The Role of Images in Astronomical Discovery
Page 16
Fig. 4.3 Schematic of the “sidereal system” derived by William Herschel from his stellar counts.
From Herschel (1785), Philosophical Transactions of the Royal Society of London.
To frame his endeavour soundly, Herschel made two simplifying assumptions: first, that
his instruments allowed him to see into the farthest limits of the sidereal system; and sec-
ondly, that the stars were distributed regularly throughout space. He then defined approxi-
mately 300 sections having the same solid angle, spread across the sky that was visible from
13:59:10, subject to the Cambridge Core
.006
4. Portraying “Nebulae” for the Mind
97
his observing location. Then he counted the stars in each of these conic sections. He counted
and counted, using the brightness of the stars as an indicator of their distance, assuming that
the fainter ones were further away. Herschel’s method was primitive and his assumptions
were incorrect. But overall his statistical sampling procedure was valid. The Kapteyn pro-
gram that I discussed in Chapter 3 was a rebirth of Herschel’s dream.
Herschel’s counting method was adopted and improved by other astronomers during
the nineteenth century, including his son John Herschel. It deeply influenced the work of
Russian astronomer Friedrich Georg Wilhelm Struve (1793–1864), director of the Pulkovo
Observatory in St. Petersburg.13 Applying a similar counting method, Struve demonstrated
that the brighter stars tended to conglomerate in the plane of the Milky Way. In 1847, Struve
even suggested from his counts the existence of an attenuation of stellar light by one magni-
tude per c. 3,000 light-years of the line of sight. Although he did not explain the extinction
mechanism, Struve had discovered an important component of the matter filling the space
between the stars: fine dust particles distributed between the stars that absorb or diminish
the intensity of stellar light as it travels through sidereal space, causing the so-called “zone
of avoidance.” This dimming effect was ignored for the next 70 years.14
In the 1910s, Heber Curtis used Herschel’s statistical counting approach to estimate the
number of spiral galaxies observable with the 36-inch Crossley reflector. Always doubtful
of the reality of the large recession velocities of galaxies, Edwin Hubble conducted a sys-
tematic count of galaxies as a function of apparent brightness during the 1930s. He tried to
use the number of galaxies counted to discriminate between models that explained galaxy
spectral redshift as being due to the expansion of the universe, and models proposing instead
some unknown phenomenon such as light “fatigue.”
Confronting Bias
Naturally there is a subjective aspect to synoptic representation. For example, decisions
have to be made about what features are important or what quantities need to be illustrated.
As large set of data points are averaged, and a consistent method for smoothing the data has
to be adopted and described. Furthermore, all scientific measurements are subject to biases.
The designer needs to state the appropriate warning and limitations. For example, the use
of a particular telescope, instrument and detector biases the astronomical detection in ways
that depend on the observing wavelength, and hence the filter used. Biases and systematic
effects are introduced by optical effects (e.g. diffraction or limits of the optics), quantum
physics effects (threshold of detection or sensitivity as a function of wavelength), the dura-
tion of exposure (fainter objects remain undetected), and varying atmospheric conditions.
As noted by James Keeler, the difference in the response of the human eye compared to that
of the photographic emulsion explained some of the differences between objects recorded
13 J. North, Cosmos: An Illustrated History of Astronomy and Cosmology, Chicago: University of Chicago Press, 2007, p. 448–
449.
14 Modern estimates of interstellar extinction in the Milky Way are 0.7–1.0 magnitudes per kiloparsec, or a factor of 2 to 2.5 of attenuation per 3,200 light-years.
13:59:10, subject to the Cambridge Core
.006
98
Part I – Images and the Cosmos
by drawing and by photography.15 The use of different photographic emulsions has been
driven both intentionally and by practical reasons. As we will see in Chapters 9 and 10,
such choices affect the process of galaxy classification and influence interpretation.16
Another biasing effect is brought about by sampling. For example, as we observe further
and further away in the universe, intrinsically faint objects drop out. A sample of distant
objects gets skewed towards the more luminous objects, as intrinsically brighter objects can
be observed further away. This creates a false trend of increasing average luminosity as a
function of distance. This is called the Malmquist bias, an important selection effect popu-
larized by the Swedish astronomer Gunnar Malmquist (1893–1982).17 The effect must be
understood and corrected for, which is complicated by the fact that there are also evolu-
tionary effects that modify the underlying population. Distant objects are seen at a time the
universe and these objects were younger. Furthermore, as objects evolve with time, their
population representation changes over different periods of time.
For extended objects, i.e. those that are not as point-like as stars, the surface brightness
can introduce a biasing trend. The night sky has an intrinsic brightness, partly due to the flu-
orescence of the Earth’s atmosphere, but also as a result of the general stellar background. It
is easier to detect objects of high rather than low surface brightness. If you examine Plate 6.3
closely, you will find dozens of faint galaxies, which are barely visible. This greatly affects
the detection and measurement of extended faint objects, either regular nebulae in our own
galaxy or galaxies of low surface brightness in the extragalactic world. The knowledge that
these objects exist and the need to assess and count them galvanize efforts to optimize the
transmission and control of scattered light in optical systems. Observations of the nearby
universe and the modeling of galaxy evolution can correct for this. However, it remains
extremely difficult to detect the large populations of faint galaxies.
The Drive to Classify
Synoptic representation triggers another epistemological stage: a classification process that
tries to combine identical or similar features of several objects to create an encompassing
class of objects or extract an archetypal object. In his 1811 paper, William Herschel pre-
sented two plates to show the main nebular shapes (Fig. 2.4). It was an undifferentiated
mix of what we now know to be galactic nebulae and external galaxies. Herschel saw all of
them as one class of objects and he tried to sequence them.18 He suggested that the degree of
condensation of “nebulae” was related to gravity, thus determining nebular shapes. Perhaps
more importantly, in the light of later developments, Herschel inferred that the shapes of
15 J. E. Keeler, Note on a Cause of Differences Between Drawings and Photographs of Nebulae, Publications of the Astronomical Society of the Pacific, 1895, Vol. 7, pp. 279–282.
16 The impact of filters and wavebands with regard to galaxy morphology an
d classification is discussed by the authors in R. J.
Buta, H. G. Corwin Jr. and S. C. Odewahn, The de Vaucouleurs Atlas of Galaxies, Cambridge: Cambridge University Press, 2007.
17 G. Malmquist, On Some Relations in Stellar Statistics, Arkiv för Mathematik, Astronomi och Fysik, 1922, Vol. 16, pp. 1–52.
18 M. J. Way, Dismantling Hubble’s Legacy? in Origins of the Expanding Universe: 1912–1932, Astronomical Society of the Pacific Conference Series, 2013, Vol. 471, pp. 102–103.
13:59:10, subject to the Cambridge Core
.006
4. Portraying “Nebulae” for the Mind
99
“nebulae” were physically meaningful as criteria to be used by classifiers. Stephen Alexan-
der also sought such an ordering (see Fig. 2.11 for a small collection of shapes assembled
by Alexander).
Almost a century after Herschel, the German astronomer Max Wolf (1863–1932) of Hei-
delberg Observatory, also a pioneer of astrophotography and wide-field photography, intro-
duced a classification scheme for “small nebulae” ( kleine Nebelflecken) (Fig. 4.4). Having
commented on the limited power of spectroscopy to sort nebulae – this had been attempted
by American astronomer Edward Charles Pickering (1846–1919)19 – Wolf used morphol-
ogy as a criterion for classifying all “nebulae.” Avoiding any inference about the intrinsic
nature of the “nebulae,” he wrote cautiously: “You will therefore need to be purely descrip-
tive, without any consideration of a hypothesis about the origin of the mist, or the nature
of the spectrum.”20 Wolf’s drawings, derived from photographs, appeared to be of poor
quality compared to those produced at Birr Castle 50 years before. While William Parsons’
team depicted specific objects as accurately as possible, Wolf purposely mixed and aver-
aged features. What mattered was not the accuracy of the sketch but the common structural
information that the morphology conveyed.
In putting together his global scheme, Wolf extracted 23 “classes” that summarized the
main shapes of the foggy nebular images. He rightly emphasized the dependence of the
quality and shapes of the images delivered on the optical instruments used. Like Herschel’s
1811 plates, Wolf’s set is a mix of objects that included Milky Way nebulae and galaxies.
Although spiral shapes and edge-on systems were clearly recognized, Wolf did not add
much to the work of Parsons. Wolf’s merit resided in his teleological purpose: to show that
the natural sequence of shapes betrays deeper physical phenomena and processes. We will
return to the topic of galaxy classification in Chapter 9.
High-Level Representations
As more quantitative physical measurements grew, the use of synoptic representations in
nebular and galactic research increased. With the advent of photography, quantitative pho-
tometry became possible and the technique was quickly applied to images of galaxies;
images or charts in different wavebands were calibrated and constructed.
Isophotal maps, connecting portions of equal brightness in the image of an object,
turned out to be essential for comparing the same objects imaged at different wavelengths,
especially for those obtained in the non-visible domains. It was particularly important to
relate these findings with observations and images in the radio and the X-ray domains that
astronomers had started working with during the second half of the twentieth century. Omar
Nasim has given a fine description of the work of the young American astronomer Ebenezer
Mason, who was introduced at the start of this chapter. Mason was probably the first to use
19 Pickering led a large effort at Harvard College Observatory that resulted in an effective and successful spectral classification system of stars.
20 M. Wolf, Die Klassifizierung der kleinen Nebelflecken, Publikationen des Astrophysikalischen Instituts Königstuhl-Heidelberg, 1908, Vol. 3, pp. 109–112 (translation by the author).
13:59:10, subject to the Cambridge Core
.006
100
Part I – Images and the Cosmos
Fig. 4.4 Early classification of “nebulae” by Max Wolf. From Wolf (1908), Publikationen des Astro-
physikalischen Instituts Königstuhl-Heidelberg. Courtesy of John G. Wolbach Library, Harvard
College Observatory. 2016.
13:59:10, subject to the Cambridge Core
.006
4. Portraying “Nebulae” for the Mind
101
the method of lines of equal brightness in astronomical observation.21 “It was first sug-
gested by the method usually adopted for the representation of heights above the sea-level
on geographic maps, by drawing curves which represent horizontal sections of a hill and
valley at successive elevations.”22 In a parallel approach, John Herschel produced descrip-
tive maps where a nebular drawing was started by making an accurate reference grid of
stars, which served as a sort of triangulation basis; he then drew nebular intensity, carefully
linking areas of equal brightness.
Isocontour maps of physical properties became the standard means of non-homomorphic
representation. Nasim refers to the “isoline craze” that embraced the natural sciences dur-
ing the nineteenth century.23 Astronomy was one of these “infected” sciences. Isophotal
maps as Mason invented them became extensively used in astronomy and continue to be a
universal way to present imaging data (see Plate 4.1; Plate 7.2).
Digital imaging enables an additional step, that of creating intensity ratios of images
of different wavelengths. Another dazzling example has been the use of isovelocity maps:
the joining up of locations of equal radial velocity points across an object (Plate 4.2). Such
maps have been used to establish the behavior of the orbital velocities of stars or gas as a
function of the distance to a galaxy center. These rotational curves and maps have become
essential tools for deriving the mass distribution within individual galaxies and their total
mass. The use of “false colours” is most effective in highlighting the information to be
conveyed.
Probably one of the most stunning non-homomorphic maps in modern astronomy is that
of the cosmic microwave background (CMB), a relic of the distribution of matter in the uni-
verse as it was about 380,000 years after the Big Bang (Chapter 5). Since the serendipitous
discovery by Arno Penzias and Robert Wilson in 1964, we know that the universe is filled
with microwave radiation. The intensity distribution across wavelengths corresponds to a
perfect black body at 2.725 K. The fossil glow peaks in the millimeter–centimeter wave-
length range of the spectrum, the microwave region. The present-day CMB is the remnant
light from the time when the mean temperature of the universe had cooled enough, due
to expansion, for the free electrons to be captured by the protons. The universe became
transparent and the photons were left to cool as the universe continued its expansion.
There were no stars or galaxies this far back in time. Intriguingly, the microwave back-
ground is not absolutely uniform. It is spotted with tiny fluctuations in temperature (Plate
4.3). These are of the order of only 1/100,000 K but have been precisely measured with
very sensitive radio telescopes in space and on the ground. When mapped over the surface
of the sky, the fluctuations in temper
ature show an orderly angular structure; the mottling
has a pattern. The most straightforward interpretation of the pattern is that these structures
correspond to regions of the young universe where matter had started to assemble under the
21 J. Shears, et al., In the Footsteps of Ebenezer Porter Mason and His Nebulae, 2014arXiv:1401.7960. See also O. W. Nasim, Observing by Hand: Sketching the Nebulae in the Nineteenth Century, Chicago: University of Chicago Press, 2013, pp. 131–
137.
22 Cited in J. Shears et al., In the Footsteps of Ebenezer Porter Mason and His Nebulae, 2014arXiv:1401.7960, p. 3.
23 O. W. Nasim, Observing by Hand: Sketching the Nebulae in the Nineteenth Century, Chicago: University of Chicago Press, 2013, p. 131.
13:59:10, subject to the Cambridge Core
.006
102
Part I – Images and the Cosmos
action of gravity. As these enhanced structures grew, they led to large-scale structures and
to the superclusters of galaxies we observe in the universe of today. The map of the spatial
temperature fluctuations of the CMB is a powerful non-homomorphic representation of the
distribution of matter in the young universe a few hundred thousand years after the Big
Bang.
These few examples show that images as representations of nature do not need to be an
exact replica of what an omnipotent human eye would see, also assisted by an instrument
like a telescope or a microscope. Non-homomorphic images can convey information of a
higher level.
This chapter concludes the overview of the different approaches and techniques that
astronomers have developed over the centuries for viewing, sketching and photograph-
ing “nebulae,” in brief, to make images of the “nebulae.” In the next four chapters, the
style changes. I present the world of galaxies using a more thematic approach, highlighting
key individuals and techniques, as well as the specificities of imaging in each wavelength
domain (radio, infrared, visible, ultraviolet, X-rays and gamma-rays) that have all helped
to elucidate the surprising objects and phenomena of the extragalactic universe.
13:59:10, subject to the Cambridge Core
.006
Part II
Images as Galaxy Discovery Engines
14:07:19, subject to the Cambridge Core