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/3.10.19/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();

2025-11-13 20:08:26,497 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:26,498 | synference | INFO     | Features: 8 features over 100 samples
2025-11-13 20:08:26,499 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:26,500 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:26,501 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:26,501 | synference | INFO     | JWST/NIRCam.F070W: 7.131974 - 42.758 AB
2025-11-13 20:08:26,503 | synference | INFO     | JWST/NIRCam.F090W: 7.108530 - 39.933 AB
2025-11-13 20:08:26,503 | synference | INFO     | JWST/NIRCam.F115W: 7.012560 - 38.354 AB
2025-11-13 20:08:26,504 | synference | INFO     | JWST/NIRCam.F150W: 6.969396 - 36.997 AB
2025-11-13 20:08:26,505 | synference | INFO     | JWST/NIRCam.F200W: 7.133157 - 35.470 AB
2025-11-13 20:08:26,506 | synference | INFO     | JWST/NIRCam.F277W: 7.670149 - 33.243 AB
2025-11-13 20:08:26,507 | synference | INFO     | JWST/NIRCam.F356W: 8.072730 - 32.490 AB
2025-11-13 20:08:26,508 | synference | INFO     | JWST/NIRCam.F444W: 8.353975 - 31.965 AB
2025-11-13 20:08:26,508 | synference | INFO     | ---------------------------------------------

[4]:

fitter.plot_histogram_feature_array();

2025-11-13 20:08:26,899 | synference | INFO     | saving /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/models/test/plots//feature_histogram.png
../_images/sbi_train_feature_array_6_1.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();

2025-11-13 20:08:28,110 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:28,111 | synference | INFO     | Features: 8 features over 100 samples
2025-11-13 20:08:28,112 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:28,113 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:28,114 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:28,115 | synference | INFO     | JWST/NIRCam.F070W: -4.543123 - 9.707 log10 nJy
2025-11-13 20:08:28,115 | synference | INFO     | JWST/NIRCam.F090W: -3.413210 - 9.717 log10 nJy
2025-11-13 20:08:28,116 | synference | INFO     | JWST/NIRCam.F115W: -2.781743 - 9.755 log10 nJy
2025-11-13 20:08:28,117 | synference | INFO     | JWST/NIRCam.F150W: -2.238772 - 9.772 log10 nJy
2025-11-13 20:08:28,118 | synference | INFO     | JWST/NIRCam.F200W: -1.627814 - 9.707 log10 nJy
2025-11-13 20:08:28,119 | synference | INFO     | JWST/NIRCam.F277W: -0.737114 - 9.492 log10 nJy
2025-11-13 20:08:28,120 | synference | INFO     | JWST/NIRCam.F356W: -0.436000 - 9.331 log10 nJy
2025-11-13 20:08:28,120 | synference | INFO     | JWST/NIRCam.F444W: -0.225931 - 9.218 log10 nJy
2025-11-13 20:08:28,121 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:28,492 | synference | INFO     | saving /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/models/test/plots//feature_histogram.png
../_images/sbi_train_feature_array_8_1.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);

2025-11-13 20:08:29,956 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,956 | synference | INFO     | Features: 8 features over 100 samples
2025-11-13 20:08:29,957 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,958 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:29,959 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,960 | synference | INFO     | JWST/NIRCam.F070W: 0.000029 - 5095777214.931 nJy
2025-11-13 20:08:29,960 | synference | INFO     | JWST/NIRCam.F090W: 0.000386 - 5207006253.097 nJy
2025-11-13 20:08:29,961 | synference | INFO     | JWST/NIRCam.F115W: 0.001653 - 5688217291.120 nJy
2025-11-13 20:08:29,962 | synference | INFO     | JWST/NIRCam.F150W: 0.005771 - 5918909018.545 nJy
2025-11-13 20:08:29,962 | synference | INFO     | JWST/NIRCam.F200W: 0.023561 - 5090228277.719 nJy
2025-11-13 20:08:29,963 | synference | INFO     | JWST/NIRCam.F277W: 0.183183 - 3104132896.571 nJy
2025-11-13 20:08:29,964 | synference | INFO     | JWST/NIRCam.F356W: 0.366437 - 2142437494.029 nJy
2025-11-13 20:08:29,964 | synference | INFO     | JWST/NIRCam.F444W: 0.594386 - 1653522697.555 nJy
2025-11-13 20:08:29,966 | 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);

2025-11-13 20:08:29,976 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,977 | synference | INFO     | Features: 8 features over 100 samples
2025-11-13 20:08:29,978 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,978 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:29,979 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,980 | synference | INFO     | JWST/NIRCam.F070W: 7.132040 - 31.400 asinh
2025-11-13 20:08:29,981 | synference | INFO     | JWST/NIRCam.F090W: 7.108595 - 31.400 asinh
2025-11-13 20:08:29,981 | synference | INFO     | JWST/NIRCam.F115W: 7.012625 - 31.399 asinh
2025-11-13 20:08:29,982 | synference | INFO     | JWST/NIRCam.F150W: 6.969461 - 31.397 asinh
2025-11-13 20:08:29,983 | synference | INFO     | JWST/NIRCam.F200W: 7.133222 - 31.387 asinh
2025-11-13 20:08:29,983 | synference | INFO     | JWST/NIRCam.F277W: 7.670215 - 31.301 asinh
2025-11-13 20:08:29,984 | synference | INFO     | JWST/NIRCam.F356W: 8.072795 - 31.202 asinh
2025-11-13 20:08:29,985 | synference | INFO     | JWST/NIRCam.F444W: 8.354040 - 31.082 asinh
2025-11-13 20:08:29,986 | 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"]
);

2025-11-13 20:08:29,994 | synference | INFO     | Removing 1 photometry filters: ['JWST/NIRCam.F090W'].
2025-11-13 20:08:29,996 | synference | INFO     | Tokenizing feature: F444W-F356W
2025-11-13 20:08:29,996 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,997 | synference | INFO     | Features: 9 features over 100 samples
2025-11-13 20:08:29,998 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:29,999 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:29,999 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,000 | synference | INFO     | JWST/NIRCam.F070W: 7.131974 - 42.758 AB
2025-11-13 20:08:30,001 | synference | INFO     | JWST/NIRCam.F115W: 7.012560 - 38.354 AB
2025-11-13 20:08:30,001 | synference | INFO     | JWST/NIRCam.F150W: 6.969396 - 36.997 AB
2025-11-13 20:08:30,002 | synference | INFO     | JWST/NIRCam.F200W: 7.133157 - 35.470 AB
2025-11-13 20:08:30,003 | synference | INFO     | JWST/NIRCam.F277W: 7.670149 - 33.243 AB
2025-11-13 20:08:30,003 | synference | INFO     | JWST/NIRCam.F356W: 8.072730 - 32.490 AB
2025-11-13 20:08:30,004 | synference | INFO     | JWST/NIRCam.F444W: 8.353975 - 31.965 AB
2025-11-13 20:08:30,005 | synference | INFO     | redshift: 0.000953 - 4.979 None
2025-11-13 20:08:30,006 | synference | INFO     | F444W-F356W: -0.757010 - 0.393 None
2025-11-13 20:08:30,006 | 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");

2025-11-13 20:08:30,014 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,015 | synference | INFO     | Features: 8 features over 100 samples
2025-11-13 20:08:30,016 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,017 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:30,017 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,018 | synference | INFO     | JWST/NIRCam.F070W: -0.001183 - 7.830 AB
2025-11-13 20:08:30,020 | synference | INFO     | JWST/NIRCam.F090W: -0.024627 - 5.701 AB
2025-11-13 20:08:30,021 | synference | INFO     | JWST/NIRCam.F115W: -0.120597 - 3.614 AB
2025-11-13 20:08:30,022 | synference | INFO     | JWST/NIRCam.F150W: -0.163761 - 2.019 AB
2025-11-13 20:08:30,022 | synference | INFO     | JWST/NIRCam.F277W: -2.226750 - 0.537 AB
2025-11-13 20:08:30,023 | synference | INFO     | JWST/NIRCam.F356W: -2.979535 - 0.940 AB
2025-11-13 20:08:30,023 | synference | INFO     | JWST/NIRCam.F444W: -3.504708 - 1.221 AB
2025-11-13 20:08:30,025 | synference | INFO     | norm_JWST/NIRCam.F200W_AB: 7.133157 - 35.470 AB
2025-11-13 20:08:30,025 | 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)

2025-11-13 20:08:30,032 | synference | INFO     | Using depth-based noise models with True scatters per row.
2025-11-13 20:08:30,034 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,034 | synference | INFO     | Features: 8 features over 100 samples
2025-11-13 20:08:30,035 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,036 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:30,037 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,037 | synference | INFO     | JWST/NIRCam.F070W: 7.131974 - 50.000 AB
2025-11-13 20:08:30,038 | synference | INFO     | JWST/NIRCam.F090W: 7.108530 - 50.000 AB
2025-11-13 20:08:30,039 | synference | INFO     | JWST/NIRCam.F115W: 7.012560 - 50.000 AB
2025-11-13 20:08:30,040 | synference | INFO     | JWST/NIRCam.F150W: 6.969396 - 50.000 AB
2025-11-13 20:08:30,041 | synference | INFO     | JWST/NIRCam.F200W: 7.133157 - 50.000 AB
2025-11-13 20:08:30,042 | synference | INFO     | JWST/NIRCam.F277W: 7.670149 - 50.000 AB
2025-11-13 20:08:30,043 | synference | INFO     | JWST/NIRCam.F356W: 8.072730 - 50.000 AB
2025-11-13 20:08:30,044 | synference | INFO     | JWST/NIRCam.F444W: 8.353975 - 32.465 AB
2025-11-13 20:08:30,044 | synference | INFO     | ---------------------------------------------

[10]:

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         22.89238375, 21.89978021, 24.53910189, 31.73224584, 50.        ,
         28.21702386, 20.2639975 , 21.18701417, 32.97644829, 27.28108154,
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         17.66484934, 29.67425457, 29.29561048, 23.82374241, 26.20644556,
         21.87437346, 24.43487502, 22.83852163, 21.37479599, 21.26871009,
         20.69049095, 23.55257373, 26.6258772 , 28.59872434, 28.99143734,
         22.53069715, 29.42330665, 26.89368183, 19.61535251, 28.60590347,
         24.64504242, 29.47052228, 28.08387334, 21.27079213, 50.        ,
         22.83018415, 25.54723107, 17.42602738, 22.46264397, 27.22589544,
         23.98342429, 27.59415966, 22.47600562, 26.52050853, 22.52610916],
        [23.20064216, 23.73168317, 21.47696883, 24.40827067, 26.552023  ,
         19.12455828, 20.51031545, 20.31784488, 27.80866627, 25.46204314,
         24.05984356, 19.19374397, 18.44239846, 29.87164162, 25.01807219,
         22.87145341, 25.37275098, 24.73564264,  7.67014923, 24.60579431,
         25.38885793, 28.54810676, 27.39958421, 16.41602591, 28.15833326,
         23.48647165, 24.07885967, 25.64839269, 25.32033028, 20.29699881,
         26.12439227, 28.3822433 , 23.95121932, 26.14578356, 50.        ,
         31.46586629, 29.70500157, 20.92698123, 25.54026606, 26.98670625,
         23.22402627, 26.95018462, 29.69977263, 28.00827513, 18.76561334,
         25.07555844, 25.36951262, 19.40053939, 17.48199572, 27.98438309,
         20.76615461, 19.81709801, 28.43392167, 29.01959336, 20.47466145,
         21.36067895, 18.57365211, 29.26940499, 19.1693879 , 25.52727347,
         21.53194594, 21.93421481, 29.08799693, 25.21172894, 29.40772461,
         18.17501327, 28.55735329, 28.54386069, 23.48428147, 25.18994924,
         21.44193893, 24.16509407, 21.9243355 , 21.19427511, 20.53759088,
         20.94928748, 23.13829576, 26.31529263, 27.92325412, 28.55748861,
         21.55476824, 28.50656527, 26.33271256, 19.31434653, 28.43913971,
         24.17108636, 28.60021977, 27.67200975, 21.45503645, 32.12284523,
         21.82433631, 24.38607642, 17.42006034, 21.83824689, 27.05304165,
         23.31341677, 27.18149306, 21.93661165, 26.47117037, 22.39119149],
        [22.96482526, 23.52161853, 21.12799405, 24.12700283, 26.21846386,
         19.14324062, 20.79639837, 20.06847803, 27.45899324, 24.89585346,
         23.740957  , 19.04108591, 18.38000654, 29.45313195, 24.80489996,
         22.48358892, 25.00215788, 24.4003001 ,  8.0727296 , 24.37846942,
         24.99726618, 27.96423944, 27.39815284, 16.784362  , 28.01396763,
         23.12398375, 23.77815824, 24.89555397, 25.15951807, 20.05814196,
         25.99267252, 28.1698241 , 23.75897206, 25.75309204, 50.        ,
         31.46263292, 29.51699942, 20.69189939, 25.0807729 , 26.44338011,
         23.01547022, 26.72577007, 29.11697882, 27.40747742, 19.21770791,
         25.32689466, 25.13688337, 19.15453637, 17.82344938, 27.69349142,
         20.57472582, 19.40265855, 28.04752003, 28.78940732, 20.4004316 ,
         21.26715698, 18.93096237, 28.45067679, 19.57703943, 25.3252735 ,
         21.07469829, 21.52993086, 28.89783996, 25.049619  , 29.35174897,
         18.57330843, 28.10605618, 28.2066649 , 23.29208602, 24.60538166,
         21.71092512, 23.95384576, 21.5849338 , 21.14419081, 20.65267192,
         21.23178641, 22.98031834, 26.24035376, 27.69561202, 28.28248694,
         21.32368085, 27.97922968, 26.0711153 , 19.09579164, 28.40201113,
         23.85922952, 28.0612119 , 27.48159852, 21.89511535, 32.06619229,
         21.45962145, 23.90961646, 17.82867427, 21.41813339, 26.93877241,
         22.83678814, 26.9774419 , 21.65956601, 26.34225579, 22.68042478],
        [22.81996681, 23.39606743, 20.92698682, 23.90391776, 26.0858014 ,
         19.4658365 , 21.08432168, 19.88987241, 27.11394256, 24.51820461,
         23.55110158, 18.96483105, 18.75695762, 29.12225625, 24.7887608 ,
         22.2586741 , 24.74384051, 24.17685228,  8.3539746 , 24.60558889,
         24.8398718 , 27.42693206, 27.26573253, 17.1162241 , 27.83111519,
         22.80545784, 23.84913147, 24.26402449, 25.17081225, 19.880843  ,
         25.92763986, 28.02536458, 23.90215159, 25.23118391, 32.4647013 ,
         30.4438894 , 29.34187325, 20.39336704, 24.69724739, 26.12235239,
         22.77183734, 26.59345066, 28.894557  , 26.97271137, 19.5756813 ,
         25.57116587, 25.1903951 , 19.01551885, 17.95342469, 27.82542282,
         20.46698154, 19.05283767, 27.79679355, 28.55126346, 20.74259737,
         21.2311666 , 19.14325009, 27.65115232, 19.86995743, 25.60721157,
         20.7694777 , 21.6633068 , 28.86453433, 24.74724599, 29.0252608 ,
         18.87026197, 27.94736706, 27.90017594, 23.02996985, 24.21409025,
         21.91318541, 23.75672243, 21.37086463, 21.0256978 , 20.61270271,
         21.46393787, 22.77166115, 26.59957202, 27.81098198, 27.89904813,
         21.23878885, 27.665591  , 25.75673191, 18.92788724, 28.3770439 ,
         23.63285937, 27.82164066, 27.38989784, 22.10871853, 30.98382191,
         21.24993891, 23.8050094 , 18.14345487, 21.0373165 , 27.06897946,
         22.6492128 , 26.78495714, 21.52987799, 26.31997123, 23.07330897]]),
 ['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();

2025-11-13 20:08:30,064 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,065 | synference | INFO     | Features: 8 features over 100 samples
2025-11-13 20:08:30,066 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,067 | synference | INFO     | Feature: Min - Max
2025-11-13 20:08:30,068 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,069 | synference | INFO     | JWST/NIRCam.F070W: 7.131974 - 42.758 AB
2025-11-13 20:08:30,070 | synference | INFO     | JWST/NIRCam.F090W: 7.108530 - 39.933 AB
2025-11-13 20:08:30,071 | synference | INFO     | JWST/NIRCam.F115W: 7.012560 - 38.354 AB
2025-11-13 20:08:30,071 | synference | INFO     | JWST/NIRCam.F150W: 6.969396 - 36.997 AB
2025-11-13 20:08:30,072 | synference | INFO     | JWST/NIRCam.F200W: 7.133157 - 35.470 AB
2025-11-13 20:08:30,073 | synference | INFO     | JWST/NIRCam.F277W: 7.670149 - 33.243 AB
2025-11-13 20:08:30,074 | synference | INFO     | JWST/NIRCam.F356W: 8.072730 - 32.490 AB
2025-11-13 20:08:30,075 | synference | INFO     | JWST/NIRCam.F444W: 8.353975 - 31.965 AB
2025-11-13 20:08:30,076 | synference | INFO     | ---------------------------------------------
2025-11-13 20:08:30,128 | synference | INFO     | saving /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/models/test/plots//param_histogram.png
../_images/sbi_train_feature_array_21_1.png