331 citations · 972 across the 22 of their papers we have counts for
23 papers
An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
Maximilian Dax, Theo Heimel, Gilles Louppe
Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and…
Flexible Gravitational-Wave Parameter Estimation with Transformers
Annalena Kofler, Maximilian Dax, Stephen R. Green +6
Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of obs…
Reparameterized LLM Training via Orthogonal Equivalence Transformation
Zeju Qiu, Simon Buchholz, Tim Z. Xiao +3
While large language models (LLMs) are driving the rapid advancement of artificial intelligence, effectively and reliably training these large models remains one of the field's mos…
Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax +4
Inferring atmospheric properties of exoplanets from observed spectra is key to understanding their formation, evolution, and habitability. Since traditional Bayesian approaches to…
Real-time gravitational-wave inference for binary neutron stars using machine learning
Maximilian Dax, Stephen R. Green, Jonathan Gair +7
Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817…
Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run
Gayathri Raman, Samuele Ronchini, James Delaunay +1819
We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network usin…
Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA
Nihar Gupte, Antoni Ramos-Buades, Alessandra Buonanno +9
Binary black holes (BBHs) in eccentric orbits produce distinct modulations in gravitational waves (GWs); measuring orbital eccentricity provides evidence for dynamical binary forma…
Ultralight vector dark matter search using data from the KAGRA O3GK run
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1800
Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating l…
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax +3
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem.…
Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1772
Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Or…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Flow Matching for Scalable Simulation-Based Inference
Maximilian Dax, Jonas Wildberger, Simon Buchholz +3
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional pro…
Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1692
Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about funda…
Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1741
The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data…
Search for subsolar-mass black hole binaries in the second part of Advanced LIGO's and Advanced Virgo's third observing run
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1702
We describe a search for gravitational waves from compact binaries with at least one component with mass 0.2 -- and mass ratio in Advanced LIGO…
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Jonas Wildberger, Maximilian Dax, Stephen R. Green +5
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accur…
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
Maximilian Dax, Stephen R. Green, Jonathan Gair +5
We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian po…
Model-based cross-correlation search for gravitational waves from the low-mass X-ray binary Scorpius X-1 in LIGO O3 data
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1692
We present the results of a model-based search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1 using LIGO detector data from the third observing run…
Real-time gravitational-wave science with neural posterior estimation
Maximilian Dax, Stephen R. Green, Jonathan Gair +3
We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions…
Dispersive analysis of the Primakoff reaction
Maximilian Dax, Dominik Stamen, Bastian Kubis
We provide a dispersion-theoretical representation of the reaction amplitudes in all charge channels, based on modern pion-kaon -wave phase shift input. Crossed-chan…
Explicitly Modeled Attention Maps for Image Classification
Andong Tan, Duc Tam Nguyen, Maximilian Dax +2
Self-attention networks have shown remarkable progress in computer vision tasks such as image classification. The main benefit of the self-attention mechanism is the ability to cap…
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi +4
Deep neural network (DNN) based salient object detection in images based on high-quality labels is expensive. Alternative unsupervised approaches rely on careful selection of multi…
Quark-mass dependence in decays
Maximilian Dax, Tobias Isken, Bastian Kubis
We study the quark-mass dependence of decays, based on a dispersion-theoretical framework. We rely on the quark-mass-dependent scattering phase shift for the pion-pion …