arXiv subjects

Christoph Weniger

Publications and source records attributed to Christoph Weniger.

At least 55 records · Page 3Linked to original sources

arXiv2022

Strong-Lensing Source Reconstruction with Denoising Diffusion Restoration Models

Analysis of galaxy--galaxy strong lensing systems is strongly dependent on any prior assumptions made about the appearance of the source. Here we present a method of imposing a data-driven prior / regularisation for source galaxies based on denoising diffusion probabilistic models (DDPMs). We use a pre-trained model for galaxy images, AstroDDPM, and a chain of conditional reconstruction steps called denoising diffusion reconstruction model (DDRM) to obtain samples consistent both with the noisy observation and with the distribution of training data for AstroDDPM. We show that these samples have the qualitative properties associated with the posterior for the source model: in a low-to-medium noise scenario they closely resemble the observation, while reconstructions from uncertain data show greater variability, consistent with the distribution encoded in the generative model used as prior.

astro-ph.IM↗

arXiv2022

Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods. In this paper we describe how Truncated Marginal Neural Ratio Estimation (TMNRE) (a new approach in so-called simulation-based inference) naturally evades these issues, improving the $(i)$ efficiency, $(ii)$ scalability, and $(iii)$ trustworthiness of the inferred posteriors. Using measurements of the Cosmic Microwave Background (CMB), we show that TMNRE can achieve converged posteriors using orders of magnitude fewer simulator calls than conventional Markov Chain Monte Carlo (MCMC) methods. Remarkably, the required number of samples is effectively independent of the number of nuisance parameters. In addition, a property called \emph{local amortization} allows the performance of rigorous statistical consistency checks that are not accessible to sampling-based methods. TMNRE promises to become a powerful tool for cosmological data analysis, particularly in the context of extended cosmologies, where the timescale required for conventional sampling-based inference methods to converge can greatly exceed that of simple cosmological models such as $Λ$CDM. To perform these computations, we use an implementation of TMNRE via the open-source code \texttt{swyft}.

astro-ph.CO↗

arXiv2022

Snowmass Theory Frontier: Astrophysics and Cosmology

We summarize progress made in theoretical astrophysics and cosmology over the past decade and areas of interest for the coming decade. This Report is prepared as the TF09 "Astrophysics and Cosmology" topical group summary for the Theory Frontier as part of the Snowmass 2021 process.

hep-ph↗

arXiv2022

Axion-Photon Conversion in Neutron Star Magnetospheres: The Role of the Plasma in the Goldreich-Julian Model

The most promising indirect search for the existence of axion dark matter uses radio telescopes to look for narrow spectral lines generated from the resonant conversion of axions in the magnetospheres of neutron stars. Unfortunately, a large list of theoretical uncertainties has prevented this search strategy from being fully accepted as robust. In this work we attempt to address major outstanding questions related to the role and impact of the plasma, including: $(i)$ does refraction and reflection of radio photons in the magnetosphere induce strong inhomogeneities in the flux, $(ii)$ can refraction induce premature axion-photon de-phasing, $(iii)$ to what extent do photon-plasma interactions induce a broadening of the spectral line, $(iv)$ does the flux have a strong time dependence, and $(v)$ can radio photons sourced by axions be absorbed by the plasma. We present an end-to-end analysis pipeline based on ray-tracing that exploits a state-of-the-art auto-differentiation algorithm to propagate photons from the conversion surface to asymptotically large distances. Adopting a charge symmetric Goldreich-Julian model for the magnetosphere, we show that for reasonable parameters one should expect a strong anisotropy of the signal, refraction induced axion-photon de-phasing, significant line-broadening, a variable time-dependence of the flux, and, for large enough magnetic fields, anisotropic absorption. Our simulation code is flexible enough to serve as the basis for follow-up studies with a large range of magnetosphere models.

hep-ph↗

arXiv2022

Towards reconstructing the halo clustering and halo mass function of N-body simulations using neural ratio estimation

High-resolution cosmological N-body simulations are excellent tools for modelling the formation and clustering of dark matter haloes. These simulations suggest complex physical theories of halo formation governed by a set of effective physical parameters. Our goal is to extract these parameters and their uncertainties in a Bayesian context. We make a step towards automatising this process by directly comparing dark matter density projection maps extracted from cosmological simulations, with density projections generated from an analytical halo model. The model is based on a toy implementation of two body correlation functions. To accomplish this we use marginal neural ratio estimation, an algorithm for simulation-based inference that allows marginal posteriors to be estimated by approximating marginal likelihood-to-evidence ratios with a neural network. In this case, we train a neural network with mock images to identify the correct values of the physical parameters that produced a given image. Using the trained neural network on cosmological N-body simulation images we are able to reconstruct the halo mass function, to generate mock images similar to the N-body simulation images and to identify the lowest mass of the haloes of those images, provided that they have the same clustering with our training data. Our results indicate that this is a promising approach in the path towards developing cosmological simulations assisted by neural networks.

astro-ph.CO↗

arXiv2022

TangoSIDM: Tantalizing models of Self-Interacting Dark Matter

We introduce the TangoSIDM project, a suite of cosmological simulations of structure formation in a $Λ$-Self-Interacting Dark Matter (SIDM) universe. TangoSIDM explores the impact of large dark matter (DM) scattering cross sections over dwarf galaxy scales. Motivated by DM interactions that follow a Yukawa potential, the cross section per unit mass, $σ/m_χ$, assumes a velocity dependent form that avoids violations of current constraints on large scales. We demonstrate that our implementation accurately models not only core formation in haloes, but also gravothermal core collapse. For central haloes in cosmological volumes, frequent DM particle collisions isotropise the particles orbit, making them largely spherical. We show that the velocity-dependent $σ/m_χ$ models produce a large diversity in the circular velocities of satellites haloes, with the spread in velocities increasing as the cross sections reach 20, 60 and 100 cm$^2$/g in $10^9~\rm{M}_{\odot}$ haloes. The large variation in the haloes internal structure is driven by DM particles interactions, causing in some haloes the formation of extended cores, whereas in others gravothermal core collapse. We conclude that the SIDM models from the Tango project offer a promising explanation for the diversity in the density and velocity profiles of observed dwarf galaxies.

astro-ph.GA↗

arXiv2022

Astrophysical and Cosmological Probes of Dark Matter

While astrophysical and cosmological probes provide a remarkably precise and consistent picture of the quantity and general properties of dark matter, its fundamental nature remains one of the most significant open questions in physics. Obtaining a more comprehensive understanding of dark matter within the next decade will require overcoming a number of theoretical challenges: the groundwork for these strides is being laid now, yet much remains to be done. Chief among the upcoming challenges is establishing the theoretical foundation needed to harness the full potential of new observables in the astrophysical and cosmological domains, spanning the early Universe to the inner portions of galaxies and the stars therein. Identifying the nature of dark matter will also entail repurposing and implementing a wide range of theoretical techniques from outside the typical toolkit of astrophysics, ranging from effective field theory to the dramatically evolving world of machine learning and artificial-intelligence-based statistical inference. Through this work, the theory frontier will be at the heart of dark matter discoveries in the upcoming decade.

hep-ph↗

arXiv2022

Extraterrestrial Axion Search with the Breakthrough Listen Galactic Center Survey

Axion dark matter (DM) may efficiently convert to photons in the magnetospheres of neutron stars (NSs), producing nearly monochromatic radio emission. This process is resonantly triggered when the plasma frequency induced by the underlying charge distribution approximately matches the axion mass. We search for evidence of this process using archival Green Bank Telescope data collected in a survey of the Galactic Center in the C-Band by the Breakthrough Listen project. While Breakthrough Listen aims to find signatures of extraterrestrial life in the radio band, we show that their high-frequency resolution spectral data of the Galactic Center region is ideal for searching for axion-photon transitions generated by the population of NSs in the inner pc of the Galaxy. We use data-driven models to capture the distributions and properties of NSs in the inner Galaxy and compute the expected radio flux from each NS using state-of-the-art ray tracing simulations. We find no evidence for axion DM and set leading constraints on the axion-photon coupling, excluding values down to the level $g_{a γγ} \sim 10^{-11}$ GeV$^{-1}$ for DM axions for masses between 15 and 35 $μ$eV.

astro-ph.CO↗

arXiv2021

Stellar Disruption of Axion Miniclusters in the Milky Way

Axion miniclusters are dense bound structures of dark matter axions that are predicted to form in the post-inflationary Peccei-Quinn symmetry breaking scenario. Although dense, miniclusters can easily be perturbed or even become unbound by interactions with baryonic objects such as stars. Here, we characterize the spatial distribution and properties of miniclusters in the Milky Way (MW) today after undergoing these stellar interactions throughout their lifetime. We do this by performing a suite of Monte Carlo simulations which track the miniclusters' structure and, in particular, accounts for partial disruption and mass loss through successive interactions. We consider two density profiles - Navarro-Frenk-White (NFW) and Power-law (PL) - for the individual miniclusters in order to bracket the uncertainties on the minicluster population today due to their uncertain formation history. For our fiducial analysis at the Solar position, we find a survival probability of 99% for miniclusters with PL profiles and 46% for those with NFW profiles. Our work extends previous estimates of this local survival probability to the entire MW. We find that towards the Galactic center, the survival probabilities drop drastically. Although we present results for a particular initial halo mass function, our simulations can be easily recast to different models using the provided data and code. Finally, we comment on the impact of our results on lensing, direct, and indirect detection.

astro-ph.GA↗

arXiv2021

Truncated Marginal Neural Ratio Estimation

Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter inference in this context can be challenging. We present a neural simulation-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability, which is unique among modern algorithms. Our approach is simulation efficient by simultaneously estimating low-dimensional marginal posteriors instead of the joint posterior and by proposing simulations targeted to an observation of interest via a prior suitably truncated by an indicator function. Furthermore, by estimating a locally amortized posterior our algorithm enables efficient empirical tests of the robustness of the inference results. Since scientists cannot access the ground truth, these tests are necessary for trusting inference in real-world applications. We perform experiments on a marginalized version of the simulation-based inference benchmark and two complex and narrow posteriors, highlighting the simulator efficiency of our algorithm as well as the quality of the estimated marginal posteriors.

stat.ML↗

arXiv2021

Transient Radio Signatures from Neutron Star Encounters with QCD Axion Miniclusters

The QCD axion is expected to form dense structures known as axion miniclusters if the Peccei-Quinn symmetry is broken after inflation. Miniclusters that have survived until today will interact with neutron stars (NSs) in the Milky Way to produce transient radio signals from axion-photon conversion in the NS magnetosphere. We quantify the properties of these encounters and find that they occur frequently ($\mathcal{O}(1-100)\,\mathrm{day}^{-1}$); last between a day and a few months; are spatially clustered towards the Galactic center; and can reach observable fluxes. These radio transients are within reach of current generation telescopes and therefore offer a promising pathway to discovering QCD axion dark matter.

hep-ph↗

arXiv2021

Strong-lensing source reconstruction with variationally optimised Gaussian processes

Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution datasets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are: (A) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator; (B) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation; and (C) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimising hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper.

astro-ph.IM↗

arXiv2020

Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization

Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of waveform models that link between signal shapes and the source parameters, running Markov chains until convergence is typically expensive and requires days of computation. In this extended abstract, we provide a proof of concept that demonstrates how the latest advances in neural simulation-based inference can speed up the inference time by up to three orders of magnitude -- from days to minutes -- without impairing the performance. Our approach is based on a convolutional neural network modeling the likelihood-to-evidence ratio and entirely amortizes the computation of the posterior. We find that our model correctly estimates credible intervals for the parameters of simulated gravitational waves.

astro-ph.IM↗

arXiv2020

Towards constraining warm dark matter with stellar streams through neural simulation-based inference

A statistical analysis of the observed perturbations in the density of stellar streams can in principle set stringent contraints on the mass function of dark matter subhaloes, which in turn can be used to constrain the mass of the dark matter particle. However, the likelihood of a stellar density with respect to the stream and subhaloes parameters involves solving an intractable inverse problem which rests on the integration of all possible forward realisations implicitly defined by the simulation model. In order to infer the subhalo abundance, previous analyses have relied on Approximate Bayesian Computation (ABC) together with domain-motivated but handcrafted summary statistics. Here, we introduce a likelihood-free Bayesian inference pipeline based on Amortised Approximate Likelihood Ratios (AALR), which automatically learns a mapping between the data and the simulator parameters and obviates the need to handcraft a possibly insufficient summary statistic. We apply the method to the simplified case where stellar streams are only perturbed by dark matter subhaloes, thus neglecting baryonic substructures, and describe several diagnostics that demonstrate the effectiveness of the new method and the statistical quality of the learned estimator.

astro-ph.GA↗

arXiv2020

Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies

The analysis of optical images of galaxy-galaxy strong gravitational lensing systems can provide important information about the distribution of dark matter at small scales. However, the modeling and statistical analysis of these images is extraordinarily complex, bringing together source image and main lens reconstruction, hyper-parameter optimization, and the marginalization over small-scale structure realizations. We present here a new analysis pipeline that tackles these diverse challenges by bringing together many recent machine learning developments in one coherent approach, including variational inference, Gaussian processes, differentiable probabilistic programming, and neural likelihood-to-evidence ratio estimation. Our pipeline enables: (a) fast reconstruction of the source image and lens mass distribution, (b) variational estimation of uncertainties, (c) efficient optimization of source regularization and other hyperparameters, and (d) marginalization over stochastic model components like the distribution of substructure. We present here preliminary results that demonstrate the validity of our approach.

astro-ph.CO↗

arXiv2020

Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time

We present algorithms (a) for nested neural likelihood-to-evidence ratio estimation, and (b) for simulation reuse via an inhomogeneous Poisson point process cache of parameters and corresponding simulations. Together, these algorithms enable automatic and extremely simulator efficient estimation of marginal and joint posteriors. The algorithms are applicable to a wide range of physics and astronomy problems and typically offer an order of magnitude better simulator efficiency than traditional likelihood-based sampling methods. Our approach is an example of likelihood-free inference, thus it is also applicable to simulators which do not offer a tractable likelihood function. Simulator runs are never rejected and can be automatically reused in future analysis. As functional prototype implementation we provide the open-source software package swyft.

astro-ph.IM↗

arXiv2020

A frequentist analysis of three right-handed neutrinos with GAMBIT

The extension of the Standard Model by right-handed neutrinos can not only explain the active neutrino masses via the seesaw mechanism, it is also able solve a number of long standing problems in cosmology. Especially, masses below the TeV scale are of particular interest as they can lead to a plethora of signatures in experimental searches. We present the first full frequentist analysis of the extension of the Standard Model by three right-handed neutrinos, with masses between 60 MeV and 500 GeV, using the Global and Modular BSM (beyond the Standard Model) Inference Tool GAMBIT. Our analysis is based on the Casas-Ibarra parametrisation and includes a large range of experimental constraints: active neutrino mixing, indirect constraints from, e.g., electroweak precision observables and lepton universality, and numerous direct searches for right-handed neutrinos. To study their overall effect, we derive combined profile likelihood results for the phenomenologically most relevant parameter projections. Furthermore, we discuss the role of (marginally) statistically preferred regions in the parameter space. Finally, we explore the flavour mixing pattern of the three right-handed neutrinos for different values of the lightest neutrino mass. Our results comprise the most comprehensive assessment of the model with three right-handed neutrinos model below the TeV scale so far, and provide a robust ground for exploring the impact of future constraints or detections.

hep-ph↗

arXiv2020

Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming

Since upcoming telescopes will observe thousands of strong lensing systems, creating fully-automated analysis pipelines for these images becomes increasingly important. In this work, we make a step towards that direction by developing the first end-to-end differentiable strong lensing pipeline. Our approach leverages and combines three important computer science developments: (a) convolutional neural networks, (b) efficient gradient-based sampling techniques, and (c) deep probabilistic programming languages. The latter automatize parameter inference and enable the combination of generative deep neural networks and physics components in a single model. In the current work, we demonstrate that it is possible to combine a convolutional neural network trained on galaxy images as a source model with a fully-differentiable and exact implementation of gravitational lensing physics in a single probabilistic model. This does away with hyperparameter tuning for the source model, enables the simultaneous optimization of nearly one hundred source and lens parameters with gradient-based methods, and allows the use of efficient gradient-based posterior sampling techniques. These features make this automated inference pipeline potentially suitable for processing a large amount of data. By analyzing mock lensing systems with different signal-to-noise ratios, we show that lensing parameters are reconstructed with percent-level accuracy. More generally, we consider this work as one of the first steps in establishing differentiable probabilistic programming techniques in the particle astrophysics community, which have the potential to significantly accelerate and improve many complex data analysis tasks.

astro-ph.CO↗