Optimized Blind Gamma-ray Pulsar Searches at Fixed Computing Budget

TL;DR

This paper develops optimized methods for blind gamma-ray pulsar searches that maximize sensitivity within fixed computational budgets, using multistage strategies and improved analysis techniques.

Contribution

It introduces a multistage search strategy and efficiency-enhanced interpolation methods to improve sensitivity without increasing computational costs.

Findings

Multistage approach effectively balances sensitivity and computational resources.

Incoherent harmonic summing is not beneficial at fixed cost for typical pulsars.

Optimizations can reduce minimum detectable pulsed fraction by nearly 50%.

Abstract

The sensitivity of blind gamma-ray pulsar searches in multiple years worth of photon data, as from the Fermi LAT, is primarily limited by the finite computational resources available. Addressing this "needle in a haystack" problem, we here present methods for optimizing blind searches to achieve the highest sensitivity at fixed computing cost. For both coherent and semicoherent methods, we consider their statistical properties and study their search sensitivity under computational constraints. The results validate a multistage strategy, where the first stage scans the entire parameter space using an efficient semicoherent method and promising candidates are then refined through a fully coherent analysis. We also find that for the first stage of a blind search incoherent harmonic summing of powers is not worthwhile at fixed computing cost for typical gamma-ray pulsars. Further enhancing sensitivity, we present efficiency-improved interpolation techniques for the semicoherent search stage. Via realistic simulations we demonstrate that overall these optimizations can significantly lower the minimum detectable pulsed fraction by almost 50% at the same computational expense.