| --- |
| library_name: pytorch |
| tags: |
| - astronomy |
| - supernovae |
| - transients |
| - pan-starrs |
| - cnn |
| - pytorch |
| - host-galaxy-identification |
| datasets: |
| - PRISM-Astro/h2f_delight_radius_norm_p4 |
| - PRISM-Astro/h2f_delight_test |
| --- |
| |
| # DELIGHT-Synth |
|
|
| The DELIGHT model (Förster et al. 2022) trained without any real label: the hosts of the DELIGHT training sample, each with synthetic transient positions drawn uniformly in area within three effective radii of its PS1 r-band Sersic ellipse; the number of positions per host, n = max[1, round((R_e / 8 arcsec)^4)], grows with its size (12,610 positions for 7,093 hosts, most hosts with a single one). It locates the host galaxy of a transient from PS1 r-band stamps centred on the transient. |
| |
| ## Model |
| |
| - Backbone: the DELIGHT convolutional backbone (Förster et al. 2022), shared by the five stamp levels. |
| - Input: PS1 stamps of 30 x 30 pixels at five levels (0.25 arcsec per pixel at level 0, doubling at each level), band(s) r. |
| - Output: the host offset (x, y). |
| |
| The host offset is predicted in level-0 pixels of 0.25 arcsec from the stamp centre. Each prediction is the average of the eight rotations and flips of the stamp, transformed back to the original orientation. M is the mean of min(r, 1), with r the residual distance in units of the host's SEP ellipse scaled by three (semi-axes A = 3a, B = 3b); RMSE_d is the root mean square of the residual distance. |
|
|
| ## Training data and cross-validation |
|
|
| Trained on [PRISM-Astro/h2f_delight_radius_norm_p4](https://huggingface.co/datasets/PRISM-Astro/h2f_delight_radius_norm_p4) with five-fold cross-validation: there is |
| one model per fold, trained on the other four folds and validated on its own fold. The host locators are tested on the real host-transient pairs of [PRISM-Astro/h2f_delight_test](https://huggingface.co/datasets/PRISM-Astro/h2f_delight_test). |
|
|
| For each fold we keep the checkpoint of the epoch with the lowest validation M. |
|
|
| ## Validation results |
|
|
| Validation metrics of each fold model on its own validation fold (synthetic transient positions for the models trained on synthetic labels, real hand-labelled pairs for DELIGHT-Real). |
|
|
| | Fold | Epoch | Step | M | RMSE_d (arcsec) | |
| |---|---|---|---|---| |
| | 1 | 31 | 7812 | 0.07335 | 1.811 | |
| | 2 | 31 | 7812 | 0.08089 | 1.815 | |
| | 3 | 39 | 10023 | 0.07726 | 1.472 | |
| | 4 | 33 | 7986 | 0.07834 | 1.955 | |
| | 5 | 36 | 9324 | 0.07808 | 1.228 | |
| | **Mean ± std** | | | 0.07758 ± 0.00244 | 1.656 ± 0.267 | |
| |
| ## Files |
| |
| ``` |
| config.yaml Hydra configuration of the run (model, dataset, training arguments) |
| selection.json checkpoint, epoch, step and validation metrics of each fold |
| fold_k/model.safetensors |
| fold_k/trainer_state.json |
| ``` |
| |
| ## Usage |
| |
| Access is gated: request access on this page and log in (`huggingface-cli login` or `HF_TOKEN`) before downloading. |
| |
| ```python |
| from hydra.utils import instantiate |
| from huggingface_hub import hf_hub_download |
| from omegaconf import OmegaConf |
| from safetensors.torch import load_file |
|
|
| repo = "PRISM-Astro/delight-synth" |
| cfg = OmegaConf.load(hf_hub_download(repo, "config.yaml")) |
| model = instantiate(cfg.model, _convert_="partial") # needs the PRISM package |
| model.load_state_dict(load_file(hf_hub_download(repo, "fold_1/model.safetensors"))) |
| model.eval() |
| ``` |
| |
| `config.yaml` is the Hydra configuration of the run, with `dataset.fold_idx: null`; the model of `fold_k` was |
| trained on the other four folds of the dataset's `fold` column and validated on fold k. All fold models were trained |
| with an effective batch of 40 stamps (per-device batch times the number of GPUs, which varied between folds), so the |
| per-device batch size in the file does not apply to every fold. |
| |
| The input stamps must be preprocessed as in training (band selection and order, inverse hyperbolic sine of the |
| g band when it is used, five levels); see `prism.dataset` and `scripts/inferencia_test.py` in the code. |
| |
