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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Online Data: Black-hole mass estimation through accretion disk spectral fitting for high-redshift blazars

Authors: Kyriopoulos, Georgios; Petropoulou, Maria; Vasilopoulos, Georgios;

Online Data: Black-hole mass estimation through accretion disk spectral fitting for high-redshift blazars

Abstract

# High-z Blazar SED Fitting Results This package contains archival data, MCMC fitting results, and Python scripts for modeling the spectral energy distributions (SEDs) of high-redshift blazars. It is designed to allow reproduction of the analysis and generation of plots for individual sources. --- ## 📂 Contents of the ZIP folder - **DLAcoeff.txt**, **LAFcoeff.txt** Coefficients used in the analytical model of Inoue et al. (2014), MNRAS, 442 for the effective opacity to neutral hydrogen in the IGM (https://ui.adsabs.harvard.edu/abs/2014ascl.soft02019I) - **sources.txt** List of sources in the sample (columns: `id`, `name`, `redshift`, `model` [1 = single-component, 2 = two-component]). - **1.txt – 23.txt** Archival observations (photometric data) for each source in the sample. - **\*.h5** MCMC fitting results stored with `emcee.backends.HDFBackend`. - **functions.py** Helper functions used by script.py - **script.py** Main plotting script. Run with: ```bash python script.py [you will be prompted to enter the source id you wish] ⚙️ Requirements This code depends on the following Python packages: numpy pandas matplotlib scipy emcee corner astropy (Python ≥ 3.8 recommended) ▶️ Usage Select a source of interest from sources.txt (using the id column). Run the plotting script with the source ID: python script.py You will be prompted to enter the ID of a source. You will be prompted to enter a number (1-4) for the plot you wish to create (Type 1: chain plot, Type 2: corner plot, Type 3: SED plot, Type 4: All). Output plots are saved in the current directory (or in a plots/ folder, if present). 📊 Outputs Corner plots of posterior distributions (from MCMC fits). SED plots with archival photometry and model results. The code also applies IGM attenuation using the supplied LAFcoeff.txt and DLAcoeff.txt. 🆕 **What’s new in Version 2** 🔭 Updated source list. ⚙️ Improved jet/disk model implementation. 📈 MCMC fitting results recalculated using longer chains. 📚 Citation If you use this material in your work, please cite:Kyriopoulos et al. arxiv (submitted)

Keywords

Black holes, accretion disks, quasars: supermassive black holes, Blazars

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average