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Support Measure Machines for Quasar Target Selection




In this talk I will discuss the problem of quasar target selection. The objects attributes in astronomy such as fluxes are often subjected to substantial and heterogeneous measurement uncertainties, especially for the medium-redshift between 2.2 and 3.5 quasars which is relatively rare and must be targeted down to g ~ 22 mag. Most of the previous works for quasar target selection includes UV-excess, kernel density estimation, a likelihood approach, and artificial neural network cannot directly deal with the heterogeneous input uncertainties. Recently, extreme deconvolution (XD) has been used to tackle this problem in a well-posed manner. In this work, we present a discriminative approach for quasar target selection that can deal with input uncertainties directly. To do so, we represent each object as a Gaussian distribution whose mean is the object's attribute vector and covariance is the given flux measurement uncertainty. Given a training set of Gaussian distributions, the support measure machines (SMMs) algorithm are trained and used to build the quasar targeting catalog. Preliminary results will also be presented. Joint work with Jo Bovy and Bernhard Sch{\"o}lkopf

Author(s): Muandet, K.
Year: 2012
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: Astro Imaging Workshop
Event Place: Alpine Astro Village, L{\"u}-Stailas, Switzerland
Talk Type: Invited Lecture

Links: Web


  title = {Support Measure Machines for Quasar Target Selection},
  author = {Muandet, K.},
  year = {2012}