arXiv subjects

Florian List

Publications and source records attributed to Florian List.

At least 19 recordsLinked to original sources

arXiv2025

DISCO-DJ II: a differentiable particle-mesh code for cosmology

The mildly non-linear regime of cosmic structure formation holds much of the information that upcoming large-scale structure surveys aim to exploit, making fast and accurate predictions on these scales essential. We present the $N$-body module of DISCO-DJ (DIfferentiable Simulations for COsmology - Done with Jax), designed to deliver high-fidelity, GPU-accelerated, and differentiable particle-mesh simulations tailored for cosmological inference. Theory-informed time integrators such as the recently introduced BullFrog method allow for accurate predictions already with few time steps (e.g. $6$ steps for per-cent-level accuracy in terms of the present-day power spectrum at $k \approx 0.2 \, h / \mathrm{Mpc}$ using $N = 512^3$ particles, which takes just a few seconds). To control discreteness effects and achieve high accuracy, the code incorporates a suite of advanced techniques, for example a custom non-uniform FFT implementation for force evaluation. Both forward- and reverse-mode differentiation are supported, with memory requirements independent of the number of time steps; in the reverse case, this is achieved through an adjoint formulation. We extensively study the effect of various numerical parameters on the accuracy. As an application of DISCO-DJ, we perform field-level inference by recovering $\sigma_8$ and the initial conditions from a noisy Gadget matter density field. Coupled with our recently introduced Einstein--Boltzmann solver, the DISCO-DJ ecosystem provides a self-consistent, fully differentiable pipeline for modelling the large-scale structure of the universe. The code is available at https://github.com/cosmo-sims/DISCO-DJ.

astro-ph.CO↗

arXiv2025

Gravitational Microlensing of the Galactic Centre $\gamma$-Ray Excess: A New Test for Point-Like or Extended Emission?

We present a potential test of the origin of the $\gamma$-ray Galactic Centre Excess (GCE). We demonstrate how gravitational microlensing by stellar mass objects along the line of sight to the Galactic Bulge can distinguish between the possibility of extensive emission due to dark matter self-annihilation from more prosaic astrophysical sources, namely millisecond pulsars. Such an astrophysical origin would result in emission from a population of small, currently unresolved point-like sources - in contrast to the expected smoother emission resulting from dark matter annihilation. Given that the scale of gravitational microlensing, that is, the Einstein radius for stellar mass lenses, and hence, the degree of induced magnification, is sensitive to the size of the emitting region, such microlensing will induce time variability in the emission of astrophysical sources, whereas $\gamma$-ray emission from dark matter annihilation will effectively be immune to such influences. However, we find that detecting microlensing-induced variability requires significantly greater sensitivity than that of current or planned $\gamma$-ray detectors. For a small population of bright GCE sources, more than an order-of-magnitude increase in effective area over Fermi-LAT would be required, with events remaining extremely rare. For a large population of faint sources, events would occur multiple times a year, but would only be detectable with a four-order-of-magnitude improvement. Whilst microlensing might not be a definitive test of the origin of the GCE, in future observations, it may prove useful in determining the properties of any point-like source population.

astro-ph.HE↗

arXiv2025

On the Energy Distribution of the Galactic Center Excess' Sources

The Galactic Center Excess (GCE) may yet herald the discovery of annihilating dark matter. Weighing against that conclusion are analyses showing evidence for dim point sources within the spatial structure of the emission. Due to technical limitations these analyses are purely spatial with all spectral information that could disentangle the excess from astrophysical backgrounds discarded. Here, we demonstrate that a neural network simulation-based inference approach can jointly analyze the spatial and spectra data. The addition is profound: energy information drives the putative point sources to be significantly dimmer, indicating either the GCE is truly diffuse in nature or made of an exceptionally large number of sources. Quantitatively, for our best fit background model, the excess is essentially consistent with Poisson emission as predicted by dark matter. If due to point sources, our median prediction is $\mathcal{O}(10^5)$ sources, or more than 35,000 at 90\% confidence, both orders of magnitude larger than the hundreds preferred by earlier point-source analyses of the GCE, although variations allowed by background systematics could reduce the required number of sources by roughly an order of magnitude.

astro-ph.HE↗

arXiv2025

Differentiable Halo Mass Prediction and the Cosmology-Dependence of Halo Mass Functions

Modern cosmological inference increasingly relies on differentiable models to enable efficient, gradient-based parameter estimation and uncertainty quantification. Here, we present a novel approach for predicting the abundance of dark matter haloes and their cosmology dependence using a differentiable, field-level neural network (NN) model, and study how well the cosmology dependence is captured by common parametrisations of the halo mass function (HMF), and by our NN-based approach. By training a 3D U-Net on initial density fields from fast N-body simulations with varying cosmological parameters, we enable direct, differentiable mapping from the linear density field to protohalo patches and their mass bins. Our method achieves competitive accuracy in identifying protohalo regions and in capturing the dependence of the HMF on cosmological parameters. Our NN derivatives agree well with finite differences of both analytical and emulated HMFs, at the level of the disagreement among the different models. We further demonstrate how the NN model can additionally be used to investigate the response of the HMF to changes in the initial Gaussian random field. Finally, we also demonstrate that a differentiable model can be used to extrapolate existing models at very high precision.

astro-ph.CO↗

arXiv2025

A robust neural determination of the source-count distribution of the Fermi-LAT sky at high latitudes

Over the past 16 years, the Fermi Large Area Telescope (LAT) has significantly advanced our view of the GeV gamma-ray sky, yet several key questions remain - such as the composition of the isotropic gamma-ray background, the origin of the Fermi Bubbles or the potential presence of signatures from exotic physics like dark matter. Addressing these challenges requires sophisticated astrophysical modeling and robust statistical methods capable of handling high-dimensional parameter spaces. In this work, we analyze 14 years of high-latitude ($|b|\geq30^{\circ}$) Fermi-LAT data in the range from 1 to 10 GeV using simulation-based inference (SBI) via neural ratio estimation. This approach allows us to detect individual gamma-ray sources and derive a list of significant gamma-ray emitters containing more than 98\% of all sources listed in the Fermi-LAT Fourth Source Catalog (4FGL) with a flux $S>3\times10^{-10}\;\mathrm{cm}^{-2}\,\mathrm{s}^{-1}$ (about a factor of three larger than the flux above which 4FGL is nearly complete), without any non-4FGL source detected in that flux range. Additionally, we reconstruct the source-count distribution in both parametric and non-parametric forms, achieving large agreement with previous literature results as well as those sources detected by our SBI pipeline. We also quantitatively validate our gamma-ray emission simulator via an anomaly detection technique, demonstrating that the synthetic data closely reproduces the complexity of the real observations.

astro-ph.HE↗

arXiv2025

Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation

Knowledge of the primordial matter density field from which the large-scale structure of the Universe emerged over cosmic time is of fundamental importance for cosmology. However, reconstructing these cosmological initial conditions from late-time observations is a notoriously difficult task, which requires advanced cosmological simulators and sophisticated statistical methods to explore a multi-million-dimensional parameter space. We show how simulation-based inference (SBI) can be used to tackle this problem and to obtain data-constrained realisations of the primordial dark matter density field in a simulation-efficient way with general non-differentiable simulators. Our method is applicable to full high-resolution dark matter $N$-body simulations and is based on modelling the posterior distribution of the constrained initial conditions to be Gaussian with a diagonal covariance matrix in Fourier space. As a result, we can generate thousands of posterior samples within seconds on a single GPU, orders of magnitude faster than existing methods, paving the way for sequential SBI for cosmological fields. Furthermore, we perform an analytical fit of the estimated dependence of the covariance on the wavenumber, effectively transforming any point-estimator of initial conditions into a fast sampler. We test the validity of our obtained samples by comparing them to the true values with summary statistics and performing a Bayesian consistency test.

astro-ph.CO↗

arXiv2024

Mean-Field Simulation-Based Inference for Cosmological Initial Conditions

Reconstructing cosmological initial conditions (ICs) from late-time observations is a difficult task, which relies on the use of computationally expensive simulators alongside sophisticated statistical methods to navigate multi-million dimensional parameter spaces. We present a simple method for Bayesian field reconstruction based on modeling the posterior distribution of the initial matter density field to be diagonal Gaussian in Fourier space, with its covariance and the mean estimator being the trainable parts of the algorithm. Training and sampling are extremely fast (training: $\sim 1 \, \mathrm{h}$ on a GPU, sampling: $\lesssim 3 \, \mathrm{s}$ for 1000 samples at resolution $128^3$), and our method supports industry-standard (non-differentiable) $N$-body simulators. We verify the fidelity of the obtained IC samples in terms of summary statistics.

astro-ph.CO↗

arXiv2024

BullFrog: Multi-step perturbation theory as a time integrator for cosmological simulations

Modelling the cosmic large-scale structure can be done through numerical N-body simulations or by using perturbation theory. Here, we present an N-body approach that effectively implements a multi-step forward model based on Lagrangian Perturbation Theory (LPT) in a $\Lambda$CDM Universe. This is achieved by introducing the second-order accurate BullFrog integrator, which automatically performs 2LPT time steps to second order without requiring the explicit computation of 2LPT displacements. Importantly, we show that BullFrog trajectories rapidly converge to the exact solution as the number of time steps increases, at any moment in time, even though 2LPT becomes invalid after shell-crossing. As a validation test, we compare BullFrog against other N-body integrators and high-order LPT, both for a realistic $\Lambda$CDM cosmology and for simulations with a sharp UV cutoff in the initial conditions. The latter scenario enables controlled experiments against LPT and, in practice, is particularly relevant for modelling coarse-grained fluids arising in the context of effective field theory. We demonstrate that BullFrog significantly improves upon other LPT-inspired integrators, such as FastPM and COLA, without incurring any computational overhead compared to standard N-body integrators. Implementing BullFrog in any existing N-body code is straightforward, particularly if FastPM is already integrated.

astro-ph.CO↗

arXiv2024

A deep learning framework for jointly extracting spectra and source-count distributions in astronomy

Astronomical observations typically provide three-dimensional maps, encoding the distribution of the observed flux in (1) the two angles of the celestial sphere and (2) energy/frequency. An important task regarding such maps is to statistically characterize populations of point sources too dim to be individually detected. As the properties of a single dim source will be poorly constrained, instead one commonly studies the population as a whole, inferring a source-count distribution (SCD) that describes the number density of sources as a function of their brightness. Statistical and machine learning methods for recovering SCDs exist; however, they typically entirely neglect spectral information associated with the energy distribution of the flux. We present a deep learning framework able to jointly reconstruct the spectra of different emission components and the SCD of point-source populations. In a proof-of-concept example, we show that our method accurately extracts even complex-shaped spectra and SCDs from simulated maps.

astro-ph.IM↗

arXiv2023

DISCO-DJ I: a differentiable Einstein-Boltzmann solver for cosmology

We present the Einstein-Boltzmann module of the DISCO-DJ (DIfferentiable Simulations for COsmology - Done with JAX) software package. This module implements a fully differentiable solver for the linearised cosmological Einstein-Boltzmann equations in the JAX framework, and allows computing Jacobian matrices of all solver output with respect to all input parameters using automatic differentiation. This implies that along with the solution for a given set of parameters, the tangent hyperplane in parameter space is known as well, which is a key ingredient for cosmological inference and forecasting problems as well as for many other applications. We discuss our implementation and demonstrate that our solver agrees at the per-mille level with the existing non-differentiable solvers CAMB and CLASS, including massive neutrinos and a dark energy fluid with parameterised equation of state. We illustrate the dependence of various summary statistics in large-scale structure cosmology on model parameters using the differentiable solver, and finally demonstrate how it can be easily used for Fisher forecasting, with a forecast for Euclid as an example. Since the implementation is significantly shorter and more modular than existing solvers, we believe it will be more straightforward to extend our solver to include additional physics, such as additional dark energy and dark matter models, modified gravity, or other non-standard physics in the future.

astro-ph.CO↗

arXiv2023

Bayesian Simulation-based Inference for Cosmological Initial Conditions

Reconstructing astrophysical and cosmological fields from observations is challenging. It requires accounting for non-linear transformations, mixing of spatial structure, and noise. In contrast, forward simulators that map fields to observations are readily available for many applications. We present a versatile Bayesian field reconstruction algorithm rooted in simulation-based inference and enhanced by autoregressive modeling. The proposed technique is applicable to generic (non-differentiable) forward simulators and allows sampling from the posterior for the underlying field. We show first promising results on a proof-of-concept application: the recovery of cosmological initial conditions from late-time density fields.

astro-ph.CO↗

arXiv2023

Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models

In recent years, deep learning models have been successfully employed for augmenting low-resolution cosmological simulations with small-scale information, a task known as "super-resolution". So far, these cosmological super-resolution models have relied on generative adversarial networks (GANs), which can achieve highly realistic results, but suffer from various shortcomings (e.g. low sample diversity). We introduce denoising diffusion models as a powerful generative model for super-resolving cosmic large-scale structure predictions (as a first proof-of-concept in two dimensions). To obtain accurate results down to small scales, we develop a new "filter-boosted" training approach that redistributes the importance of different scales in the pixel-wise training objective. We demonstrate that our model not only produces convincing super-resolution images and power spectra consistent at the percent level, but is also able to reproduce the diversity of small-scale features consistent with a given low-resolution simulation. This enables uncertainty quantification for the generated small-scale features, which is critical for the usefulness of such super-resolution models as a viable surrogate model for cosmic structure formation.

astro-ph.CO↗

arXiv2023

Starting Cosmological Simulations from the Big Bang

The cosmic large-scale structure (LSS) provides a unique testing ground for connecting fundamental physics to astronomical observations. Modeling the LSS requires numerical $N$-body simulations or perturbative techniques that both come with distinct shortcomings. Here we present the first unified numerical approach, enabled by new time integration and discreteness reduction schemes, and demonstrate its convergence at the field level. In particular, we show that our simulations (1) can be initialized directly at time zero, and (2) can be made to agree with high-order Lagrangian perturbation theory in the fluid limit. This enables fast, self-consistent, and UV-complete forward modeling of LSS observables.

astro-ph.CO↗

arXiv2023

Perturbation-theory informed integrators for cosmological simulations

Large-scale cosmological simulations are an indispensable tool for modern cosmology. To enable model-space exploration, fast and accurate predictions are critical. In this paper, we show that the performance of such simulations can be further improved with time-stepping schemes that use input from cosmological perturbation theory. Specifically, we introduce a class of time-stepping schemes derived by matching the particle trajectories in a single leapfrog/Verlet drift-kick-drift step to those predicted by Lagrangian perturbation theory (LPT). As a corollary, these schemes exactly yield the analytic Zel'dovich solution in 1D in the pre-shell-crossing regime (i.e. before particle trajectories cross). One representative of this class is the popular FastPM scheme by Feng et al. 2016, which we take as our baseline. We then construct more powerful LPT-inspired integrators and show that they outperform FastPM and standard integrators in fast simulations in two and three dimensions with $\mathcal{O}(1 - 100)$ timesteps, requiring less steps to accurately reproduce the power spectrum and bispectrum of the density field. Furthermore, we demonstrate analytically and numerically that, for any integrator, convergence is limited in the post-shell-crossing regime (to order 3/2 for planar wave collapse), owing to the lacking regularity of the acceleration field, which makes the use of high-order integrators in this regime futile. Also, we study the impact of the timestep spacing and of a decaying mode present in the initial conditions. Importantly, we find that symplecticity of the integrator plays a minor role for fast approximate simulations with a small number of timesteps.

astro-ph.CO↗

arXiv2021

Dim but not entirely dark: Extracting the Galactic Center Excess' source-count distribution with neural nets

The two leading hypotheses for the Galactic Center Excess (GCE) in the $\textit{Fermi}$ data are an unresolved population of faint millisecond pulsars (MSPs) and dark-matter (DM) annihilation. The dichotomy between these explanations is typically reflected by modeling them as two separate emission components. However, point-sources (PSs) such as MSPs become statistically degenerate with smooth Poisson emission in the ultra-faint limit (formally where each source is expected to contribute much less than one photon on average), leading to an ambiguity that can render questions such as whether the emission is PS-like or Poissonian in nature ill-defined. We present a conceptually new approach that describes the PS and Poisson emission in a unified manner and only afterwards derives constraints on the Poissonian component from the so obtained results. For the implementation of this approach, we leverage deep learning techniques, centered around a neural network-based method for histogram regression that expresses uncertainties in terms of quantiles. We demonstrate that our method is robust against a number of systematics that have plagued previous approaches, in particular DM / PS misattribution. In the $\textit{Fermi}$ data, we find a faint GCE described by a median source-count distribution (SCD) peaked at a flux of $\sim4 \times 10^{-11} \ \text{counts} \ \text{cm}^{-2} \ \text{s}^{-1}$ (corresponding to $\sim3 - 4$ expected counts per PS), which would require $N \sim \mathcal{O}(10^4)$ sources to explain the entire excess (median value $N = \text{29,300}$ across the sky). Although faint, this SCD allows us to derive the constraint $\eta_P \leq 66\%$ for the Poissonian fraction of the GCE flux $\eta_P$ at 95% confidence, suggesting that a substantial amount of the GCE flux is due to PSs.

astro-ph.HE↗

arXiv2021

The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression

Although ubiquitous in the sciences, histogram data have not received much attention by the Deep Learning community. Whilst regression and classification tasks for scalar and vector data are routinely solved by neural networks, a principled approach for estimating histogram labels as a function of an input vector or image is lacking in the literature. We present a dedicated method for Deep Learning-based histogram regression, which incorporates cross-bin information and yields distributions over possible histograms, expressed by $\tau$-quantiles of the cumulative histogram in each bin. The crux of our approach is a new loss function obtained by applying the pinball loss to the cumulative histogram, which for 1D histograms reduces to the Earth Mover's distance (EMD) in the special case of the median ($\tau = 0.5$), and generalizes it to arbitrary quantiles. We validate our method with an illustrative toy example, a football-related task, and an astrophysical computer vision problem. We show that with our loss function, the accuracy of the predicted median histograms is very similar to the standard EMD case (and higher than for per-bin loss functions such as cross-entropy), while the predictions become much more informative at almost no additional computational cost.

cs.LG↗

arXiv2020

Lux ex tenebris: The imprint of annihilating dark matter on the intergalactic medium during Cosmic Dawn

Upcoming measurements of the highly redshifted 21cm line with next-generation radio telescopes such as HERA and SKA will provide the intriguing opportunity to probe dark matter (DM) physics during the Epoch of Reionization (EoR), Cosmic Dawn, and the Dark Ages. With HERA already under construction, there is a pressing need to thoroughly understand the impact of DM physics on the intergalactic medium (IGM) during these epochs. We present first results of a hydrodynamic simulation suite with $2 \times 512^3$ particles in a $(100 \ h^{-1} \ \text{Mpc})^3$ box with DM annihilation and baryonic cooling physics. We focus on redshift $z \sim 11$, just before reionization starts in our simulations, and discuss the imprint of DM annihilation on the IGM and on structure formation. We find that whereas structure formation is not affected by thermal WIMPs heavier than $m_\chi \gtrsim 100 \ \text{MeV}$, heating from $\mathcal{O}$(GeV) DM particles may leave a significant imprint on the IGM that alters the 21cm signal. Cold gas in low density regions is particularly susceptible to the effects of DM heating. We note, however, that delayed energy deposition is not currently accounted for in our simulations.

astro-ph.CO↗

arXiv2020

The GCE in a New Light: Disentangling the $\gamma$-ray Sky with Bayesian Graph Convolutional Neural Networks

A fundamental question regarding the Galactic Center Excess (GCE) is whether the underlying structure is point-like or smooth. This debate, often framed in terms of a millisecond pulsar or annihilating dark matter (DM) origin for the emission, awaits a conclusive resolution. In this work we weigh in on the problem using Bayesian graph convolutional neural networks. In simulated data, our neural network (NN) is able to reconstruct the flux of inner Galaxy emission components to on average $\sim$0.5%, comparable to the non-Poissonian template fit (NPTF). When applied to the actual $\textit{Fermi}$-LAT data, we find that the NN estimates for the flux fractions from the background templates are consistent with the NPTF; however, the GCE is almost entirely attributed to smooth emission. While suggestive, we do not claim a definitive resolution for the GCE, as the NN tends to underestimate the flux of point-sources peaked near the 1$\sigma$ detection threshold. Yet the technique displays robustness to a number of systematics, including reconstructing injected DM, diffuse mismodeling, and unmodeled north-south asymmetries. So while the NN is hinting at a smooth origin for the GCE at present, with further refinements we argue that Bayesian Deep Learning is well placed to resolve this DM mystery.

astro-ph.HE↗