DeepGlow: An efficient neural network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events

Boersma, Oliver M.; Leeuwen, J. van

doi:10.1017/pasa.2023.32

Cited by 3 publications

(1 citation statement)

References 38 publications

(70 reference statements)

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“…An additional type of application can be found in simulating GRB prompt or afterglow emission. In the work presented by [43], a neural network called DeepGlow is trained to simulate a GRB afterglow lightcurve. This simulation is conditioned by pre-defined afterglow parameters, thus ensuring fidelity to the underlying physical properties.…”

Section: Grb Simulationmentioning

confidence: 99%

New detailed characterization of the residual luminescence emitted by the GAGG:Ce scintillator crystals for the HERMES Pathfinder mission

Della Casa,

Zampa,

Cirrincione

et al. 2024

Nuclear Instruments and Methods in Physics Research Section A:

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Section: Grb Simulationmentioning

confidence: 99%

New detailed characterization of the residual luminescence emitted by the GAGG:Ce scintillator crystals for the HERMES Pathfinder mission

Della Casa,

Zampa,

Cirrincione

et al. 2024

Nuclear Instruments and Methods in Physics Research Section A:

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Transformer models for astrophysical time series and the GRB prompt–afterglow relation

Boersma,

Ayache,

van Leeuwen

2024

RAS Techniques and Instruments

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Transformer models have recently become very successful in the natural language domain. Their value as sequence-to-sequence translators there, also makes them a highly interesting technique for learning relationships between astrophysical time series. Our aim is investigate how well such a transformer neural network can establish causal temporal relations between different channels of a single-source signal. We thus apply a transformer model to the two phases of Gamma-Ray Bursts (GRBs), reconstructing one phase from the other. GRBs are unique instances where a single process and event produces two distinct time variable phenomena: the prompt emission and the afterglow. We here investigate if a transformer model can predict the afterglow flux from the prompt emission. If successful, such a predictive scheme might then be distilled to the most important underlying physics drivers in the future. We combine the transformer model with a novel dense neural network setup to directly estimate the starting value of the prediction. We find that the transformer model can, in some instances, successfully predict different phases of canonical afterglows, including the plateau phase. Hence it is a useful and promising new astrophysical analysis technique. For the GRB test case, the method marginally exceeds the baseline model overall, but still achieves accurate recovery of the prompt-afterglow fluence-fluence correlation in reconstructed light curves. Despite this progress, we conclude that consistent improvement over the baseline model is not yet achieved for the GRB case. We discuss the future improvements in data and modeling that are required to identify new physical-relation parameters or new insights into the single process driving both GRB phases.

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Modeling Blazar Broadband Emission with a Convolutional Neural Network. I. Synchrotron Self-Compton Model

Bégué,

Sahakyan,

Dereli-Bégué

et al. 2024

ApJ

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Modeling the multiwavelength spectral energy distributions (SEDs) of blazars provides key insights into the underlying physical processes responsible for the emission. While SED modeling with self-consistent models is computationally demanding, it is essential for a comprehensive understanding of these astrophysical objects. We introduce a novel, efficient method for modeling the SEDs of blazars by the mean of a convolutional neural network (CNN). In this paper, we trained the CNN on a leptonic model that incorporates synchrotron and inverse Compton emissions, as well as self-consistent electron cooling and pair creation–annihilation processes. The CNN is capable of reproducing the radiative signatures of blazars with high accuracy. This approach significantly reduces the computational time, thereby enabling real-time fitting to multiwavelength data sets. As a demonstration, we used the trained CNN with MultiNest to fit the broadband SEDs of Mrk 421 and 1ES 1959+650, successfully obtaining their parameter posterior distributions. This novel framework for fitting the SEDs of blazars will be further extended to incorporate more sophisticated models based on external Compton and hadronic scenarios, allowing for multimessenger constraints in the analysis. The models will be made publicly available via a web interface at the Markarian Multiwavelength Data Center to facilitate self-consistent modeling of multimessenger data from blazar observations.

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DeepGlow: An efficient neural network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events

Cited by 3 publications

References 38 publications

New detailed characterization of the residual luminescence emitted by the GAGG:Ce scintillator crystals for the HERMES Pathfinder mission

New detailed characterization of the residual luminescence emitted by the GAGG:Ce scintillator crystals for the HERMES Pathfinder mission

Transformer models for astrophysical time series and the GRB prompt–afterglow relation

Modeling Blazar Broadband Emission with a Convolutional Neural Network. I. Synchrotron Self-Compton Model

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