Strategies for optimal sky subtraction in the low surface brightness regime
Strategies for optimal sky subtraction in the low surface brightness regime
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低地表亮度条件下的最佳天空减法策略
DOI：
10.1093/mnras/stae236
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发表时间：
2024-01
影响因子：
4.8
通讯作者：
A. Watkins;S. Kaviraj;Chris C Collins;J. Knapen;L. Kelvin;Pierre-Alain Duc;Javier Román;J. C. Miho
中科院分区：
文献类型：
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作者：
A. Watkins;S. Kaviraj;Chris C Collins;J. Knapen;L. Kelvin;Pierre-Alain Duc;Javier Román;J. C. Miho
低表面亮度（LSB）区域（μg ≳ 26 mag arcsec−2）包含一个巨大的、大部分未经探索的发现空间，从矮星系到弥漫的星际介质。进入这个状态需要精确地去除仪器特征和光污染，包括最关键的夜空发射。这并不是一件小事，因为微弱的天体物理和仪器污染可能会使天空模型在表征 LSB 结构所需的精度上产生偏差。使用理想化的合成图像，我们评估了这种偏差如何影响两种常见的面向 LSB 的天空估计算法：(1) 掩蔽和参数化建模，以及 (2) 堆叠和平滑抖动曝光。未检测到的通量通过对所有派生的天空模型施加基座偏移来限制这两种方法。对固定源进行仔细、深度的掩蔽可以缓解这种情况，但源密度始终会带来根本性的限制。即使在低密度场中，恒星散射光也能贡献~28–29 mag arcsec−2 的背景通量；在天空估计之前去除它是至关重要的。对于复杂的天空，图像组合是一种有效的非参数方法，尽管它强烈依赖于观测策略并在平滑核尺度上向图像添加噪声。固定源的先发制人的减法可能是鲁棒天空估计的唯一实用方法。因此，我们测试了第三种算法，从曝光中减去初步的天空减去 coadd 以隔离天空发射。不幸的是，天空估计中的初始误差会传播到所有后续的天空模型，使得该方法不切实际。对于像遗产时空调查这样的大规模调查，关键的科学目标限制了观测策略，假设首先去除恒星散射光，掩蔽和建模仍然是最佳的天空估计方法。
The low surface brightness (LSB) regime (μg ≳ 26 mag arcsec−2) comprises a vast, mostly unexplored discovery space, from dwarf galaxies to the diffuse interstellar medium. Accessing this regime requires precisely removing instrumental signatures and light contamination, including, most critically, night sky emission. This is not trivial, as faint astrophysical and instrumental contamination can bias sky models at the precision needed to characterize LSB structures. Using idealized synthetic images, we assess how this bias impacts two common LSB-oriented sky-estimation algorithms: (1) masking and parametric modelling, and (2) stacking and smoothing dithered exposures. Undetected flux limits both methods by imposing a pedestal offset to all derived sky models. Careful, deep masking of fixed sources can mitigate this, but source density always imposes a fundamental limit. Stellar scattered light can contribute ∼28–29 mag arcsec−2 of background flux even in low-density fields; its removal is critical prior to sky estimation. For complex skies, image combining is an effective non-parametric approach, although it strongly depends on observing strategy and adds noise to images on the smoothing kernel scale. Preemptive subtraction of fixed sources may be the only practical approach for robust sky estimation. We thus tested a third algorithm, subtracting a preliminary sky-subtracted coadd from exposures to isolate sky emission. Unfortunately, initial errors in sky estimation propagate through all subsequent sky models, making the method impractical. For large-scale surveys like Legacy Survey of Space and Time, where key science goals constrain observing strategy, masking and modelling remain the optimal sky estimation approach, assuming stellar scattered light is removed first.