Creating a Feature Array¶
The first part in training an model with Synference is to create a feature array from the generated model library. This feature array will be used as the training data for the inference model.
This can included any of the following:
Normalization: Scaling the features to a common range.
Noise modelling: Adding realistic noise to the features to simulate observational conditions.
Customising features: Selecting specific features or combinations of features that are most relevant for the inference task.
Adding new features: E.g. colors, supplementary parameters, or moving parameters from being inferred to being part of the features.
Simulating missing data in the training set.
Transforming parameters or features to improve model performance.
First lets load the library and create a basic feature array.
[1]:
from synference import SBI_Fitter, test_data_dir
fitter = SBI_Fitter.init_from_hdf5(
model_name="test", hdf5_path=f"{test_data_dir}/example_model_library.hdf5"
)
/opt/hostedtoolcache/Python/3.10.20/x64/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
The create_feature_array method exposes some of the basic functionality for creating a feature array, but more complex feature array creation can be achieved with the create_feature_array_from_raw_photometry method.
So we can see that the basic functionality will create a feature array with noiseless fluxes in AB magnitudes for all filters in the library, and the parameters to be inferred will be the full set of model parameters.
[2]:
fitter.observation_type
[2]:
'photometry'
[3]:
fitter.create_feature_array();
2026-05-01 18:07:42,626 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:42,627 | synference | INFO | Features: 8 features over 100 samples
2026-05-01 18:07:42,627 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:42,628 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:42,629 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:42,630 | synference | INFO | JWST/NIRCam.F070W: 7.131974 - 42.758 AB
2026-05-01 18:07:42,630 | synference | INFO | JWST/NIRCam.F090W: 7.108530 - 39.933 AB
2026-05-01 18:07:42,631 | synference | INFO | JWST/NIRCam.F115W: 7.012560 - 38.354 AB
2026-05-01 18:07:42,632 | synference | INFO | JWST/NIRCam.F150W: 6.969396 - 36.997 AB
2026-05-01 18:07:42,632 | synference | INFO | JWST/NIRCam.F200W: 7.133157 - 35.470 AB
2026-05-01 18:07:42,633 | synference | INFO | JWST/NIRCam.F277W: 7.670149 - 33.243 AB
2026-05-01 18:07:42,634 | synference | INFO | JWST/NIRCam.F356W: 8.072730 - 32.490 AB
2026-05-01 18:07:42,634 | synference | INFO | JWST/NIRCam.F444W: 8.353975 - 31.965 AB
2026-05-01 18:07:42,635 | synference | INFO | ---------------------------------------------
[4]:
fitter.plot_histogram_feature_array();
2026-05-01 18:07:43,197 | synference | INFO | saving /opt/hostedtoolcache/Python/3.10.20/x64/lib/python3.10/models/test/plots//feature_histogram.png
Now there are some easy changes we could try out here. First, let’s try changing the flux unit to log10 nJy.
[5]:
fitter.create_feature_array(flux_units="log10 nJy")
fitter.plot_histogram_feature_array();
2026-05-01 18:07:44,363 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:44,364 | synference | INFO | Features: 8 features over 100 samples
2026-05-01 18:07:44,365 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:44,365 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:44,367 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:44,367 | synference | INFO | JWST/NIRCam.F070W: -4.543123 - 9.707 log10 nJy
2026-05-01 18:07:44,368 | synference | INFO | JWST/NIRCam.F090W: -3.413210 - 9.717 log10 nJy
2026-05-01 18:07:44,369 | synference | INFO | JWST/NIRCam.F115W: -2.781743 - 9.755 log10 nJy
2026-05-01 18:07:44,369 | synference | INFO | JWST/NIRCam.F150W: -2.238772 - 9.772 log10 nJy
2026-05-01 18:07:44,371 | synference | INFO | JWST/NIRCam.F200W: -1.627814 - 9.707 log10 nJy
2026-05-01 18:07:44,371 | synference | INFO | JWST/NIRCam.F277W: -0.737114 - 9.492 log10 nJy
2026-05-01 18:07:44,372 | synference | INFO | JWST/NIRCam.F356W: -0.436000 - 9.331 log10 nJy
2026-05-01 18:07:44,373 | synference | INFO | JWST/NIRCam.F444W: -0.225931 - 9.218 log10 nJy
2026-05-01 18:07:44,374 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:44,718 | synference | INFO | saving /opt/hostedtoolcache/Python/3.10.20/x64/lib/python3.10/models/test/plots//feature_histogram.png
We can also make it a unyt quantity by passing in flux_units=nJy.
[6]:
from unyt import nJy
fitter.create_feature_array(flux_units=nJy);
2026-05-01 18:07:45,839 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,840 | synference | INFO | Features: 8 features over 100 samples
2026-05-01 18:07:45,841 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,842 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:45,843 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,843 | synference | INFO | JWST/NIRCam.F070W: 0.000029 - 5095777214.931 nJy
2026-05-01 18:07:45,844 | synference | INFO | JWST/NIRCam.F090W: 0.000386 - 5207006253.097 nJy
2026-05-01 18:07:45,845 | synference | INFO | JWST/NIRCam.F115W: 0.001653 - 5688217291.120 nJy
2026-05-01 18:07:45,846 | synference | INFO | JWST/NIRCam.F150W: 0.005771 - 5918909018.545 nJy
2026-05-01 18:07:45,847 | synference | INFO | JWST/NIRCam.F200W: 0.023561 - 5090228277.719 nJy
2026-05-01 18:07:45,848 | synference | INFO | JWST/NIRCam.F277W: 0.183183 - 3104132896.571 nJy
2026-05-01 18:07:45,848 | synference | INFO | JWST/NIRCam.F356W: 0.366437 - 2142437494.029 nJy
2026-05-01 18:07:45,849 | synference | INFO | JWST/NIRCam.F444W: 0.594386 - 1653522697.555 nJy
2026-05-01 18:07:45,850 | synference | INFO | ---------------------------------------------
Finally we can also use asinh magnitudes, but we will need to specify a softening parameter for the asinh magnitudes. Here we will use 1 nJy.
[7]:
fitter.create_feature_array(flux_units="asinh", asinh_softening_parameters=1 * nJy);
2026-05-01 18:07:45,858 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,859 | synference | INFO | Features: 8 features over 100 samples
2026-05-01 18:07:45,860 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,861 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:45,862 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,862 | synference | INFO | JWST/NIRCam.F070W: 7.132040 - 31.400 asinh
2026-05-01 18:07:45,864 | synference | INFO | JWST/NIRCam.F090W: 7.108595 - 31.400 asinh
2026-05-01 18:07:45,864 | synference | INFO | JWST/NIRCam.F115W: 7.012625 - 31.399 asinh
2026-05-01 18:07:45,865 | synference | INFO | JWST/NIRCam.F150W: 6.969461 - 31.397 asinh
2026-05-01 18:07:45,866 | synference | INFO | JWST/NIRCam.F200W: 7.133222 - 31.387 asinh
2026-05-01 18:07:45,866 | synference | INFO | JWST/NIRCam.F277W: 7.670215 - 31.301 asinh
2026-05-01 18:07:45,867 | synference | INFO | JWST/NIRCam.F356W: 8.072795 - 31.202 asinh
2026-05-01 18:07:45,868 | synference | INFO | JWST/NIRCam.F444W: 8.354040 - 31.082 asinh
2026-05-01 18:07:45,868 | synference | INFO | ---------------------------------------------
We can also add features from the parameter array or colors. We can also remove some features such as fluxes in certain filters. Here we will add ‘redshift’ and ‘F444W-F356W’ color and remove the F090W filter.
[8]:
fitter.create_feature_array(
extra_features=["redshift", "F444W-F356W"], photometry_to_remove=["JWST/NIRCam.F090W"]
);
2026-05-01 18:07:45,875 | synference | INFO | Removing 1 photometry filters: ['JWST/NIRCam.F090W'].
2026-05-01 18:07:45,877 | synference | INFO | Tokenizing feature: F444W-F356W
2026-05-01 18:07:45,878 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,878 | synference | INFO | Features: 9 features over 100 samples
2026-05-01 18:07:45,879 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,880 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:45,881 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,882 | synference | INFO | JWST/NIRCam.F070W: 7.131974 - 42.758 AB
2026-05-01 18:07:45,883 | synference | INFO | JWST/NIRCam.F115W: 7.012560 - 38.354 AB
2026-05-01 18:07:45,884 | synference | INFO | JWST/NIRCam.F150W: 6.969396 - 36.997 AB
2026-05-01 18:07:45,885 | synference | INFO | JWST/NIRCam.F200W: 7.133157 - 35.470 AB
2026-05-01 18:07:45,886 | synference | INFO | JWST/NIRCam.F277W: 7.670149 - 33.243 AB
2026-05-01 18:07:45,887 | synference | INFO | JWST/NIRCam.F356W: 8.072730 - 32.490 AB
2026-05-01 18:07:45,888 | synference | INFO | JWST/NIRCam.F444W: 8.353975 - 31.965 AB
2026-05-01 18:07:45,889 | synference | INFO | redshift: 0.000953 - 4.979 None
2026-05-01 18:07:45,889 | synference | INFO | F444W-F356W: -0.757010 - 0.393 None
2026-05-01 18:07:45,891 | synference | INFO | ---------------------------------------------
Since most SED fitting parameters are sensitive only to colors rather than absolute fluxes, we may wish to normalise the fluxes in some way. Here we will normalise the fluxes to the F200W filter.
[9]:
fitter.create_feature_array(normalize_method="JWST/NIRCam.F200W");
2026-05-01 18:07:45,899 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,899 | synference | INFO | Features: 8 features over 100 samples
2026-05-01 18:07:45,900 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,902 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:45,902 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,903 | synference | INFO | JWST/NIRCam.F070W: -0.001183 - 7.830 AB
2026-05-01 18:07:45,904 | synference | INFO | JWST/NIRCam.F090W: -0.024627 - 5.701 AB
2026-05-01 18:07:45,905 | synference | INFO | JWST/NIRCam.F115W: -0.120597 - 3.614 AB
2026-05-01 18:07:45,906 | synference | INFO | JWST/NIRCam.F150W: -0.163761 - 2.019 AB
2026-05-01 18:07:45,906 | synference | INFO | JWST/NIRCam.F277W: -2.226750 - 0.537 AB
2026-05-01 18:07:45,907 | synference | INFO | JWST/NIRCam.F356W: -2.979535 - 0.940 AB
2026-05-01 18:07:45,908 | synference | INFO | JWST/NIRCam.F444W: -3.504708 - 1.221 AB
2026-05-01 18:07:45,908 | synference | INFO | norm_JWST/NIRCam.F200W_AB: 7.133157 - 35.470 AB
2026-05-01 18:07:45,909 | synference | INFO | ---------------------------------------------
Modelling Noise¶
We can apply a simple scatter model to the feature array to simulate observational noise.
[10]:
depths = 3 * nJy # 5 sigma depth of 30.2 AB magnitudes
fitter.create_feature_array(scatter_fluxes=True, depths=depths)
2026-05-01 18:07:45,915 | synference | INFO | Using depth-based noise models with True scatters per row.
2026-05-01 18:07:45,917 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,917 | synference | INFO | Features: 8 features over 100 samples
2026-05-01 18:07:45,918 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,919 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:45,919 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,920 | synference | INFO | JWST/NIRCam.F070W: 7.131974 - 50.000 AB
2026-05-01 18:07:45,920 | synference | INFO | JWST/NIRCam.F090W: 7.108530 - 50.000 AB
2026-05-01 18:07:45,921 | synference | INFO | JWST/NIRCam.F115W: 7.012560 - 50.000 AB
2026-05-01 18:07:45,922 | synference | INFO | JWST/NIRCam.F150W: 6.969396 - 50.000 AB
2026-05-01 18:07:45,923 | synference | INFO | JWST/NIRCam.F200W: 7.133157 - 33.527 AB
2026-05-01 18:07:45,923 | synference | INFO | JWST/NIRCam.F277W: 7.670149 - 50.000 AB
2026-05-01 18:07:45,924 | synference | INFO | JWST/NIRCam.F356W: 8.072730 - 50.000 AB
2026-05-01 18:07:45,925 | synference | INFO | JWST/NIRCam.F444W: 8.353975 - 32.222 AB
2026-05-01 18:07:45,926 | synference | INFO | ---------------------------------------------
[10]:
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22.89300156, 21.89981542, 24.53706129, 31.94385763, 32.35652269,
28.21004816, 20.26405733, 21.18694064, 32.57073644, 27.25712802,
27.4123656 , 29.77726517, 27.37473094, 7.13197392, 27.86504391,
28.95525718, 50. , 28.86469702, 16.71648458, 30.98592135,
27.59983457, 26.86127864, 32.34379112, 27.7093619 , 22.70302672,
28.22037401, 31.52689619, 26.4602559 , 30.53272823, 50. ,
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26.86354866, 29.0208855 , 21.26850138, 20.082008 , 30.46541249,
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23.01409774, 20.81842334, 33.71971044, 19.90570989, 29.16272049,
25.97066713, 25.95870938, 31.94081032, 29.1321622 , 50. ,
17.91975559, 50. , 50. , 26.49808603, 33.004173 ,
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23.18184159, 23.21501708, 31.05438 , 50. , 22.06318484,
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27.47598732, 29.47082498, 24.47507672, 27.97042757, 50. ,
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17.63502352, 30.91596816, 30.20768787, 24.88926508, 27.5103036 ,
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21.7549094 , 20.45679915, 29.01776857, 30.5480353 , 20.85740443,
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23.98320734, 27.59367962, 22.47593128, 26.52989247, 22.52563513],
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19.12456626, 20.51034719, 20.31780839, 27.79413876, 25.46000671,
24.05871171, 19.19372622, 18.442401 , 29.95817774, 25.01586076,
22.87161084, 25.37324532, 24.73716072, 7.67014923, 24.60784385,
25.3869236 , 28.55145709, 27.38949743, 16.41602537, 28.14720294,
23.48603508, 24.07905392, 25.64947794, 25.32623235, 20.29697733,
26.12965739, 28.39508967, 23.95097364, 26.15198015, 50. ,
32.57041576, 29.94147194, 20.92700017, 25.53859903, 26.97020895,
23.22371676, 26.93526787, 29.69250996, 27.97969482, 18.76561707,
25.07708455, 25.37339768, 19.4005389 , 17.48199618, 27.94700407,
20.76620537, 19.81707307, 28.57897158, 28.95980226, 20.47463505,
21.36064618, 18.57366143, 29.18707007, 19.16939645, 25.52878168,
21.53201344, 21.93437148, 29.25732059, 25.21602903, 29.27483719,
18.17501199, 28.46541015, 28.69143349, 23.48440081, 25.18321211,
21.44181335, 24.16431269, 21.92426913, 21.19419193, 20.53760832,
20.94930655, 23.13756546, 26.3153606 , 27.89134428, 28.50154125,
21.55476312, 28.46208878, 26.33097519, 19.31435333, 28.46582867,
24.17013271, 28.61302661, 27.65281189, 21.45509489, 50. ,
21.82447007, 24.3835965 , 17.4200593 , 21.83812622, 27.02821107,
23.31262367, 27.19585183, 21.93660367, 26.4842342 , 22.39112601],
[22.96494207, 23.52243565, 21.12805642, 24.12551158, 26.2300273 ,
19.14324792, 20.79636416, 20.06849219, 27.47469091, 24.89504067,
23.73995271, 19.04107123, 18.38001672, 29.28910426, 24.80208321,
22.48364918, 25.00849099, 24.39992105, 8.0727296 , 24.37891083,
24.99748989, 27.98689183, 27.36216609, 16.78436192, 28.02245444,
23.1243893 , 23.77947423, 24.89441124, 25.15885295, 20.05815645,
25.99770498, 28.15635632, 23.75734591, 25.75115521, 32.93304708,
50. , 29.64096327, 20.69184504, 25.08161331, 26.44632132,
23.01549142, 26.71792049, 29.38874042, 27.41707996, 19.21770538,
25.32574934, 25.13733247, 19.15452437, 17.82344973, 27.68860664,
20.57473324, 19.40266316, 28.10455571, 28.726918 , 20.40050611,
21.26713147, 18.93095893, 28.36941986, 19.57707454, 25.32560246,
21.07464238, 21.52992822, 29.07403307, 25.05202248, 29.3892281 ,
18.57330135, 28.13097479, 28.16520093, 23.29202443, 24.60726862,
21.7109319 , 23.95501698, 21.58481786, 21.14420744, 20.6526004 ,
21.23184646, 22.98055815, 26.25512741, 27.68484306, 28.29537027,
21.32354225, 27.94383305, 26.07551062, 19.09580082, 28.44365242,
23.85707699, 28.03845694, 27.52538917, 21.89509636, 31.72240429,
21.45955673, 23.90952928, 17.82867051, 21.41802714, 26.91964946,
22.83679137, 26.98377664, 21.65950407, 26.35946502, 22.68068183],
[22.82034097, 23.39731413, 20.92690624, 23.90451805, 26.08869312,
19.46581467, 21.08438126, 19.88987035, 27.14323967, 24.51829407,
23.55102879, 18.96484709, 18.75695446, 28.98476508, 24.78780603,
22.25869314, 24.74472157, 24.17509004, 8.3539746 , 24.60293067,
24.84149753, 27.46341308, 27.29587519, 17.11622639, 27.83283268,
22.80535284, 23.84970922, 24.26345355, 25.17061559, 19.88086761,
25.93085038, 28.05059299, 23.90282478, 25.23724415, 32.2219489 ,
30.98742319, 29.27399047, 20.39339709, 24.69813392, 26.12035451,
22.77209597, 26.57554608, 28.8716333 , 26.95752136, 19.57570531,
25.57481622, 25.19193103, 19.01550769, 17.9534278 , 27.80213182,
20.46703926, 19.052833 , 27.79569232, 28.56459672, 20.74265955,
21.2311541 , 19.14324876, 27.66826452, 19.86997189, 25.60739825,
20.76940113, 21.6631797 , 28.74506153, 24.74899392, 29.06220757,
18.87027538, 27.89319577, 27.89883457, 23.0304636 , 24.21580458,
21.91313652, 23.75658242, 21.37090199, 21.02578226, 20.61268858,
21.46395503, 22.77152315, 26.60468215, 27.82557919, 27.89338812,
21.23892995, 27.64613136, 25.75105464, 18.92788385, 28.35195831,
23.6326445 , 27.8481436 , 27.39206672, 22.10880491, 31.22696432,
21.24994223, 23.80492272, 18.1434544 , 21.03735649, 27.07922099,
22.64996816, 26.79345089, 21.52988318, 26.3237514 , 23.07340052]]),
['JWST/NIRCam.F070W',
'JWST/NIRCam.F090W',
'JWST/NIRCam.F115W',
'JWST/NIRCam.F150W',
'JWST/NIRCam.F200W',
'JWST/NIRCam.F277W',
'JWST/NIRCam.F356W',
'JWST/NIRCam.F444W'])
Missing Fluxes¶
Changing the Parameter Array¶
We can also change our parameter array, which is created automatically when we create the feature array. Here we will only infer ‘stellar_mass’.
[11]:
fitter.create_feature_array(
parameters_to_remove=["redshift", "tau_v", "tau", "peak_age", "log10metallicity"]
)
fitter.plot_histogram_parameter_array();
2026-05-01 18:07:45,938 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,939 | synference | INFO | Features: 8 features over 100 samples
2026-05-01 18:07:45,939 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,941 | synference | INFO | Feature: Min - Max
2026-05-01 18:07:45,941 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,942 | synference | INFO | JWST/NIRCam.F070W: 7.131974 - 42.758 AB
2026-05-01 18:07:45,943 | synference | INFO | JWST/NIRCam.F090W: 7.108530 - 39.933 AB
2026-05-01 18:07:45,943 | synference | INFO | JWST/NIRCam.F115W: 7.012560 - 38.354 AB
2026-05-01 18:07:45,944 | synference | INFO | JWST/NIRCam.F150W: 6.969396 - 36.997 AB
2026-05-01 18:07:45,945 | synference | INFO | JWST/NIRCam.F200W: 7.133157 - 35.470 AB
2026-05-01 18:07:45,945 | synference | INFO | JWST/NIRCam.F277W: 7.670149 - 33.243 AB
2026-05-01 18:07:45,947 | synference | INFO | JWST/NIRCam.F356W: 8.072730 - 32.490 AB
2026-05-01 18:07:45,947 | synference | INFO | JWST/NIRCam.F444W: 8.353975 - 31.965 AB
2026-05-01 18:07:45,949 | synference | INFO | ---------------------------------------------
2026-05-01 18:07:45,997 | synference | INFO | saving /opt/hostedtoolcache/Python/3.10.20/x64/lib/python3.10/models/test/plots//param_histogram.png
