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arXiv 0910.3770astro-ph.CO
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用神经网络选择QSO和测光红移

QSO Selection and Photometric Redshifts with Neural Networks

Ch. Yeche, P. Petitjean, J. Rich, E. Aubourg, N. Busca, J. -Ch. Hamilton, J. -M. Le Goff, I. Paris, S. Peirani, Ch. Pichon, E. Rollinde, M. Vargas-Magana

摘要:

利用类星体视线上物质密度场的莱曼α吸收特征,可以研究重子声振荡及其对物质功率谱的影响。一个足够精确的测量,以提供有用的宇宙学约束,需要观测到约100000个类星体在红移范围2.2<z<3.5超过约8000 deg 2。Sloan数字巡天(SDSS-III)的重子振荡光谱巡天(BOSS)计划进行这样的巡天,实际上需要两个数量级以上的恒星剔除,对g ~ 22等的类星体的选择效率优于50%。为了获得一个合适的目标列表和估计类星体红移,我们开发了一个人工神经网络(NN)与多层感知器结构。输入变量是光度测量值,即SDSS光度测量的五个波段(ugriz)中的物体星等及其误差。对于目标选择,我们实现了99.6%和98.5%的非类星体点状物体拒绝率,类星体效率分别为50%和85%。测光红移精度的顺序为0.1以上的区域相关的BAO研究。

英文摘要:

Baryonic Acoustic Oscillations (BAO) and their effects on the matter power spectrum can be studied by using the Lyman-alpha absorption signature of the matter density field along quasar (QSO) lines of sight. A measurement sufficiently accurate to provide useful cosmological constraints requires the observation of ~100000 quasars in the redshift range 2.2<z<3.5 over ~8000 deg2. Such a survey is planned by the Baryon Oscillation Spectroscopic Survey (BOSS) project of the Sloan Digital Sky Survey (SDSS-III).In practice, one needs a stellar rejection of more than two orders of magnitude with a selection efficiency for quasars better than 50% up to magnitudes as large as g ~ 22. To obtain an appropriate target list and estimate quasar redshifts, we have developed an Artificial Neural Networks (NN) with a multilayer perceptron architecture. The input variables are photometric measurements, i.e. the object magnitudes and their errors in the five bands (ugriz) of the SDSS photometry. For target selection, we achieve a non-quasar point-like object rejection of 99.6% and 98.5% for a quasar efficiency of, respectively, 50% and 85%. The photometric redshift precision is of the order of 0.1 over the region relevant for BAO studies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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