The Collective Development of Scientific Methods

From exoplanet astronomy to the study of consciousness, seeing is believing

By Georgeann Sack

“Looking out of an observatory in Vicuna, Chile.” By DC_Colombia.

Exoplanet astronomers analyze data from telescopes to create visual representations of supposed planets. The catch? All of their data comes from the light of stars. Exoplanets cannot be seen directly.

In the third chapter of “Placing Outer Space: An Earthly Ethnography of Other Worlds,” author Lisa Messeri writes with incredible insight about how new exoplanet visualizations are developed, and what makes them successful or not. Key to an analysis method’s success is convincing the rest of your scientific community to see what you see in the data.

Messeri, who is Assistant Professor of Anthropology at Yale University, began fieldwork for her book in 2009 and published it in 2016. She writes, “As exoplanet astronomy is a new field, I had the privilege of observing the community at a time when the techniques of seeing were still being developed. Such development is a collective task.”

If you are interested in the practice and philosophy of science, or are actively pushing at the boundaries of what can be seen and known, you may benefit from reading what Messeri has to say. For Awake & Alive Mind readers, Messeri’s research is especially relevant to the science of consciousness, where “the techniques of seeing” are in active development. Neuroscientists searching for consciousness in the brain have at least as difficult a task as astronomers searching for exoplanets in the universe.

New ways of seeing

In pushing the boundaries of what can be visually represented, exoplanet astronomers are forging a new visual culture, in Bruno Latour’s sense that current practice “redefines both what it is to see, and what there is to see.”

Messeri, in Placing Outer Space

Exoplanet astronomy is based on data from telescopic observations of stars. Telescopes are used to measure the brightness or color signature of stars over time, and that data is analyzed for evidence of exoplanets. Messeri describes these first steps of collecting and analyzing data as ‘seeing with the system.’

From there, exoplanet astronomers use more convoluted types of analysis, and some amount of imagination, to speculate about what type of planet they are looking at. Messeri describes this step as ‘seeing beyond the signal.’

We also describe what and how we see through written and verbal language. Messeri calls this third type of seeing ‘seeing through language.’

Seeing with the system

We learned from our visitors that to see the data properly we had to have an image of the whole system (star, satellite, Earth) as a lens for interpretation.

Messeri, in Placing Outer Space

Astronomy relies on the use of telescopes to gather and focus light. In essence, telescopes enable us to see distant objects that are invisible to our eyes. As Michael S. Turner put it in New Eyes on the Universe: 400 Years of Physicist Astronomers, “the telescope and microscope were the first instruments of science that extended our ability to explore the physical world.”

Never satisfied, we have continued to push the boundaries of what can be made visible. One example of this is the search for exoplanets, which can only be “seen” indirectly as having an effect on their star.

In exoplanet astronomy no image of the object itself exists. Astronomers have thus crafted many different representations — from light curves to radial velocity graphs to visualized statistics — to stand in for planets. Exoplanet astronomy as a new visual culture is one with many layered and new ways of seeing. [Sara] Seager elegantly described this way of seeing as understanding “data as art.”

Messeri, in Placing Outer Space

Exoplanets that happen to cross between their star and our telescopes can be “seen” as a dip in star brightness. Time-lapse imaging of the star reveals a periodic dimming as the exoplanet transits across it.

Transit photometry method of exoplanet detection. The brightness of the star is measured over time. Image credit: NASA (source)

For the many exoplanets that do not cross between their star and our telescopes, astronomers look for stars that wobble. Stars that move in periodic loops are part of a larger system, likely including an exoplanet, orbiting around a common center of mass.

To detect wobble, astronomers measure the star’s color signature over time. Light gathered by the telescope is fed into a spectrograph so that it’s color signature (component wavelengths) can be measured. Changes in color signature are correlated with the movement of the star relative to the observer, a result of the doppler effect. As the star moves toward us, spectral lines shift toward blue. As the star moves away, they shift toward red.

Astronomers convert spectrograph data into a measure of radial velocity of the star using math. Radial velocity = ((wavelength of spectral lines at given point in time minus wavelength of spectral lines that would be observed if star at rest)/(wavelength at rest)) X (the speed of light).

If the radial velocity creates a repeating S-shaped curve when graphed over time, its movement may be caused by the tug of an orbiting exoplanet.

Radial velocity (in meters per second) of the star 18 Delphini over time (in years). The periodic changes in radial velocity are caused by the orbit of exoplanet 18 Delphini b. (source)

As you might imagine, the detected signal, a periodic change in either the brightness or spectrum of light emitted by a star, is small, while the detected noise, such as the flux of the star, is large. The data must be “cleaned-up” through filtering algorithms to reduce the noise and boost the signal.

Distinguishing ‘real’ data from ‘artifacts’ is a common pursuit across many sciences. Michael Lynch writes: “The possibility of artifact is an almost inevitable accompaniment of research which relies upon specialized techniques and machinery for making initially ‘invisible’ theoretic entities visible in documentary formats.”

Messeri, in Placing Outer Space

Any neuroscientist, whether they use imaging or electrophysiology, can relate to the myriad issues that come with this type of data analysis. Astronomers and neuroscientists alike are looking for small signals in huge noisy datasets.

The data is then displayed as a visualization. Data visualizations are useful to see the big picture of what is happening, but they are also several steps removed from the so-called “raw data.” That makes it difficult for those not intimately familiar with the raw data and the analysis steps that led to the polished visualization to understand whether or not what they see is a trustworthy interpretation.

Seeing beyond the signal

Despite what astronomers desire, there is a very large gap between the light curve and truly understanding an exoplanet as a world. Across this gap, astronomers weave ‘strange associations’ as they make these alien planets familiar places.

Messeri, in Placing Outer Space

Messeri shares how the same data used to locate an exoplanet is then massaged to speculate about the nature of the planet. For example, an exoplanet astronomer might ask, “What kind of atmosphere does this exoplanet have?” With limited data, there are many possibilities. Messeri shadows a new graduate student (given the pseudonym Jessica) frustrated by the uncertainty of her analysis, who says ‘there is not enough information to constrain the problem.’ Messeri asks the important open question, “How far beyond the data can astronomers go in speculating about a planet?”

Seeing through language

As Messeri points out, when astronomers speculate about their planet of interest, it is hard to get away from analogy to planets in our solar system. Or as she puts it, “language games are played to aid in the practice of place-making. … Language and metaphor mimic the work of visualization to make visible the invisible and create new realities.” Comparison to something better known has the unfortunate consequence of creating imagined comparisons in the reader’s mind that far exceed what can be justified by the data.

In provoking conclusion, Messeri writes, “To see beyond the signal is to see the exoplanet as multiple and recognize the fragility of one’s object of study; it is to expose worlds as things that are made.”

“Dawn on an alien planet, and a giant, ringed planet looms over a tranquil lakeside scene.” By simonbradfield.

Indoctrination into a new way of seeing

Here is where Messeri’s perspective as an anthropologist comes in. Messeri turns the lens back on ourselves in her studies. How do we become convinced that a new way of seeing is to be trusted? And how does one individual’s new way of seeing become a common way of seeing, such that it becomes “perception as a collective as opposed to an individual form of cognition?”

What is common across all of the projects discussed is the importance of both learning the different ways of ‘seeing’ the data and of training readers of scientific publications to see as the author does. This indoctrinates the student into the communities shared visual practices, even as these practices are themselves taking form.

Messeri, in Placing Outer Space

One interesting point Messeri makes is that we tend to accept new methods that have some resonance with what is already established. She writes that visualizations are more likely to be successful when they have a “visual resonance with more established conventions.” Likewise, she shares an anecdote about an unnamed astronomer whose publication was largely ignored, writing, “He realized why his article had failed where the other had succeeded: ‘ocean planet’ was a familiar, imaginable world, while ‘volatile-rich planet’ did not evoke an immediately recognizable kind of place.”

The milky way viewed from the Atacama Desert, Chile. Photo by Jorge Corante. For me, the best moment of the five hour astronomy lesson was pointing a telescope at what looked like a cloud and finding that it was in fact a cluster of individual stars.

A second important point is that ways of seeing must be taught. Teaching can occur in written form, as when the author(s) of a research article explain their data and analysis. Messeri writes, “Whether or not the authors can get their readers to ‘see’ the data the same way they do is crucial for community acceptance.” Teaching can also take place in oral form, as when a mentor talks through a new approach with a trainee. Messeri writes,“[The undergraduate astronomy students] were taught to see with the system, which translated to knowing how to interpret light curves. To believe that there is a world in a light curve is to see and trust the methods by which they are produced.”

I think a third teacher is worth mentioning here, and that is experience. For most, it is difficult to understand the wisdom of a certain approach over others unless they have personally looked through and analyzed data. For those not actively engaged in exoplanet research, we must rely on the authority of those who are and take their word for it. More than trusting any individual, we trust the consensus of the exoplanet astronomy community.

Insights into science communication

There are a few takeaways from Messeri’s research I think we can apply to science communication, a form of sharing what we see through language. One is to think in terms of ‘seeing with the system’ and ‘seeing beyond the signal.’ What are the components of the system you are studying or writing about? What assumptions are you making about the system? When are you speculating beyond what the data shows? These should all be clearly stated in any research findings.

The second is to think of your explanation as indoctrinating the reader or trainee into a new way of seeing. We have a tendency to omit the details, especially about the logical why and how of steps taken to transition between the raw data to polished visualization, when we are familiar with with the process. These details are essential if you hope to indoctrinate someone into your way of seeing. They need to understand each step, and to agree that they would have made the same choices you did, if they are to believe your conclusions.

It always helps to imagine yourself as a naive viewer or reader. For example, if you show a graph during a talk, imagine you are looking at it for the first time. What would you need to understand about that data visualization to understand what it is showing? How about to believe what it shows? Too often people throw up graphs and don’t explain what the axes represent or how the data was collected and analyzed.

The third one, often overlooked, is that the development of new ways of seeing is always a collective task. The lone genius does not get very far if they do not convince others to see as they do. More than that, new ways of seeing evolve stepwise from established ways of seeing, such that many people are likely to invent a similar new way of seeing at the same time. Over time, one approach becomes the preferred one used by the community.

These periods of time when everyone is using slightly different approaches can be frustrating, because it is difficult to compare results between groups. Collective tasks benefit from leadership. Having leaders in the field who systematically compare different approaches and get everyone discussing what is best and why can expedite the development of defined standards and practices.

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