline the performance of JWST’s primary imaging instrument, NIRCam, with regards to exoplanet science. They found that with 1000 seconds of integration time, R=500 spectra of Jupiter-sized exoplanets in primary transit and secondary eclipse will be attainable with signal-to-noise ratios (SNRs) ranging from $\sim$5 for faint (M=10 mag) G2V stars and up to $\sim$90 for bright (M=5 mag) G2V stars. Deming et al. (2009) created a sensitivity model for NIRSpec and MIRI, and predicted that JWST will be able to measure temperature and absorption of CO2 and H2O in 1-4 habitable Earth-like planets discovered by the Transiting Exoplanet Survey Satellite (TESS).

Since then, many have sought to baseline the performance of JWST using independent sensitivity models (Kaltenegger & Traub, 2009; Beichman et al., 2014; Batalha et al., 2015; Cowan et al., 2015; Barstow et al., 2015, 2016; Greene et al., 2016; Mordasini et al., 2016; Mollière et al., 2016,in press; Howe et al., 2017; Batalha et al., 2017). For example, Batalha et al. (2015) reported that primary transit spectroscopy with NIRSpec of 1-10 M⊕, 400-1000 K planets orbiting M dwarfs would result in high SNR spectra if the planets were within $\sim$50 pc and if 25 transits were co-added. These results were based off noise simulations which included spacecraft jitter, drift, flat field errors and background noise. In reality, exoplanet observations with JWST could suffer from other systematics as well. Barstow et al. (2015) explored these effects by including time varying astrophysical and instrumental systematics in their observational simulations. Greene et al. (2016) used a retrieval algorithm with an independent noise simulator for NIRISS, NIRCam, and MIRI in order to determine what atmospheric properties could be retrieved from a hot Jupiter, warm Neptune, warm sub-Neptune and cool super-Earth. All of these were pivotal to our knowledge and understanding of the functionality of JWST observing modes and all of these relied on simulating noise sources.

With just two years left until launch, it is imperative that the exoplanet community begins to digest and integrate these studies into their observing plans and strategies. In order to encourage this and to allow all members of the community access to HST and JWST simulations, we present here an open source python package (also available as an online tool111http://pandexo.science.psu.edu:1111) for creating observation simulations of all observatory-supported time-series spectroscopy modes, called PandExo. This noise simulator uses portions of Space Telescope Science Institute’s (STScI) Exposure Time Calculator, named Pandeia. We briefly describe Pandeia in $\S$2 and how it is utilized within PandExo in $\S$3. In $\S$4, we baseline PandExo’s performance against the JWST instrument team’s simulators in order to show that they are in agreement. In $\S$5 we describe the methodology for simulating HST observations. We end with concluding remarks in $\S$6.

2. Pandeia: Simulating Noise Sources

The source code for STScI’s exposure time calculator, named Pandeia, was recently released to the observing community 222http://jwst.etc.stsci.edu. Although Pandeia supports all officially-supported observing modes, we limit our discussion to the modes that will be useful for exoplanet transit spectroscopy.

Pandeia is a hybrid instrument simulator. It simulates observations using a three-dimensional, pixel-based approach but its ultimate goal is to provide the user with accurate predictions of SNRs for specific observing scenarios. Therefore, it does not fully simulate the entire field of view of the instrument, and it does not include optical field distortion, intra-pixel response variations, other detector systematic noise, or the effects of spacecraft jitter and drift. Pandeia does include accurate and up-to-date estimates for background noise, point-spread-functions (PSFs), instrument throughputs and optical paths, saturation levels, ramp noise, correlated read noise, flat field errors and data extraction for all the JWST instruments. We briefly describe Pandeia below, but a full description can be found in Pontoppidan et al. (2016).

For each calculation, a three dimensional cube is created with spatial and spectral dimensions. Astronomical scenes are modeled by specifying a spectral energy distribution along these two dimensions. In the case of transit spectroscopy, this is always a stellar spectrum or star$+$planet spectrum placed at the center of the optical axis and normalized at a specific reference wavelength (see §3).

After the scene is created, Pandeia uses pre-calculated low, medium and high background cases adopted from Glasse et al. (2015). For the calculations in this analysis we employ the “medium” background case shown in Figure 1.

After the background is added, Pandeia convolves each plane in the three-dimensional astronomical scene with the unique, two dimensional PSF for the instrument mode being simulated. All PSFs are calculated using WebbPSF, which is described in Perrin (2011). For spectroscopy modes (except NIRISS), Pandeia assumes that the PSF profile is independent of spatial location. The inclusion of the PSFs can be seen in Pandeia’s 2-dimensional simulations of the detector (Figure 2).

All JWST exoplanet time series spectroscopy modes will acquire sampled-up-the-ramp data at a constant cadence of one frame (Rauscher et al., 2007). A frame is a unit of data that results from sequentially clocking and digitizing all pixels in the rectangular area of the detector. The time it takes to read out one frame ($t_{f}$) depends on the observation mode or, more specifically, on the subarray size. In JWST terminology, a group is a number ($n$) of consecutively read frames with no intervening resets. For all exoplanet time series modes, there is one frame per group. An integration is composed of a reset of the detector followed by a series of non-destructively sampled groups (n = # groups per integration). The time it takes to reset the detector in between integrations is equivalent to the frame time, $t_{f}$.

The measured signal can be calculated in two ways. The first, referred to as MULTIACCUM, is the standard procedure within Pandeia. It computes the final signal by fitting each point up the ramp. The second, referred to as Last-Minus-First (LMF) is the standard procedure within PandExo. In this procedure, the final signal within an integration is equal to the final readout value minus the first readout value. We describe MULTIACCUM below and describe LMF in §3, where we also discuss differences between the two methods.

In the MULTIACCUM procedure, correlations between the number of groups and the number of averaged frames per group are considered when computing the individual noise on a single pixel. Generally, this data is modeled using a standard two-parameter least-squares fitting procedure. Rauscher et al. (2007) generalized this least-squares approach for fitting nondestructive reads for the JWST readout mode, accounting for the correlated noise in the integrating charge. Pandeia calculates the total noise via this formula (Rauscher et al., 2007):

[TABLE]

where $m$ is the number of frames per group (for transit time series $m=1$), $n$ is the number of groups, $\sigma_{\textrm{read}}$ is the read noise per frame, $t_{g}$ is the time per group, $f$ is the electron rate calculated from the astronomical scene cube ($e^{-1}$s*-1pixel-1*) and $t_{f}$ is the time per frame.

The read noise, $\sigma_{\textrm{read}}$ (e- rms), is calculated by considering the effects of correlated noise. It is well-known that both the near-infrared H2RG detectors and the mid-infrared detectors are affected by correlated noise. Therefore, regardless of the amount of incident light on the detector, the read noise in one pixel will depend on the read noise in other pixels. This effect becomes even larger in the fast-read direction and ignoring it would lead to an underestimation of the noise. The greatest consequence of adding correlated noise is that the error propagation must be handled with a covariance matrix and the noise in each pixel cannot simply be added in quadrature sum.

3. PandExo: Simulating JWST Observations

Our JWST transit simulator tool, called PandExo, is built around the core capabilities of Pandeia’s throughput calculations. Pandeia is packaged as a Python package that is called by PandExo, and therefore any updates to Pandeia, by STScI, will automatically (assuming user keeps python packages updated) be incorporated into PandExo. In addition to the observatory inputs required for Pandeia, PandExo requires:

• •

A stellar SED model ($F_{*,\lambda}$, taken from Phoenix Stellar Atlas (Husser et al., 2013))

• •

Apparent magnitude

• •

Planet spectrum (primary or secondary)

• •

Transit duration($T_{14}$)

• •

Fraction of time spent observing in-transit versus out-of-transit

• •

Number of transits

• •

Exposure level considered to be the saturation (% full well)

• •

User-defined noise floor

Using the star and planet models, an out-of-transit ($F_{*,\lambda}$) and in-transit model ($F_{*,\lambda}(1-(R_{p,\lambda}/R_{*})^{2})$ for primary transit or $(F_{p,\lambda}+F_{*})$ for secondary transits) is calculated. PandExo does not create full light curve models with an ingress and egress. Likewise, it does not include the effects of time-varying stellar noise. Doing so would require frame-by-frame simulations and would be too computationally demanding for a community tool. We leave an in-depth analysis of these effects for a future paper and treat the transit as a box model.

With the out-of-transit spectrum, PandExo calls Pandeia to create a 2D simulated image of the flux on the detector with n = 2 (minimum number of groups required for an observation). Then, PandExo calculates the maximum number of groups allowed in an integration before the pixel on the detector receiving the highest flux reaches the user-defined saturation limit. Determining how many groups per integration is a crucial step within PandExo because it sets the observing efficiency, also known as the duty cycle, where:

[TABLE]

The above equation is exact for the near-IR detectors, but MIRI is more efficient. MIRI reads pixels in two rows and then resets the two rows before going on to the next two rows. This dramatically shortens the dead time between the last read and reset (the denominator is only a little more than $n$ and little less than $n+1$. Therefore, while this is formula is exact for the near-IR instruments, MIRI is somewhat more efficient at small $n$ and so in that regime, PandExo values for MIRI may be slightly conservative.

After the timing information is calculated, PandExo uses Pandeia to compute two simulated extracted spectral rates (e${}^{-}s^{-1}$): one for the out-of-transit component and one for the in-transit component. As discussed in $\S$2, Pandeia returns (among other products) a 1D extracted flux rate ($F_{in,\lambda}$,$F_{out,\lambda}$ in e- s*-1*).

If there are $n_{i,in}$ integrations taken in, and $n_{i,out}$ integrations taken out of transit, the pure shot noise can be easily calculated from those fluxes via:

[TABLE]

One important correction that is made to $F_{in/out,\lambda}$ from Pandeia, is the contribution from quantum yield. Quantum yield is the number of charge carriers generated per interacting photon (Janesick, 2011). It ultimately has the effect of increasing the electron rate and by default, the saturation rate, of the detectors by a factor of $\sim$1.8 at 0.5 $\micron$, dropping to a factor of $\sim$1.0 at 1.9 $\micron$ (for the nir-IR detectors) (Pontoppidan et al., 2016). In Pandeia, this is added to the extracted flux product and corrected for in the noise product. In PandExo, we divide Pandeia’s extracted flux by the quantum yield before computing $\sigma_{shot}^{2}$.

Then, to compute the total noise, we must add in the contributions from the background and the read noise. The background signal, $F_{bkg}$, is directly computed from Pandeia. The contribution from the read noise is:

[TABLE]

where RN is the total contribution of read noise in electrons (see Table 1), $n_{pix}$ is the number of extracted pixels. The factor of two comes from the fact that Pandeia’s RN values (Table 1) are given in units of e-/frame. Since we are subtracting the last frame from the first, we must account for both frames. The total noise, calculated for the in-transit and out-of-transit data separately, is then

[TABLE]

This traditional formulation does not assume any correlations between the number of groups. The total number of electrons collected and the associated noise is simply computed by subtracting the first group from the last group, LMF. The last group can always be used because PandExo does not model non-linearity. Once JWST’s non-linearity is more accurately known PandExo will be updated accordingly.

As discussed above, in Pandeia’s MULTIACCUM formulation (Eqn. 1) all the groups in the data are used to fit a slope. The first group has $t_{g}*F_{out}$ electrons, the second as $2t_{g}*F_{out}$ electrons, etc. And although each of these groups has a separate photon noise component, the noise is correlated between all of them. Therefore, the MULTIACCUM method will only be equivalent to the LMF method in the case where the flux rate, $F$, is much larger than the expected read noise and $n=2$. In this limit, Eqn 1 simplifies to:

[TABLE]

The number of groups will be $2$ in the cases where the magnitude of the target is very close to the saturation limit of the instrument mode, which will be the case for a small number of exoplanet targets. In these cases, the MULTIACCUM method and the LMF method will yield similar results, barring small correlated noise contributions from the MULTIACCUM method. However, in cases where the magnitude of the target is at least $\sim 1$ magnitude greater than the saturation limit of the instrument mode, $n$ will be much larger than 2 and the flux rate will still be much larger than the expected read noise. In this limit, the MULTIACCUM formulation simplifies to:

[TABLE]

In this limit, the factor of $\frac{6}{5}$ comes from the second expression in Eqn. 1. When $n=2$, $\frac{6(n^{2}+1)}{5n(n+1)}=1$, but the $\frac{6}{5}$ factor remains when $n>2$. Therefore in the $n>2$ regime, the uncertainty calculated using the MULTIACCUM method will be a factor $\sim\frac{6}{5}$ greater than the LMF method (Eqn. 3).

To reconcile these two different noise formulations, PandExo has the capability to derive the noise using either method by simply changing a key word in the input file. However, the default noise calculation is the simplified LMF method.

The final simulated transmission ($-$) and emission ($+$) spectra combines these and adds a random noise component via the equation:

[TABLE]

where $N_{out/in,\lambda}$ is the total number of photons collected out-of- and in-transit and $N(0,1)$ is a standard normal distribution. The 1$\sigma$ propagated error on the final spectrum, $\sigma_{prop}$ is:

[TABLE]

Greene et al. (2016) argue that a systematic noise floor might inhibit JWST observations to get below 20 ppm, 30 ppm, and 50 ppm, for NIRISS SOSS, NIRCam grism, and MIRI LRS, respectively. This argument was based off of a comparison of the lowest noise achieved with an HST WFC3 G141 observations (Kreidberg et al., 2014b) for the NIR instruments and the lowest noise achieved with a Spitzer Si:As observation (Knutson et al., 2009) for MIRI. However, as with the actual effective saturation limit, the noise floor ($\sigma_{f,\lambda}$) will not be known until after commissioning and Early Release Science (Stevenson et al., 2016). Therefore we do not adopt these same noise floors and leave it up to the observer to input their own. In contrast to Greene et al. (2016), noise floors are not added to $\sigma_{prop,\lambda}$ in quadrature. Instead, PandExo sets $\sigma_{prop,\lambda}(\sigma_{prop,\lambda}<\sigma_{f,\lambda})=\sigma_{f,\lambda}$. This is done solely to increase the transparency of the calculation. The major final PandExo products are shown in Figure 3.

4. Benchmarking PandExo Performance

In the absence of JWST observations, we test the accuracy of PandExo against each instrument team’s independently written noise simulators. Each of the instrument teams used the LMF noise formulation. For completeness we show the LMF (always in blue) and the MULTIACCUM noise derivations (always in red) as well as the pure shot noise (always dashed lines). The following calculations are also all done using a stellar SED from the Phoenix Stellar Database (Husser et al., 2013) with T=4000 K, Fe/H=0.0 and, log$g$=4.0 normalized to a J=8, a model of WASP-12b in transmission from Madhusudhan et al. (2014), and $n_{i,in}=n_{i,out}$ (chosen for simplicity). The results of the comparisons are shown in Figure 4, 5, 7, and 8, and discussed in the following sections.

4.1. NIRCam

To benchmark NIRCam, we used the NIRCam F444W grism mode in conjunction with the SUBGRISM64 subarray ($t_{g}$=0.34 secs). The results are shown in Figure 4. For a target with J=8, we selected $n=55$ groups to optimize the duty cycle without saturating the detectors (eff=0.96) and ran the simulation for a single integration in transit and a single integration out of transit. Because of the high number of groups, PandExo MULTIACCUM, as expected, is a factor of $\sim\frac{6}{5}$ higher than PandExo LMF (see discussion in $\S 3$) and PandExo LMF, as expected, matches within 10% with the instrument team’s results.

NIRCam is also slitless. While PandExo does not directly incorporate position angles to prevent overlapping spectra, it is important to consider this when planning observations.

4.2. NIRISS

To benchmark NIRISS, we used the NIRISS SOSS mode in conjunction with the SUBSTRIP256 subarray ($t_{g}$=5.491 secs). The results are shown in Figure 5. For a target with J=8, only the minimum number of groups, $n=2$, is possible. And even so, this results in a partial saturation of pixels at the peak of the stellar SED. NIRISS simulations were computed with a 2 hour observation in transit, and 2 hour baseline observation out of transit.

Because $n=2$, the MULTIACCUM (red) approximately follows the PandExo LMF (blue). The omitted points in the red curve, and the purple diamonds represent saturated pixels in PandExo and the instrument team’s simulator, respectively. Both teams are saturating identical pixels.

PandExo produces 2-dimensional simulations of detector images and of the saturation profiles. Figure 6 shows the exact pixels that saturated, colored in gray. Because of NIRISS’ widely sampled PSF (23 pixels), it is likely still possible to extract a spectrum by excluding saturated pixels (a decision the observer must make). Ultimately though, if an observation only contains 2 groups, PandExo marks every wavelength bin which contains at least one saturated pixel as completely saturated, regardless of whether or not it may be possible to extract unsaturated data from that bin. PandExo will then produce the following warning statement: “There are [# OF PIXELS] saturated pixels at the end of the first group. These pixels cannot be recovered.” The NIRISS team also alerts users by flagging each pixel considering saturated. By adding in these obvious warnings, users will know they are in a region of parameter space where they will, to some degree, encounter saturated pixels.

An important limitation with NIRISS is contamination by field stars because it is slitless. It is crucial to run the instrument team’s contamination tool to select observing position angles and dates that minimize spectral trace contamination. It complements the instrument team’s 1D simulator 333http://jwst.astro.umontreal.ca/?page_id=401 used for comparison with PandExo.

4.3. NIRSpec

To benchmark NIRSpec, the G395M/F290LP grism/filter was used with the the 32x2048 subarray ($t_{g}=0.90156$ secs). The results are shown in Figure 7. For the benchmarking, we simulated an observation with $n=2$ for a single integration out-of-transit and a single integration in-transit. The instrument team’s simulator, described in Nielsen et al. (2016), can either implement the LMF noise procedure or the ”Last-Minus-Zero” (LMZ) procedure. In this strategy, the observer implements a reset-read-reset scheme with $n=1$. Currently in PandExo the number of groups must be $n\geq 2$. This requirement is a result of Pandeia’s requirements and will be lifted as soon as Pandeia is updated. Here, we only consider the instrument team’s LMF noise formula. As expected, these match within 10%.

4.4. MIRI

For MIRI, the LRS slitless mode was used ($t_{g}$=0.159 secs). Figure 8 shows the results. For a J=8 target, we selected $n=10$ groups to optimize the duty cycle without saturation (eff=0.81) and ran the simulation for a single integration in transit and a single integration out of transit. Similar to NIRCam, the MULTIACCUM PandExo results are offset by $\sim\frac{6}{5}$ because $n>2$.

The PandExo LMF formulation results are in good agreement with the instrument team’s simulations. It should be pointed out that the jagged behavior of the noise curve is solely a result of binning ($\lambda/\Delta\lambda=30$ creates variable pixels per bin) and not an instrument systematic. Also, in both teams simulations MIRI is slightly dominated by read noise and background at long wavelengths ($\lambda>10\micron$) for a target with J=8. This adds another high degree of certainty to the correctness of both calculations. MIRI is also technically slitless, but the SLITLESSPRISM subarray is small enough so that overlapping spectra are not a major issue.

5. PandExo: Simulating HST Observations

In addition to simulating JWST observations, PandExo can simulate realistic uncertainties for HST/WFC3 transmission and emission spectra, optimize instrument setups, and generate scheduling requirements. Accurate spectrophotometric uncertainties are necessary to correctly determine the number of transit/eclipse visits required to obtain a meaningful constraint.

The HST/WFC3 implementation of PandExo predicts spectrophotometric uncertainties for any specified system by first scaling measured flux, variance, and exposure time values from previously-observed systems published in Kreidberg et al. (2014a) and Kreidberg et al. (2014b), then computing the expected rms per spectrophotometric channel per exposure, and finally estimating the transit/eclipse depth error based on the anticipated number of individual valid in- and out-of-transit exposures. The uncertainty estimates depend on the orbital properties of the system, instrument configuration, and observation duration. The code assumes Gaussian-distributed white noise and uniform uncertainties over the G102 and G141 grisms, both of which are consistent with published results (e.g. Kreidberg et al., 2014a; Kreidberg et al., 2014b; Stevenson et al., 2014). PandExo also recommends an observing strategy (best NSAMP and SAMP-SEQ values) optimized to achieve the highest duty cycle (lowest photon-noise rms) and computes an observation start range in units of orbital phase. These instrument and scheduling requirements are important factors to consider when planning proposals and observations in the Astronomer’s Proposal Tools (APT) and can be tedious to compute/optimize manually for a large number of targets.

As inputs, PandExo requires the stellar H-band magnitude, full transit/eclipse duration, number of transits/eclipses, number of spectrophotometric channels, disperser type (G102 or G141), scan direction (forward or round trip), subarray size (GRISM256 or GRISM512), and schedulability (30% for small/medium programs or 100% for large programs). Optional inputs that may be optimized include the number of HST orbits per visit and WFC3’s instrument parameters (NSAMP and SAMP-SEQ). Additional inputs for the scheduling requirement include the orbital parameters (transit/eclipse depth, inclination, separation, eccentricity, longitude of periastron, and period) and the observation start window size (usually 20 – 30 minutes).

Accepting a user-provided model transmission/emission spectrum, PandExo will simulate binned spectrophotometric data with realistic uncertainties and plot the results against the supplied model. As an example, Figure 9A depicts a model emission spectrum of WASP-43b at secondary eclipse as well as simulated and published WFC3/G141 data. For the utilized instrument configuration, the simulated uncertainty is 37.6 ppm and the mean published uncertainty is $36.5{\pm}3.5$ ppm (Stevenson et al., 2014). These values are consistent at 0.3$\sigma$.

For the same example system, Figure 9B & C display simulated WFC3 light curves with the earliest and latest possible observation start times, respectively, that correspond to the computed minimum and maximum phase values of 0.3071 and 0.3241, respectively. The actual observations would commence anywhere in between these two extremes.

Future work for this noise simulator includes adding functionality for the STIS G430 and G750 grisms, computing wavelength-dependent uncertainties, and exploring more sophisticated calculation methods beyond scaling values from previously-observed systems.

6. Conclusion

We introduced a new open-source Python package, called PandExo, which is used for modeling instrumental noise from each of the exoplanet transit time series exoplanet spectroscopy modes with JWST (NIRISS, NIRCam, NIRSpec and, MIRI LRS) and HST (WFC3). PandExo computes noise with two different noise formulations: 1) subtracting the last group from the first group (LMF method) and 2) independently fitting each group up the ramp (MULTIACCUM method) and accounting for correlated noise. The instrument teams’ calculations are in good agreement with PandExo’s noise calculations employing the LMF method.

PandExo currently does not include any photometry modes. However, it is expected to continue evolving as we approach JWST launch date in 2018.

PandExo is available for download on github444https://github.com/natashabatalha/PandExo and there is an associated github pages with full documentation and tutorials 555https://natashabatalha.github.io/PandExo. The online interface is also currently available 666https://pandeexo.science.psu.edu:1111.

7. Acknowledgments

First and foremost, we thank the STScI team responsible for writing and developing Pandeia. This was a huge undertaking which will undoubtedly improve the astrophysics community’s understanding of JWST. We also thank Hannah R. Wakeford and Sarah Blumenthal for countless discussions regarding how to create the most useful and user-friendly tool. Lastly, we thank Natalie M. Batalha, Laura Kreidberg, Ian Crossfield, Kamen Todorov, Nicolas Crouzet and Zach Berta-Thompson for testing PandExo, which resulted in bug fixes and code/installation improvements. This material is based upon work supported by the National Science Foundation under Grant No. DGE1255832 to N.E.B. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NASA or the National Science Foundation.