MIRI Observations of Vesta Family

MIRI Observations of Vesta Family#

This tutorial outlines the observation planning for Vesta family members using the MIRI instrument during JWST Cycle 6. Our main objective is to study silicate composition, which requires achieving a target Signal-to-Noise Ratio (SNR) of 300 around 10μm. As the 10μm feature is accessible with both MIRI Low Resolution Spectrometer (LRS) and Medium Resolution Spectrometer (MRS), we use both modes.

We place the following constraints:

· Targets must be Vesta family members with a known diameter.

· The sample must span a range of diameters to encompass different sizes.

· Required SNR is 300 around 10μm.

Identifying potential targets#

We start by identifying all Vesta family members that have a known diameter, a prerequisite for accurately modelling their thermal emission. We use the rocks package to access the SsODNet/BFT asteroid database.

import rocks

# Load table of asteroid properties (SsODNet/BFT)
bft = rocks.load_bft()

# Select candidate targets: Vesta family members with known diameter
is_vestoid = bft["family.family_name"] == "Vesta"
has_diameter = bft["diameter.value"].notnull()

vestoids = bft[is_vestoid & has_diameter]

This initial selection yields 2041 Vesta family members with known diameters at the time of writing, all of which are potential targets.

Defining MIRI configurations#

We configure the MIRI MRS and LRS modes using jayrock, setting the optics, exposure times, and readout patterns for each.

MRS Mode#

For the MRS, we set a standard 4-point dither (nexp=4). To target the 10μm silicate feature, we initially set the aperture to Channel 2 and the Long disperser.

import jayrock

miri_mrs = jayrock.Instrument('MIRI', mode='MRS')

miri_mrs.detector.nexp = 4  # 4-pt dither
miri_mrs.detector.readout_pattern = 'fastr1'  # recommended for MRS

miri_mrs.aperture = "ch2"
miri_mrs.disperser = "long"

LRS Mode#

We configure the LRS for SLIT mode and specify a 2-point dither (nexp=2). Since LRS has only one valid optical component, no further configuration is required.

miri_lrs = jayrock.Instrument('MIRI', mode='LRSSLIT')

miri_lrs.detector.nexp = 2  # 2-pt dither
miri_lrs.detector.readout_pattern = 'fastr1'  # recommended for LRS

Determining observable diameters#

To understand the diameter range achievable with each MIRI mode, we select the largest Vesta family member within a set of representative diameter bins.

import pandas as pd

# ------
# Get largest Vestoid within diameter range
diameter_bins = [1, 2, 4, 8, 12, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024]

vestoids["diameter_bin"] = pd.cut(
    vestoids["diameter.value"], bins=diameter_bins, right=True, include_lowest=True
)

vestoids_largest_per_diameter_bin = vestoids.loc[
    vestoids.groupby("diameter_bin", observed=True)["diameter.value"].idxmax()
]
print(vestoids_largest_per_diameter_bin[["sso_name", "diameter.value", "diameter_bin"]])

   sso_name  diameter.value     diameter_bin
   2000 WE8           1.998     (0.999, 2.0]
 2000 QA163           3.995       (2.0, 4.0]
     Kollaa           7.922       (4.0, 8.0]
Koskenniemi          11.172      (8.0, 12.0]
       Mila          13.243     (12.0, 16.0]
 Robelmonte          23.834     (16.0, 24.0]
    Ausonia          93.000     (64.0, 96.0]
      Vesta         525.400  (512.0, 1024.0]

(4) Vesta is by far the largest family member, followed by (63) Ausonia at 93km diameter. No family members have sizes between 24 and 64km.

Estimating exposure times#

We now iterate through this sample to estimate the minimum exposure time required to achieve an SNR between 300 and 400 at 10μm. Using an SNR range (instead of a single value) speeds up the binary search, as the process stops once any ngroup and nint combination is found. The resulting exposure times are thus appropriate upper limits. Once we have our target list, we will refine the exposure times further.

For each target, we:

Compute its ephemeris for Cycle 6.
Identify the date of minimum thermal flux (thermal_min) during its visibility window, representing the most challenging observation scenario.
Estimate the necessary exposure settings (ngroup and nint) for both MRS and LRS modes.

# ------
# Estimate texp to reach SNR 300-400
for idx, asteroid in vestoids_largest_per_diameter_bin.iterrows():
    # Define target and compute ephemeris for JWST Cycle 6
    target = jayrock.Target(asteroid["sso_name"])
    target.compute_ephemeris(cycle=6)
    target.print_ephemeris()

    # Add total length of visibility windows to dataframe
    vestoids.loc[idx, "N_days_observable"] = len(target.ephemeris)

    # Get date of minimum thermal flux during visibility window
    date_obs = target.get_date_obs(at="thermal_min")

    # Compute exposure times for SNR targets
    for inst in [miri_mrs, miri_lrs]:
        # Estimate ngroup and nint
        success = inst.set_snr_target([300, 400], target, date_obs)

        if not success:
            print(f" Could not estimate SNR, skipping...")
            continue

        # Record results
        vestoids.loc[idx, f"nint_{inst.mode}"] = inst.detector.nint
        vestoids.loc[idx, f"ngroup_{inst.mode}"] = inst.detector.ngroup
        vestoids.loc[idx, f"texp_{inst.mode}"] = inst.texp
        vestoids.loc[idx, f"snr_{inst.mode}"] = inst.estimated_snr

Example outputs of these calculations for three targets are shown below.

The table below shows the exposure times (in seconds). A value of NaN indicates that the target saturates even at the minimum exposure settings.

   sso_name  diameter.value     texp_mrs  texp_lrsslit
   2000 WE8           1.998  15684.52608    1115.56608
 2000 QA163           3.995   3352.24832     288.60416
     Kollaa           7.922   1110.01600      88.80128
Koskenniemi          11.172    577.20832      55.50080
       Mila          13.243    577.20832      55.50080
 Robelmonte          23.834     77.70112           NaN
    Ausonia          93.000     55.50080           NaN
      Vesta         525.400          NaN           NaN

The exposure times confirm that MIRI MRS (IFU mode) is less sensitive than MIRI LRS (slit mode). MRS can observe larger, brighter targets without saturation, yet it requires considerably longer exposure times for the smaller targets compared to LRS. LRS saturates for targets larger than ∼24 km, and Vesta saturates in both modes.

We can visualise this trade-off by plotting the exposure time against the diameter:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.scatter(
    vestoids_largest_per_diameter_bin["diameter.value"],
    vestoids_largest_per_diameter_bin["texp_mrs"] / 60,
    label="MIRI MRS",
    s=50,
)

ax.scatter(
    vestoids_largest_per_diameter_bin["diameter.value"],
    vestoids_largest_per_diameter_bin["texp_lrsslit"] / 60,
    label="MIRI LRS",
    s=50,
    marker="s"
)

ax.set(xscale="log", yscale="log", xlabel="Diameter / km", ylabel="Exposure time / min")
ax.legend()
plt.show()

_images/vesta_lrsmrs_dark.png — **Fig. 1:** *Required total exposure times for SNRs between 300-400 versus diameter for selected Vestoids.*#

_images/vesta_lrsmrs.png — **Fig. 1:** *Required total exposure times for SNRs between 300-400 versus diameter for selected Vestoids.*#

Defining final observing strategy#

Based on the exposure time analysis:

· MIRI MRS is suitable for targets down to approximately 4km diameter to meet the SNR requirement within a reasonable exposure time.

· MIRI LRS is necessary for the smallest targets, those <4km.

For this example, we select the five targets ranging from 8km to 93km for observations in MRS mode and the two targets under 4km for observations in LRS mode.

Configuring MRS mode#

For MRS, we must calculate the required exposure settings across all four channels and three dispersers. Channels 2 and 3 cover the key silicate features, so we set their target SNR to 300. Channel 4 is less sensitive and is set to SNR 100. For Channel 1, which has low throughput and is observed concurrently with Channel 2, we use the minimum exposure settings (ngroup=5, nint=1).

TARGETS_MRS = ["Kollaa", "Koskenniemi", "Mila", "Robelmonte", "Ausonia"]

SNR_targets = {
    'ch2': {'short': 300, 'medium': 300, 'long': 300},
    'ch3': {'short': 300, 'medium': 300, 'long': 300},
    'ch4': {'short': 100, 'medium': 100, 'long': 100},
}

We now loop over our targets and instrument configurations to compute the required nint and ngroup for the APT. We always use 4-point dithers (nexp=4) for the target observations. We store these parameters in a dictionary to avoid recomputing them later.

MRS_OBSERVING_PARAMETERS = {}

for target in TARGETS_MRS:
    # Define target and compute ephemeris for JWST Cycle 6
    target = jayrock.Target(target)
    target.compute_ephemeris(cycle=6)

    # Get date of minimum thermal flux during visibility window
    date_obs = target.get_date_obs(at="thermal_min")

    for channel in ["ch1", "ch2", "ch3", "ch4"]:
        for disperser in ["short", "medium", "long"]:
            # Set aperture and disperser
            miri_mrs.aperture = channel
            miri_mrs.disperser = disperser

            if channel == "ch1":
                # Use minimum ngroup/nint
                miri_mrs.detector.ngroup = 5
                miri_mrs.detector.nint = 1
            else:
                # Get SNR target
                snr_target = SNR_targets[channel][disperser]
                # Estimate ngroup and nint based on SNR target
                miri_mrs.set_snr_target(snr_target, target, date_obs)

            print(
                f"{target.name:15s} | {channel:3s} | {disperser:6s} | "
                f"ngroup: {miri_mrs.detector.ngroup:2d} | nint: {miri_mrs.detector.nint:2d} | "
                f"texp: {miri_mrs.texp / 60:6.2f} min"
            )

            if target.name not in MRS_OBSERVING_PARAMETERS:
                MRS_OBSERVING_PARAMETERS[target.name] = {}

            MRS_OBSERVING_PARAMETERS[target.name][(channel, disperser)] = {
                "ngroup": miri_mrs.detector.ngroup,
                "nint": miri_mrs.detector.nint,
            }

MRS_OBSERVING_PARAMETERS = {
 "Kollaa": {
     ("ch1", "short"): {"ngroup": 5, "nint": 1},
     ("ch1", "medium"): {"ngroup": 5, "nint": 1},
     ("ch1", "long"): {"ngroup": 5, "nint": 1},
     ("ch2", "short"): {"ngroup": 83, "nint": 4},
     ("ch2", "medium"): {"ngroup": 69, "nint": 2},
     ("ch2", "long"): {"ngroup": 74, "nint": 1},
     ("ch3", "short"): {"ngroup": 62, "nint": 1},
     ("ch3", "medium"): {"ngroup": 47, "nint": 1},
     ("ch3", "long"): {"ngroup": 40, "nint": 1},
     ("ch4", "short"): {"ngroup": 28, "nint": 1},
     ("ch4", "medium"): {"ngroup": 48, "nint": 1},
     ("ch4", "long"): {"ngroup": 91, "nint": 3},
 },
 "Koskenniemi": {
     ("ch1", "short"): {"ngroup": 5, "nint": 1},
     ("ch1", "medium"): {"ngroup": 5, "nint": 1},
     ("ch1", "long"): {"ngroup": 5, "nint": 1},
     ("ch2", "short"): {"ngroup": 86, "nint": 2},
     ("ch2", "medium"): {"ngroup": 72, "nint": 1},
    # ...
    },
    # ...
}

Checking SNR and saturation#

We check the full SNR curve at both the date of minimum and maximum thermal flux to confirm the observation avoids saturation across the full visibility window.

for target, params in MRS_OBSERVING_PARAMETERS.items():
    observations = []

    print(f"Target: {target}")
    target = jayrock.Target(target)
    target.compute_ephemeris(cycle=6)

    date_obs_thermal_min = target.get_date_obs(at="thermal_min")
    date_obs_thermal_max = target.get_date_obs(at="thermal_max")

    for (channel, disperser), settings in params.items():
        ngroup = settings["ngroup"]
        nint = settings["nint"]

        miri_mrs.aperture = channel
        miri_mrs.disperser = disperser
        miri_mrs.detector.ngroup = ngroup
        miri_mrs.detector.nint = nint

        obs_thermal_min = jayrock.observe(target, miri_mrs, date_obs_thermal_min)
        obs_thermal_max = jayrock.observe(target, miri_mrs, date_obs_thermal_max)
        observations.append(obs_thermal_min)
        observations.append(obs_thermal_max)

    # Save plot to file
    jayrock.plot_snr(
        observations, show=False, save_to=f"{target.name}_miri_mrs_snr.ng"
    )

Configuring LRS mode#

We calculate the settings for the two smallest targets using MIRI LRS, aiming for an SNR of 300 at 10μm.

TARGETS_LRS = ["2000 WE8", "2000 QA163"]
SNR_TARGET = 300

LRS_OBSERVING_PARAMETERS = {}

for target in TARGETS_LRS:
    # Define target and compute ephemeris for JWST Cycle 6
    target = jayrock.Target(target)
    target.compute_ephemeris(cycle=6)

    # Get date of minimum thermal flux during visibility window
    date_obs = target.get_date_obs(at="thermal_min")

    # Compute exposure times for SNR target at 10 micron
    miri_lrs.set_snr_target(SNR_TARGET, target, date_obs, wave=10)

    print(
        f"{target.name:15s} | "
        f"ngroup: {miri_lrs.detector.ngroup:2d} | nint: {miri_lrs.detector.nint:2d} | "
        f"texp: {miri_lrs.texp / 60:6.2f} min"
    )

    LRS_OBSERVING_PARAMETERS[target.name] = {
        "ngroup": miri_lrs.detector.ngroup,
        "nint": miri_lrs.detector.nint,
    }

LRS_OBSERVING_PARAMETERS = {
    "2000 WE8": {"ngroup": 65, "nint": 2},
    "2000 QA163": {"ngroup": 36, "nint": 1},
}

Checking SNR and saturation#

We confirm that the LRS observations also avoid saturation at the date of highest thermal emission.

for target, params in LRS_OBSERVING_PARAMETERS.items():
    observations = []

    print(f"Target: {target}")
    target = jayrock.Target(target)
    target.compute_ephemeris(cycle=6)

    date_obs_thermal_min = target.get_date_obs(at="thermal_min")
    date_obs_thermal_max = target.get_date_obs(at="thermal_max")

    miri_lrs.detector.ngroup = params["ngroup"]
    miri_lrs.detector.nint = params["nint"]

    obs_thermal_min = jayrock.observe(target, miri_lrs, date_obs_thermal_min)
    obs_thermal_max = jayrock.observe(target, miri_lrs, date_obs_thermal_max)
    observations.append(obs_thermal_min)
    observations.append(obs_thermal_max)

    # Save plot to file
    jayrock.plot_snr(
        observations, show=False, save_to=f"{target.name}_miri_lrs_snr.png"
    )

The results show no saturation issues for the two small LRS targets at either thermal extreme.

Verification with JWST ETC and APT#

The MRS_OBSERVING_PARAMETERS and LRS_OBSERVING_PARAMETERS dictionaries contain all required information to set up the observations in APT. To verify that the SNRs and saturation levels match between Jayrock and the JWST ETC, we can export the spectra for each target and import them into the ETC.

TARGETS = TARGETS_MRS + TARGETS_LRS

for target in TARGETS:
    target = jayrock.Target(target)
    target.compute_ephemeris(cycle=6)

    date_obs = target.get_date_obs(at="thermal_min")
    target.export_spectrum(f"{target.name}_spectrum_thermal_minimum.txt", date_obs=date_obs)

MIRI Observations of Vesta Family

Contents

MIRI Observations of Vesta Family#

Identifying potential targets#

Defining MIRI configurations#

MRS Mode#

LRS Mode#

Determining observable diameters#

Estimating exposure times#

Defining final observing strategy#

Configuring MRS mode#

Checking SNR and saturation#

Configuring LRS mode#

Checking SNR and saturation#

Verification with JWST ETC and APT#