arXiv subjects

Sajad Abbar

Publications and source records attributed to Sajad Abbar.

At least 19 recordsLinked to original sources

arXiv2026

Energy-dependent slow collective flavor conversion of supernova neutrinos

We study collective slow flavor conversion (SFC) of supernova neutrinos with multi-energy, multi-angle simulations for three representative neutrino spectra in the early accretion, late accretion, and cooling phases, in which multiple crossings between the initial electron- and heavy-lepton-flavor spectra are present. By numerically solving the neutrino quantum kinetic equations in a local periodic box, we find that SFC triggered predominantly by the spatially inhomogeneous instabilities drives the system toward a spatially coarse-grained, quasi-stationary state, whose flavor conversion probability depends strongly on energy, angle, and the neutrino mass ordering. While we find that not all of the initial spectral crossings are completely erased in the final state, a simple, box-like analytical prescription inspired by studies of fast flavor conversions, which eliminates the spectral crossings, can reasonably approximate the post-SFC spectra. Using the initial and post-SFC spectra, we also evaluate the changes of the corresponding $\nu_e$ and $\bar\nu_e$ heating rates as well as the absorption equilibrium electron fraction ($Y_e$). Within the considered scenarios, we find that the heating rates are generally enhanced by up to $\sim 80\%$ due to the net conversion of $\nu_x$ to $\nu_e$ (and $\bar\nu_x$ to $\bar\nu_e$) above their crossing energy, provided that the energy spectra above the crossing energy differ substantially. For the absorption equilibrium $Y_e$, spectra changes due to SFC increase it by $\sim 0.03$ due to the relatively more enhanced $\nu_e$ absorption rate than $\bar\nu_e$, which potentially drives supernova materials to be more proton-rich. These results highlight the importance of energy-dependent treatments of SFC for supernova neutrinos.

astro-ph.HE↗

arXiv2026

Machine Learning Detection of Non-Axisymmetric Fast Flavor Instabilities in Compact Objects

Neutrinos in dense astrophysical environments such as core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo FFCs, which could develop on extremely small scales. A necessary condition for the occurrence of FFCs is the presence of a zero crossing in the electron lepton number (ELN) angular distribution of neutrinos. In this work, we explore machine learning (ML) approaches to detect non-axisymmetric ELN crossings in these environments, based on input features of the $\nu_e$ and $\bar\nu_e$ zeroth and first angular moments. Overall, the ML models demonstrate relatively good generalizability for most of the unseen test datasets generated by various methods that do not assume the same underlying angular distributions as used in the training set. Interestingly, while the model's performance is mediocre for an axisymmetric distribution dataset derived by solving the discretized Boltzmann transport equation under 1D CCSN background, imposing an artificial non-axisymmetry substantially improves the performance. We also find that for the flavor-equilibrated angular distributions, although our ML model trained based solely on ELN inputs performs poorly when the true crossings depend on the post-equilibrated angular distributions of heavy lepton neutrinos and antineutrinos, which become different, it delivers strong performance in detecting ELN crossings when the heavy-lepton neutrino and antineutrino distributions are artificially removed. This highlights the need for additional input features to further improve the model. This is a crucial step toward successfully integrating FFCs into large-scale CCSN and NSM simulations.

astro-ph.HE↗

arXiv2026

Gravitational-Wave Signatures of Nonstandard Neutrino Properties in Collapsing Stellar Cores

We present a novel multi-messenger approach for probing nonstandard neutrino properties through the detection of gravitational waves (GWs) from collapsing stellar cores and associated supernova explosions. We show that neutrino flavor conversion inside the proto-neutron star (PNS), motivated by physics Beyond the Standard Model (BSM), can significantly boost PNS convection. This effect leads to large-amplitude GW emission over a wide frequency range during an otherwise relatively quiescent GW phase shortly after core bounce. Such a signal provides a promising new avenue for exploring nonstandard neutrino phenomena and other BSM physics impacting PNS convection.

astro-ph.HE↗

arXiv2025

Flavor Equilibration of Supernova Neutrinos: Exploring the Dynamics of Slow Modes

Neutrinos experience collective flavor conversion in extreme astrophysical environments such as core-collapse supernovae (CCSNe). One manifestation of collective conversion is slow flavor conversion (SFC), which has recently attracted renewed interest owing to its ubiquity across different regions of the supernova environment. In this study, we systematically examine the evolution of kinematic decoherence in a dense neutrino gas undergoing SFC, considering lepton number asymmetries as large as $30\%$. Our findings show that the neutrino gas asymptotically evolves toward a generic state of coarse-grained flavor equilibration which is constrained by approximate lepton number conservation. The equilibration occurs within a few factors of the inverse vacuum oscillation frequency, $ω^{-1}$, which corresponds to (anti)neutrinos reaching near flavor equipartition after a few kilometers for typical supernova neutrino energies. Notably, the quasi-steady state of the neutrino number densities can be quantitatively described by the neutrino-antineutrino number density ratio $n_{\barν_e}/n_{ν_e}$ alone. Such a simple estimation opens new opportunities for incorporating SFC into CCSN simulations, particularly in regions where SFC develops on scales much shorter than those of collisions.

astro-ph.HE↗

arXiv2025

Fast Neutrino Flavor Conversions can Help and Hinder Neutrino-Driven Explosions

We present the first simulations of core-collapse supernovae in axial symmetry with feedback from fast neutrino flavor conversion (FFC). Our schematic treatment of FFCs assumes instantaneous flavor equilibration under the constraint of lepton-number conservation individually for each flavor. Systematically varying the spatial domain where FFCs are assumed to occur, we find that they facilitate SN explosions in low-mass (9-12 solar masses) progenitors that otherwise explode with longer time delays, whereas FFCs weaken the tendency to explode of higher-mass (around 20 solar masses) progenitors.

astro-ph.HE↗

arXiv2024

Detecting Fast Neutrino Flavor Conversions with Machine Learning

Neutrinos in dense environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo fast flavor conversions (FFCs) once the angular distribution of neutrino lepton number crosses zero along a certain direction. Recent advancements have demonstrated the effectiveness of machine learning (ML) in detecting these crossings. In this study, we enhance prior research in two significant ways. Firstly, we utilize realistic data from CCSN simulations, where neutrino transport is solved using the full Boltzmann equation. We evaluate the ML methods' adaptability in a real-world context, enhancing their robustness. In particular, we demonstrate that when working with artificial data, simpler models outperform their more complex counterparts, a noteworthy illustration of the bias-variance tradeoff in the context of ML. We also explore methods to improve artificial datasets for ML training. In addition, we extend our ML techniques to detect the crossings in the heavy-leptonic channels, accommodating scenarios where $ν_x$ and $\barν_x$ may differ. Our research highlights the extensive versatility and effectiveness of ML techniques, presenting an unparalleled opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations.

astro-ph.HE↗

arXiv2024

Physics-Informed Neural Networks for Predicting the Asymptotic Outcome of Fast Neutrino Flavor Conversions

In the most extreme astrophysical environments, such as core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), neutrinos can undergo fast flavor conversions (FFCs) on exceedingly short scales. Intensive simulations have demonstrated that FFCs can attain equilibrium states in certain models. In this study, we utilize physics-informed neural networks (PINNs) to predict the asymptotic outcomes of FFCs, by specifically targeting the first two moments of neutrino angular distributions. This makes our approach suitable for state-of-the-art CCSN and NSM simulations. Through effective feature engineering and the incorporation of customized loss functions that penalize discrepancies in the predicted total number of $ν_e$ and $\barν_e$, our PINNs demonstrate remarkable accuracies, with an error margin of $\lesssim3\%$. Our study represents a substantial leap forward in the potential incorporation of FFCs into simulations of CCSNe and NSMs, thereby enhancing our understanding of these extraordinary astrophysical events.

astro-ph.HE↗

arXiv2024

Oscillations of High-Energy Cosmic Neutrinos in the Copious MeV Neutrino Background

The core-collapse of massive stars and merger of neutron star binaries are among the most promising candidate sites for the production of high-energy cosmic neutrinos. We demonstrate that the high-energy neutrinos produced in such extreme environments can experience efficient flavor conversions on scales much shorter than those expected in vacuum, due to their coherent forward scatterings with the bath of decohered low-energy neutrinos emitted from the central engine. These low-energy neutrinos, which exist as mass eigenstates, provide a very special and peculiar dominant background for the propagation of the high-energy ones. We point out that the high-energy neutrino flavor ratio is modified to a value independent of neutrinos energies, which is distinct from the conventional prediction with the matter effect. We also suggest that the signals can be used as a novel probe of new neutrino interactions beyond the Standard Model. This is yet another context where neutrino-neutrino interactions can play a crucial role in their flavor evolution.

hep-ph↗

arXiv2024

Application of Neural Networks for the Reconstruction of Supernova Neutrino Energy Spectra Following Fast Neutrino Flavor Conversions

Neutrinos can undergo fast flavor conversions (FFCs) within extremely dense astrophysical environments such as core-collapse supernovae (CCSNe) and neutron star mergers (NSMs). In this study, we explore FFCs in a \emph{multi-energy} neutrino gas, revealing that when the FFC growth rate significantly exceeds that of the vacuum Hamiltonian, all neutrinos (regardless of energy) share a common survival probability dictated by the energy-integrated neutrino spectrum. We then employ physics-informed neural networks (PINNs) to predict the asymptotic outcomes of FFCs within such a multi-energy neutrino gas. These predictions are based on the first two moments of neutrino angular distributions for each energy bin, typically available in state-of-the-art CCSN and NSM simulations. Our PINNs achieve errors as low as $\lesssim6\%$ and $\lesssim 18\%$ for predicting the number of neutrinos in the electron channel and the relative absolute error in the neutrino moments, respectively.

astro-ph.HE↗

arXiv2024

Using Bayesian Inference to Distinguish Neutrino Flavor Conversion Scenarios via a Prospective Supernova Neutrino Signal

The upcoming galactic core-collapse supernova is expected to produce a considerable number of neutrino events within terrestrial detectors. By using Bayesian inference techniques, we address the feasibility of distinguishing among various neutrino flavor conversion scenarios in the supernova environment, using such a neutrino signal. In addition to the conventional MSW, we explore several more sophisticated flavor conversion scenarios, such as spectral swapping, fast flavor conversions, flavor equipartition caused by non-standard neutrino interactions, magnetically-induced flavor equilibration, and flavor equilibrium resulting from slow flavor conversions. Our analysis demonstrates that with a sufficiently large number of neutrino events during the supernova accretion phase (exceeding several hundreds), there exists a good probability of distinguishing among feasible neutrino flavor conversion scenarios in the supernova environment.

astro-ph.HE↗

arXiv2024

Machine Learning-Based Detection of Non-Axisymmetric Fast Neutrino Flavor Instabilities in Core-Collapse Supernovae

In dense neutrino environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), neutrinos can undergo fast flavor conversions (FFC) when their angular distribution of neutrino electron lepton number ($ν$ELN) crosses zero along some directions. While previous studies have demonstrated the detection of axisymmetric $ν$ELN crossings in these extreme environments, non-axisymmetric crossings have remained elusive, mostly due to the absence of models for their angular distributions. In this study, we present a pioneering analysis of the detection of non-axisymmetric $ν$ELN crossings using machine learning (ML) techniques. Our ML models are trained on data from two CCSN simulations, one with rotation and one without, where non-axisymmetric features in neutrino angular distributions play a crucial role. We demonstrate that our ML models achieve detection accuracies exceeding 90\%. This is an important improvement, especially considering that a significant portion of $ν$ELN crossings in these models eluded detection by earlier methods.

astro-ph.HE↗

arXiv2023

Evaluating approximate asymptotic distributions for fast neutrino flavor conversions in a periodic 1D box

The fast flavor conversions (FFCs) of neutrinos generally exist in core-collapse supernovae and binary neutron-star merger remnants, and can significantly change the flavor composition and affect the dynamics and nucleosynthesis processes. Several analytical prescriptions were proposed recently to approximately explain or predict the asymptotic outcome of FFCs for systems with different initial or boundary conditions, with the aim for providing better understandings of FFCs and for practical implementation of FFCs in hydrodynamic modeling. In this work, we obtain the asymptotic survival probability distributions of FFCs in a survey over thousands of randomly sampled initial angular distributions by means of numerical simulations in one-dimensional boxes with the periodic boundary condition. We also propose improved prescriptions that guarantee the continuity of the angular distributions after FFCs. Detailed comparisons and evaluation of all these prescriptions with our numerical survey results are performed. The survey dataset is made publicly available to inspire the exploration and design for more effective methods applicable to realistic hydrodynamic simulations.

astro-ph.HE↗

arXiv2023

Applications of Machine Learning to Detecting Fast Neutrino Flavor Instabilities in Core-Collapse Supernova and Neutron Star Merger Models

Neutrinos propagating in a dense neutrino gas, such as those expected in core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), can experience fast flavor conversions on relatively short scales. This can happen if the neutrino electron lepton number ($ν$ELN) angular distribution crosses zero in a certain direction. Despite this, most of the state-of-the-art CCSN and NSM simulations do not provide such detailed angular information and instead, supply only a few moments of the neutrino angular distributions. In this study we employ, for the \emph{first} time, a machine learning (ML) approach to this problem and show that it can be extremely successful in detecting $ν$ELN crossings on the basis of its zeroth and first moments. We observe that an accuracy of $\sim95\%$ can be achieved by the ML algorithms, which almost corresponds to the Bayes error rate of our problem. Considering its remarkable efficiency and agility, the ML approach provides one with an unprecedented opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations \emph{on the fly}. We also provide our ML methodologies on GitHub.

astro-ph.HE↗

arXiv2023

Fast Neutrino Flavor Conversion in Core-Collapse Supernovae: A Parametric Study in 1D Models

We explore the impact of small-scale flavor conversions of neutrinos, the so-called fast flavor conversions (FFCs), on the dynamical evolution and neutrino emission of core-collapse supernovae (CCSNe). In order to do that, we implement FFCs in the spherically symmetric (1D) CCSN simulations of a 20 solar-mass progenitor model parametrically, assuming that FFCs happen at densities lower than a systematically varied threshold value and lead to an immediate flavor equilibrium consistent with lepton number conservation. We find that besides hardening the electron neutrino and antineutrino spectra, which helps the expansion of the shock by enhanced postshock heating, FFCs can cause significant, nontrivial modifications of the energy transport in the SN environment via increasing the heavy-lepton neutrino luminosities. In our non-exploding models this results in extra cooling of the layers around the neutrinospheres, which triggers a faster contraction of the proto-neutron star and hence, in our 1D models, hampers the CCSN explosion. Although our study is limited by the 1D nature of our simulations, it provides valuable insights into how neutrino flavor conversions in the deepest CCSN regions can impact the neutrino release and the corresponding response of the stellar medium.

astro-ph.HE↗

arXiv2022

Non-Standard Neutrino Self-Interactions Can Cause Neutrino Flavor Equipartition Inside the Supernova Core

We show that non-standard neutrino self-interactions can lead to total flavor equipartition in a dense neutrino gas, such as those expected in core-collapse supernovae. In this first investigation of this phenomenon in the multi-angle scenario, we demonstrate that such a flavor equipartition can occur on very short scales, and therefore very deep inside the newly formed proto-neutron star, with a possible significant impact on the physics of core-collapse supernovae. Our findings imply that future galactic core-collapse supernovae can appreciably probe non-standard neutrino self-interactions, for certain cases even when they are many orders of magnitude smaller than the Standard Model terms.

astro-ph.HE↗

arXiv2022

Fast Neutrino Conversion in Hydrodynamic Simulations of Neutrino-Cooled Accretion Disks

The outflows from neutrino-cooled black-hole (BH) accretion disks formed in neutron-star mergers or cores of collapsing stars are expected to be neutron-rich enough to explain a large fraction of elements created by the rapid neutron-capture (r-) process, but their precise chemical composition remains elusive. Here, we investigate the role of fast neutrino flavor conversion, motivated by the findings of our post-processing analysis that shows evidence of electron-neutrino lepton-number (ELN) crossings deep inside the disk, hence suggesting possibly non-trivial effects due to neutrino flavor mixing. We implement a parametric, dynamically self-consistent treatment of fast conversion in time-dependent simulations and examine the impact on the disk and its outflows. By activating the, otherwise inefficient, emission of heavy-lepton neutrinos, fast conversions enhance the disk cooling rates and reduce the absorption rates of electron-type neutrinos, causing a reduction of the electron fraction in the disk by 0.03-0.06 and in the ejected material by 0.01-0.03. The r-process yields are enhanced by typically no more than a factor of two, rendering the overall impact of fast conversions modest. The kilonova is prolonged as a net result of increased lanthanide opacities and enhanced radioactive heating rates. We observe only mild sensitivity to the disk mass, the condition for the onset of flavor conversion, and to the considered cases of flavor mixing. Remarkably, parametric models of flavor mixing that conserve the lepton numbers per family result in an overall smaller impact than models invoking three-flavor equipartition, often assumed in previous works.

astro-ph.HE↗

arXiv2022

Suppression of Fast Neutrino Flavor Conversions Occurring at Large Distances in Core-Collapse Supernovae

Neutrinos propagating in dense neutrino media such as core-collapse supernovae and neutron star merger remnants can experience the so-called fast flavor conversions on scales much shorter than those expected in vacuum. A very generic class of fast flavor instabilities is the ones which are produced by the backward scattering of neutrinos off the nuclei at relatively large distances from the supernova core. In this study we demonstrate that despite their ubiquity, such fast instabilities are unlikely to cause significant flavor conversions if the population of neutrinos in the backward direction is not large enough. Indeed, the scattering-induced instabilities can mostly impact the neutrinos traveling in the backward direction, which represent only a small fraction of neutrinos at large radii. We show that this can be explained by the shape of the unstable flavor eigenstates, which can be extremely peaked at the backward angles.

astro-ph.HE↗

arXiv2021

Fast neutrino flavor conversions in one-dimensional core-collapse supernova models with and without muon creation

In very dense environments, neutrinos can undergo fast flavor conversions on scales as short as a few centimeters provided that the angular distribution of the neutrino lepton number crosses zero. This work presents the first attempt to establish whether the non-negligible abundance of muons and their interactions with neutrinos in the core of supernovae can affect the occurrence of such crossings. For this purpose we employ state-of-the-art one-dimensional core-collapse supernova simulations, considering models that include muon-neutrino interactions as well as models without these reactions. Although a consistent treatment of muons in the equation of state and neutrino transport does not seem to modify significantly the conditions for the occurrence of fast modes, it allows for the existence of an interesting phenomenon, namely fast instabilities in the $μ-τ$ sector. We also show that crossings below the supernova shock are a relatively generic feature of the one-dimensional simulations under investigation, which contrasts with the previous reports in the literature. Our results highlight the importance of multi-dimensional simulations with muon creation, where our results must be tested in the future.

astro-ph.HE↗