arXiv subjects

M. Hohmann

Publications and source records attributed to M. Hohmann.

6 recordsLinked to original sources

arXiv2025

Data-Driven Temperature Modelling of Machine Tools by Neural Networks: A Benchmark

Thermal errors in machine tools significantly impact machining precision and productivity. Traditional thermal error correction/compensation methods rely on measured temperature-deformation fields or on transfer functions. Most existing data-driven compensation strategies employ neural networks (NNs) to directly predict thermal errors or specific compensation values. While effective, these approaches are tightly bound to particular error types, spatial locations, or machine configurations, limiting their generality and adaptability. In this work, we introduce a novel paradigm in which NNs are trained to predict high-fidelity temperature and heat flux fields within the machine tool. The proposed framework enables subsequent computation and correction of a wide range of error types using modular, swappable downstream components. The NN is trained using data obtained with the finite element method under varying initial conditions and incorporates a correlation-based selection strategy that identifies the most informative measurement points, minimising hardware requirements during inference. We further benchmark state-of-the-art time-series NN architectures, namely Recurrent NN, Gated Recurrent Unit, Long-Short Term Memory (LSTM), Bidirectional LSTM, Transformer, and Temporal Convolutional Network, by training both specialised models, tailored for specific initial conditions, and general models, capable of extrapolating to unseen scenarios. The results show accurate and low-cost prediction of temperature and heat flux fields, laying the basis for enabling flexible and generalisable thermal error correction in machine tool environments.

cs.LG↗

arXiv2025

An Open Dataset for Temperature Modelling in Machine Tools

This data set descriptor introduces a structured, high-resolution dataset of transient thermal simulations for a vertical axis of a machine tool test rig. The data set includes temperature and heat flux values recorded at 29 probe locations at 1800 time steps, sampled every second over a 30-minute range, across 17 simulation runs derived from a fractional factorial design. First, a computer-aided design model was de-featured, segmented, and optimized, followed by finite element (FE) modelling. Detailed information on material, mesh, and boundary conditions is included. To support research and model development, the dataset provides summary statistics, thermal evolution plots, correlation matrix analyses, and a reproducible Jupyter notebook. The data set is designed to support machine learning and deep learning applications in thermal modelling for prediction, correction, and compensation of thermally induced deviations in mechanical systems, and aims to support researchers without FE expertise by providing ready-to-use simulation data.

cs.CE↗

arXiv2025

Evidence for Two SNe Type Triggering GRB 220101A: a Pair SN and a Rotating Magnetized Core Collapse SN

The traditional interpretation of gamma ray bursts (GRBs) as originating from a single black hole has been extended by the Binary Driven Hypernova (BdHN) model, in which a GRB arises from a binary system composed of a carbon oxygen (CO) core and a neutron star (NS) companion. This framework successfully reproduces the six canonical emission episodes observed in GRBs. Recent observations of energetic events, such as GRB 220101 and GRB 240825, suggest a more powerful variant involving a rapidly rotating, strongly magnetized CO core in a binary system with an NS. In this scenario, the collapse and possible fission of the CO core lead to the formation of a highly magnetized, rapidly rotating newborn neutron star. A pair instability supernova (pair SN) is triggered when rotation and magnetic effects drive the core to instability, influencing its collapse dynamics. This process results in a millisecond neutron star that later evolves into a pulsar. Concurrently, accretion of supernova ejecta onto the NS companion can induce its collapse into a black hole, powering high energy emission. This framework introduces two distinct classes of supernovae: (i) pair instability supernovae leaving no compact remnant, and (ii) magnetized, rotating core collapses producing pulsars. The model further incorporates the role of magnetic field amplification and magnetohydrodynamic processes, including the generation of overcritical fields and electron positron pair production. This represents a significant departure from earlier non rotating models and aligns with modern pair SN scenarios. BdHNe are characterized by seven physical episodes; notably, in pair SN cases, the final episode is not powered by radioactive nickel decay but by pulsar formation. These modifications are described within a leading order analytical framework.

astro-ph.HE↗

arXiv2023

Measurement of the Ratio of Partial Branching Fractions of Inclusive $\overline{B} \to X_u \ell \overline{\nu}$ to $\overline{B} \to X_{c} \ell \overline{\nu}$ and the Ratio of their Spectra with Hadronic Tagging

We present a measurement of the ratio of partial branching fractions of the semi-leptonic inclusive decays, $\overline{B} \to X_{u} \ell \overline{\nu}$ to $\overline{B} \to X_{c} \ell \overline{\nu}$, where $\ell = (e, \mu)$, using the full Belle sample of $772 \times 10^{6}$ $B \kern 0.18em\overline{\kern -0.18em B}$ pairs collected at the $\Upsilon(4S)$ resonance. The ratio is measured via a two-dimensional fit to the squared four-momentum transfer to the lepton pair, and the charged lepton energy in the $B$ meson rest frame, where the latter must be larger than $1$ Ge\kern -0.1em V, covering approximately $86\%$ and $78\%$ of the $\overline{B} \to X_{u} \ell \overline{\nu}$ and $\overline{B} \to X_{c} \ell \overline{\nu}$ phase space, respectively. We find $\Delta \mathcal{B}(\overline{B} \to X_{u} \ell \overline{\nu})/ \Delta \mathcal{B}(\overline{B} \to X_{c} \ell \overline{\nu}) = (1.99 \pm 0.17_{\rm stat} \pm 0.16_{\rm syst}) \times 10^{-2}$ where the uncertainties are statistical and systematic, respectively. In addition, we report the partial branching fractions separately for charged and neutral $B$ meson decays, and for electron and muon decay channels. We place a limit on isospin breaking in $\overline{B} \to X_{u} \ell \overline{\nu}$ decays, and find no indication of lepton flavor universality violation in either the charmed or charmless mode. Furthermore, we unfold the $\overline{B} \to X_{u} \ell \overline{\nu}$ and $\overline{B} \to X_{c} \ell \overline{\nu}$ yields and report the differential ratio in lepton energy and four-momentum transfer squared.

hep-ex↗

arXiv2021

Punzi-loss: A non-differentiable metric approximation for sensitivity optimisation in the search for new particles

We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.

hep-ex↗

arXiv2021

$B$-flavor tagging at Belle II

We report on new flavor tagging algorithms developed to determine the quark-flavor content of bottom ($B$) mesons at Belle II. The algorithms provide essential inputs for measurements of quark-flavor mixing and charge-parity violation. We validate and evaluate the performance of the algorithms using hadronic $B$ decays with flavor-specific final states reconstructed in a data set corresponding to an integrated luminosity of $62.8$ fb$^{-1}$, collected at the $\Upsilon$(4$S$) resonance with the Belle II detector at the SuperKEKB collider. We measure the total effective tagging efficiency to be $\varepsilon_{\rm eff} = \big(30.0 \pm 1.2(\text{stat}) \pm 0.4(\text{syst})\big)\%$ for a category-based algorithm and $\varepsilon_{\rm eff} = \big(28.8 \pm 1.2(\text{stat}) \pm 0.4(\text{syst})\big)\%$ for a deep-learning-based algorithm.

hep-ex↗