Forecasting constraints on the baryon mass fraction in the IGM from fast radio bursts and type Ia supernovae

Abstract

Fast Radio Bursts (FRBs) are millisecond transient radio events with a high energy. By identifying the origin of the burst, it is possible to measure the redshift of the host galaxy, which can be used to constrain cosmological and astrophysical parameters and test aspects of fundamental physics when combined with the dispersion measure (DM). However, some factors limit the cosmological application of FRBs: (i) the poor modelling of the fluctuations in the DM due to spatial variation in the cosmic electrons density; (ii) the fact that the fraction of baryon mass in the intergalactic medium (fIGM\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f_{IGM}$$\\end{document}) is degenerated with some cosmological parameters; (iii) the limited knowledge about host galaxy contribution (DMhost\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$DM_{host}$$\\end{document}). In this work, we investigate the impact of different redshift distribution models of FRBs to constrain the baryon fraction in the IGM and host galaxy contribution. We use a cosmological model-independent method developed in previous work Lemos (EPJC 83:138, 2023) to perform the analysis and combine simulated FRB data from Monte Carlo simulation and supernovae data. We assume four distribution models for the FRBs: gamma-ray bursts (GRB), star formation rate (SFR), uniform and equidistant (ED). Also, we consider samples with N=15,30,100\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N = 15, 30, 100$$\\end{document} and 500 points and different values of the fluctuations of electron density in the DM, δ=0,100,200,400,230z\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta = 0, 100, 200, 400, 230\\sqrt{z}$$\\end{document} pc/cm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{3}$$\\end{document}. Our analysis shows that all the distribution models present consistent results within 2σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\\sigma $$\\end{document} for the free parameters fIGM\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f_{IGM}$$\\end{document} and DMhost,0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$DM_{host,0}$$\\end{document} and highlights the crucial role of DM fluctuations in obtaining more precise measurements.