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The ALHAMBRA survey: Accurate merger fractions by PDF analysis of photometric close pairs

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arxiv 1409.1142 v1 pith:WNKTVDKC submitted 2014-09-03 astro-ph.GA

The ALHAMBRA survey: Accurate merger fractions by PDF analysis of photometric close pairs

classification astro-ph.GA
keywords photometricgalaxiesmergerbluemethodologyaccuratealhambraclose
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Our goal is to develop and test a novel methodology to compute accurate close pair fractions with photometric redshifts. We improve the current methodologies to estimate the merger fraction f_m from photometric redshifts by (i) using the full probability distribution functions (PDFs) of the sources in redshift space, (ii) including the variation in the luminosity of the sources with z in both the selection of the samples and in the luminosity ratio constrain, and (iii) splitting individual PDFs into red and blue spectral templates to deal robustly with colour selections. We test the performance of our new methodology with the PDFs provided by the ALHAMBRA photometric survey. The merger fractions and rates from the ALHAMBRA survey are in excellent agreement with those from spectroscopic work, both for the general population and for red and blue galaxies. With the merger rate of bright (M_B <= -20 - 1.1z) galaxies evolving as (1+z)^n, the power-law index n is larger for blue galaxies (n = 2.7 +- 0.5) than for red galaxies (n = 1.3 +- 0.4), confirming previous results. Integrating the merger rate over cosmic time, we find that the average number of mergers per galaxy since z = 1 is N_m = 0.57 +- 0.05 for red galaxies and N_m = 0.26 +- 0.02 for blue galaxies. Our new methodology exploits statistically all the available information provided by photometric redshift codes and provides accurate measurements of the merger fraction by close pairs only using photometric redshifts. Current and future photometric surveys will benefit of this new methodology.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Performance of morphological classifiers for galaxy mergers compared to current machine learning methods

    astro-ph.GA 2026-07 conditional novelty 5.0

    Updated G-M20 and G-C morphological cuts achieve ~70% merger precision comparable to ML, with better high-z robustness, but only select pre-mergers.