Simulating observations with MUSTANG-2¶
MUSTANG-2 is a bolometric array on the Green Bank Telescope. In this notebook we simulate an observation of the Crab Nebula (M1).
[1]:
import maria
input_map = maria.map.get("maps/M1.h5").to("uK_RJ")
input_map.plot(slices=dict(nu=[0, 1, 2]))
print(input_map)
2026-06-02 15:49:15.106 INFO: Fetching https://github.com/thomaswmorris/maria-data/raw/master/maps/M1.h5
Downloading: 100%|██████████| 21.8M/21.8M [00:00<00:00, 169MB/s]
ProjectionMap:
data(1, 3, 1205, 1187):
min: -4.145e+09
max: 6.287e+08
units: uK_RJ
quantity: rayleigh_jeans_temperature
stokes(1):
components: ['I']
nu(3):
values: [149.8962 214.1375 272.5386] GHz
eta(1205):
height: 20.07’
res: -1”
xi(1187):
width: 19.77’
res: 1”
frame: ra/dec
center:
ra: 05ʰ34ᵐ31.95ˢ
dec: 22°00’52.16”
beam(maj, min, psi): ragged
memory: 34.33 MB
[2]:
from maria import Planner
planner = Planner(target=input_map, site="green_bank", constraints={"el": (60, 90)})
plans = planner.generate_plans(total_duration=600, sample_rate=100)
plans[0].plot()
print(plans)
PlanList(1 plans, 600 s):
start_time duration target(ra,dec) center(az,el)
chunk
0 2026-06-02 16:13:43.462 +00:00 600 s (83.63°, 22.01°) (116.2°, 60.94°)
[3]:
instrument = maria.get_instrument("MUSTANG-2")
print(instrument)
instrument.plot()
Instrument(1 array)
├ arrays:
│ n FOV baseline bands polarized
│ array1 217 4.2’ 0 m [m2/f093] False
│
└ bands:
name center width η NEP NET_RJ NET_CMB FWHM
0 m2/f093 86.21 GHz 20.98 GHz 0.1 15 aW√s 571.1 uK_RJ√s 690.5 uK_CMB√s 9.133”
[4]:
sim = maria.Simulation(
instrument,
plans=plans,
site="green_bank",
map=input_map,
atmosphere="2d",
)
print(sim)
2026-06-02 15:49:27.084 INFO: Fetching https://github.com/thomaswmorris/maria-data/raw/master/atmosphere/spectra/am/v3/green_bank.h5
Downloading: 100%|██████████| 22.0M/22.0M [00:00<00:00, 185MB/s]
2026-06-02 15:49:27.832 INFO: Fetching https://github.com/thomaswmorris/maria-data/raw/master/atmosphere/weather/era5/green_bank.h5
Downloading: 100%|██████████| 12.0M/12.0M [00:00<00:00, 138MB/s]
Simulation
├ Instrument(1 array)
│ ├ arrays:
│ │ n FOV baseline bands polarized
│ │ array1 217 4.2’ 0 m [m2/f093] False
│ │
│ └ bands:
│ name center width η NEP NET_RJ NET_CMB FWHM
│ 0 m2/f093 86.21 GHz 20.98 GHz 0.1 15 aW√s 571.1 uK_RJ√s 690.5 uK_CMB√s 9.133”
├ Site:
│ region: green_bank
│ timezone: America/New_York
│ location:
│ longitude: 79°50’23.28” W
│ latitude: 38°25’59.16” N
│ altitude: 825 m
│ seasonal: True
│ diurnal: True
├ PlanList(1 plans, 600 s):
│ start_time duration target(ra,dec) center(az,el)
│ chunk
│ 0 2026-06-02 16:13:43.462 +00:00 600 s (83.63°, 22.01°) (116.2°, 60.94°)
├ '2d'
└ ProjectionMap:
data(1, 3, 1, 1205, 1187):
min: -4.145e+09
max: 6.287e+08
units: uK_RJ
quantity: rayleigh_jeans_temperature
stokes(1):
components: ['I']
nu(3):
values: [149.8962 214.1375 272.5386] GHz
t(1):
values: [1.78041535e+09] s
eta(1205):
height: 20.07’
res: -1”
xi(1187):
width: 19.77’
res: 1”
frame: ra/dec
center:
ra: 05ʰ34ᵐ31.95ˢ
dec: 22°00’52.16”
beam(maj, min, psi): ragged
memory: 34.33 MB
[5]:
tods = sim.run()
tods[0].plot()
2026-06-02 15:49:28.482 INFO: Simulating observation 1 of 1
Constructing atmosphere: 100%|██████████| 8/8 [00:00<00:00, 13.37it/s]
Generating turbulence: 100%|██████████| 8/8 [00:00<00:00, 47.99it/s]
Sampling turbulence: 100%|██████████| 8/8 [00:03<00:00, 2.04it/s]
Computing atmospheric emission: 100%|██████████| 1/1 [00:00<00:00, 1.44it/s, band=m2/f093]
Sampling map: 100%|██████████| 1/1 [00:06<00:00, 6.49s/it, band=m2/f093, channel=(0 Hz, 182 GHz)]
Generating noise: 100%|██████████| 1/1 [00:01<00:00, 1.30s/it, band=m2/f093]
2026-06-02 15:49:50.395 INFO: Simulated observation 1 of 1 in 21.9 s
[6]:
from maria.mappers import MaximumLikelihoodMapper
mapper = MaximumLikelihoodMapper(
stokes="I",
tod_preprocessing={
"remove_spline": {"knot_spacing": 60, "remove_el_gradient_order": 1},
},
units="uK_RJ",
tods=tods,
resolution=2 * input_map.resolution,
)
mapper.map.plot()
2026-06-02 15:49:56.114 INFO: Inferring center {'ra': '05ʰ34ᵐ31.55ˢ', 'dec': '22°00’47.24”'} for mapper
2026-06-02 15:49:56.124 INFO: Inferring mapper width 23.66’ for mapper from observation patch
2026-06-02 15:49:56.124 INFO: Inferring mapper height 23.66’ to match supplied width
Preprocessing TODs: 100%|██████████| 1/1 [00:04<00:00, 4.87s/it]
Computing pointing matrices: 100%|██████████| 1/1 [00:02<00:00, 2.45s/it]
[7]:
mapper.fit(epochs=2, steps_per_epoch=25)
mapper.map.plot()
Updating noise model: 100%|██████████| 1/1 [00:02<00:00, 2.95s/it, tod=1/1]
Fitting map (epoch 1/2): 100%|██████████| 25/25 [00:54<00:00, 2.17s/it, mll=-8.275e-12, pgrad=0.932, step=1.503e+21]
Updating noise model: 100%|██████████| 1/1 [00:02<00:00, 2.91s/it, tod=1/1]
Fitting map (epoch 2/2): 100%|██████████| 25/25 [00:56<00:00, 2.25s/it, mll=-6.398e-12, pgrad=0.549, step=1.326e+21]