| 1. |
- Dainotti, Maria Giovanna, et al.
(author)
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On the Evolution of the Hubble Constant with the SNe Ia Pantheon Sample and Baryon Acoustic Oscillations : A Feasibility Study for GRB-Cosmology in 2030
- 2022
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In: Galaxies. - : MDPI AG. - 2075-4434. ; 10:1
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Journal article (peer-reviewed)abstract
- The difference from 4 to 6 σ in the Hubble constant (H0 ) between the values observed with the local (Cepheids and Supernovae Ia, SNe Ia) and the high-z probes (Cosmic Microwave Background obtained by the Planck data) still challenges the astrophysics and cosmology community. Previous analysis has shown that there is an evolution in the Hubble constant that scales as f (z) = H0/(1 + z)η, where H0 is H0 (z = 0) and η is the evolutionary parameter. Here, we investigate if this evolution still holds by using the SNe Ia gathered in the Pantheon sample and the Baryon Acoustic Oscillations. We assume H0 = 70 km s−1 Mpc−1 as the local value and divide the Pantheon into three bins ordered in increasing values of redshift. Similar to our previous analysis but varying two cosmological parameters contemporaneously (H0, Ω0m in the ΛCDM model and H0, wa in the w0waCDM model), for each bin we implement a Markov-Chain Monte Carlo analysis (MCMC) obtaining the value of H0 assuming Gaussian priors to restrict the parameters spaces to values we expect from our prior knowledge of the current cosmological models and to avoid phantom Dark Energy models with w < −1. Subsequently, the values of H0 are fitted with the model f (z). Our results show that a decreasing trend with η ∼ 10−2 is still visible in this sample. The η coefficient reaches zero in 2.0 σ for the ΛCDM model up to 5.8 σ for w0waCDM model. This trend, if not due to statistical fluctuations, could be explained through a hidden astrophysical bias, such as the effect of stretch evolution, or it requires new theoretical models, a possible proposition is the modified gravity theories, f (R). This analysis is meant to further cast light on the evolution of H0 and it does not specifically focus on constraining the other parameters. This work is also a preparatory to understand how the combined probes still show an evolution of the H0 by redshift and what is the current status of simulations on GRB cosmology to obtain the uncertainties on the Ω0m comparable with the ones achieved through SNe Ia.
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| 2. |
- Gibson, Spencer James, et al.
(author)
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Using Multivariate Imputation by Chained Equations to Predict Redshifts of Active Galactic Nuclei
- 2022
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In: Frontiers in Astronomy and Space Sciences. - : Frontiers Media SA. - 2296-987X. ; 9
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Journal article (peer-reviewed)abstract
- Redshift measurement of active galactic nuclei (AGNs) remains a time-consuming and challenging task, as it requires follow up spectroscopic observations and detailed analysis. Hence, there exists an urgent requirement for alternative redshift estimation techniques. The use of machine learning (ML) for this purpose has been growing over the last few years, primarily due to the availability of large-scale galactic surveys. However, due to observational errors, a significant fraction of these data sets often have missing entries, rendering that fraction unusable for ML regression applications. In this study, we demonstrate the performance of an imputation technique called Multivariate Imputation by Chained Equations (MICE), which rectifies the issue of missing data entries by imputing them using the available information in the catalog. We use the Fermi-LAT Fourth Data Release Catalog (4LAC) and impute 24% of the catalog. Subsequently, we follow the methodology described in Dainotti et al. (ApJ, 2021, 920, 118) and create an ML model for estimating the redshift of 4LAC AGNs. We present results which highlight positive impact of MICE imputation technique on the machine learning models performance and obtained redshift estimation accuracy.
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| 3. |
- Narendra, Aditya, et al.
(author)
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Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II
- 2022
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In: Astrophysical Journal, Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 259:2
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Journal article (peer-reviewed)abstract
- Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide insight into the star formation rate, the luminosity function, and the density rate evolution. Hence, there is a requirement for alternative redshift measurement techniques. In this project, we aim to use the Fermi Gamma-ray Space Telescope's 4LAC Data Release 2 catalog to train a machine-learning (ML) model capable of predicting the redshift reliably. In addition, this project aims at improving and extending with the new 4LAC Catalog the predictive capabilities of the ML methodology published in Dainotti et al. Furthermore, we implement feature engineering to expand the parameter space and a bias correction technique to our final results. This study uses additional ML techniques inside the ensemble method, the SuperLearner, previously used in Dainotti et al. Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation. Using these methods, we provide a catalog of estimated redshift values for those AGNs that do not have a spectroscopic redshift measurement. These estimates can serve as a redshift reference for the community to verify as updated Fermi catalogs are released with more redshift measurements.
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