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David Barnes

Publications and source records attributed to David Barnes.

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Where are the High Velocity Clouds in Local Group Analogs?

High-velocity clouds (HVCs) are clouds of HI seen around the Milky Way with velocities inconsistent with Galactic rotation, have unknown distances and masses and controversial origins. One possibility is that HVCs are associated with the small dark matter halos seen in models of galaxy formation and distributed at distances of 150 kpc - 1 Mpc. We report on our attempts to detect the analogs to such putative extragalactic clouds in three groups of galaxies similar to our own Local Group using the ATNF Parkes telescope and Compact Array. Eleven dwarf galaxies were found, but no HI clouds lacking stars were detected. Using the population of compact HVCs around the Milky Way as a template, we find that our non-detection of analogs implies that they must be clustered within 160 kpc of the Milky Way (and other galaxies) with an average HI mass <4x10^5 M(sun) at the 95% confidence level. This is in accordance with recent limits derived by other authors. If our groups are true analogs to the Local Group, then this makes the original Blitz et al. and Braun & Burton picture of HVCs residing out to 1 Mpc from the Milky Way extremely unlikely. The total HI mass in HVCs, < 10^8 M(sun), implies that there is not a large reservoir of neutral hydrogen waiting to be accreted onto the Milky Way. Any substantial reservoir of baryonic matter must be mostly ionized or condensed enough as to be undetectable.

astro-ph↗

An Algorithm for Optimal Partitioning of Data on an Interval

Many signal processing problems can be solved by maximizing the fitness of a segmented model over all possible partitions of the data interval. This letter describes a simple but powerful algorithm that searches the exponentially large space of partitions of $N$ data points in time $O(N^2)$. The algorithm is guaranteed to find the exact global optimum, automatically determines the model order (the number of segments), has a convenient real-time mode, can be extended to higher dimensional data spaces, and solves a surprising variety of problems in signal detection and characterization, density estimation, cluster analysis and classification.

math.NA↗