Supernova search with active learning in ZTF DR3

Data sources for results presented in Pruzhinskaya et al., 2022. Results from the Active Anomaly Discovery (AAD) algorithm and the feature data set extracted from ZTF DR3 light curves. "log/anomalies_feature_*.txt" files contain the list of OIDs classified by the expert as anomalies, i.e. supernova candidates, for each ZTF field. "log/answers_feature_*.csv" files contain answers to the AAD output given by the expert in order of their appearance. "log/fields.csv" contains supernova statistics for each ZTF field. "features/" directory represent the dataset we used for supernova search in ZTF photometric data with AAD. "feature_*.dat" files contain object-ordered light curve feature data, every object is built on 42 feature values, which are encoded as little endian single precision IEEE-754 float (32bit float) numbers. Feature code-names are the same for all three data sets and are listed in plain text files "feature_*.name", one code-name per line. "oid_*.dat" files contain ZTF DR object identifiers encoded as little endian 64-bit unsigned integer numbers.<br> "oid_*.dat" and "feature_*.dat" have same object order, for example the first 8 bytes of "oid_796.dat" files contain the OID of the ZTF DR3 light curve which feature are presented in the first 168 bytes of "feature_796.dat" file. Note that only observations between 58194 ≤ MJD ≤ 58483 are used, see Malanchev et al. 2021 for features details. The sample Python code to access the data as Numpy arrays: <pre><code class="language-python">import numpy as np oid = np.memmap('oid_796.dat', mode='r', dtype=np.uint64) with open('feature_796.name') as f: names = f.read().split() dtype = [(name, np.float32) for name in names] feature = np.memmap('feature_796.dat', mode='r', dtype=dtype, shape=oid.shape) idx = np.argmax(feature['amplitude']) print('Object {} has maximum amplitude {:.3f}'.format(oid[idx], feature['amplitude'][idx]))</code></pre> It should print "Object 796206400001779 has maximum amplitude 3.739"