AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY

AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY
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DOI：
10.1088/0004-6256/150/3/82
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发表时间：
2015-04
期刊：
The Astronomical Journal
影响因子：
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通讯作者：
D. Goldstein;D. Goldstein;C. D'Andrea;J. Fischer;R. Foley;Ravi R. Gupta;R. Kessler;A. Kim;R. Nichol;P. Nugent;P. Nugent;A. Papadopoulos;M. Sako;Mathew Smith;M. Sullivan;R. Thomas;W. Wester;R. Wolf;F. Abdalla;M. Banerji;A. Benoit-Lévy;E. Bertin;D. Brooks;A. Rosell;F. Castander;L. Costa;R. Covarrubias;D. Depoy;S. Desai;H. Diehl;P. Doel;T. Eifler;T. Eifler;A. F. Neto;D. Finley;B. Flaugher;P. Fosalba;J. Frieman;J. Frieman;D. Gerdes;D. Gruen;R. Gruendl;R. Gruendl;D. James;K. Kuehn;N. Kuropatkin;O. Lahav;Ting S. Li;M. Maia;M. Makler;M. March;J. Marshall;P. Martini;K. Merritt;R. Miquel;B. Nord;R. Ogando;A. Plazas;A. Plazas;A. Romer;A. Roodman;A. Roodman;E. Sánchez;V. Scarpine;M. Schubnell;I. Sevilla-Noarbe;R. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;J. Thaler;A. Walker
D. Goldstein;D. Goldstein;C. D'Andrea;J. Fischer;R. Foley;Ravi R. Gupta;R. Kessler;A. Kim;R. Nichol;P. Nugent;P. Nugent;A. Papadopoulos;M. Sako;Mathew Smith;M. Sullivan;R. Thomas;W. Wester;R. Wolf;F. Abdalla;M. Banerji;A. Benoit-Lévy;E. Bertin;D. Brooks;A. Rosell;F. Castander;L. Costa;R. Covarrubias;D. Depoy;S. Desai;H. Diehl;P. Doel;T. Eifler;T. Eifler;A. F. Neto;D. Finley;B. Flaugher;P. Fosalba;J. Frieman;J. Frieman;D. Gerdes;D. Gruen;R. Gruendl;R. Gruendl;D. James;K. Kuehn;N. Kuropatkin;O. Lahav;Ting S. Li;M. Maia;M. Makler;M. March;J. Marshall;P. Martini;K. Merritt;R. Miquel;B. Nord;R. Ogando;A. Plazas;A. Plazas;A. Romer;A. Roodman;A. Roodman;E. Sánchez;V. Scarpine;M. Schubnell;I. Sevilla-Noarbe;R. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;J. Thaler;A. Walker
中科院分区：
其他
文献类型：
--
作者：
D. Goldstein;D. Goldstein;C. D'Andrea;J. Fischer;R. Foley;Ravi R. Gupta;R. Kessler;A. Kim;R. Nichol;P. Nugent;P. Nugent;A. Papadopoulos;M. Sako;Mathew Smith;M. Sullivan;R. Thomas;W. Wester;R. Wolf;F. Abdalla;M. Banerji;A. Benoit-Lévy;E. Bertin;D. Brooks;A. Rosell;F. Castander;L. Costa;R. Covarrubias;D. Depoy;S. Desai;H. Diehl;P. Doel;T. Eifler;T. Eifler;A. F. Neto;D. Finley;B. Flaugher;P. Fosalba;J. Frieman;J. Frieman;D. Gerdes;D. Gruen;R. Gruendl;R. Gruendl;D. James;K. Kuehn;N. Kuropatkin;O. Lahav;Ting S. Li;M. Maia;M. Makler;M. March;J. Marshall;P. Martini;K. Merritt;R. Miquel;B. Nord;R. Ogando;A. Plazas;A. Plazas;A. Romer;A. Roodman;A. Roodman;E. Sánchez;V. Scarpine;M. Schubnell;I. Sevilla-Noarbe;R. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;J. Thaler;A. Walker

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我们描述了一种算法，用于识别点源瞬态和移动物体的参考减去光学图像包含工件的处理和仪器。该算法使用了被称为随机森林的监督机器学习技术。我们介绍了它在暗能量调查超新星计划（DES-SN）中的使用结果，在该计划中，它使用瞬态检测管道生成的898，963个信号和背景事件的样本进行训练。在使用该算法对第一个DES-SN观测季节（2013年9月至2014年2月）收集的数据进行重新处理后，符合人类扫描条件的瞬态候选者数量减少了13.4倍，而只有1.0%的人工Ia型超新星（SNe）被注入到搜索图像中以监测调查效率，其中大部分是非常微弱的事件。在这里，我们详细描述了算法的性能，我们讨论了它如何为未来的时域成像调查，如大型综合巡天望远镜和Zwicky瞬态设施的管道设计决策提供信息。本文中使用的算法和训练数据的实现可在http://portal.nersc.gov/project/dessn/autoscan上获得。
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