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C. Tiede

Publications and source records attributed to C. Tiede.

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Supermassive black hole binaries in the multi-messenger context of ground and spaceborne VLBI

Evidence of a gravitational wave background suggests the existence of a population of sub-parsec supermassive black hole binaries (SMBHBs), with characteristic angular separations on the order of 1-10 $\mu$as. Spaceborne extensions of Very Long Baseline Interferometry (VLBI) and the next generation ground arrays introduce the possibility of directly imaging these SMBHBs. In this work, a binary Spectral Energy Distribution (SED) model is used to predict the detectability of SMBHBs with ground and spaceborne VLBI. We consider the Black Hole Explorer (BHEX), a proposal for a spaceborne VLBI mission, as our primary case study. We explore the detectable SMBHB parameter space and identify distinguishable binary signatures that could exist in the visibility domain. We find that for a flux-density-limited sample, ground array observations are more effective at detecting a wider region of the binary parameter space, with $M_\mathrm{tot} \gtrsim 10^9$ solar mass systems detectable out to redshift, $z=0.075$ and beyond. Conversely, inclusion of a spaceborne element such as BHEX, offering finer angular resolution ($\sim6\,\mu$as) and sampling of the (u,v) plane not limited by Earth rotation synthesis, will provide significant benefits in constraining binary properties, resulting in improvements in characterisation of the separation and position angle of SMBHBs by a factor of $\sim4$. Near-future ground and/or spaceborne VLBI may achieve the first direct observation of a SMBHB, contributing significantly to multi-messenger studies of such systems with pulsar timing arrays and observations across the electromagnetic spectrum.

astro-ph.HE

Finding rare objects and building pure samples: Probabilistic quasar classification from low resolution Gaia spectra

We develop and demonstrate a probabilistic method for classifying rare objects in surveys with the particular goal of building very pure samples. It works by modifying the output probabilities from a classifier so as to accommodate our expectation (priors) concerning the relative frequencies of different classes of objects. We demonstrate our method using the Discrete Source Classifier, a supervised classifier currently based on Support Vector Machines, which we are developing in preparation for the Gaia data analysis. DSC classifies objects using their very low resolution optical spectra. We look in detail at the problem of quasar classification, because identification of a pure quasar sample is necessary to define the Gaia astrometric reference frame. By varying a posterior probability threshold in DSC we can trade off sample completeness and contamination. We show, using our simulated data, that it is possible to achieve a pure sample of quasars (upper limit on contamination of 1 in 40,000) with a completeness of 65% at magnitudes of G=18.5, and 50% at G=20.0, even when quasars have a frequency of only 1 in every 2000 objects. The star sample completeness is simultaneously 99% with a contamination of 0.7%. Including parallax and proper motion in the classifier barely changes the results. We further show that not accounting for class priors in the target population leads to serious misclassifications and poor predictions for sample completeness and contamination. (Truncated)

astro-ph