Anomaly detection in the Zwicky Transient Facility DR3

<p>The feature data set extracted from <a href="https://www.ztf.caltech.edu/page/dr3">ZTF DR3</a> light curves. It was used in <a href="https://arxiv.org/abs/2012.01419">Malanchev et al. 2020</a> to detect anomalous astrophysical sources in ZTF data.&nbsp;</p> <p>&quot;feature_XXX.dat&quot; 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 &quot;feature_XXX.name&quot;, one code-name per line. &quot;oid_XXX.dat&quot; files contain ZTF DR object identifiers encoded as little endian 64-bit unsigned integer numbers. &quot;oid_XXX.dat&quot; and &quot;feature_XXX.dat&quot; have same object order, for example the first 8 bytes of &quot;oid_m31.dat&quot; files contain the OID of the ZTF DR3 light curve which feature are presented in the first 168 bytes of &quot;feature_m31.dat&quot; file. &quot;m31&quot;, &quot;deep&quot; and &quot;disk&quot; denote different ZTF fields and contain 57 546, 406 611, 1 790 565 objects. Note that observations between 58194 &le; MJD &le; 58483 are used, see <a href="https://doi.org/10.1093/mnras/stab316">the paper</a> for field and features details.</p> <p>The sample Python code to access the data as Numpy arrays:</p> <pre><code class="language-python">import numpy as np oid = np.memmap('oid_m31.dat', mode='r', dtype=np.uint64) with open('feature_m31.name') as f: names = f.read().split() dtype = [(name, np.float32) for name in names] feature = np.memmap('feature_m31.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> <p>&nbsp;</p>