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Prof. Wolfgang Rhode (TU Dortmund)15/10/2026, 10:10
This talk explores the shift from classical critical rationalism to probabilistic rationalism in modern experimental physics. Using Plato's Cave as a metaphor, it argues that modern detectors produce probabilistically smeared shadows of underlying physical processes, making the inference from data to theory inherently non-unique. The presentation identifies three epistemological shifts in...
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Prof. Pierre-Hugues Beauchemin (Department of Physics and Astronomy, Tufts University, Department of Philosophy)15/10/2026, 10:55
Unfolding is an inverse data transformation process that aims to correct high-level physics observable quantities obtained from a set of data for possible distortions introduced by the instrument. Unfolding is a crucial step to enable experimental outcomes to be directly comparable to theoretical predictions. Traditional unfolding methods used in experimental High Energy Physics consist in...
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Dr Mirko Bunse (Lamarr Institute, TU Dortmund University)15/10/2026, 11:40
Modern unfolding techniques pay special attention to
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systematic effects, such as imprecisely known or varying properties of the detection process. This necessary, although fairly novel, shift towards the modeling of systematic effects corresponds to a desirable relaxation of assumptions previously imposed by the physicist, so that unfolding can represent the observed data more closely than... -
Prof. Michael Schmelling (Max-Planck-Institut für Kernphysik Heidelberg)15/10/2026, 14:00
Data analysis is often faced with the problem to determine the properties of a signal in the presence of background. A measurement is possible if the two components can be statistically separated in a discriminant variable, provided the probability density functions in this variable are known or can be estimated from the data. This is realized by Custom Orthogonal Weight functions (COWs). The...
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Prof. Katja Ickstadt (TU Dortmund)15/10/2026, 14:45
The quality of results of machine learning models highly depends on the quality of the data used to train the model. But what do we mean by good data quality? Are representative data, e.g., provided by the German micro census or the census of good quality? And does the term “representativity” suffice to define data quality? In this talk, we will discuss the terms “data quality” and...
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Dr Dominik Elsässer (TU Dortmund)15/10/2026, 15:45
Research in astronomy and astrophysics has over centuries made huge progress along the frontier of ever increasing observational capabilities, which supply a wealth of data spanning the electromagnetic spectrum and different messengers.
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With this treasure drove of data sitting in archives and being continuously added to, questions remain on how to best ensure equal access for members of the... -
Prof. Kent W. Staley (Department of Philosophy, Saint Louis University)15/10/2026, 16:30
The study of particle signatures at High Energy Colliders provides a concrete context to think about pattern identification in experimental data. An inquiry into pattern identification in relation to particle signatures stands to yield new insights on long-standing philosophical questions about patterns (highlighted in an influential paper by Dennett), such as their relationship to phenomena...
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Prof. Michael Stöltzner (Department of Philosophy, University of South Carolina)15/10/2026, 17:45
In a recent paper, Peter Mättig and I developed a generalized version of the concept of signatures broadly used in elementary particle physics and discussed its relation to phenomena and data models. My paper explores, for one, how this concept can be extended to other fields and where such an extension is most likely to fail. Secondly, I discuss to what extent signatures can be a target for...
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Dr Dinah Pfau (Institute for the History of Science and Technology, Deutsches Museum Munich)15/10/2026, 18:30
In the course of the “industrialization” (Galison 1997) of high-energy physics during the 1950s and beyond, the evaluation of vast quantities of experimentally produced photographs was initially delegated to untrained women. However, their “practices of seeing” (Schürmann 2008) were soon identified as the economic and epistemic bottleneck of the experimental process — a barrier that was to be...
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Prof. Brigitte Falkenburg (TU Dortmund)16/10/2026, 10:00
Astroparticle physics measures cosmic rays using particle detector systems that function as telescopes for detecting subatomic particles of extraterrestrial origin. As part of current multi-messenger astronomy, its models and methods bridge the gap between astrophysics, cosmology, and particle physics. My talk focuses on the causal models underlying the data analysis of the IceCube neutrino...
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Dr Marlene Doert (Department of Physics, TU Dortmund)16/10/2026, 10:45
Modern physics is deeply shaped by the development and use of models. From detector simulations and statistical inference to the interpretation of experimental data, scientific knowledge is rarely derived from observations alone but emerges through layers of modeling that represent physical processes, instruments, and uncertainties.
At the same time, the role of models as representations of...
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Dr Mahdi Khalili (Institute of Philosophy, University of Bern)16/10/2026, 11:45
In this talk, I ask whether and how the use of machine learning (ML) in contemporary science affects the scientific realism debate. After discussing the current state of the debate, I focus on the case of exoplanet detection, where ML has recently been used to distinguish transit signals produced by planets from false positives. I ask, first, whether ML can provide a methodologically distinct...
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Dr Silvia De Bianchi (Department of Philosophy, Università degli Studi di Milano)16/10/2026, 14:00
In recent work, Constantin et al. (2026) suggested the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery. Thanks to structural insights, they believe that one can inform the training or fine-tuning of language models for symbolic reasoning, encouraging them to generate expressions whose statistical and...
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Dr Koray Karaca (Middle East Technical University Ankara)16/10/2026, 14:45
Deep learning (DL) models are increasingly used in astroparticle physics for tasks such as gamma–hadron separation, neutrino event reconstruction, and cosmic-ray classification. While these models achieve remarkable predictive accuracy, their opacity poses a challenge to the epistemic standards of discovery. Heatmap-based explainable AI (XAI) techniques —such as heatmaps — promise insight into...
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Dr Johannes Mierau (TU Dortmund)16/10/2026, 15:45
The No Miracles Argument is arguably the strongest case for scientific realism. Yet, it seems that machine-learning-driven scientific discovery evades its justificatory force. Machine learning models are explicitly trained for the purpose of detecting patterns in data, their predictive success comes by design and does not indicate the truth of the underlying representation. Moreover, such...
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