The multi-messenger detection of the gravitational-wave signal the corresponding kilonova AT2017gfo and the short gamma-ray burst GRB170817A, as well as the observed afterglow has delivered a scientiﬁc breakthrough. For an accurate interpretation of all these different messengers, one requires robust theoretical models that describe the emitted gravitational-wave, the electromagnetic emission, and dense matter reliably. In addition, one needs efﬁcient and accurate computational tools to ensure a correct cross-correlation between the models and the observational data. For this purpose, we have developed the NMMA (Nuclear-physics and Multi-Messenger Astrophysics) framework. The code allows incorporation of nuclear-physics constraints at low densities as well as X-ray and radio observations of isolated neutron stars. It also enables us to classify electromagnetic observations, e.g., to dis-tinguish between supernovae and kilonovae. In previous works, the NMMA code has allowed us to constrain the equation of state of supranuclear dense matter, to measure the Hubble constant, and to compare dense-matter physics probed in neutron-star mergers and in heavy-ion collisions. The extension of the NMMA code presented here is the ﬁrst attempt of analysing the gravitational-wave signal, the kilonovae, and the GRB afterglow simultaneously, which re-duces the uncertainty of our constraints. Incorporating all available information, we estimate the radius of a 1 . 4 M (cid:12) neutron star to be R =11 . 98 +0 . 35 − 0 . 40 km.

pdf/nmma-a-nuclear-physics-and-multi-messenger-astrophysics-33rrijy2.pdf

NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers

The detection of a sub-solar mass black hole could yield dramatic new insights into the nature of dark matter and early-Universe physics, as such objects lack a traditional astrophysical formation mechanism. Gravitational waves allow for the direct measurement of compact object masses during binary mergers, and we expect the gravitational-wave signal from a low-mass coalescence to remain within the LIGO frequency band for thousands of seconds. However, it is unclear whether one can confidently measure the properties of a sub-solar mass compact object and distinguish between a sub-solar mass black hole or other exotic objects. To this end, we perform Bayesian parameter estimation on simulated gravitational-wave signals from sub-solar mass black hole mergers to explore the measurability of their source properties. We find that the LIGO/Virgo detectors during the O4 observing run would be able to confidently identify sub-solar component masses at the threshold of detectability; these events would also be well-localized on the sky and may reveal some information on their binary spin geometry. Further, next-generation detectors such as Cosmic Explorer and the Einstein Telescope will allow for precision measurement of the properties of sub-solar mass mergers and tighter constraints on their compact-object nature.

pdf/too-small-to-fail-characterizing-sub-solar-mass-black-hole-18n68w8b.pdf

Too small to fail: characterizing sub-solar mass black hole mergers with gravitational waves

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22 248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multiband inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. Published by the American Physical Society 2024 

Kilonova light-curve interpolation with neural networks

. The detection of a sub-solar mass black hole could yield dramatic new insights into the nature of dark matter and early-Universe physics, as such objects lack a traditional astrophysical formation mechanism. Gravitational waves allow for the direct measurement of compact object masses during binary mergers, and we expect the gravitational-wave signal from a low-mass coalescence to remain within the LIGO frequency band for thousands of seconds. However, it is unclear whether one can confidently measure the properties of a sub-solar mass compact object and distinguish between a sub-solar mass black hole or other exotic objects. To this end, we perform Bayesian parameter estimation on simulated gravitational-wave signals from sub-solar mass black hole mergers to explore the measurability of their source properties. We find that the LIGO/Virgo detectors during the O4 observing run would be able to confidently identify sub-solar component masses at the threshold of detectability; these events would also be well-localized on the sky and may reveal some information on their binary spin geometry. Further, next-generation detectors such as Cosmic Explorer and the Einstein Telescope will allow for precision measurement of the properties of sub-solar mass mergers and tighter constraints on their compact-object nature.

Too small to fail: assessing the measurability of sub-solar mass compact object mergers

Abstract With GW170817 being the only multimessenger gravitational-wave (GW) event with an associated kilonova detected so far, there exists a pressing need for realistic estimation of the GW localization uncertainties and rates, as well as optimization of available telescope time to enable the detection of new kilonovae. We simulate GW events assuming a data-driven distribution of binary parameters for the LIGO/Virgo/KAGRA fourth and fifth observing runs (O4 and O5). We map the binary neutron star (BNS) and neutron star–black hole (NSBH) properties to the kilonova optical light curves. We use the simulated population of kilonovae to generate follow-up observing plans, with the primary goal of optimizing detection with the Gravitational Wave Multi-Messenger Astronomy DECam Survey. We explore the dependence of kilonova detectability on the mass, distance, inclination, and spin of the binaries. Assuming that no BNS was detected during O4 until the end of 2024, we present updated GW BNS (NSBH) merger detection rates. We expect to detect BNS (NSBH) kilonovae with DECam at a per-year rate of 0–2.0 (0) in O4 and 2.0–19 (0–1.0) in O5. We expect the majority of BNS detections and also those accompanied by a detectable kilonova to produce a hypermassive NS remnant, with a significant fraction of the remaining BNSs promptly collapsing to a BH. We release GW simulations and depths required to detect kilonovae based on our predictions to support the astronomical community in their multimessenger follow-up campaigns and analyses. 

pdf/detecting-electromagnetic-counterparts-to-ligo-virgo-kagra-u45aumb6hmrg.pdf

Detecting Electromagnetic Counterparts to LIGO/Virgo/KAGRA Gravitational-wave Events with DECam: Neutron Star Mergers

Over the last few years, there has been an increasing interest in sub-solar mass black holes due to their potential to provide valuable information about cosmology or the black hole population. Motivated by this, we study observable phenomena connected to the merger of a sub-solar mass black hole with a neutron star. For this purpose, we perform new numerical-relativity simulations of a binary system composed of a black hole with mass $0.5M_\odot$ and a neutron star with mass $1.4 M_\odot$. We investigate the merger dynamics of this exotic system and provide information about the connected gravitational-wave and kilonova signals. Our study indicates that current gravitational-waveform models are unable to adequately describe such systems and that phenomenological relations connecting the binary parameters with the ejecta and remnant properties are not applicable to our system. Furthermore, we find a dependence of the kilonova signal on the azimuthal viewing angle due to the asymmetric mass ejection. This first-of-its-kind simulation opens the door for the study of sub-solar mass black hole - neutron star mergers and could serve as a testing ground for future model development.

pdf/general-relativistic-hydrodynamics-simulation-of-a-neutron-22q362xi.pdf

General-Relativistic Hydrodynamics Simulation of a Neutron Star - Sub-Solar-Mass Black Hole Merger

Accurate modeling of the multi-messenger signatures connected to binary neutron star mergers requires proper knowledge on the final remnant's fate and the conditions under which black holes (BHs) can form in such mergers. In this article, we use a suite of 84 numerical-relativity simulations in 28 different physical setups to explore the impact of the individual stars' spin on the merger outcome and on the early postmerger dynamics. We find that for setups close to the prompt-collapse threshold, the stars' intrinsic spin significantly changes the lifespan of the remnant before collapse and that the mass of the debris disk surrounding the BH is also altered. To enable a better understanding of BH formation, we check if there is at least a theoretical chance of observing densities that are above the maximum density allowed in a stable isolated neutron star, and we investigate the importance of different pressure contributions on the evolution of the postmerger remnant and BH formation.

Black-hole formation in binary neutron star mergers: The impact of spin on the prompt-collapse scenario

Neutrino interactions are essential for an accurate understanding of the binary neutron star merger process. In this article, we extend the code infrastructure of the well-established numerical-relativity code BAM that until recently neglected neutrino-driven interactions. In fact, while previous work allowed already the usage of nuclear-tabulated equations of state and employing a neutrino leakage scheme, we are moving forward by implementing a first-order multipolar radiation transport scheme (M1) for the advection of neutrinos. After testing our implementation on a set of standard scenarios, we apply it to the evolution of four low-mass binary systems, and we perform an analysis of ejecta properties. We also show that our new ejecta analysis infrastructure is able to provide numerical relativity-informed inputs for the codes $\texttt{POSSIS}$ and $\texttt{Skynet}$, for the computation of kilonova lightcurves and nucleosynthesis yields, respectively.

pdf/m1-neutrino-transport-within-the-numerical-relativistic-code-1m7gj46e.pdf

M1 neutrino transport within the numerical-relativistic code BAM with application to low mass binary neutron star mergers

Accurate numerical-relativity simulations are essential to study the rich phenomenology of binary neutron star systems. In this work, we focus on the material that is dynamically ejected during the merger process and on the kilonova transient it produces. Typically, radiative transfer simulations of kilonova light curves from ejecta make the assumption of homologous expansion, but this condition might not always be met at the end of usually very short numerical-relativity simulations. In this article, we adjust the infrastructure of the bam code to enable longer simulations of the dynamical ejecta with the aim of investigating when the condition of homologous expansion is satisﬁed. In fact, we observe that the deviations from a perfect homologous expansion are about (cid:46) 30% at roughly 100 ms after the merger. To determine how these deviations might aﬀect the calculation of kilonova light curves, we extract the ejecta data for diﬀerent reference times and use them as input for radiative transfer simulations. Our results show that the light curves for extraction times later than 80 ms after the merger deviate by (cid:46) 0 . 4 mag and are mostly consistent with numerical noise. Accordingly, deviations from the homologous expansion for the dynamical ejecta component are negligible for the purpose of kilonova modelling.

Long-term simulations of dynamical ejecta: Homologous expansion and kilonova properties

GW170817 and its associated electromagnetic counterpart AT2017gfo continue to be a treasure trove as observations and modeling continue. Recent precision astrometry of AT2017gfo with the Hubble Space Telescope combined with previous constraints from Very Long Baseline Interferometry (VLBI) constraints narrowed down the inclination angle to 19-25 deg (90\% confidence). This paper explores how the inclusion of precise inclination information can reveal new insights about the ejecta properties, in particular, about the composition of the dynamical ejecta of AT2017gfo. Our analysis relies on updated kilonova modeling, which includes state-of-the-art heating rates, thermalization efficiencies, and opacities and is parameterized by $\bar{Y}_{\rm e,dyn}$, the average electron fraction of the dynamical ejecta component. Using this model, we incorporate the latest inclination angle constraint of AT2017gfo into a light curve fitting framework to derive updated parameter estimates. Our results suggest that the viewing angle of the observer is pointed towards the lanthanide-poor ($Y_{\rm e,dyn}\gtrsim0.25$), squeezed polar dynamical ejecta component, which can explain the early blue emission observed in the light curve of AT2017gfo. In contrast to a recent claim of spherical ejecta powering AT2017gfo, our study indicates that the composition of the dynamical ejecta has a strong angular dependence, with a lanthanide-rich ($Y_{\rm e,dyn}\lesssim0.25$), tidal component distributed around the merger plane with a half-opening angle of $35^\circ$. The inclination angle constraint reduces $\bar{Y}_{\rm e,dyn}$ from $0.24$ to $0.22$, with values $0.17\lesssim Y_{\rm e, dyn} \lesssim0.41$ enabling the robust production of $r$-process elements up to the $3^{\rm rd}$ peak in the tidal dynamical ejecta.

Chemical Distribution of the Dynamical Ejecta in the Neutron Star Merger GW170817

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Anna Neuweiler

Author Tools

Chat about Author