arXiv subjects

David Huber

Publications and source records attributed to David Huber.

At least 19 recordsLinked to original sources

arXiv2026

High-resolution simulations of non-thermal emission from LS 5039

In a previous study, we investigated the relativistic wind dynamics in the LS 5039 system. In this work, we analyse energetic-particle transport within this modelling context, where we simulate the high-energy particle distribution and ensuing emission of non-thermal radiation. From these high-resolution simulations, we compute the non-thermal emission from this system and compare it to corresponding observations. We modelled the LS 5039 system assuming a wind-driven scenario. Our numerical model uses a joint simulation of the dynamical wind interaction together with the transport of energetic leptons from the shocked pulsar wind. We computed the non-thermal emission from this system in a post-processing step from the resulting distribution of energetic leptons. In this computation, we took into account the synchrotron and inverse Compton emission, relativistic beaming, and {\gamma}{\gamma}-absorption in the stellar radiation field. We investigated the dynamical variation of the energetic particle spectra on both orbital and on short timescales. Our model successfully reproduces many of the spectral features of LS 5039. We also find a better correspondence between our predicted orbital light curves and the corresponding observations in soft x-rays, low-energy, and high-energy gamma rays than in our previous modelling efforts. We find that our high-resolution and large-scale simulations can successfully capture the relevant parts of the wind-collision region that are related to particle acceleration and emission of non-thermal radiation. The quality of the fit strengthens the wind-driven assumption underlying our model. Desirable extensions for the future include a dynamical magnetic-field model for the synchrotron regime, a revision of our injection parameters, and a consideration of an additional hadronic component that could explain recent observations in the 100~TeV regime.

astro-ph.HE↗

arXiv2025

GRAVITY+ adaptive optics (GPAO) tests in Europe

We present in this proceeding the results of the test phase of the GRAVITY+ adaptive optics. This extreme AO will enable both high-dynamic range observations of faint companions (including exoplanets) thanks to a 40x40 sub-apertures wavefront control, and sensitive observations (including AGNs) thanks to the addition of a laser guide star to each UT of the VLT. This leap forward is made thanks to a mostly automated setup of the AO, including calibration of the NCPAs, that we tested in Europe on the UT+atmosphere simulator we built in Nice. We managed to reproduce in laboratory the expected performances of all the modes of the AO, including under non-optimal atmospheric or telescope alignment conditions, giving us the green light to proceed with the Assembly, Integration and Verification phase in Paranal.

astro-ph.IM↗

arXiv2024

Enhanced magnetic dichroism in darkfield UV photoemission electron microscopy

Photoemission electron microscopy (PEEM) has evolved into an indispensable tool for structural and magnetic characterization of surfaces at the nanometer scale. In strong contrast to synchrotron-radiation-based X-ray PEEM as a leading method for element-specific magnetic properties via magnetic circular dichroism (MCD), laboratory ultraviolet (UV) PEEM has seen limited application with much smaller dichroism effects for in-plane magnetization. Here we introduce darkfield PEEM as a novel approach to enhance MCD contrast in threshold photoemission, enabling efficient MCD imaging with significantly enhanced contrast by an order-of-magnitude for Fe(001). This advancement paves the way for MCD imaging on femtosecond timescales using modern lasers. The experimental results will be quantitatively benchmarked against advanced relativistic photoemission calculations.

cond-mat.mtrl-sci↗

arXiv2024

SACR\'E BLEU: Self-Assessed Creator Royalties \'Enforced by Balancing Liquidity Estimation & Utility (A formal definition and analysis of Ethereum Request for Comment ERC-7526)

The secondary market for Ethereum non-fungible tokens (NFTs) has resulted in over $1.8bn being paid to creators in the form of a sales tax commonly called creator royalties. This was despite royalty payments being enforced by no more than social contract alone. Predictably, such an incentive structure led to zero-royalty alternatives becoming abundant and payments dwindled. A purely programmatic solution to royalty enforcement is hampered by the prevailing NFT standard, ERC-721, which is ignorant of sale values and royalty enforcement therefore relies on (potentially dishonest) third parties. We thus introduce an incentive-compatible mechanism for which there is a single rationalisable solution, in which royalties are paid in full, while maintaining full ERC-721 compatibility. The mechanism constitutes the core of ERC-7526.

cs.GT↗

arXiv2023

System for Analysis of Wind Collocations (SAWC): A Novel Archive and Collocation Software Application for the Intercomparison of Winds from Multiple Observing Platforms

Accurate atmospheric 3D wind observations are a high priority in the science community. To address this requirement and to support researchers' needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019-August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. Recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC's capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture

physics.ao-ph↗

arXiv2023

Evaluating synthetic fuel production: A case study on the influence of electricity and CO2 price variations

To combat climate change, we need to reduce emissions from the transport sector. Synthetic fuels are a long-term solution for aviation, maritime and heavy machinery. Large-scale use requires cost-effectiveness, efficient production and resilience to price changes. In this case study, we simultaneously optimize the cell voltage of the solid oxide electrolysis cell, the heat exchanger network and the heat supply of a PtL-plant. PtL-efficiency and production costs are used as objectives to generate multiple Pareto fronts for future price scenarios. The results show that the sensitivity to price changes has different impacts on design and operating parameters, which can lead to unattractive solution domains in the Pareto front. Currently, synthetic fuels can be produced at 1.83-2.36 EUR/kg. In the best case, at 1.42-1.97 EUR/kg and 3.88-4.28 EUR/kg in the worst case. This paper supports decision-makers in planning PtL-plants to ensure sustainable synthetic fuel availability on a global scale.

math.OC↗

arXiv2023

Tractable Bounding of Counterfactual Queries by Knowledge Compilation

We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an inner approximation of those bounds by sampling the initialisation parameters. Such a method requires multiple (Bayesian network) queries over models sharing the same structural equations and topology, but different exogenous probabilities. This setup makes a compilation of the underlying model to an arithmetic circuit advantageous, thus inducing a sizeable inferential speed-up. We show how a single symbolic knowledge compilation allows us to obtain the circuit structure with symbolic parameters to be replaced by their actual values when computing the different queries. We also discuss parallelisation techniques to further speed up the bound computation. Experiments against standard Bayesian network inference show clear computational advantages with up to an order of magnitude of speed-up.

cs.AI↗

arXiv2023

Unlocking the Potential of Synthetic Fuel Production: Coupled Optimization of Heat Exchanger Network and Operating Parameters of a 1 MW Power-to-Liquid Plant

The use of synthetic fuels is a promising way to reduce emissions significantly. To accelerate cost-effective large-scale synthetic fuel deployment, we optimize a novel 1 MW PtL-plant in terms of PtL-efficiency and fuel production costs. For numerous plants, the available waste heat and temperature level depend on the operating point. Thus, to optimize efficiency and costs, the choice of the operating point is included in the heat exchanger network synthesis. All nonlinearities are approximated using piecewise linear models and transferred to MILP. Adapting the epsilon constraint method allows us to solve the multi-criteria problem with uniformly distributed solutions on the Pareto front. The results show that compared to the conventional design process, the production cost can be reduced to 1.83 EUR/kg and the PtL-efficiency can be increased to 61.30 %. By applying the presented method, climate-neutral synthetic fuels can be promoted and emissions can be reduced in the long term.

math.OC↗

arXiv2023

Piecewise linear approximation for MILP leveraging piecewise convexity to improve performance

Mixed integer linear programming (MILP) has seen a sharp rise in use for engineering optimization applications in recent years. Even for initially non-linear problems, it is often the method of choice. Then, the non-linear functions have to be approximated in a way, that allows for an efficient implementation in MILP. To realize adaptive operation planning with MILP unit commitment, piecewise-linear approximations of the functions that describe the operating behavior of devices in the energy system have to be computed. We present an algorithm to compute a piecewise-linear approximation of a multi-variate non-linear function. The algorithm splits the domain into two regions and approximates each region with a set of hyperplanes that can be translated to a convex set of constraints in MILP. The main advantage of this "piecewise-convex approximation" (PwCA) compared to more general piecewise-linear approximation with simplices is that the MILP representation of PwCA requires only one auxiliary binary variable. For this reason, PwCA yields significantly faster solving times in large MILP problems where the MILP representation of certain functions has to be replicated many times, such as in unit commitment. To quantify the impact on solving time, we compare the performance using PwCA with the performance of simplex approximation with logarithmic formulation and show that PCA outperforms the latter by a big margin. For this reason, we conclude that PCA will be a useful tool to set up and solve large MILP problems such as arise in unit commitment and similar engineering optimization problems.

math.OC↗

arXiv2023

Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources

We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset affected by a selection bias. We show that the likelihood of the available data has no local maxima. This enables us to use the causal expectation-maximisation scheme to approximate the bounds for partially identifiable counterfactual queries, which are the focus of this paper. We then show how the same approach can address the general case of multiple datasets, no matter whether interventional or observational, biased or unbiased, by remapping it into the former one via graphical transformations. Systematic numerical experiments and a case study on palliative care show the effectiveness of our approach, while hinting at the benefits of fusing heterogeneous data sources to get informative outcomes in case of partial identifiability.

stat.ME↗

arXiv2023

Efficient Computation of Counterfactual Bounds

We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes. The question we want to answer is how we can compute bounds for partially identifiable counterfactual queries from such an input. We start by giving a map from structural casual models to credal networks. This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models. Exact computation is going to be inefficient in general given that, as we show, causal inference is NP-hard even on polytrees. We target then approximate bounds via a causal EM scheme. We evaluate their accuracy by providing credible intervals on the quality of the approximation; we show through a synthetic benchmark that the EM scheme delivers accurate results in a fair number of runs. In the course of the discussion, we also point out what seems to be a neglected limitation to the trending idea that counterfactual bounds can be computed without knowledge of the structural equations. We also present a real case study on palliative care to show how our algorithms can readily be used for practical purposes.

cs.AI↗

arXiv2023

High-Resolution Simulations of LS 5039

Context. We present an analysis of our high-resolution relativistic-hydrodynamics model of the stellar- and pulsar-wind interaction in the LS-5039 system. Aims. With our high-resolution simulation covering three orbital periods, we analyse the impact of turbulence with a particular focus on short-term and orbit-to-orbit variations. Methods. Our model uses a relativistic hydrodynamics description of the wind interaction in the LS-5039 system assuming a pulsar-wind driven scenario. The corresponding system of equations is solved using the finite-volume code Cronos. We compute statistical quantities, also relevant for particle acceleration in this system, from results of multiple consecutive timesteps. Results. In our simulation we find the previously observed shock structures related to the wind-collision region (WCR), including the pulsar-wind termination, being dynamically influenced by orbital motion. In our high-resolution simulation we find high turbulence levels following from instabilities driven at the WCR. These instabilities lead to strong fluctuations of several dynamical quantities especially around and after apastron. These fluctuations are expected to impact the particle transport and also especially the related emission of non-thermal radiation. As an important example, the region from which gamma-ray emission has been found to be boosted due to relativistic beaming in previous studies shows strong variations in size both on short and on orbital timescales. Conclusions. Using a large computational domain together with high spatial resolution allowed a detailed study of fluctuations in the stellar- and pulsar-wind interaction. The results indicate a possible influence on the non-thermal emission from this system, which will be analysed with dedicated simulations in a forthcoming publication.

astro-ph.HE↗

arXiv2023

HENS unchained: MILP implementation of multi-stage utilities with stream splits, variable temperatures and flow capacities

Heat exchanger network synthesis (HENS) is a well-studied method in research for determining cost-optimal heat exchanger networks. In this paper, we present a modified superstructure formulation to implement streams with variable temperatures and flow capacities. To apply fast MILP solvers, all nonlinear terms, such as those of LMTD, HEX areas and energy balances, are piecewise-linear approximated with simplex or hyperplane models. The translation to MILP is achieved with highly efficient logarithmic coding. One promising application is implementing utilities as streams with variable temperatures and flow capacities. On the one hand, this enables multi-stage heat transfer with stream splits and intermediate utility placement. On the other hand, the temperatures of the utilities can be included as a design parameter in optimizing the heat exchanger network. This makes sense if only the sensible heat of, e.g., thermal oil, water or flue gas, is used as a utility where the inlet and outlet temperatures do not necessarily have to be specified a priori. To examine whether the implementation of utilities as streams leads to more cost-effective solutions, three representative case studies were considered. The results show that reducing the outlet temperature of cold utilities or increasing the outlet temperature of hot utilities leads to significant cost savings. We show that implementing utilities as multi-staged streams with stream splits, variable temperatures and flow capacities is a highly efficient tool for indirect, cost-efficient utility design.

math.OC↗

arXiv2022

Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data

We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset affected by a selection bias. We show that the likelihood of the available data has no local maxima. This enables us to use the causal expectation-maximisation scheme to compute approximate bounds for partially identifiable counterfactual queries, which are the focus of this paper. We then show how the same approach can solve the general case of multiple datasets, no matter whether interventional or observational, biased or unbiased, by remapping it into the former one via graphical transformations. Systematic numerical experiments and a case study on palliative care show the effectiveness and accuracy of our approach, while hinting at the benefits of integrating heterogeneous data to get informative bounds in case of partial identifiability.

cs.AI↗

arXiv2022

Bounding Counterfactuals under Selection Bias

Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal. This enables us to use the causal expectation-maximisation scheme to obtain the values of causal queries in the identifiable case, and to compute bounds otherwise. Experiments demonstrate the approach to be practically viable. Theoretical convergence characterisations are provided.

stat.ML↗

arXiv2022

Magnetic impurity in a one-dimensional few-fermion system

We present a numerical analysis of spin-$\frac{1}{2}$ fermions in a one-dimensional harmonic potential in the presence of a magnetic point-like impurity at the center of the trap. The model represents a few-body analogue of a magnetic impurity in the vicinity of an $s$-wave superconductor. Already for a few particles we find a ground-state level crossing between sectors with different fermion parities. We interpret this crossing as a few-body precursor of a quantum phase transition, which occurs when the impurity `breaks' a Cooper pair. This picture is further corroborated by analyzing density-density correlations in momentum space. Finally, we discuss how the system may be realized with existing cold-atoms platforms.

cond-mat.quant-gas↗

arXiv2022

A modular implementation of an effective interaction approach for harmonically trapped fermions in 1D

We introduce a generic and accessible implementation of an exact diagonalization method for studying few-fermion models. Our aim is to provide a testbed for the newcomers to the field as well as a stepping stone for trying out novel optimizations and approximations. This userguide consists of a description of the algorithm, and several examples in varying orders of sophistication. In particular, we exemplify our routine using an effective-interaction approach that fixes the low-energy physics. We benchmark this approach against the existing data, and show that it is able to deliver state-of-the-art numerical results at a significantly reduced computational cost.

cond-mat.quant-gas↗

arXiv2021

Special Relativistic Hydrodynamics with CRONOS

We describe the special relativistic extension of the CRONOS code, which has been used for studies of gamma-ray binaries in recent years. The code was designed to be easily adaptable, allowing the user to easily change existing functionalities or introduce new modules tailored to the problem at hand. Numerically, the equations are treated using a finite-volume Godunov scheme on rectangular grids, which currently support Cartesian, spherical, and cylindrical coordinates. The employed reconstruction technique, the approximate Riemann solver and the equation of state can be chosen dynamically by the user. Further, the code was designed with stability and robustness in mind, detecting and mitigating possible failures early on. We demonstrate the code's capabilities on an extensive set of validation problems.

astro-ph.HE↗