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Pair similarity regularization (#273)
* added component cross-entropy regularizer * added per-band responsibility option * added Responsibility class to collect options * added PairSimilarity loss as more specific regularizer * example uses of new regularizer * more accurate similarity calculation * made similarity weight relative to NLL; updated notebooks * removed cross-entropy responsibility penalty; faster similarity calc * reduced iterations for LSBG fit to avoid RTD timeout
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‎docs/0-example.ipynb‎

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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-06T03:30:43.079757Z",
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"start_time": "2026-05-06T03:30:41.963960Z"
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}
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"outputs": [],
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"source": [
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"# Import Packages and setup\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"end_time": "2026-05-06T03:30:43.322047Z",
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"start_time": "2026-05-06T03:30:43.080885Z"
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}
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},
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"outputs": [],
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"source": [
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"from huggingface_hub import hf_hub_download\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"end_time": "2026-05-06T03:30:44.358652Z",
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}
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},
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"obs = sc2.Observation(data, weights=weights, psf=psf, channels=channels, name=\"HSC\")"
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{
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"cell_type": "code",
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"execution_count": null,
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}
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},
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"norm = sc2.plot.AsinhAutomaticNorm(obs)\n",
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"execution_count": null,
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"end_time": "2026-05-06T03:30:46.307450Z",
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"start_time": "2026-05-06T03:30:44.941167Z"
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}
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},
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"model_frame = sc2.Frame.from_observations(obs)\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"end_time": "2026-05-06T03:30:47.519240Z",
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"start_time": "2026-05-06T03:30:46.321171Z"
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}
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},
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"outputs": [],
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"obs.match(model_frame)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"end_time": "2026-05-06T03:30:50.770854Z",
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"start_time": "2026-05-06T03:30:47.529312Z"
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}
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},
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"source": [
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"with sc2.Scene(model_frame) as scene:\n",
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{
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}
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"# we know source 0 is a star:\n",
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{
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}
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"sc2.plot.scene(\n",
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{
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"end_time": "2026-05-06T03:30:52.628502Z",
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"start_time": "2026-05-06T03:30:52.347148Z"
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}
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"scene_array = scene() # evaluate the model\n",
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{
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"print(\"Avg. chi^2:\", obs.goodness_of_fit(scene_array))"
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"end_time": "2026-05-06T03:30:53.573610Z",
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}
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"source": [
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"from functools import partial\n",
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"maxiter = 1000\n",
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"end_time": "2026-05-06T03:31:07.790719Z",
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"start_time": "2026-05-06T03:31:07.711428Z"
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"plt.semilogy(scene_.fit_info['losses'])\n",
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"plt.semilogy(scene_.fit_info['loss'])\n",
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"plt.ylabel('Loss')\n",
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"plt.xlabel('Iteration')\n",
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"plt.show()\n",
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"end_time": "2026-05-06T03:31:09.561022Z",
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"start_time": "2026-05-06T03:31:09.189173Z"
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"sc2.plot.scene(\n",
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{
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"end_time": "2026-05-06T03:31:12.759044Z",
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"start_time": "2026-05-06T03:31:12.531067Z"
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"# Get models for source 1\n",
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"end_time": "2026-05-06T03:31:18.908174Z",
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"start_time": "2026-05-06T03:31:16.632802Z"
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"sc2.plot.sources(\n",
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"The model of object 0 assumes the simple Gaussian shape of the model PSF, which is the internal representation of a point source.\n",
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"But it's noticeable that all other sources show pixel patterns that don't seem right. Some of them pick up flux from other sources (especially source 1) or noise (sources 4-6). What we see here is the limitation of non-parametric models: they can fit anything, but the likelihood may not have enough information to constrain all of these degrees of freedom. More/stronger constraints or priors can help, and we will describe their use in [a separate tutorial](howto/priors).\n",
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"But it's noticeable that all other sources show pixel patterns that don't seem right. Some of them pick up flux from other sources (especially source 1) or noise (sources 4-6). What we see here is the limitation of non-parametric models: they can fit anything, but the likelihood may not have enough information to constrain all of these degrees of freedom. More/stronger constraints or priors can help, and we will describe their use in [a separate tutorial](howto/priors). Another option is the addition of a model-free regularizer, e.g. {py:class}`~scarlet2.PairSimilarity`, which we demonstrate in the [LSBG tutorial](howto/lsb).\n",
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"\n",
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"### Source Measurements\n",
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"\n",

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