Broad Applications

Predicting neutrino flavor evolution in supernova astrophysics, and acoustic structure in birdsong: two unconventional applications of data assimilation

Eve Armstrong (University of Pennsylvania), George Fuller (University of CA, San Diego), Chad Kishimoto (University of San Diego), Amol Patwardhan (University of Wisconsin, Madison), Lucas Johns (University of CA, San Diego), Henry Abarbanel (University of CA, San Diego), Alicia Zeng (University of Pennsylvania), Marc Schmidt (University of Pennsylvania), David White (Wilfred Lauriel University, Canada)

Abstract

The evolution of neutrino “flavor” following a core-collapse supernova (SN) event is an important question for cosmology, because that flavor evolution in-part sets the abundances of the heavy elements in the Universe. We sought to ascertain what information an earth-based detector must receive in order to infer the flavor evolution history of neutrinos that emanated from such an event. Using a simplified model of neutrino interactions, a method of statistical data assimilation (DA) can infer the complete flavor evolution history, given measurements only at the location of the detector.

A similar DA protocol can be employed to synthesize the acoustic time series pressure wave generated by a songbird. This discovery we made with the aim of identifying a predictive metric for observed song preferences in female birds. To date, most acoustic analysis tools have failed to identify such a metric. This is not surprising, given that those tools are based on linear spectral analysis, while vocalization is a complex nonlinear phenomenon. We show how DA can reliably reconstruct the acoustic structure of song, by assuming that it was generated by a nonlinear dynamical model.

• Presentation file

Contact information

Dr. Eve Armstrong University of Pennsylvania