These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST (Gagliano+2024, submitted). The contents of the data files are detailed in the file ReadmeMESA.txt. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication.

These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST (Gagliano+2024, submitted). The contents of the data files are detailed in the file ReadmeMESA.txt. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication.

This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plus -- This version corresponds to commit: 956b5d555f58800c01a74b3977e0a3b5476ea9cd and tag v0.0.8.dataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.model_0.yaml: Configuration file associated with model_0.pt.model_z_0.pt: Same as model_0.pt, but trained using redshift information.model_z_0.yaml: Configuration file associated with model_z_0.pt.early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.probs_concat.csv: Spectroscopic set's classification results without using redshift information.probs_z_concat.csv: Spectroscopic set's classification results using redshift information.probs_photometric_v2.mrt: Superphot+'s probabilities for the photometric set without using redshift information. Updated to correct for missing IAU names.

This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plus -- This version corresponds to commit: 956b5d555f58800c01a74b3977e0a3b5476ea9cd and tag v0.0.8.dataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.model_0.yaml: Configuration file associated with model_0.pt.model_z_0.pt: Same as model_0.pt, but trained using redshift information.model_z_0.yaml: Configuration file associated with model_z_0.pt.early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.probs_concat.csv: Spectroscopic set's classification results without using redshift information.probs_z_concat.csv: Spectroscopic set's classification results using redshift information.probs_photometric_v2.mrt: Superphot+'s probabilities for the photometric set without using redshift information. Updated to correct for missing IAU names.

OverviewThis dataset is derived from the Big Sobol Sequence (BSQ) of the Quijote simulations, a collection of N-body simulations designed for machine learning applications. Each simulation consists of a point cloud (points in space, with 3D coordinates attached to them) generated under a varying set of cosmological parameters. Each point represents a simulated galaxy and is accompanied by associated properties such as velocity and mass. The cardinality of each point cloud is 5000 points. The dataset is split into 11,200 simulations in the training set, 608 in the validation set, and 576 in the test set. File structureThe dataset is provided in TFRecord format. The training simulations are split across 50 TFRecord files following the naming convention halos_train_.tfrecord. The validation and test sets are provided in halos_val_1.tfrecord and halos_test_1.tfrecord, respectively.Each dataset can be loaded using TensorFlow as shown in the code example below:import tensorflow as tffiles = tf.io.gfile.glob(f"halostrain.tfrecord") # replace 'train' with 'val' or 'test'dataset = tf.data.TFRecordDataset(files)TFRecord structureEach record (corresponding to a point cloud) in a TFRecord file contains the following feature fields:"x": (tensor of shape [5000], dtype=float) - Position along x axis"y": (tensor of shape [5000], dtype=float) - Position along y axis"z": (tensor of shape [5000], dtype=float) - Position along z axis"v_x": (tensor of shape [5000], dtype=float) - Velocity along x axis"v_y": (tensor of shape [5000], dtype=float) - Velocity along y axis"v_z": (tensor of shape [5000], dtype=float) - Velocity along z axis"J_x": (tensor of shape [5000], dtype=float) - Angular momentum along x axis"J_y": (tensor of shape [5000], dtype=float) - Angular momentum along y axis"J_z": (tensor of shape [5000], dtype=float) - Angular momentum along z axis"M200c": (tensor of shape [5000], dtype=float) - Virial mass"Omega_m": (float) - Matter density"Omega_b": (float) - Baryon density"h": (float) - Hubble parameter"n_s": (float) - Density perturbation spectral index"sigma_8": (float) - RMS matter fluctuation amplitude on a scale of 8 Mpc/h"tpcf": (tensor of shape [24], dtype=float) - Two-point correlation function

OverviewThis dataset is derived from the Big Sobol Sequence (BSQ) of the Quijote simulations, a collection of N-body simulations designed for machine learning applications. Each simulation consists of a point cloud (points in space, with 3D coordinates attached to them) generated under a varying set of cosmological parameters. Each point represents a simulated galaxy and is accompanied by associated properties such as velocity and mass. The cardinality of each point cloud is 5000 points. The dataset is split into 11,200 simulations in the training set, 608 in the validation set, and 576 in the test set. File structureThe dataset is provided in TFRecord format. The training simulations are split across 50 TFRecord files following the naming convention halos_train_.tfrecord. The validation and test sets are provided in halos_val_1.tfrecord and halos_test_1.tfrecord, respectively.Each dataset can be loaded using TensorFlow as shown in the code example below:import tensorflow as tffiles = tf.io.gfile.glob(f"halostrain.tfrecord") # replace 'train' with 'val' or 'test'dataset = tf.data.TFRecordDataset(files)TFRecord structureEach record (corresponding to a point cloud) in a TFRecord file contains the following feature fields:"x": (tensor of shape [5000], dtype=float) - Position along x axis"y": (tensor of shape [5000], dtype=float) - Position along y axis"z": (tensor of shape [5000], dtype=float) - Position along z axis"v_x": (tensor of shape [5000], dtype=float) - Velocity along x axis"v_y": (tensor of shape [5000], dtype=float) - Velocity along y axis"v_z": (tensor of shape [5000], dtype=float) - Velocity along z axis"J_x": (tensor of shape [5000], dtype=float) - Angular momentum along x axis"J_y": (tensor of shape [5000], dtype=float) - Angular momentum along y axis"J_z": (tensor of shape [5000], dtype=float) - Angular momentum along z axis"M200c": (tensor of shape [5000], dtype=float) - Virial mass"Omega_m": (float) - Matter density"Omega_b": (float) - Baryon density"h": (float) - Hubble parameter"n_s": (float) - Density perturbation spectral index"sigma_8": (float) - RMS matter fluctuation amplitude on a scale of 8 Mpc/h"tpcf": (tensor of shape [24], dtype=float) - Two-point correlation function

This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plusdataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.model_0.yaml: Configuration file associated with model_0.pt.model_z_0.pt: Same as model_0.pt, but trained using redshift information.model_z_0.yaml: Configuration file associated with model_z_0.pt.early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.probs_concat.csv: Spectroscopic set's classification results without using redshift information.probs_z_concat.csv: Spectroscopic set's classification results using redshift information.probs_photometric.mrt: Superphot+'s probabilities for the photometric set without using redshift information.