Real-bogus scores for active anomaly detection

Data description for Semenikhin et al., 2024

The dataset consists of the following files:

feature_snad4_r_100.dat contains light curve feature data for objects, where each object is represented by 54 feature values. These values are encoded as little-endian single-precision IEEE-754 floating-point numbers (32-bit floats). Feature names are listed in the plain text file feature_snad4_r_100.name, with one name per line.
sid_snad4_r_100.dat contains ZTF DR object identifiers, encoded as little-endian 64-bit unsigned integers.

exp_feature_snad4_r_100.dat contains the same features as feature_snad4_r_100.dat, but with an additional column representing the real-bogus classifier prediction. Each object in this file corresponds to 55 features: the original 54 features plus 1 additional feature. Feature names for this file are provided in exp_feature_snad4_r_100.name.

The files sid_snad4_r_100.dat, feature_snad4_r_100.dat, and exp_feature_snad4_r_100.dat share the same object order.

Below is a sample Python script for accessing the data using NumPy:

import numpy as np

# Load object IDs
oid     =  np.memmap('sid_snad4_r_100.dat', mode='c', dtype=np.uint64)

# Load features and reshape
feature = np.memmap('feature_snad4_r_100.dat', mode='c', dtype=np.float32).reshape(oid.shape[0], -1)

# Print dataset information
print(f'Number of objects: {len(oid)}')
print(f'Features shape: {feature.shape}')