A team of researchers has discovered 11 previously undetected spatial anomalies, seven of which are supernova candidates.
The study was published in the journal “New Astronomy”. The researchers analyzed digital images of the northern sky taken in 2018 using a kD tree to detect anomalies using the “nearest neighbor” method. Machine learning algorithms helped automate the search. Most astronomical discoveries have been based on observations with subsequent calculations. While the total number of observations in the 20th century was still relatively small, data volumes increased dramatically with the advent of large-scale astronomical surveys.
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For example, the Zwicky Transient Facility (ZTF), which uses a wide-field camera to monitor the northern sky, generates about 1.4 TB of data per night of observation and its catalog contains billions of objects. Manual processing of huge amounts of data is both expensive and time-consuming, which is why the team of SNAD researchers from Russia, France and the United States came together to develop an automated solution.
When scientists examine astronomical objects, they observe their light curves, which show how an object’s brightness changes over time. Observers first identify a flash of light in the sky and then track its progress to see if the light brightens or dims over time or dies out. In this study, researchers examined one million real lightcurves from the ZTF’s 2018 catalog and seven simulated livecurve models of the object types under study. In total, they tracked around 40 parameters, including the magnitude of an object’s brightness and the delay. “We described the properties of our simulations using a set of features expected to be observed in real astronomical bodies. In the dataset of around one million objects, we were looking for super-powerful supernovae, type Ia supernovae, type II supernovae and tidal disturbance events,” explains Konstantin Malanchev, co-author of article and postdoctoral fellow at the University of Illinois at Urbana-Champagne. “We refer to these object classes as anomalies. They are either very rare, with little-known properties, or seem interesting enough to merit further study.
“Lightcurve data from real objects was then compared to that from simulations using the kD tree algorithm. A kD tree is a geometric data structure for dividing space into smaller parts by cutting it with hyperplanes, planes, lines or points. In the current research, this algorithm was used to narrow the search range when searching for real objects with properties similar to those described in the seven simulations.
Subsequently, the team identified 15 nearest neighbors, i.e. real objects from the ZTF database, for each simulation – 105 matches in total, which the researchers then visually examined to verify. the anomalies. Hand checking confirmed 11 anomalies, of which seven were supernova candidates and four were active galactic nuclei candidates where tidal disturbance events could occur. “It’s a very good result,” comments Maria Pruzhinskaya, co-author of the article and researcher at the Sternberg Institute of Astronomy. “In addition to the rare objects already discovered, we were able to detect several new ones hitherto missed by astronomers. This means that existing search algorithms can be improved to avoid missing such objects.
“This study demonstrates that the method is very effective, while being relatively easy to apply. The proposed algorithm for detecting space phenomena of a certain type is universal and can be used to discover all interesting astronomical objects, not limited to rare types of supernovae. “Astronomical and astrophysical phenomena that have not yet been discovered are in fact anomalies,” according to Matvey Kornilov, associate professor in the faculty of physics at HSE University. “Their observed manifestations should differ from the properties of known objects. In the future, we will try to use our method to discover new classes of objects.’
