Improving Photometric Redshift Estimation using GPz: size information, post processing and improved photometry
Monthly Notices of the Royal Astronomical Society Blackwell Publishing Inc.
Abstract:
The next generation of large scale imaging surveys (such as those conducted with the Large Synoptic Survey Telescope and Euclid) will require accurate photometric redshifts in order to optimally extract cosmological information. Gaussian Processes for photometric redshift estimation (GPz) is a promising new method that has been proven to provide efficient, accurate photometric redshift estimations with reliable variance predictions. In this paper, we investigate a number of methods for improving the photometric redshift estimations obtained using GPz (but which are also applicable to others). We use spectroscopy from the Galaxy and Mass Assembly Data Release 2 with a limiting magnitude of r<19.4 along with corresponding Sloan Digital Sky Survey visible (ugriz) photometry and the UKIRT Infrared Deep Sky Survey Large Area Survey near-IR (YJHK) photometry. We evaluate the effects of adding near-IR magnitudes and angular size as features for the training, validation and testing of GPz and find that these improve the accuracy of the results by ~15-20 per cent. In addition, we explore a post-processing method of shifting the probability distributions of the estimated redshifts based on their Quantile-Quantile plots and find that it improves the bias by ~40 per cent. Finally, we investigate the effects of using more precise photometry obtained from the Hyper Suprime-Cam Subaru Strategic Program Data Release 1 and find that it produces significant improvements in accuracy, similar to the effect of including additional features.JWST detection of heavy neutron capture elements in a compact object merger
Large Synoptic Survey Telescope White Paper; The Case for Matching U-band on Deep Drilling Fields
Abstract:
U-band observations with the LSST have yet to be fully optimized in cadence. The straw man survey design is a simple coverage of the medium-deep-fast survey. Here we argue that deep coverage of the four deep drilling fields (XMM-LSS, ECDFS, ELAIS-S1 and COSMOS) has a much higher scientific return, given that these are also the target of the Southern Hemisphere's Square Kilometer Array Pathfinder, the MeerKAT specifically, deep radio observations.
MIGHTEE - HI: The relation between the HI gas in galaxies and the cosmic web
Monthly Notices of the Royal Astronomical Society, Volume 513, Issue 2, pp.2168-2177
Abstract:
We study the 3D axis of rotation (3D spin) of 77 H I galaxies from the MIGHTEE-H I Early Science observations, and its relation to the filaments of the cosmic web. For this HI-selected sample, the alignment between the spin axis and the closest filament (|cos ψ|) is higher for galaxies closer to the filaments, with ⟨|cos ψ|⟩ = 0.66 ± 0.04 for galaxies <5 Mpc from their closest filament compared to ⟨|cos ψ|⟩ = 0.37 ± 0.08 for galaxies at 5 < d < 10 Mpc. We find that galaxies with a low HI-to-stellar mass ratio (log10(MHI/M⋆) < 0.11) are more aligned with their closest filaments, with ⟨|cos ψ|⟩ = 0.58 ± 0.04; whilst galaxies with (log10(MHI/M⋆) > 0.11) tend to be mis-aligned, with ⟨|cos ψ|⟩ = 0.44 ± 0.04. We find tentative evidence that the spin axis of HI-selected galaxies tend to be aligned with associated filaments (d < 10 Mpc), but this depends on the gas fractions. Galaxies that have accumulated more stellar mass compared to their gas mass tend towards stronger alignment. Our results suggest that those galaxies that have accrued high gas fraction with respect to their stellar mass may have had their spin axis alignment with the filament disrupted by a recent gas-rich merger, whereas the spin vector for those galaxies in which the neutral gas has not been strongly replenished through a recent merger tend to orientate towards alignment with the filament. We also investigate the spin transition between galaxies with a high HI content and a low HI content at a threshold of MHI≈10^9.5M⊙ found in simulations; however, we find no evidence for such a transition with the current data.
Neural Deprojection of Galaxy Stellar Mass Profiles
Machine Learning and the Physical Sciences Workshop, NeurIPS 2025