X-ray Luminosity Functions of Normal Galaxies in the GOODS
A. Ptak (1), B.Mobasher (2), A. Hornschemeier (1,3), F. Bauer (4) and, C. Norman (1,2) ((1) Johns Hopkins University, (2) Space Telescope Institute,, (3) NASA/GSFC, (4) Columbia University)

TL;DR
This study analyzes the X-ray luminosity functions of normal galaxies in the GOODS fields at different redshifts, revealing significant evolution and providing new models for galaxy X-ray emission over cosmic time.
Contribution
It introduces a detailed analysis of galaxy XLFs at z~0.25 and 0.75, applying advanced fitting techniques and models to understand their evolution.
Findings
XLFs differ significantly between z<0.5 and z>0.5
Best-fit luminosity evolution follows (1+z)^1.6 for early-type and (1+z)^2.3 for late-type galaxies
Models are robust across different functional forms
Abstract
We present soft (0.5-2 keV) X-ray luminosity functions (XLFs) in the Great Observatories Origins Deep Survey (GOODS) fields, derived for galaxies at z~0.25 and 0.75. SED fitting was used to estimate photometric redshifts and separate galaxy types, resulting in a sample of 40 early-type galaxies and 46 late-type galaxies. We estimate k-corrections for both the X-ray/optical and X-ray/NIR flux ratios, which facilitates the separation of AGN from the normal/starburst galaxies. We fit the XLFs with a power-law model using both traditional and Markov-Chain Monte Carlo (MCMC) procedures. The XLFs differ between z<0.5 and z>0.5, at >99% significance levels for early-type, late-type and all (early and late-type) galaxies.We also fit Schechter and log-normal models to the XLFs, fitting the low and high redshift XLFs for a given sample simultaneously assuming only pure luminosity evolution. In…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical and numerical algorithms · Galaxies: Formation, Evolution, Phenomena · Gaussian Processes and Bayesian Inference
