---
license: mit
tags:
- time series
- astrophysics
- pretraining
- connect-later
size_categories:
- 100K<n<1M
---
# AstroClassification and Redshifts Datasets

<!-- Provide a quick summary of the dataset. -->

This dataset was used for the AstroClassification and Redshifts introduced in [Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations](). This is a dataset of simulated astronomical time-series (e.g., supernovae, active galactic nuclei), and the task is to classify the object type (AstroClassification) or predict the object's redshift (Redshifts).

- **Repository:** https://github.com/helenqu/connect-later
- **Paper:** will be updated
- **Point of Contact: Helen Qu (<helenqu@sas.upenn.edu>)** 

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
- **object_id**: unique object identifier
- **times_wv**: 2D array of shape (N, 2) containing the observation times (modified Julian days, MJD) and filter (wavelength in nm) for each observation, N=number of observations
- **lightcurve**: 2D array of shape (N, 2) containing the flux (arbitrary units) and flux error for each observation
- **label**: integer representing the class of the object (see below for details)
- **redshift**: redshift of the object

## Dataset Creation

### Source Data

This is a modified version of the dataset from the 2018 Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) Kaggle competition
The original Kaggle competition can be found [here](https://www.kaggle.com/c/PLAsTiCC-2018). [This note](https://arxiv.org/abs/1810.00001) from the competition describes the dataset in detail. Astronomers may be interested in [this paper](https://arxiv.org/abs/1903.11756) describing the simulations used to generate the data.

- **Train**: 80% of the original PLAsTiCC training set augmented using the redshifting targeted augmentation described in the Connect Later paper
- **Validation**: Remaining 20% of the original PLAsTiCC training set, *not* augmented or modified
- **Test**: Subset of 10,000 objects randomly selected from the PLAsTiCC test set

### Object Types
```
 0: microlens-single
 1: tidal disruption event (TDE)
 2: eclipsing binary (EB)
 3: type II supernova (SNII)
 4: peculiar type Ia supernova (SNIax)
 5: Mira variable
 6: type Ibc supernova(SNIbc)
 7: kilonova (KN)
 8: M-dwarf
 9: peculiar type Ia supernova (SNIa-91bg)
 10: active galactic nuclei (AGN)
 11: type Ia supernova (SNIa)
 12: RR-Lyrae (RRL)
 13: superluminous supernova (SLSN-I)
 14: 5 "anomalous" types that are not present in training set: microlens-binary, intermediate luminosity optical transient (ILOT), calcium-rich transient (CaRT), pair instability supernova (PISN), microlens-string
```

## Citation
will be updated