Table 3.
Biases in the mean redshift estimation (Δ⟨z⟩ = ⟨z⟩est − ⟨z⟩true), per tomographic bin, for our various runs of the MICE2 simulations.
| Dataset | Reconstruction bias (Δ⟨z⟩) | |||||
|---|---|---|---|---|---|---|
| Type | Phot | Bin1 | Bin2 | Bin3 | Bin4 | Bin5 |
| Perfect | Exact | 0.001 ± < 10−3 | ∅ | ∅ | ∅ | ∅ |
| KV450 | Exact | 0.002 ± 0.001 | 0.002 ± 0.002 | 0.003 ± 0.001 | 0.003 ± 0.001 | −0.001 ± 0.001 |
| Perfect | Noisy | 0.004 ± 0.003 | <10−3 ± 0.002 | 0.006 ± 0.003 | 0.004 ± 0.003 | 0.003 ± 0.003 |
| KV450 | Noisy | 0.009 ± 0.005 | 0.004 ± 0.006 | 0.023 ± 0.006 | 0.012 ± 0.004 | −0.007 ± 0.005 |
| KV450 | Noisy+QC1 | <10−3 ± 0.005 | 0.002 ± 0.006 | 0.013 ± 0.006 | 0.011 ± 0.004 | −0.006 ± 0.005 |
| KV450 | Noisy+QC2 | 0.002 ± 0.005 | 0.003 ± 0.006 | 0.007 ± 0.005 | 0.009 ± 0.004 | −0.006 ± 0.004 |
| Using kNN association | ||||||
| KV450 | Noisy | 0.047 ± 0.005 | 0.025 ± 0.004 | 0.032 ± 0.005 | −0.004 ± 0.004 | −0.013 ± 0.004 |
Notes. We show the results using both KV450-like and perfectly representative spectroscopic data, using both noisy and exact photometry. Values shown are the mean biases over 100 different lines of sight (MICE2), as well as the stdev population scatters from the same. Entries with both bias and scatter less than 1 × 10−3 are simply shown with a null symbol ∅. Conversely, entries with large biases (Δ⟨z⟩ > 0.01) are highlighted via boldface. The results demonstrate that the SOM method is unbiased in the absence of photometric noise, even in the presence of sample variance and spectroscopic selection effects. Photometric noise at the level of KV450 introduces colour redshift degeneracies which subsequently introduce a maximal bias of Δ⟨z⟩ = 0.023 in some tomographic bins. Basic quality cuts (“QC1”) are able to reduce the maximal bias to Δ⟨z⟩ = 0.013, or Δ⟨z⟩ ≤ 0.025 at 97.5% confidence, at the cost of Δneff = {2.0, 0.3, 2.4, 0.6, 0.1}% in the five tomographic bins respectively. More stringent quality cuts (“QC2”) reduce the biases to Δ⟨z⟩ = 0.010, but at further cost to the effective number density (Δneff = {15.9, 13.6, 23.3, 26.2, 21.1}%). The results computed when using the previous kNN association are shown in the final row.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.