We alleviate the circularity problem, whereby gamma-ray bursts are not perfect distance indicators, by means of a new model-independent technique based on B'ezier polynomials. To do so, we use the well consolidate textit{Amati} and textit{Combo} correlations. We consider improved calibrated catalogs of mock data from differential Hubble rate points. To get our mock data, we use those machine learning scenarios that well adapt to gamma ray bursts, discussing in detail how we handle small amounts of data from our machine learning techniques. In particular, we explore only three machine learning treatments, i.e. emph{linear regression}, emph{neural network} and emph{random forest}, emphasizing quantitative statistical motivations behind these choices. Our calibration strategy consists in taking Hubble's data, creating the mock compilation using machine learning and calibrating the aforementioned correlations through B'ezier polynomials with a standard chi-square analysis first and then by means of a hierarchical Bayesian regression procedure. The corresponding catalogs, built up from the two correlations, have been used to constrain dark energy scenarios. We thus employ Markov Chain Monte Carlo numerical analyses based on the most recent Pantheon supernova data, baryonic acoustic oscillations and our gamma ray burst data. We test the standard $Lambda$CDM model and the Chevallier-Polarski-Linder parametrization. We discuss the recent $H_0$ tension in view of our results. Moreover, we highlight a further severe tension over $Omega_m$ and we conclude that a slight evolving dark energy model is possible.
{"title":"Model-independent calibrations of gamma-ray bursts using machine learning","authors":"O. Luongo, M. Muccino","doi":"10.1093/MNRAS/STAB795","DOIUrl":"https://doi.org/10.1093/MNRAS/STAB795","url":null,"abstract":"We alleviate the circularity problem, whereby gamma-ray bursts are not perfect distance indicators, by means of a new model-independent technique based on B'ezier polynomials. To do so, we use the well consolidate textit{Amati} and textit{Combo} correlations. We consider improved calibrated catalogs of mock data from differential Hubble rate points. To get our mock data, we use those machine learning scenarios that well adapt to gamma ray bursts, discussing in detail how we handle small amounts of data from our machine learning techniques. In particular, we explore only three machine learning treatments, i.e. emph{linear regression}, emph{neural network} and emph{random forest}, emphasizing quantitative statistical motivations behind these choices. Our calibration strategy consists in taking Hubble's data, creating the mock compilation using machine learning and calibrating the aforementioned correlations through B'ezier polynomials with a standard chi-square analysis first and then by means of a hierarchical Bayesian regression procedure. The corresponding catalogs, built up from the two correlations, have been used to constrain dark energy scenarios. We thus employ Markov Chain Monte Carlo numerical analyses based on the most recent Pantheon supernova data, baryonic acoustic oscillations and our gamma ray burst data. We test the standard $Lambda$CDM model and the Chevallier-Polarski-Linder parametrization. We discuss the recent $H_0$ tension in view of our results. Moreover, we highlight a further severe tension over $Omega_m$ and we conclude that a slight evolving dark energy model is possible.","PeriodicalId":8431,"journal":{"name":"arXiv: Cosmology and Nongalactic Astrophysics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75467846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-27DOI: 10.1103/PHYSREVD.103.063530
L. F. Guimarães, F. Falciano
We show how to build a curvaton inflationary model motivated by scale-dependent non-Gaussianities of cosmological perturbations. In particular, we study the change of sign in the $f_{NL}$ parameter as a function of the curvaton field value at horizon crossing and identify it with the cosmic microwave background pivot scale. We devise a procedure to recover the curvaton model that provides the desired $f_{NL}$ parameter. We then present a concrete example of $f_{NL}$ and construct its parent model. We study the constraints applied to this model based on considerations taken on $f_{NL}$. We show that the hemispherical asymmetry can also be used to constrain the scale-dependence of $f_{NL}$ and the model parameters.
{"title":"Viable curvaton models from the \u0000fNL\u0000 parameter","authors":"L. F. Guimarães, F. Falciano","doi":"10.1103/PHYSREVD.103.063530","DOIUrl":"https://doi.org/10.1103/PHYSREVD.103.063530","url":null,"abstract":"We show how to build a curvaton inflationary model motivated by scale-dependent non-Gaussianities of cosmological perturbations. In particular, we study the change of sign in the $f_{NL}$ parameter as a function of the curvaton field value at horizon crossing and identify it with the cosmic microwave background pivot scale. We devise a procedure to recover the curvaton model that provides the desired $f_{NL}$ parameter. We then present a concrete example of $f_{NL}$ and construct its parent model. We study the constraints applied to this model based on considerations taken on $f_{NL}$. We show that the hemispherical asymmetry can also be used to constrain the scale-dependence of $f_{NL}$ and the model parameters.","PeriodicalId":8431,"journal":{"name":"arXiv: Cosmology and Nongalactic Astrophysics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87462200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}