arXiv subjects

Maria Giovanna Dainotti

Publications and source records attributed to Maria Giovanna Dainotti.

At least 37 records · Page 2Linked to original sources

arXiv2025

Gamma-ray Bursts as Distance Indicators by a Statistical Learning Approach

Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts ($z$) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9 $yr^{-1} Gpc^{-1}$ for $1.9<z<2.3$. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate highlighting a discrepancy of a factor of 3 at $z<1$.

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arXiv2025

Theoretical Modelling of Gamma-Ray Burst 090510

Gamma-ray bursts detected at high energies provide valuable insights into the emission mechanisms behind these still puzzling enigmatic events. In this study, we focus on GRB 090510, which is an unusual short GRB exhibiting plateau emission observed by the Fermi-LAT. Using the general relativistic magnetohydrodynamic code (HARM), we aim to infer the key properties of this GRB, such as the jet opening angle, the energetics, the Lorentz Gamma factor, the jet structure and its variability, and the progenitor parameters of the compact binary system. We explored both the 2D and 3D models and estimated the variability timescales. Our findings show that the predicted jet opening angle is within $88\%$ of the observed upper limit from observations, and the energetics are in general agreement with observed values when accounting for the evolution of jet opening angle with redshift. This work establishes the foundation for ongoing exploration, which will further align the theoretical model simulations with observational data.

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arXiv2025

Supernovae Ia, high-redshift probes, and the Hubble tension: current status and future perspectives

The Hubble constant ($H_0$) tension is one of the biggest challenges in modern cosmology. This consists of the discrepancy, at around $5σ$, between the local value of $H_0$ measured through Supernovae Ia (SNe Ia) constrained with the Cepheids and the value inferred from the observations of Cosmic Microwave Background (CMB) by Planck data. According to the most appealing cosmological models, such as the flat $Λ$CDM, the $H_0$ should not vary according to the measurement method or the redshift $z$ of the probe used for estimating it. Thus, many ideas have been proposed in the literature to face this tension. In the current work, we summarize the results obtained with the binned analysis of SNe Ia, showing a decreasing trend for $H_0$ with $z$ with an evolutionary coefficient $η\sim 0.01$, and we further discuss the impact of high-$z$ probes such as Gamma-ray Bursts (GRBs) and quasars (QSOs) that allow reaching constraints on the cosmological parameters that will extend the Hubble diagram to high-$z$ values.

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arXiv2025

The Machine Learning to reconstruct GRB lightcurves

The current knowledge in cosmology deals with open problems whose solutions are still under investigation. The main issue is the so-called Hubble constant ($H_0$) tension, namely, the $4-6 σ$ discrepancy between the local value of $H_0$ obtained with Cepheids+Supernovae Ia (SNe Ia) and the cosmological one estimated from the observations of the Cosmic Microwave Background (CMB). For the investigation of this problem, probes that span all over the redshift $z$ ranges are needed. Cepheids are local objects, SNe Ia reached up to $z=2.9$, and CMB is observed at $z=1100$. In this context, the use of probes at intermediate redshift $z>3$ is auspicious for casting more light on modern cosmology. The Gamma-ray Bursts (GRBs) are particularly suitable for this task, given their observability up to $z=9.4$. The use of GRBs as standardizable candles requires the use of tight and reliable astrophysical correlations and the presence of gaps in the GRB time-domain data represents an obstacle in this sense. In this work, we propose to improve the precision of the lightcurve (LC) parameters through a reconstruction process performed with the functional forms of GRB LCs and the Gaussian Processes (GP). The filling of gaps in the GRB LCs through these processes shows an improvement up to $41.5\%$ on the precision of the LC parameters fitting, which lead to a reduced scatter in the astrophysical correlations and, thus, in the estimation of cosmological parameters.

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arXiv2025

GRB Redshift Classifier to Follow-up High-Redshift GRBs Using Supervised Machine Learning

Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift ($z \sim 10$) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need a large sample of high$-z$ GRBs, currently limited due to the difficulty in securing time at the large aperture Telescopes. Thus, it is painstaking to determine quickly whether a GRB is high$z$ or low$-z$, which hampers the possibility of performing rapid follow-up observations. Previous efforts to distinguish between high$-$ and low$-z$ GRBs using GRB properties and machine learning (ML) have resulted in limited sensitivity. In this study, we aim to improve this classification by employing an ensemble ML method on 251 GRBs with measured redshifts and plateaus observed by the Neil Gehrels Swift Observatory. Incorporating the plateau phase with the prompt emission, we have employed an ensemble of classification methods to enhance the sensitivity unprecedentedly. Additionally, we investigate the effectiveness of various classification methods using different redshift thresholds, $z_{threshold}$=$z_t$ at $z_{t}=$ 2.0, 2.5, 3.0, and 3.5. We achieve a sensitivity of 87\% and 89\% with a balanced sampling for both $z_{t}=3.0$ and $z_{t}=3.5$, respectively, representing a 9\% and 11\% increase in the sensitivity over Random Forest used alone. Overall, the best results are at $z_{t} = 3.5$, where the difference between the sensitivity of the training set and the test set is the smallest. This enhancement of the proposed method paves the way for new and intriguing follow-up observations of high$-z$ GRBs.

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arXiv2025

Neutrino cosmology after DESI: tightest mass upper limits, preference for the normal ordering, and tension with terrestrial observations

The recent DESI Baryon Acoustic Oscillation measurements have led to tight upper limits on the neutrino mass sum, potentially in tension with oscillation constraints requiring $\sum m_ν \gtrsim 0.06\,{\text{eV}}$. Under the physically motivated assumption of positive $\sum m_ν$, we study the extent to which these limits are tightened by adding other available cosmological probes, and robustly quantify the preference for the normal mass ordering over the inverted one, as well as the tension between cosmological and terrestrial data. Combining DESI data with Cosmic Microwave Background measurements and several late-time background probes, the tightest $2σ$ limit we find without including a local $H_0$ prior is $\sum m_ν<0.05\,{\text{eV}}$. This leads to a strong preference for the normal ordering, with Bayes factor relative to the inverted one of $46.5$. Depending on the dataset combination and tension metric adopted, we quantify the tension between cosmological and terrestrial observations as ranging between $2.5σ$ and $5σ$. These results are strenghtened when allowing for a time-varying dark energy component with equation of state lying in the physically motivated non-phantom regime, $w(z) \geq -1$, highlighting an interesting synergy between the nature of dark energy and laboratory probes of the mass ordering. If these tensions persist and cannot be attributed to systematics, either or both standard neutrino (particle) physics or the underlying cosmological model will have to be questioned.

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arXiv2024

High-redshift Cosmology by Gamma-Ray Bursts: an overview

Several correlations among Gamma-Ray Bursts (GRBs) quantities, both in the prompt and afterglow emissions, have been established during the last decades, thus enabling the standardization of GRBs as cosmological probes. Since GRBs are observed up to redshift $z \sim 9$, they represent a valuable tool to fill in the gap of information on the Universe evolution between the farthest type Ia supernovae and the Cosmic Microwave Background Radiation and to shed new light on the current challenging cosmological tensions. Without claiming for completeness, here we describe the state of the art of GRB correlations, their theoretical interpretations, and their cosmological applications both as standalone probes and in combination with other probes. In this framework, we pinpoint the importance of correcting the correlations for selection biases and redshift evolution to derive intrinsic relations, the assets of combining probes at different scales, and the need for the employment of the appropriate cosmological likelihood to precisely constrain cosmological parameters. Furthermore, we emphasize the benefits of the cosmographic approach to avoid any cosmological assumptions and the valuable applications of machine learning techniques to reconstruct GRB light curves and predict unknown GRB redshifts. Finally, we stress the relevance of all these factors, along with future observations, to definitely boost the power of GRBs in cosmology.

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arXiv2024

The Density and Ionization Profile of Optically Dark and High Redshift GRBs Probed by X-ray Absorption

The X-ray column density (NHX) of gamma-ray bursts (GRBs) can probe the local environment of their progenitors over a wide redshift range. Previous work has suggested an increasing trend as a function of redshift. The relevance of the current analysis relies on investigating the selection bias method, such as the effect of the X-ray spectrum in high-redshift GRBs, which complicates the measurement of small NHX, have yet to be fully evaluated or discussed elsewhere. In this work, we evaluated these effects through simulations to define appropriate observational limits in the NHX versus redshift plane. We then applied a one-sided nonparametric method developed by Efron and Petrosian. Within the framework of this method, we investigated the redshift dependence of NHX and the local distribution function. Our results show that the evolution of NHX with redshift firmly exists with a significance of more than four sigma and follows a power law of $(1+z)^{1.39 (+0.22, -0.27)}$. Based on these analyses and previous studies, the GRB progenitor mass varies but is more massive in the high redshift environment and has a higher gas column density. This suggests that part of the luminosity evolution of GRBs, which has been widely reported, may be due to the evolution of the progenitor's mass. Using the same method, we demonstrate that optically dark GRBs show a consistent evolution as $(1+z)^{1.15(+0.67, -0.83)}$. By applying the Kolmogorov-Smirnov (KS) test, it is shown that optically dark GRBs have statistically identical flux and photon index distributions compared to normal GRBs, but the NHX is systematically larger. This result suggests that the darkness of some GRB populations is not due to an intrinsic mechanism, but that a higher density surrounds them.

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arXiv2024

Towards a new model-independent calibration of Gamma-Ray Bursts

Current data on baryon acoustic oscillations and Supernovae of Type Ia cover up to $z\sim 2.5$. These observations play a very important role in the determination of cosmological parameters and have been widely used to constrain the $Λ$CDM and models beyond it. To extend the investigation to higher $z$, Gamma-Ray Bursts (GRBs) stand out as one of the most promising observables since can probe the universe up to $z\sim9.4$. The use of GRB correlations is still a challenge due to the spread in their intrinsic properties. In this work, we propose an innovative and cosmology-independent method of calibration of the so-called 3D Dainotti correlation. We employ state-of-the-art data on Cosmic Chronometers (CCH) at $z\lesssim2$ and use the Gaussian Processes reconstruction tool. To match the CCH redshift range, we select 20 long GRBs in $0.553\leq z\leq1.96$ from the Platinum sample, which consists of well-defined GRB plateau properties that obey the fundamental plane relation. We verify that the choice of priors on the parameters of the Dainotti relation and the modelling of CCH uncertainties and covariance have negligible impact on our results. We also consider the case in which the redshift evolution of the physical features of the plane is accounted for. We find that the use of CCH allows us to identify a sub-sample of GRBs that adhere even more closely to the fundamental plane relation, with an intrinsic scatter of $σ_{int}=0.20^{+0.03}_{-0.05}$ when evolutionary effects are considered. In an epoch in which we strive to reduce uncertainties on the GRB correlations variables to tighten constraints on cosmological parameters, we have found a novel model-independent approach to pinpoint a sub-sample that can thus represent a valuable set of standardizable candles. This allows us to extend the distance ladder presenting a new catalogue of calibrated luminosity distances up to $z=5$.

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arXiv2024

The Wide-field Spectroscopic Telescope (WST) Science White Paper

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate

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arXiv2024

A new binning method to choose a standard set of Quasars

Although the Lambda Cold Dark Matter model is the most accredited cosmological model, information at intermediate redshifts (z) between type Ia Supernovae (z = 2.26) and the Cosmic Microwave Background (z = 1100) is crucial to validate this model further. Here, we present a detailed and reliable methodology for binning the quasars (QSO) data that allows the identification of a golden sample of QSOs to be used as standard candles. This procedure has the advantage of being very general. Thus, it can be applied to any astrophysical sources at cosmological distances. This methodology allows us to avoid the circularity problem since it involves a flux-flux relation and includes the analysis of removing selection biases and the redshift evolution. With this method, we have discovered a sample of 1253 quasars up to z = 7.54 with reduced intrinsic dispersion of the relation between Ultraviolet and X-ray fluxes, with $δ_{int} = 0.096\pm 0.003$ (56\% less than the original sample where $δ_{int} =0.22$). Once the luminosities are corrected for selection biases and redshift evolution, this `gold' sample allows us to determine the matter density parameter to be $Ω_M=0.240 \pm 0.064$. This value is aligned with the results of the $ΛCDM$ model obtained with SNe Ia.

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arXiv2024

The Scavenger Hunt for Quasar Samples to Be Used as Cosmological Tools

Although the $Λ$ Cold Dark Matter model is the most accredited cosmological model, information at high redshifts ($z$) between type Ia supernovae ($z=2.26$) and the Cosmic Microwave Background ($z=1100$) is crucial to validate this model further. To this end, we have discovered a sample of 1132 quasars up to $z=7.54$ exhibiting a reduced intrinsic dispersion of the relation between ultraviolet and X-ray fluxes, $δ_\mathrm{F}=0.22$ vs. $δ_\mathrm{F}=0.29$ ($24\%$ less), than the original sample. This gold sample, once we correct the luminosities for selection biases and redshift evolution, enables us to determine the matter density parameter $Ω_M$ with a precision of 0.09. Unprecedentedly, this quasar sample is the only one that, as a standalone cosmological probe, yields such tight constraints on $Ω_M$ while being drawn from the same parent population of the initial sample.

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arXiv2024

Reduced uncertainties up to 43\% on the Hubble constant and the matter density with the SNe Ia with a new statistical analysis

Type Ia Supernovae (SNe Ia) are considered the most reliable \textit{standard candles} and they have played an invaluable role in cosmology since the discovery of the Universe's accelerated expansion. During the last decades, the SNe Ia samples have been improved in number, redshift coverage, calibration methodology, and systematics treatment. These efforts led to the most recent \textit{``Pantheon"} (2018) and \textit{``Pantheon +"} (2022) releases, which enable to constrain cosmological parameters more precisely than previous samples. In this era of precision cosmology, the community strives to find new ways to reduce uncertainties on cosmological parameters. To this end, we start our investigation even from the likelihood assumption of Gaussianity, implicitly used in this domain. Indeed, the usual practise involves constraining parameters through a Gaussian distance moduli likelihood. This method relies on the implicit assumption that the difference between the distance moduli measured and the ones expected from the cosmological model is Gaussianly distributed. In this work, we test this hypothesis for both the \textit{Pantheon} and \textit{Pantheon +} releases. We find that in both cases this requirement is not fulfilled and the actual underlying distributions are a logistic and a Student's t distribution for the \textit{Pantheon} and \textit{Pantheon +} data, respectively. When we apply these new likelihoods fitting a flat $Λ$CDM model, we significantly reduce the uncertainties on $Ω_M$ and $H_0$ of $\sim 40 \%$. This boosts the SNe Ia power in constraining cosmological parameters, thus representing a huge step forward to shed light on the current debated tensions in cosmology.

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arXiv2024

Inferring the redshift of more than 150 GRBs with a Machine Learning Ensemble model

Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up to redshift 10, and thus have the potential to be vital cosmological probes of early processes in the universe. Fulfilling this potential requires a large sample of GRBs with known redshifts, but due to observational limitations, only 11\% have known redshifts ($z$). There have been numerous attempts to estimate redshifts via correlation studies, most of which have led to inaccurate predictions. To overcome this, we estimated GRB redshift via an ensemble supervised machine learning model that uses X-ray afterglows of long-duration GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts are strongly correlated (a Pearson coefficient of 0.93) and have a root mean square error, namely the square root of the average squared error $\langleΔz^2\rangle$, of 0.46 with the observed redshifts showing the reliability of this method. The addition of GRB afterglow parameters improves the predictions considerably by 63\% compared to previous results in peer-reviewed literature. Finally, we use our machine learning model to infer the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with plateaus by 94\%, a significant milestone for enhancing GRB population studies that require large samples with redshift.

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arXiv2023

Shedding new light on the Hubble constant tension through Supernovae Ia

The standard cosmological model, the $Λ$CDM model, is the most suitable description for our universe. This framework can explain the accelerated expansion phase of the universe but still is not immune to open problems when it comes to the comparison with observations. One of the most critical issues is the so-called Hubble constant ($H_0$) tension, namely, the difference of about $5σ$ as an average between the value of $H_0$ estimated locally and the cosmological value measured from the Last Scattering Surface. The value of this tension changes from 4 to 6 $σ$ according to the data used. The current analysis explores the $H_0$ tension in the \textit{Pantheon} sample (PS) of SNe Ia. Through the division of the PS in 3 and 4 bins, the value of $H_0$ is estimated for each bin and all the values are fitted with a decreasing function of the redshift ($z$). Remarkably, $H_0$ undergoes a slow decreasing evolution with $z$, having an evolutionary coefficient compatible with zero up to $5.8σ$. If this trend is not caused by hidden astrophysical biases or $z$-selection effects, then the $f(R)$ modified theories of gravity represent a valid model for explaining such a trend.

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arXiv2023

From the gravitational waves to the exoplanets: the Research Highlights

In this Research Highlights, we summarize 31 contributions provided during the Workshop \textit{Multifrequency Behaviour of High Energy Cosmic Sources - XIV}, held in Palermo (Italy) from the 12th to the 17th of June 2023. We will start with the most recent discoveries in the field of gravitational waves (GWs). We will connect this topic to the contributions of Gamma-Ray Bursts (GRBs) associated with GWs and with the Kilonovae (KNe) hunting and, more in general, on GRBs. Continuing on high-energy astrophysics objects, we will delve into Active Galactic Nuclei (AGNs), neutrino astronomy and the study of the primordial universe, both from the space telescopes' observation and from the very recent proposals in terms of cosmological models. From the faraway universe, we will move to the more local scales and discuss the recent observations in Supernova Remnants (SNRs), massive star binaries, globular cluster dynamics, and exoplanets observed by Kepler.

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arXiv2023

The two-dimensional and three-dimensional relations in the plateau emission in multi-wavelengths

Gamma-Ray Bursts (GRBs) are interesting objects for testing the emission models in highly energetic regimes and are very promising standardizable candles, given their observability at high redshift (up to $z=9.4$) that allows the extension of the Hubble diagram much further the limit of Supernovae Ia (SNe Ia), the most distant one being at $z=2.26$. In this study, we demonstrate that the fundamental plane relation involving the prompt peak luminosity in X-rays, the X-rays plateau-end luminosity, and the plateau-end rest-frame time is not only a robust benchmark for testing GRB emission models like the magnetar but also a promising avenue for high-$z$ cosmology exploration. First, we discuss the connection between the magnetar model and the GRB afterglow correlations. Second, through the simulation of GRBs, we count how many years are needed to achieve the same precision of modern SNe Ia samples in the estimation of $Ω_{M}$.

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arXiv2023

Fermi LAT AGN classification using supervised machine learning

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data Release 3. Missing data hinders the use of machine learning to classify AGN. A previous paper found that Multiple Imputation by Chain Equations (MICE) imputation is useful for estimating missing values. Since many AGN have missing redshift and the highest energy, we use data imputation with MICE and K-nearest neighbor (kNN) algorithm to fill in these missing variables. Then, we classify AGN into the BLLs or the Flat Spectrum Radio Quasars (FSRQs) using the SuperLearner, an ensemble method that includes several classification algorithms like logistic regression, support vector classifiers, Random Forests, Ranger Random Forests, multivariate adaptive regression spline (MARS), Bayesian regression, Extreme Gradient Boosting. We find that a SuperLearner model using MARS regression and Random Forests algorithms is 91.1% accurate for kNN imputed data and 91.2% for MICE imputed data. Furthermore, the kNN-imputed SuperLearner model predicts that 892 of the 1519 unclassified blazars are BLLs and 627 are Flat Spectrum Radio Quasars (FSRQs), while the MICE-imputed SuperLearner model predicts 890 BLLs and 629 FSRQs in the unclassified set. Thus, we can conclude that both imputation methods work efficiently and with high accuracy and that our methodology ushers the way for using SuperLearner as a novel classification method in the AGN community and, in general, in the astrophysics community.

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