arXiv subjects

Fabian Schmidt

Publications and source records attributed to Fabian Schmidt.

At least 37 records · Page 2Linked to original sources

arXiv2025

On the Connection between Field-Level Inference and $n$-point Correlation Functions

Bayesian field-level inference of galaxy clustering guarantees optimal extraction of all cosmological information, provided that the data are correctly described by the forward model employed. The latter is unfortunately never strictly the case. A key question for field-level inference approaches then is where the cosmological information is coming from, and how to ensure that it is robust. In the context of perturbative approaches such as effective field theory, some progress on this question can be made analytically. We derive the parameter posterior given the data for the field-level likelihood given in the effective field theory, marginalized over initial conditions in the zero-noise limit. Particular attention is paid to cutoffs in the theory, the generalization to higher orders, and the error made by an incomplete forward model at a given order. The main finding is that, broadly speaking, an $m$-th order forward model captures the information in $n$-point correlation functions with $n \leqslant m+1$. Thus, by adding more terms to the forward model, field-level inference is made to automatically incorporate higher-order $n$-point functions. Also shown is how the effect of an incomplete forward model (at a given order) on the parameter inference can be estimated.

astro-ph.CO↗

arXiv2025

Monodromic Dark Energy and DESI

Recent baryon acoustic oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI) collaboration have renewed interest in dynamical dark energy models, particularly those that cross the "phantom divide" ($w_{\rm DE} = -1$). We present the first observational constraints on monodromic k-essence, a physically motivated scalar field dark energy scenario capable of realizing rapid oscillations about the phantom divide. Using cosmic microwave background (CMB) information, DESI DR2 BAO measurements, and Type Ia supernovae observations, we constrain the amplitude, frequency, phase, and power-law index describing the monodromic k-essence scenario at the background level. We find that the monodromic dark energy scenario can fit these datasets with a $χ^2$ that is comparable to the phenomenological $w_0$-$w_a$ parametrization. While the CMB and BAO data alone are consistent with the standard $Λ$CDM model, the inclusion of DESY5 supernovae shows a preference for a non-zero amplitude, $A=0.44^{+0.16}_{-0.12}$ (fully marginalized 68% C.L.). Conversely, inclusion of the Pantheon-Plus supernovae provides no evidence for monodromic k-essence, with $A<0.43$ (95% C.L.). We show that constraints on both monodromic dark energy and $w_0$-$w_a$ models are sensitive to the DESI DR2 LRG2 BAO distance, especially in the absence of supernovae data.

astro-ph.CO↗

arXiv2025

ROVER: A Multi-Season Dataset for Visual SLAM

Robust SLAM is a crucial enabler for autonomous navigation in natural, semi-structured environments such as parks and gardens. However, these environments present unique challenges for SLAM due to frequent seasonal changes, varying light conditions, and dense vegetation. These factors often degrade the performance of visual SLAM algorithms originally developed for structured urban environments. To address this gap, we present ROVER, a comprehensive benchmark dataset tailored for evaluating visual SLAM algorithms under diverse environmental conditions and spatial configurations. We captured the dataset with a robotic platform equipped with monocular, stereo, and RGBD cameras, as well as inertial sensors. It covers 39 recordings across five outdoor locations, collected through all seasons and various lighting scenarios, i.e., day, dusk, and night with and without external lighting. With this novel dataset, we evaluate several traditional and deep learning-based SLAM methods and study their performance in diverse challenging conditions. The results demonstrate that while stereo-inertial and RGBD configurations generally perform better under favorable lighting and moderate vegetation, most SLAM systems perform poorly in low-light and high-vegetation scenarios, particularly during summer and autumn. Our analysis highlights the need for improved adaptability in visual SLAM algorithms for outdoor applications, as current systems struggle with dynamic environmental factors affecting scale, feature extraction, and trajectory consistency. This dataset provides a solid foundation for advancing visual SLAM research in real-world, semi-structured environments, fostering the development of more resilient SLAM systems for long-term outdoor localization and mapping. The dataset and the code of the benchmark are available under https://iis-esslingen.github.io/rover.

cs.RO↗

arXiv2025

Large-Scale-Structure Observables in General Relativity Validated at Second Order

We present a second-order calculation of relativistic large-scale-structure observables in cosmological perturbation theory, specifically the "cosmic rulers and clock", which are the building-blocks of any other large-scale-structure observable, including galaxy number counts, on large scales. We calculate the scalar rulers (longitudinal perturbation and magnification) and the cosmic clock to second order, using a fully non-linear covariant definition of the observables. We validate our formulae on three non-trivial space-time metrics: two of them are null tests on metrics which are obtained by applying a gauge transformation to the background space-time, while the third is the "separate universe" curved background, for which we can also compute the observables exactly. We then illustrate the results by evaluating the second-order observables in a simplified symmetric setup. On large scales, they are suppressed over the linear contributions by $\sim 10^{-4}$, while they become comparable to the linear contributions on mildly non-linear scales. The results of this paper form a significant (and the most complicated) part of the relativistic galaxy number density at second order.

astro-ph.CO↗

arXiv2025

Fast, Accurate and Perturbative Forward Modeling of Galaxy Clustering Part I: Galaxies in the Restframe

Forward models of the galaxy density field enable simulation based inference as well as field level inference of galaxy clustering. However, these analysis techniques require forward models that are both computationally fast and robust to modeling uncertainties in the relation between galaxies and matter. Both requirements can be addressed with the Effective Field Theory of Large Scale Structure. Here, we focus on the physical and numerical convergence of the LEFTfield model. Based on the perturbative nature of the forward model, we derive an analytic understanding of the leading numerical errors, and we compare our estimates to high-resolution and N-body references. This allows us to derive a set of best-practice recommendations for the numerical accuracy parameters, which are completely specified by the desired order of the perturbative solution and the cut-off scale. We verify these recommendations by an extended set of parameter recovery tests from fully nonlinear mock data and find very consistent results. A single evaluation of the forward model takes seconds, making cosmological analyses of galaxy clustering data based on forward models computationally feasible.

astro-ph.CO↗

arXiv2025

Leveraging Machine Learning Models to Predict the Outcome of Digital Medical Triage Interviews

Many existing digital triage systems are questionnaire-based, guiding patients to appropriate care levels based on information (e.g., symptoms, medical history, and urgency) provided by the patients answering questionnaires. Such a system often uses a deterministic model with predefined rules to determine care levels. It faces challenges with incomplete triage interviews since it can only assist patients who finish the process. In this study, we explore the use of machine learning (ML) to predict outcomes of unfinished interviews, aiming to enhance patient care and service quality. Predicting triage outcomes from incomplete data is crucial for patient safety and healthcare efficiency. Our findings show that decision-tree models, particularly LGBMClassifier and CatBoostClassifier, achieve over 80\% accuracy in predicting outcomes from complete interviews while having a linear correlation between the prediction accuracy and interview completeness degree. For example, LGBMClassifier achieves 88,2\% prediction accuracy for interviews with 100\% completeness, 79,6\% accuracy for interviews with 80\% completeness, 58,9\% accuracy for 60\% completeness, and 45,7\% accuracy for 40\% completeness. The TabTransformer model demonstrated exceptional accuracy of over 80\% for all degrees of completeness but required extensive training time, indicating a need for more powerful computational resources. The study highlights the linear correlation between interview completeness and predictive power of the decision-tree models.

cs.AI↗

arXiv2025

CFiCS: Graph-Based Classification of Common Factors and Microcounseling Skills

Common factors and microcounseling skills are critical to the effectiveness of psychotherapy. Understanding and measuring these elements provides valuable insights into therapeutic processes and outcomes. However, automatic identification of these change principles from textual data remains challenging due to the nuanced and context-dependent nature of therapeutic dialogue. This paper introduces CFiCS, a hierarchical classification framework integrating graph machine learning with pretrained contextual embeddings. We represent common factors, intervention concepts, and microcounseling skills as a heterogeneous graph, where textual information from ClinicalBERT enriches each node. This structure captures both the hierarchical relationships (e.g., skill-level nodes linking to broad factors) and the semantic properties of therapeutic concepts. By leveraging graph neural networks, CFiCS learns inductive node embeddings that generalize to unseen text samples lacking explicit connections. Our results demonstrate that integrating ClinicalBERT node features and graph structure significantly improves classification performance, especially in fine-grained skill prediction. CFiCS achieves substantial gains in both micro and macro F1 scores across all tasks compared to baselines, including random forests, BERT-based multi-task models, and graph-based methods.

cs.CL↗

arXiv2025

Straightening the Ruler: Field-Level Inference of the BAO Scale with LEFTfield

Current inferences of the BAO scale from galaxy clustering employ a reconstruction technique at fixed cosmology and bias parameters. Here, we present the first consistent joint Bayesian inference of the isotropic BAO scale, jointly varying the initial conditions as well as all bias coefficients, based on the EFT-based field-level forward model $\texttt{LEFTfield}$. We apply this analysis to mock data generated at a much higher cutoff, or resolution, resulting in a significant model mismatch between mock data and the model used in the inference. We demonstrate that the remaining systematic bias in the BAO scale is below 2% for all data considered and below 1% when Eulerian bias is used for inference. Furthermore, we find that the inferred error on the BAO scale is typically 30%, and up to 50%, smaller compared to that from a replication of the standard post-reconstruction power-spectrum approach, using the same scales as in the field-level inference. The improvement in BAO scale precision grows towards smaller scales (higher $k$). As a validation test, we repeat this comparison on a mock dataset that is linearly biased with respect to a 1LPT (Zel'dovich) density field, following the assumption made in standard reconstruction approaches. We find that field-level inference indeed yields the same error bar as the post-reconstruction power spectrum, which is expectd to be optimal in this case. In summary, a field-level approach to BAO not only allows for a consistent inference of the BAO scale, but promises to achieve more precise measurements on realistic, nonlinearly biased tracers as well.

astro-ph.CO↗

arXiv2025

Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing

Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. These environments pose significant challenges due to variable lighting, weather conditions, and complex terrain. Visual-Inertial SLAM has emerged as a promising solution for robust localization under such conditions. This paper benchmarks several open-source Visual-Inertial SLAM systems, including traditional methods (ORB-SLAM3, VINS-Fusion, OpenVINS, Kimera, and SVO Pro) and learning-based approaches (HFNet-SLAM, AirSLAM), to evaluate their performance in unstructured natural outdoor settings. We focus on the impact of loop closing on localization accuracy and computational demands, providing a comprehensive analysis of these systems' effectiveness in real-world environments and especially their application to embedded systems in outdoor robotics. Our contributions further include an assessment of varying frame rates on localization accuracy and computational load. The findings highlight the importance of loop closing in improving localization accuracy while managing computational resources efficiently, offering valuable insights for optimizing Visual-Inertial SLAM systems for practical outdoor applications in mobile robotics. The dataset and the benchmark code are available under https://github.com/iis-esslingen/vi-slam_lc_benchmark.

cs.RO↗

arXiv2024

NeRF and Gaussian Splatting SLAM in the Wild

Navigating outdoor environments with visual Simultaneous Localization and Mapping (SLAM) systems poses significant challenges due to dynamic scenes, lighting variations, and seasonal changes, requiring robust solutions. While traditional SLAM methods struggle with adaptability, deep learning-based approaches and emerging neural radiance fields as well as Gaussian Splatting-based SLAM methods, offer promising alternatives. However, these methods have primarily been evaluated in controlled indoor environments with stable conditions, leaving a gap in understanding their performance in unstructured and variable outdoor settings. This study addresses this gap by evaluating these methods in natural outdoor environments, focusing on camera tracking accuracy, robustness to environmental factors, and computational efficiency, highlighting distinct trade-offs. Extensive evaluations demonstrate that neural SLAM methods achieve superior robustness, particularly under challenging conditions such as low light, but at a high computational cost. At the same time, traditional methods perform the best across seasons but are highly sensitive to variations in lighting conditions. The code of the benchmark is publicly available at https://github.com/iis-esslingen/nerf-3dgs-benchmark.

cs.RO↗

arXiv2024

Structure formation with primordial black holes: collisional dynamics, binaries, and gravitational waves

Primordial black holes (PBHs) could compose the dark matter content of the Universe. We present the first simulations of cosmological structure formation with PBH dark matter that consistently include collisional few-body effects, post-Newtonian orbit corrections, orbital decay due to gravitational wave emission, and black-hole mergers. We carefully construct initial conditions by considering the evolution during radiation domination as well as early-forming binary systems. We identify numerous dynamical effects due to the collisional nature of PBH dark matter, including evolution of the internal structures of PBH halos and the formation of a hot component of PBHs. We also study the properties of the emergent population of PBH binary systems, distinguishing those that form at primordial times from those that form during the nonlinear structure formation process. These results will be crucial to sharpen constraints on the PBH scenario derived from observational constraints on the gravitational wave background. Even under conservative assumptions, the gravitational radiation emitted over the course of the simulation appears to exceed current limits from ground-based experiments, but this depends on the evolution of the gravitational wave spectrum and PBH merger rate toward lower redshifts.

astro-ph.CO↗

arXiv2024

The Renormalization Group for Large-Scale Structure: Origin of Galaxy Stochasticity

The renormalization group equations for large-scale structure (RG-LSS) describe how the bias and stochastic (noise) parameters -- both of matter and biased tracers such as galaxies -- evolve as a function of the cutoff $Λ$ of the effective field theory. In previous work, we derived the RG-LSS equations for the bias parameters using the Wilson-Polchinski framework. Here, we extend these results to include stochastic contributions, corresponding to terms in the effective action that are higher order in the current $J$. We derive the general local interaction terms that describe stochasticity at all orders in perturbations, and a closed set of nonlinear RG equations for their coefficients. These imply that a single nonlinear bias term generates all stochastic moments through RG evolution. Further, the evolution is controlled by a different, lower scale than the nonlinear scale. This has implications for the optimal choice of the renormalization scale when comparing the theory with data to obtain cosmological constraints.

astro-ph.CO↗

arXiv2024

How much information can be extracted from galaxy clustering at the field level?

We present optimal Bayesian field-level cosmological constraints from nonlinear tracers of the large-scale structure, specifically the amplitude $σ_8$ of linear matter fluctuations inferred from rest-frame simulated dark matter halos in a comoving volume of $8\,(h^{-1}\mathrm{Gpc})^3$. Our constraint on $σ_8$ is entirely due to nonlinear information, and obtained by explicitly sampling the initial conditions along with bias and noise parameters via a Lagrangian EFT-based forward model, LEFTfield. The comparison with a simulation-based inference analysis employing the power spectrum and bispectrum -- likewise using the LEFTfield forward model -- shows that, when including precisely the same modes of the same data up to $k_{\mathrm{max}}= 0.10\,h\,\mathrm{Mpc}^{-1}$ ($0.12\,h\,\mathrm{Mpc}^{-1}$), the field-level approach yields a factor of 3.5 (5.2) improvement on the $σ_8$ constraint, from 20.0% to 5.7% (17.0% to 3.3%). This study provides direct insights into cosmological information encoded in galaxy clustering beyond low-order $n$-point functions.

astro-ph.CO↗

arXiv2024

EFTofLSS meets simulation-based inference: $σ_8$ from biased tracers

Cosmological inferences typically rely on explicit expressions for the likelihood and covariance of the data vector, which normally consists of a set of summary statistics. However, in the case of nonlinear large-scale structure, exact expressions for either likelihood or covariance are unknown, and even approximate expressions can become very cumbersome, depending on the scales and summary statistics considered. Simulation-based inference (SBI), in contrast, does not require an explicit form for the likelihood but only a prior and a simulator, thereby naturally circumventing these issues. In this paper, we explore how this technique can be used to infer $σ_8$ from a Lagrangian effective field theory (EFT) based forward model for biased tracers. The power spectrum and bispectrum are used as summary statistics to obtain the posterior of the cosmological, bias and noise parameters via neural density estimation. We compare full simulation-based inference with cases where the data vector is drawn from a Gaussian likelihood with sample and analytical covariances. We conclude that, for $k_{\text{max}}=0.1h\text{Mpc}^{-1}$ and $0.2h\text{Mpc}^{-1}$, the form of the covariance is more important than the non-Gaussianity of the likelihood, although this conclusion is expected to depend on the cosmological parameter inferred, the summary statistics considered and range of scales probed.

astro-ph.CO↗

arXiv2024

Vulnerability Handling of AI-Generated Code -- Existing Solutions and Open Challenges

The increasing use of generative Artificial Intelligence (AI) in modern software engineering, particularly Large Language Models (LLMs) for code generation, has transformed professional software development by boosting productivity and automating development processes. This adoption, however, has highlighted a significant issue: the introduction of security vulnerabilities into the code. These vulnerabilities result, e.g., from flaws in the training data that propagate into the generated code, creating challenges in disclosing them. Traditional vulnerability handling processes often involve extensive manual review. Applying such traditional processes to AI-generated code is challenging. AI-generated code may include several vulnerabilities, possibly in slightly different forms as developers might not build on already implemented code but prompt similar tasks. In this work, we explore the current state of LLM-based approaches for vulnerability handling, focusing on approaches for vulnerability detection, localization, and repair. We provide an overview of recent progress in this area and highlight open challenges that must be addressed in order to establish a reliable and scalable vulnerability handling process of AI-generated code.

cs.SE↗

arXiv2024

Constraining $f(R)$ gravity using future galaxy cluster abundance and weak-lensing mass calibration datasets

We present forecasts for constraints on the Hu \& Sawicki $f(R)$ modified gravity model using realistic mock data representative of future cluster and weak lensing surveys. We create mock thermal Sunyaev-Zel'dovich effect selected cluster samples for SPT-3G and CMB-S4 and the corresponding weak gravitational lensing data from next-generation weak-lensing (ngWL) surveys like Euclid and Rubin. We employ a state-of-the-art Bayesian likelihood approach that includes all observational effects and systematic uncertainties to obtain constraints on the $f(R)$ gravity parameter $\log_{10}|f_{R0}|$. In this analysis we vary the cosmological parameters $[Ω_{\rm m}, Ω_νh^2, h^2, A_s, n_s, \log_{10}|f_{R0}|]$, which allows us to account for possible degeneracies between cosmological parameters and $f(R)$ modified gravity. The analysis accounts for $f(R)$ gravity via its effect on the halo mass function which is enhanced on cluster mass scales compared to the expectations within general relativity (GR). Assuming a fiducial GR model, the upcoming cluster dataset SPT-3G$\times$ngWL is expected to obtain an upper limit of $\log_{10}|f_{R0}| < -5.95$ at $95\,\%$ credibility, which significantly improves upon the current best bounds. The CMB-S4$\times$ngWL dataset is expected to improve this even further to $\log_{10}|f_{R0}| < -6.23$. Furthermore, $f(R)$ gravity models with $\log_{10}|f_{R0}| \geq -6$, which have larger numbers of clusters, would be distinguishable from GR with both datasets. We also report degeneracies between $\log_{10}|f_{R0}|$ and $Ω_{\mathrm{m}}$ as well as $σ_8$ for $\log_{10}|f_{R0}| > -6$ and $\log_{10}|f_{R0}| > -5$ respectively. Our forecasts indicate that future cluster abundance studies of $f(R)$ gravity will enable substantially improved constraints that are competitive with other cosmological probes.

astro-ph.CO↗

arXiv2024

The Renormalization Group for Large-Scale Structure: Primordial non-Gaussianities

The renormalization group for large-scale structure (RG-LSS) describes the evolution of galaxy bias and stochastic parameters as a function of the cutoff $Λ$. In this work, we introduce interaction vertices that describe primordial non-Gaussianity into the Wilson-Polchinski framework, thereby extending the free theory to the interacting case. The presence of these interactions forces us to include new operators and bias coefficients to the bias expansion to ensure closure under renormalization. We recover the previously-derived ``scale-dependent bias'' contributions, as well as a new (subdominant) stochastic contribution. We derive the renormalization group equations governing the RG-LSS for a large class of interactions which account for vertices at linear order in $f_{\rm NL}$ that parametrize interacting scalar and massive spinning fields during inflation. Solving the RG equations, we show the evolution of the non-Gaussian contributions to galaxy clustering as a function of scale.

astro-ph.CO↗

arXiv2023

Cosmology inference at the field level from biased tracers in redshift-space

Cosmology inference of galaxy clustering at the field level with the EFT likelihood in principle allows for extracting all non-Gaussian information from quasi-linear scales, while robustly marginalizing over any astrophysical uncertainties. A pipeline in this spirit is implemented in the \texttt{LEFTfield} code, which we extend in this work to describe the clustering of galaxies in redshift space. Our main additions are: the computation of the velocity field in the LPT gravity model, the fully nonlinear displacement of the evolved, biased density field to redshift space, and a systematic expansion of velocity bias. We test the resulting analysis pipeline by applying it to synthetic data sets with a known ground truth at increasing complexity: mock data generated from the perturbative forward model itself, sub-sampled matter particles, and dark matter halos in N-body simulations. By fixing the initial-time density contrast to the ground truth, while varying the growth rate $f$, bias coefficients and noise amplitudes, we perform a stringent set of checks. These show that indeed a systematic higher-order expansion of the velocity bias is required to infer a growth rate consistent with the ground truth within errors. Applied to dark matter halos, our analysis yields unbiased constraints on $f$ at the level of a few percent for a variety of halo masses at redshifts $z=0,\,0.5,\,1$ and for a broad range of cutoff scales $0.08\,h/\mathrm{Mpc} \leq Λ\leq 0.20\,h/\mathrm{Mpc}$. Importantly, deviations between true and inferred growth rate exhibit the scaling with halo mass, redshift and cutoff that one expects based on the EFT of Large Scale Structure. Further, we obtain a robust detection of velocity bias through its effect on the redshift-space density field and are able to disentangle it from higher-derivative bias contributions.

astro-ph.CO↗