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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Cheat sheet hotkeys for Windows
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Posts
When string theory meets machine learning
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In my physics master’s thesis, titled Holographic RG flows in gauged supergravity, I studied applications of the AdS/CFT correspondence, which originates from string theory. To tackle the issues I encountered, I have designed and developed a new numerical algorithm, inspired by machine learning. Read more
Spatiotemporal coordination of the cell division cycle
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In oscillatory media, heterogeneities act as sources of waves which end up synchronizing the whole medium. In biological systems, these waves are a crucial way of transmitting information. At GelensLab, recent experiments increased an interest in spiral wave phenomena in the context of cell cycle oscillations of Xenopus laevis frogs. During my internship, my goal was to gain insight into properties of these wave phenomena through simulations of mathematical models. Read more
Resources
publications
Holographic RG flows in gauged supergravity
Published in N/A, 2022
Multiband analyses of the bright GRB 230812B and the associated SN2023pel
Published in Mon.Not.Roy.Astron.Soc. 530, 2023
Running Bayesian inference on a GRB candidate, performing model selection on the type of transient with the NMMA software.
Robust parameter estimation within minutes on gravitational wave signals from binary neutron star inspirals
Published in Phys.Rev.D 110, 2024
We develop GPU-accelerated waveform approximants for binary neutron star mergers, and demonstrate fast inference for this type of GW signals.
Classification of compact objects and model comparison using EOS knowledge
Published in Phys.Rev.D 110, 2024
We assess recent observations in light of our knowledge of the nuclear equation of state.
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows
Published in arXiv preprint arXiv:2410.21076, 2024
We run jim, a fast inference pipeline for gravitational waves, together with harmonic, which provides fast Bayesian evidence estimates from a set of samples.
Data-driven approach for modeling the temporal and spectral evolution of kilonova systematic uncertainties
Published in Phys.Rev.D 111, 2024
We extend the treatment of systemtic uncertainties in kilonova light-curve inference, and demonstrate its usefulness when models are misspecified.
Null-stream-based third-generation-ready glitch mitigation for gravitational wave measurements
Published in Phys.Rev.D 112, 2024
We show how a triangular ET, using its null stream, has unique capabilities in mitigating noise transients in the detector, which would otherwise bias parameter estimation.
Employing deep-learning techniques for the conservative-to-primitive recovery in binary neutron star simulations
Published in Eur.Phys.J.A 61, 2025
We develop machine learning methods to accelerate the conservative-to-primitive transformation, a costly step in binary neutron star mergers.
Limits on the ejecta mass during the search for kilonovae associated with neutron star-black hole mergers: A case study of S230518h, GW230529, S230627c and the low-significance candidate S240422ed
Published in Phys.Rev.D 112, 2025
We use electromagnetic follow-up observations on neutron star-black hole merger candidates to provide limits on the ejecta masses of their kilonovae.
Leveraging differentiable programming in the inverse problem of neutron stars
Published in Phys.Rev.D 112, 2025
Methods paper introducing jester, a GPU-accelerated pipeline for equation of state inference.
Revealing tensions in neutron star observations with pressure anisotropy
Published in Phys.Rev.D 113, 2025
We extend jester to account for pressure anisotropies in neutron stars and infer them with real data.
Efficient Bayesian analysis of kilonovae and gamma ray burst afterglows with FIESTA
Published in Astron.Astrophys. 704, 2025
We introduce fiesta, a GPU-accelerated pipeline for inference on kilonovae and gamma ray burst afterglows, with new surrogates for the latest models.
Analyzing GW231109_235456 and understanding its potential implications for population studies, nuclear physics, and multi-messenger astronomy
Published in arXiv preprint arXiv:2510.22290, 2025
We perform follow-up multi-messenger investigations of a candidate binary neutron star merger in LVK's O4a observing run, and provide projections of a similar source with future instruments.
AT2025ulz and S250818k: Investigating early time observations of a subsolar mass gravitational-wave binary neutron star merger candidate
Published in arXiv preprint arXiv:2510.24620, 2025
We perform multi-messenger analyses of an SSM candidate reported by the LVK, and using the follow-up from telescopes.
Incorporating neutron star physics into gravitational wave inference with physics-informed priors using normalizing flows
Published in Phys.Rev.D 114, 2025
We introduce data-driven priors for gravitational wave inference that are informed by existing knowledge on the neutron star equation of state.
Detecting Tidal Resonances in Binary Neutron Stars
Published in arXiv preprint arXiv:2606.06376, 2026
We show that neutron star resonance modes leave detectable imprints in the gravitational wave signal.
Impact of the Einstein Telescope’s duty cycle on the estimation of binary black holes parameters
Published in arXiv preprint arXiv:2606.19201, 2026
We assess the impact of a realistic duty cycle on parameter estimation of gravitational waves from binary black hole mergers.
nmma: An extended Bayesian framework for Nuclear Multimessenger Astronomy in the Era of Next-Generation Detectors
Published in arXiv preprint arXiv:2607.03045, 2026
We provide updates on the NMMA software, re-analyze the multi-messenger GW170817 merger and provide projections for future detectors.
Ab Initio Real-Time Gravitational-Wave Parameter Estimation
Published in arXiv preprint arXiv:2607.28265, 2026
We introduce a slice-within-Gibbs sampler within Jim, and show how it can infer binary neutron star properties at unprecedented sampling speeds.
Binary neutron stars in the next-generation era: Multi-messenger detection prospects and constraints on the equation of state, mass distribution, and cosmology
Published in arXiv preprint arXiv:2607.28438, 2026
We use jester to provide an extensive projection study on multi-messenger inference on binary neutron star mergers observed in future detectors.
Extending multi-messenger constraints on neutron star matter through the inclusion of direct Urca cooling
Published in arXiv preprint arXiv:2609.30112, 2026
We add constraints from the direct-Urca process into the multimessenger equation of state inference pipeline jester
software
jester Permalink
A JAX-based package for fast inference of the nuclear equation of state from neutron star data. Read more
NMMA Permalink
A pythonic library for probing nuclear physics and cosmology with multimessenger analysis. Read more
talks
Predict and Prejudice: Classification of compact objects and model comparison using EOS knowledge Permalink
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From constraints to classifications: closing the loop in neutron star data analysis Permalink
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Scalable Bayesian inference for 3G: Leveraging hardware acceleration and normalizing flows Permalink
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Scalable Bayesian Inference for Third-Generation Gravitational Wave Data with Normalizing Flows Permalink
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Towards GPU-accelerated multimessenger inference of neutron star mergers and dense matter physics Permalink
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Encoding neutron star information into neural priors for gravitational wave analyses Permalink
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Analyzing GW231109_235456 and its implications for the neutron star equation of state Permalink
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Incorporating neutron star physics into gravitational wave inference with neural priors Permalink
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Incorporating neutron star physics into gravitational wave inference with neural priors Permalink
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Analyzing GW231109_235456 in the ET era and incorporating neutron star physics into future GW inference Permalink
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Incorporating neutron star physics into gravitational wave inference with neural priors Permalink
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Incorporating neutron star physics into gravitational wave inference with neural priors Permalink
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jester v0.2.0: Scalable inference of the equation of state with multimessenger data Permalink
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Incorporating neutron star physics into gravitational wave inference with neural priors Permalink
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