Automated Transient Identification in the Dark Energy Survey

12 Apr 2015D. A. GoldsteinC. B. D'AndreaJ. A. FischerR. J. FoleyR. R. GuptaR. KesslerA. G. KimR. C. NicholP. NugentA. PapadopoulosM. SakoM. SmithM. SullivanR. C. ThomasW. WesterR. C. WolfF. B. AbdallaM. BanerjiA. Benoit-LévyE. BertinD. BrooksA. Carnero RosellF. J. CastanderL. N. da CostaR. CovarrubiasD. L. DePoyS. DesaiH. T. DiehlP. DoelT. F. EiflerA. Fausti NetoD. A. FinleyB. FlaugherP. FosalbaJ. FriemanD. GerdesD. GruenR. A. GruendlD. JamesK. KuehnN. KuropatkinO. LahavT. S. LiM. A. G. MaiaM. MaklerM. MarchJ. L. MarshallP. MartiniK. W. MerrittR. MiquelB. NordR. OgandoA. A. PlazasA. K. RomerA. RoodmanE. SanchezV. ScarpineM. SchubnellI. Sevilla-NoarbeR. C. SmithM. Soares-SantosF. SobreiraE. SuchytaM. E. C. SwansonG. TarleJ. ThalerA. R. Walker

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest... (read more)

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