Publications

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178 Publications visible to you, out of a total of 178

Abstract (Expand)

Accreting main-sequence stars expand significantly when the mass accretion timescale is much shorter than their thermal timescales. This occurs during mass transfer from an evolved giant star onto a main-sequence companion in a binary system and is an important phase in the formation of compact binaries including X-ray binaries, cataclysmic variables, and gravitational-wave sources. In this study, we compute 1D stellar models of main-sequence accretors with different initial masses and accretion rates. The calculations are used to derive semianalytical approximations to the maximum expansion radius. We assume that mass transfer remains fully conservative as long as the inflated accretor fits within its Roche lobe, leading stars to behave like hamsters, stuffing excess material behind their expanding cheeks. We suggest a physically motivated prescription for the mass growth of such "hamstars," which can be used to determine mass-transfer efficiency in rapid binary population synthesis models. With this prescription, we estimate that progenitors of high-mass X-ray binaries and gravitational-wave sources may have experienced highly nonconservative mass transfer. In contrast, for low-mass accretors, the accretion timescale can exceed the thermal timescale by a larger factor without causing significant radial expansion.

Authors: Mike Lau, Ryosuke Hirai, Ilya Mandel, Christopher A. Tout

Date Published: 24th Apr 2024

Publication Type: Journal

Abstract (Expand)

We have computed a three-dimensional hydrodynamic simulation of the merger between a massive (0.4 M_⊙) helium white dwarf (He WD) and a low-mass (0.6 M_⊙) carbon-oxygen white dwarf (CO WD). Despite the low mass of the primary, the merger triggers a thermonuclear explosion as a result of a double detonation, producing a faint transient and leaving no remnant behind. This type of event could also take place during common-envelope mergers whenever the companion is a CO WD and the core of the giant star has a sufficiently large He mass. The spectra show strong Ca lines throughout the first few weeks after the explosion. The explosion only yields <0.01 M_⊙of ^56Ni, resulting in a low-luminosity SN Ia-like lightcurve that resembles the Ca-rich transients within this broad class of objects, with a peak magnitude of M_\mathrmbol ≈-15.7 mag and a rather slow decline rate of ∆m_15^\mathrmbol≈1.5 mag. Both, its lightcurve-shape and spectral appearance, resemble the appearance of Ca-rich transients, suggesting such mergers as a possible progenitor scenario for this class of events.

Authors: Javier Morán-Fraile, Alexander Holas, Friedrich K Röpke, Rüdiger Pakmor, Fabian R N Schneider

Date Published: 4th Mar 2024

Publication Type: Journal

Abstract (Expand)

There is strong observational evidence that the convective cores of intermediate-mass and massive main sequence stars are substantially larger than those predicted by standard stellar-evolution models. However, it is unclear what physical processes cause this phenomenon or how to predict the extent and stratification of stellar convective boundary layers. Convective penetration is a thermal-timescale process that is likely to be particularly relevant during the slow evolution on the main sequence. We use our low-Mach-number SEVEN-LEAGUE HYDRO code to study this process in 2.5D and 3D geometries. Starting with a chemically homogeneous model of a 15  M⊙ zero-age main sequence star, we construct a series of simulations with the luminosity increased and opacity decreased by the same factor, ranging from 10^3 to 10^6. After reaching thermal equilibrium, all of our models show a clear penetration layer; its thickness becomes statistically constant in time and it is shown to converge upon grid refinement. The penetration layer becomes nearly adiabatic with a steep transition to a radiative stratification in simulations at the lower end of our luminosity range. This structure corresponds to the adiabatic ‘step overshoot’ model often employed in stellar-evolution calculations. The simulations with the highest and lowest luminosity differ by less than a factor of two in the penetration distance. The high computational cost of 3D simulations makes our current 3D data set rather sparse. Depending on how we extrapolate the 3D data to the actual luminosity of the initial stellar model, we obtain penetration distances ranging from 0.09 to 0.44 pressure scale heights, which is broadly compatible with observations.

Authors: R. Andrassy, G. Leidi, J. Higl, P. V. F. Edelmann, F. R. N. Schneider, F. K. Röpke

Date Published: 1st Mar 2024

Publication Type: Journal

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn’ova, Pascal Friederich, Frauke Gräter

Date Published: 14th Feb 2024

Publication Type: Journal

Abstract (Expand)

The merger of a white dwarf (WD) and a neutron star (NS) is a relatively common event that will produce an observable electromagnetic signal. Furthermore, the compactness of these stellar objects makes them an interesting candidate for gravitational wave (GW) astronomy, potentially being in the frequency range of LISA and other missions. To date, three-dimensional simulations of these mergers have not fully modelled the WD disruption, or have used lower resolutions and have not included magnetic fields even though they potentially shape the evolution of the merger remnant. In this work, we simulate the merger of a 1.4M_⊙NS with a 1M_⊙carbon oxygen WD in the magnetohydrodynamic moving mesh code \AREPO. We find that the disruption of the WD forms an accretion disk around the NS, and the subsequent accretion by the NS powers the launch of strongly magnetized, mildly relativistic jets perpendicular to the orbital plane. Although the exact properties of the jets could be altered by unresolved physics around the NS, the event could result in a transient with a larger luminosity than kilonovae. We discuss possible connections to fast blue optical transients (FBOTs) and long-duration gamma-ray bursts. We find that the frequency of GWs released during the merger is too high to be detectable by the LISA mission, but suitable for deci-hertz observatories such as LGWA, BBO or DECIGO.

Authors: J. Moran-Fraile, F. K. Roepke, R. Pakmor, M. A. Aloy, S. T. Ohlmann, F. R. N. Schneider, G. Leidi

Date Published: 5th Jan 2024

Publication Type: Journal

Abstract (Expand)

Many astrophysical applications require efficient yet reliable forecasts of stellar evolution tracks. One example is population synthesis, which generates forward predictions of models for comparison with observations. The majority of state-of-the-art rapid population synthesis methods are based on analytic fitting formulae to stellar evolution tracks that are computationally cheap to sample statistically over a continuous parameter range. The computational costs of running detailed stellar evolution codes, such as MESA, over wide and densely sampled parameter grids are prohibitive, while stellar-age based interpolation in-between sparsely sampled grid points leads to intolerably large systematic prediction errors. In this work, we provide two solutions for automated interpolation methods that offer satisfactory trade-off points between cost-efficiency and accuracy. We construct a timescale-adapted evolutionary coordinate and use it in a two-step interpolation scheme that traces the evolution of stars from zero age main sequence all the way to the end of core helium burning while covering a mass range from 0.65 to 300 M⊙. The feedforward neural network regression model (first solution) that we train to predict stellar surface variables can make millions of predictions, sufficiently accurate over the entire parameter space, within tens of seconds on a 4-core CPU. The hierarchical nearest-neighbor interpolation algorithm (second solution) that we hard-code to the same end achieves even higher predictive accuracy, the same algorithm remains applicable to all stellar variables evolved over time, but it is two orders of magnitude slower. Our methodological framework is demonstrated to work on the MESA Isochrones and Stellar Tracks (Choi et al. 2016) data set, but is independent of the input stellar catalog. Finally, we discuss the prospective applications of these methods and provide guidelines for generalizing them to higher dimensional parameter spaces.

Authors: K. Maltsev, F. R. N. Schneider, F. K. Röpke, A. I. Jordan, G. A. Qadir, W. E. Kerzendorf, K. Riedmiller, P. van der Smagt

Date Published: 2024

Publication Type: Journal

Abstract

Not specified

Authors: Pau Amaro-Seoane, Jeff Andrews, Manuel Arca Sedda, Abbas Askar, Quentin Baghi, Razvan Balasov, Imre Bartos, Simone S. Bavera, Jillian Bellovary, Christopher P. L. Berry, Emanuele Berti, Stefano Bianchi, Laura Blecha, Stéphane Blondin, Tamara Bogdanović, Samuel Boissier, Matteo Bonetti, Silvia Bonoli, Elisa Bortolas, Katelyn Breivik, Pedro R. Capelo, Laurentiu Caramete, Federico Cattorini, Maria Charisi, Sylvain Chaty, Xian Chen, Martyna Chruślińska, Alvin J. K. Chua, Ross Church, Monica Colpi, Daniel D’Orazio, Camilla Danielski, Melvyn B. Davies, Pratika Dayal, Alessandra De Rosa, Andrea Derdzinski, Kyriakos Destounis, Massimo Dotti, Ioana Dutan, Irina Dvorkin, Gaia Fabj, Thierry Foglizzo, Saavik Ford, Jean-Baptiste Fouvry, Alessia Franchini, Tassos Fragos, Chris Fryer, Massimo Gaspari, Davide Gerosa, Luca Graziani, Paul Groot, Melanie Habouzit, Daryl Haggard, Zoltan Haiman, Wen-Biao Han, Alina Istrate, Peter H. Johansson, Fazeel Mahmood Khan, Tomas Kimpson, Kostas Kokkotas, Albert Kong, Valeriya Korol, Kyle Kremer, Thomas Kupfer, Astrid Lamberts, Shane Larson, Mike Lau, Dongliang Liu, Nicole Lloyd-Ronning, Giuseppe Lodato, Alessandro Lupi, Chung-Pei Ma, Tomas Maccarone, Ilya Mandel, Alberto Mangiagli, Michela Mapelli, Stéphane Mathis, Lucio Mayer, Sean McGee, Barry McKernan, M. Coleman Miller, David F. Mota, Matthew Mumpower, Syeda S. Nasim, Gijs Nelemans, Scott Noble, Fabio Pacucci, Francesca Panessa, Vasileios Paschalidis, Hugo Pfister, Delphine Porquet, John Quenby, Angelo Ricarte, Friedrich K. Röpke, John Regan, Stephan Rosswog, Ashley Ruiter, Milton Ruiz, Jessie Runnoe, Raffaella Schneider, Jeremy Schnittman, Amy Secunda, Alberto Sesana, Naoki Seto, Lijing Shao, Stuart Shapiro, Carlos Sopuerta, Nicholas C. Stone, Arthur Suvorov, Nicola Tamanini, Tomas Tamfal, Thomas Tauris, Karel Temmink, John Tomsick, Silvia Toonen, Alejandro Torres-Orjuela, Martina Toscani, Antonios Tsokaros, Caner Unal, Verónica Vázquez-Aceves, Rosa Valiante, Maurice van Putten, Jan van Roestel, Christian Vignali, Marta Volonteri, Kinwah Wu, Ziri Younsi, Shenghua Yu, Silvia Zane, Lorenz Zwick, Fabio Antonini, Vishal Baibhav, Enrico Barausse, Alexander Bonilla Rivera, Marica Branchesi, Graziella Branduardi-Raymont, Kevin Burdge, Srija Chakraborty, Jorge Cuadra, Kristen Dage, Benjamin Davis, Selma E. de Mink, Roberto Decarli, Daniela Doneva, Stephanie Escoffier, Poshak Gandhi, Francesco Haardt, Carlos O. Lousto, Samaya Nissanke, Jason Nordhaus, Richard O’Shaughnessy, Simon Portegies Zwart, Adam Pound, Fabian Schussler, Olga Sergijenko, Alessandro Spallicci, Daniele Vernieri, Alejandro Vigna-Gómez

Date Published: 1st Dec 2023

Publication Type: Journal

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