PyCosmic: a robust method to detect cosmics in CALIFA and other fiber-fed integral-field spectroscopy datasets-猫眼课题宝

DOI：

10.1051/0004-6361/201220102

发表时间：

2012-08

期刊：

Astronomy and Astrophysics

影响因子：

6.5

通讯作者：

中科院分区：

物理与天体物理2区

文献类型：

作者：

B. Husemann;S. Kamann;C. Sandin;S. S'anchez;R. Garc'ia-Benito;D. M. L. F. A. Potsdam;Inst. de Astrof'isica de Andaluc'ia-Inst.-de-Astrof'isica-de-Andaluc'ia-102813830;Centro Astron'omico Hispano Alem'an de Calar Alto

在单次曝光的光纤馈入IFS数据中检测宇宙射线撞击（宇宙）是一项具有挑战性的任务，因为IFS仪器记录的信号很复杂。现有的检测算法通常被发现是不可靠的IFS数据的情况下，最佳的参数设置通常是未知的先验给定的数据集。CALIFA调查生成了数百个IFS数据集，需要一个可靠和强大的宇宙学检测算法作为全自动CALIFA数据简化管道的重要组成部分。我们开发了一种新的算法PyCosmic，它将L.A.Cosmic的边缘检测算法与点扩散函数卷积方案相结合。我们使用PMAS和VIMOS IFS仪器作为代表性案例，生成模拟数据来计算各种特征纤维馈送IFS数据集的不同算法的效率。PyCosmic是唯一一种对CALIFA数据实现可接受检测性能的算法。我们发现PyCosmic是最强大的工具，对于任何测试的IFS数据，检测率>~90%，错误检测率<5%。它比洛杉矶宇宙算法少了一个自由参数。只有对于严重欠采样的IFS数据，L.A.Cosmic的性能才比PyCosmic高出几个百分点。DCR永远不会达到其他两种算法的效率，只有在考虑计算速度时才应该使用。因此，PyCosmic似乎是IFS数据最通用的宇宙学检测算法。它在新的CALIFA数据简化管道以及最新版本的多仪器IFS管道P3 D中实现。

[Abridged] Detecting cosmic ray hits (cosmics) in fiber-fed IFS data of single exposures is a challenging task, because of the complex signal recorded by IFS instruments. Existing detection algorithms are commonly found to be unreliable in the case of IFS data and the optimal parameter settings are usually unknown a-priori for a given dataset. The CALIFA survey generates hundreds of IFS datasets for which a reliable and robust detection algorithm for cosmics is required as an important part of the fully automatic CALIFA data reduction pipeline. We developed a novel algorithm, PyCosmic, which combines the edge-detection algorithm of L.A.Cosmic with a point-spread function convolution scheme. We generated mock data to compute the efficiency of different algorithms for a wide range of characteristic fibre-fed IFS datasets using the PMAS and VIMOS IFS instruments as representative cases. PyCosmic is the only algorithm that achieves an acceptable detection performance for CALIFA data. We find that PyCosmic is the most robust tool with a detection rate of >~90% and a false detection rate <5% for any of the tested IFS data. It has one less free parameter than the L.A.Cosmic algorithm. Only for strongly undersampled IFS data does L.A.Cosmic exceed the performance of PyCosmic by a few per cent. DCR never reaches the efficiency of the other two algorithms and should only be used if computational speed is a concern. Thus, PyCosmic appears to be the most versatile cosmics detection algorithm for IFS data. It is implemented in the new CALIFA data reduction pipeline as well as in recent versions of the multi-instrument IFS pipeline P3D.

PyCosmic: a robust method to detect cosmics in CALIFA and other fiber-fed integral-field spectroscopy datasets

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