Searching for short-time-scale radio anomalies using non-linear dimensionality reduction techniques

Xuan Yang

doi:10.1093/mnras/staf046

Searching for short-time-scale radio anomalies using non-linear dimensionality reduction techniques

2025-01-11

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DOI：10.1093/mnras/staf046

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We have searched for anomalous events using 2520 h of archival observations from Murriyang, CSIRO's Parkes radio telescope. These observations were originally undertaken to search for pulsars. We used a machine learning algorithm based on resnet (Residual Network) and umap (Uniform Manifold Approximation and Projection) in order to identify parts of the data stream that potentially contain anomalous signals. Many of these anomalous events are radio frequency interference, which were subsequently filtered using multibeam information. We detected 202 anomalous events and provide their positions and event times. Our results show that the umap unsupervised machine learning pipeline effectively identifies anomalous signals in high-time-resolution data sets, highlighting its potential for use in future surveys. However, the pipeline is not applicable for standard searches for dispersed single pulses. We classify the detected events and, in particular, we are currently unable to determine the possible origin of events that last multiple seconds. For these, we encourage follow-up observations.

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methods: data analysis

fast radio bursts

Searching for short-time-scale radio anomalies using non-linear dimensionality reduction techniques

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