The Desert Fireball Network Clear-Sky Survey

Abstract

Estimating the meteoroid flux density at centimetre to metre sizes is notoriously difficult. Yet it is an important endeavour, as these sizes represent the transition between small meteoroids that pose a risk to spacecraft, and the Near-Earth Objects that are relevant for planetary defense. We present a novel automated methodology for debiasing meteor observations from multi-camera networks, applied to data from the Desert Fireball Network (DFN). Our approach utilizes the Hierarchical Equal Area isoLatitude Pixelisation (HEALPix) framework to partition the sky into equal-area pixels at 70 km altitude, enabling precise and convenient measurement of effective survey coverage and fireball counting across the network. We developed a comprehensive data processing pipeline that analyses millions of all-sky camera images to determine clear-sky conditions through automated star source detection and flux distribution analysis. As a case study, we apply this methodology to observations of the 2015 Southern Taurid meteor shower, during which there was significant fireball activity. Processing data from 33 cameras over a three-month period (October-December 2015), we calculate an effective observation coverage of $1.58 \times 10^{12}$ km$^2$.h and identified 54 Southern Taurid fireballs from 141 validated detections. Our results are consistent with the extrapolation of previous work done on the same meteor shower at smaller sizes, when we set a $\sim300$ kg.m$^{-3}$ mean meteoroid density, consistent with the cometary origin of the Taurid stream. The HEALPix-based approach successfully automates what was previously a labor-intensive manual process, providing a scalable solution for accurate flux measurements from distributed camera networks; it is directly applicable to other meteor surveys.

Figures (13)

• Figure 1: Black grid: HEALPix grid at order 6 over Australia (individual HEALPix elements shown as rhomboid cells with $\sim$103 km side length, $10,630\,\text{km}^2$ in area). Orange +: impact locations of all meteoroids detected by the DFN.
• Figure 2: Processing steps to arrive at a clear sky data product. Blue rectangles: high-level processing tasks. Orange circles: Data products.
• Figure 3: Clear HEALPix example. This figure illustrates the distribution of clear-sky conditions across HEALPix elements for a single observation window. Each colored cell represents a HEALPix element at 70 km altitude that was classified as clear based on the flux distribution of detected sources.
• Figure 4: Start vs end height distribution of detected meteors. This figure shows the distribution of start vs end height of the light path, highlighting the 70 km altitude shell used in the DFN clear sky survey.
• Figure 5: Single camera clear HEALPix example. This panel shows the clear-sky HEALPix elements as observed by a single DFN camera during a 15-minute window. The highlighted HEALPix elements indicate areas where the flux distribution of detected sources met the criteria for clear-sky classification (logarithmic fit $R^2 > 0.75$, at least 10 sources).