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Researcher profile

Jonas Petersen

Jonas Petersen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligenceastro-ph.GAMachine Learningastro-ph.COgr-qchep-phhep-th

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Published work

6 published item(s)

preprint2026arXiv

FactoryBench: Evaluating Industrial Machine Understanding

We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs are organized along four causal levels (state, intervention, counterfactual, decision) instantiating Pearl's ladder of causation, and span five answer formats: four structured formats are scored deterministically and free-form answers are scored by an LLM-as-judge voting protocol. We propose a scalable Q&A generation framework built around structured question templates, present FactoryWave (a dense, multitask, multivariate sensor dataset collected from a UR3 cobot and a KUKA KR10 industrial arm), and construct FactoryBench as a large-scale benchmark of over 70k Q&A items grounded in roughly 15k normalized episodes from FactoryWave, AURSAD, and voraus-AD. Zero-shot evaluation of six frontier LLMs shows that no model exceeds 50% on structured levels or 18% on decision-making, revealing a wide gap between current models and operational machine understanding.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models

We introduce the first universal pretraining corpus for industrial time-series data: FactoryNet. 51M datapoints across 23k end-to-end task executions (13.3k real, 9.8k synthetic) on six embodiments, unified by a shared schema that enables robust zero-shot cross-embodiment transfer and highly parameter-efficient anomaly detection. We introduce a novel schema: Setpoint, Effort, Feedback, Context (S-E-F-C) underlying the whole pipeline that maps any actuated system into a common representational frame. The corpus spans 27 annotated anomaly types alongside healthy baselines and counterfactual pairs across robotic manipulation and machining domains. Cross-embodiment transfer experiments yield positive results: under bias-aware metrics our model demonstrates fair cross-embodiment transfer capabilities on the evaluated source-target pair, while 24 schema-aligned signals achieves competitive anomaly detection performance compared to high-dimensional baselines. We release FactoryNet as a growing, multi-embodiment dataset to drive progress toward industrial foundation models.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series

Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce because such events are rare and costly to annotate. We introduce HEPA (Horizon-conditioned Event Predictive Architecture), built on two key principles. First, a causal Transformer encoder is pretrained via a Joint-Embedding Predictive Architecture (JEPA): a horizon-conditioned predictor learns to forecast future representations rather than future values, forcing the encoder to capture predictable temporal dynamics from unlabeled data alone. Second, we freeze the encoder and finetune only the predictor toward the target event, producing a monotonic survival cumulative distribution function (CDF) over horizons. With fixed architecture and optimiser hyperparameters across all benchmarks, HEPA handles water contamination, cyberattack detection, volatility regimes, and eight further event types across 11 domains, exceeding leading time-series architectures including PatchTST, iTransformer, MAE, and Chronos-2 on at least 10 of 14 benchmarks, with an order of magnitude fewer tuned parameters and, on lifecycle datasets, an order of magnitude less labeled data.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A first attempt to differentiate between modified gravity and modified inertia with galaxy rotation curves

The phenomenology of modified Newtonian dynamics (MOND) on galaxy scales may point to more fundamental theories of either modified gravity (MG) or modified inertia (MI). In this paper, we test the applicability of the global deep-MOND parameter $Q$ which is predicted to vary at the $10\%$ level between MG and MI theories. Using mock-observed analytical models of disk galaxies, we investigate several observational uncertainties, establish a set of quality requirements for actual galaxies, and derive systematic corrections in the determination of $Q$. Implementing our quality requirements to the SPARC database yields $15$ galaxies, which are close enough to the deep-MOND regime as well as having rotation curves that are sufficiently extended and sampled. For these galaxies, the average and median values of $Q$ seem to favor MG theories, albeit both MG and MI predictions are in agreement with the data within $1.5σ$. Improved precision in the determination of $Q$ can be obtained by measuring extended and finely-sampled rotation curves for a significant sample of extremely low-surface-brightness galaxies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Method for Discriminating Between Dark Matter Models and MOND Modified Inertia via Galactic Rotation Curves

Dark Matter (DM) and Modified Newtonian Dynamics (MOND) models of rotationally supported galaxies lead to curves with different geometries in $(g_{N},g_{tot})$-space ($g2$-space). Here $g_{tot}$ is the total acceleration and $g_{N}$ is the acceleration as obtained from the baryonic matter via Newtonian dynamics. In MOND modified inertia (MI) models the curves in $g2$-space are closed with zero area and so curve segments at radii $r\geq r_{N}$ (large radii) and $r< r_{N}$ (small radii) coincide, where $r_{N}$ is the radius where $g_N$ is greatest. In DM models with cored density profiles where $g_{tot}$ is also zero at the galactic centre, the curves are again closed, but the area of the closed curves are in general non-zero because the curve segments at radii $r\geq r_{N}$ and $r<r_{N}$ do not coincide. Finally in DM models with cuspy density profiles such as the NFW profile where $g_{tot}$ is formally non-zero at the galactic origin the curves are open, and again the curve segments at radii $r\geq r_{N}$ and $r< r_{N}$ do not coincide. We develop a test of whether data at small and large radii coincide and investigate rotation curves from the SPARC database in order to discriminate between the above geometries. Due to loosely quantified systematic uncertainties we do not underline the result of the test, but instead conclude that the test illustrates the relevance of this type of analysis and demonstrate the ability to discriminate between the considered DM and MI models in this way.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Toy Model for the Dynamical Discrepancies on Galactic Scales

In this study a simple toy model solution to the missing gravity problem on galactic scales is reverse engineered from galactic data via imposing broad assumptions. It is shown that the toy model solution can be written in terms of baryonic quantities, is highly similar to pseudo-isothermal dark matter on galactic scales and can accommodate the same observations. In this way, the toy model solution is similar to MOND modified gravity in the Bekenstein-Milgrom formulation. However, it differs in the similarity to pseudo-isothermal dark matter and in the functional form. In loose terms, it is shown that pseudo-isothermal dark matter can be written in terms of baryonic quantities. The required form suggests that it may be worth looking into a mechanism that can increase the magnitude of the post-Newtonian correction from general relativity for low accelerations.

0 citations0 reviewsNo code linkedOpen access