Implicit Changes of Model Uses in Astrophysics, Illustrated on the Paris-Durham Shock Model

Implizite Veränderungen der Verwendung astrophysikalischer Modelle am Beispiel des Paris-Durham-Modells für Stoßwellen

Abstract

This paper explores the epistemic status of models and simulations between theory, on the one hand, and observations, on the other. In particular, I will argue that the interpretation of an essentially invariant astrophysical model structure can change substantially over time. I will illustrate this claim using as an example the first 20 years (1985–2004) of development of the Paris-Durham shock code—a numerical model of slow interstellar shock waves (i.e. a disturbance of the medium that travel faster than the local speed of sound). I will show that the model’s interpretation and, in particular, its underlying representational ideal—the modeler’s (often implicit) goal governing the development and the use of the model—changed notably during this period. Whereas the code was originally used in a purely exploratory fashion, it was later taken to represent and encompass the target phenomenon as completely as possible. It is noteworthy that during this transition the model’s change of epistemic status was never explicitly acknowledged or in any way articulated. However, the impetus for the change can, I claim, be found in the role that observational data came to play in the later publications.

Zusammenfassung

Dieser Artikel untersucht den epistemischen Status von Modellen und Simulationen zwischen Theorie auf der einen Seite und Beobachtungsdaten auf der anderen. Insbesondere werde ich dafür argumentieren, dass sich die Interpretation einer im Kern unveränderlichen astrophysikalischen Modellstruktur mit der Zeit grundlegend ändert. Diese These soll anhand des Beispiels der ersten 20 Jahre (1985–2004) der Entwicklung des Paris-Durham Codes für Stoßwellen (Störungen eines Mediums, die sich schneller als die lokale Schallgeschwindigkeit fortbewegen) illustriert werden. Dabei wird sich zeigen, dass die Interpretation des Modells und insbesondere das dieser zugrunde liegende repräsentationale Ideal – das für den Modellierer (oft implizit vorliegende) bei der Entwicklung und Verwendung des Modells leitende Ziel – sich in diesem Beispiel bedeutend ändert. Während der Code ursprünglich auf rein explorative Weise genutzt wurde, wurde er später als möglichst vollständige Repräsentation und Verkörperung des modellierten Zielphänomens verstanden. Es ist bemerkenswert, dass diese Änderung des epistemischen Status des Modells nie explizit eingeräumt oder artikuliert wurde. Der Impuls für diese Veränderung, so meine Behauptung, kann in der Rolle gesehen werden, die Beobachtungsdaten in den späteren Publikationen spielen.

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Notes

  1. 1.

    Although the literature often distinguishes between models and simulations, with simulations exhibiting a temporal dimension, both terms are used interchangeably in this paper. Another related term used is “numerical code”, which is the computational structure underlying numerical models.

  2. 2.

    In an interview conducted by David DeVorkin and Spencer Weart at Princeton University (DeVorkin & Weart 1977), Martin Schwarzschild described his early vision on the importance of numerical computations in astrophysics as follows: “my feeling was very strongly that we had to get into the computing game, to push the computers and push the universities to have computers, so that in the future their effectiveness would be large.” He went on to explain von Neumann’s interest in astrophysical computations: “Von Neumann was very interested to have a problem which was nonlinear and sufficiently complicated to really need the whole power of his machine, but where lots of hand computations for checks were available; and therefore the stellar evolution work, which I think von Neumann also considered interesting in itself, though not all that deeply—he thought that that was an excellent one. So actually next to the official major program, the meteorological dynamics for which the machine officially was funded, stellar evolution got the biggest share of time.”

  3. 3.

    Notably, the relationship between the quality of data and numerical capacities also exists in the opposite direction: the available computing power and thus the quality of data processing models may limit the quality of the observational data. A prominent example is the Very Large Array (VLA), an interferometer that naturally relies on heavy image processing since the observations are done in Fourier space. This telescope’s dynamic range improved by more than a factor of 20 within the first ten years of operation without hardware modifications, the improvements being due solely to progress in the image processing software (see e.g. Astronomy and Astrophysics Panel Reports 1991).

  4. 4.

    Arabatzis calls this approach a “philosophical history of science” and describes its aim as follows: “to reconstruct particular historical episodes or to address historiographical questions by engaging with philosophical issues about, for example, experimentation or conceptual change.” (Arabatzis 2017: 69–70).

  5. 5.

    If the radiation is calculated only based on the knowledge of the gas properties, the underlying assumption is that all radiation generated will be able to escape the gas. Usually that is not the case, and the interaction of the radiation with the gas on its way out needs to be considered as well. This (nonlinear) version of the problem is called “radiative transfer calculation”. While early numerical models concentrated on the linear version because they were computationally not capable of solving this complex problem, we will see that a proper radiative transfer treatment played an important role for later versions of the numerical shock code.

  6. 6.

    A “grid of numerical models” is a set of model outputs, each calculated with a different combination of input parameters from confined intervals of possible input values.

  7. 7.

    See also Knuuttila & Merz (2009), who also stress these two dimensions of models: “models can be seen as productive objects that bring us understanding in different ways, depending upon the uses to which they are put” (Knuuttila & Merz 2009: 2306).

  8. 8.

    Scientists responsible for the IRAS satellite describe the benefits of the first infrared satellite compared to earth-bound telescopes as follows: “Without IRAS, atmospheric absorption and the thermal emission from both the atmosphere and Earthbound [sic] telescopes make the task of the infrared astronomer comparable to what an optical astronomer would face if required to work only on cloudy afternoons.” (Neugebauer et al. 1984: 14).

  9. 9.

    Notably, this paper, like the others, does not even state the origin of the observations, i.e. the specific telescopes used. Apparently, as theoretical astrophysicists, the authors did not show much interest in the observational details.

  10. 10.

    Again, the telescope itself is not named in the paper.

  11. 11.

    The exploratory function of models is, for example, analyzed in Gelfert (2018). He describes the “exploratory mode” of doing science as one “that aims at getting a grasp of a phenomenon or scientific problem in the absence of a well-understood and workable theory of the domain in question” (Gelfert 2018: 249). In the example of early interstellar shock modelling, the relevant (lower-level) physical and chemical theories are all at hand; however, their interplay in creating the complex higher-level cosmic phenomenon in question was not yet understood in practice. In fact, all four functions of exploratory models outlined by Gelfert can be found in the example: the early models served as a starting point for future inquiry, provided proofs of principle and potential explanations, and even led to a reassessment of the target system (by suggesting new types of shocks).

  12. 12.

    While a discussion of the implications of the case study for the debate on the nature of scientific representations would go beyond the scope of this article, it may be stressed that the demonstrated representational changes of the model show most clearly that scientific representation is an intentional concept, depending very much on the user’s intentions, objectives and purposes as well as contextual factors like data-availability and practical limitations to model building.

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Anderl, S. Implicit Changes of Model Uses in Astrophysics, Illustrated on the Paris-Durham Shock Model. N.T.M. 27, 515–546 (2019). https://doi.org/10.1007/s00048-019-00225-8

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Keywords

  • Astrophysics
  • Scientific modeling
  • Simulations
  • Interstellar shocks
  • Coding

Schlüsselwörter

  • Astrophysik
  • Wissenschaftliche Modellierung
  • Simulationen
  • Interstellare Stoßwellen
  • Programmierung