A $Z_1^2$ framework for rotational-parameter estimation and uncertainty quantification in high-energy pulsars
Akshat Singhal, Rohit Nair, Devendra Sahu, Gayathri Raman, and Suman Bala

TL;DR
This paper introduces a $Z_1^2$-based framework for estimating pulsar rotational parameters and their uncertainties from photon event data, improving efficiency and statistical accuracy.
Contribution
The authors develop and validate three computationally efficient estimators for pulsar parameters, with simplified uncertainty estimates that do not require extensive simulations.
Findings
Uncertainty estimates match Monte Carlo simulation results across various regimes.
Expressing frequency at the observation midpoint reduces parameter covariance.
Framework successfully applied to real high-energy pulsar data.
Abstract
We present a -based framework for estimating the spin frequency and frequency derivative of high-energy pulsars from Poisson-limited photon event lists. The key point is that the width of a coherent detection peak is not, by itself, the statistical uncertainty on the recovered rotational parameters. We develop and compare three computationally efficient estimators: segmented frequency regression, a coherent derivative scan, and a localized two-dimensional coherent fit. For sinusoidal signals, we derive the local form of the Z-squared response as a function of frequency and frequency derivative, and show that expressing the frequency at the midpoint of the observation removes the leading-order covariance between the two parameters. This gives simple uncertainty estimates in terms of the fitted peak amplitude and local widths, without requiring an exhaustive Monte Carlo simulation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
