PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys

Abstract

Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. As recently shown, photometric redshifts can be efficiently combined with the cosmic web extracted from overlapping spectroscopic surveys to improve their accuracy. In this paper we apply a similar method using a new generation of photometric redshifts based on a convolution neural network (CNN). The CNN is trained on the SDSS images with the main galaxy sample (SDSS-MGS, r ≤ 17.8) and the GAMA spectroscopic redshifts up to r ∼ 19.8. The mapping of the cosmic web is obtained with 680,000 spectroscopic redshifts from the MGS and BOSS surveys. The redshift probability distribution functions (PDF), which are well calibrated (unbiased and narrow, ≤ 120 Mpc), intercept a few cosmic web structures along the line of sight. Combining these PDFs with the density field distribution provides new photometric redshifts, z web , whose accuracy is improved by a factor of two (i.e., σ ∼ 0.004(1 + z)) for galaxies with r ≤ 17.8. For half of them, the distance accuracy is better than 10 cMpc. The narrower the original PDF, the larger the boost in accuracy. No gain is observed for original PDFs wider than 0.03. The final z web PDFs also appear well calibrated. The method performs slightly better for passive galaxies than star-forming ones, and for galaxies in massive groups since these populations better trace the underlying large-scale structure. Reducing the spectroscopic sampling by a factor of 8 still improves the photometric redshift accuracy by 25%. Finally, extending the method to galaxies fainter than the MGS limit still improves the redshift estimates for 70% of the galaxies, with a gain in accuracy of 20% at low z where the resolution of the cosmic web is the highest. As two competing factors contribute to the performance of the method, the photometric redshift accuracy and the resolution of the cosmic web, the benefit of combining cosmological imaging surveys with spectroscopic surveys at higher redshift remains to be evaluated.

Characterising filaments in the SDSS volume from the galaxy distribution

“…Surveys such as the Two-Degree Field Galaxy Redshift Survey (2dFGRS, Colless et al 2001), the Sloan Digital Sky Survey (SDSS, York et al 2000), the Galaxy And Mass Assembly survey (GAMA, Driver et al 2009), the Vimos Public Extragalactic Redshift Survey (VIPERS, Scodeggio et al 2018), or the COSMOS survey (Scoville et al 2007) have allowed us to obtain statistical samples of filaments and other LSS features. For example, Chen et al (2016) and Tempel et al (2014a) have produced filament catalogues in the SDSS (but see also the works by Aragón Calvo 2007;Sousbie et al 2011;Rost et al 2020;Shuntov et al 2020;Kraljic et al 2020, some of which also used the same algorithm as we used here). Other works such as Kraljic et al (2018) and Alpaslan et al (2014) detected filaments in GAMA, while Malavasi et al (2017) detected filaments in VIPERS.…”

Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods

“…Meanwhile, it is worth investigating hybrid methods that take advantage of complementary information from individual approaches. For instance, multiple methods may be explored and integrated for composite likelihood analysis (e.g., Shuntov et al 2020;Rau et al 2022). Domain knowledge may also be injected to fill in the gap for under-represented or missing data, which would be essential for alleviating the difficulty in the high-redshift regime.…”

Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods

“…It is also worth investigating hybrid methods that take advantage of complementary information from individual approaches. For instance, multiple methods may be explored and integrated for a composite likelihood analysis (e.g., Shuntov et al 2020;Rau et al 2022). Domain knowledge may also be injected to fill in the gap for under-represented or missing data, which would be essential to alleviate the difficulty in the highredshift regime.…”