Astronomy

Any freely available large stellar spectra catalog?

Any freely available large stellar spectra catalog?

For a non-astronomy personal project I would like to have a large set of optical stellar spectra combined with absolute magnitude when available. I search for a dataset that contains 10k - 10M objects, with total compressed size below 100Gb (ideally below 10Gb), is freely available via ftp/http/rsync and machine-readable. Presence of most 'naked eye visible' stars and their individual names in the set is desired, but not required.

I know that some digital survey data are freely available via FTP, so maybe some reasonable spectral data catalog exists as well?

Note, I'm planning to download the data for local processing, so retrieving them through on-line forms (or even automated http interfaces) is undesirable.

The best I was able to find is this catalog . Dropping aside that FITS is not something I'm used to (this apparentrly can be rectified) it is too small. I'd like at least 1k stars, 10k preferable. There is apparently a lot of surveys in 0.1-10k objects range that are focused on specific type of stars (say, nearby M-class dwarfs) and stellar libraries containing spectral data on representative objects from various classes. However I'd like a 'representative set' here, which contains stars of various nature in proportion similar to their 'natural abundance' in some region. An example would be the set of stars of solar neighborhood above, but again it's too small.


Gulati, R. K., Gupta, R., Gothoskar, P., & Khobragade, S. 1994, ApJ, 426, 340

Heck, A., Egret, D., Jaschek, M., & Jaschek, C. 1984, in IUE Low-Resolution Spectra: A Reference Atlas--Part I, Normal Stars, ESA SP-1052

Houk, N. 1983, in The MK Process and Stellar Classification, ed. R. F. Garrison, (Toronto, David Dunlop Observatory), p. 85

Houk, N., & Smith-Moore, M. 1988, in University of Michigan Catalogue of Two-Dimensional Spectral Types for the HD Stars, 1988, Vol. 4

Jacoby, G. H., Hunter, D. A., & Christian, C. A. 1984, ApJS, 56, 257

Jaschek, C., & Jaschek, M. 1990, The Classification of Stars, (Cambridge, Cambridge Univ. Press)

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1986, Nature, 323, 533

Silva, D. R., & Cornell, M. E. 1992, ApJS, 81, 865

von Hippel, T., Storrie-Lombardi, L. J., Storrie-Lombardi, M. C. & Irwin, M. J. 1994, MNRAS, 269, 97

Weaver, Wm. B. 1994, in The MK Process at 50 Years: A Powerful tool for Astrophysical Insight, ASP Conf. Series, Vol. 60, eds. C. J. Corbally, R. O. Gray, and R. F. Garrison (San Francisco, ASP), p. 303


3.2. SDSS Data¶

Much of the data made available by astroML comes from the Sloan Digital Sky Survey (SDSS), a decade-plus photometric and spectroscopic survey at the Apache Point Observatory in New Mexico. The survey obtained photometry for hundreds of millions of stars, quasars, and galaxies, and spectra for several million of these objects. In addition, the second phase of the survey performed repeated imaging over a small portion of the sky, called Stripe 82, enabling the study of the time-variation of many objects.

SDSS photometric data are observed through five filters, u , g , r , i , and z . A visualization of the range of these filters is shown below:

3.2.1. SDSS Spectra¶

The SDSS spectroscopic data is available as individual FITS files, indexed by three numbers: the plate, date, and fiber number. The fetch_sdss_spectrum() takes a plate, mjd, and fiber, and downloads the spectrum to disk. The spectral data can be visualized as follows:

As with all figures in this documentation, clicking on the image will link to a page showing the source code used to download the data and plot the result.

3.2.2. SDSS Photometry¶

The photometric data can be accessed directly using the SQL interface to the SDSS Catalog Archive Server (CAS). astroML contains a function which accesses this data directly using a Python SQL query tool. The function is called fetch_sdss_galaxy_colors() and can be used as a template for making custom data sets available with a simple Python command. Some of the results are visualized below:

3.2.3. SDSS Corrected Spectra¶

The SDSS spectra come from galaxies at a range of redshifts, and have sections of unreliable or missing data due to sky absorption, cosmic rays, bad detector pixels, or other effects. AstroML provides a set of spectra which have been moved to rest frame, corrected for masking using an iterative PCA reconstruction technique (see Example of downloading and processing SDSS spectra ), and resampled to 1000 common wavelength bins. The spectra can be downloaded using fetch_sdss_corrected_spectra() some examples of these are shown below:

These data are used in several of the example figures from Chapter 7: Dimensionality and its Reduction .

Because these spectra are meant to enable high-dimensional classification and visualization routines, it is useful to have some extra classification data for these objects. One set of features available in the data set is the line ratio measurements. These can be visualized as shown below:

3.2.4. SDSS Spectroscopic Sample¶

Along with spectra, SDSS catalogued photometric observations of the objects in the survey area. Those objects with both spectra and photometry available provide a wealth of information about many classes of objects in the night sky. The photometry from the SDSS spectroscopic galaxy sample is available using the routine fetch_sdss_specgals() , and some of the attributes are shown in the following visualization:

One well-known feature of the SDSS spectroscopic sample is the “great wall”, a filament of galaxies located several hundred megaparsecs away. The great wall data is among the spectroscopic sample: to enable easily working with it, astroML contains the function fetch_great_wall() . The data can be seen below:

3.2.5. SDSS DR7 Quasar Catalog¶

The SDSS has obtained the spectra of over 100,000 distant quasars. The quasar catalog is described on the SDSS website, and can be downloaded using the function fetch_dr7_quasar() . Some of this quasar data is used in the visualization below:

3.2.6. SDSS Imaging Sample¶

While the spectroscopically observed objects in SDSS offer a large number of measured features for each object, the total number of observed objects of each class (star, galaxy, quasar) is under one million. The full photometric sample goes much deeper, and thus contains photometric measurements of hundreds of millions of objects. astroML has a function called fetch_imaging_sample() which loads a selection of this data. Some of the returned attributes are visualized below:

3.2.7. SDSS Segue Stellar Parameters Pipeline¶

Several groups have produced various value-added catalogs which contain additional observed features for objects in the SDSS database. One example is the Segue Stellar Parameters Pipeline (SSPP), which makes available a large number of additional object features derived from SDSS photometry and spectra. This data can be downloaded using the function fetch_sdss_sspp() . Some of the metallicity and temperature data is visualized below:

The left panel shows the density of point on the temperature / log(g) plot. These parameters correlate with the familiar HR diagram based on photometric colors. The right panel shows the average metallicity in each pixel, and the contours indicate the density of points. Many more attributes are available in the SSPP data set see the fetch_sdss_sspp() documentation for details.

3.2.8. Stripe 82: Time Domain¶

During the second phase of the SDSS, the project repeatedly surveyed a small swath of sky known as Stripe 82 . This yielded an unprecedented set of data in the time domain, which yielded insight into phenomena as wide-ranging as the orbits of asteroids, the variability of certain classes of stars, and the acceleration of the expansion of the universe.

astroML contains two datasets based on Stripe 82 data: one containing observations of RR-Lyrae stars, and one containing observations of moving objects (i.e. asteroids) within the solar system.

The RR-Lyrae data can be obtained using the fetch_rrlyrae_mags() function, and result in the dataset visualized below:

The moving objects can be obtained using the fetch_moving_objects() function, giving a dataset containing not only photometric observations, but also orbital parameters. A portion of this information went into the following visualization:

3.2.9. Stripe 82: Standard Stars¶

Along with time-domain data, the repeated observations in Stripe 82 enabled stacked photometry of sources to minimize the statistical error in their measured fluxes. The Stripe 82 standard stars are a set of stars in this region which are below a specified variability criterion. The multiple exposures were combined to yield a highly precise catalog of stars. This data can be obtained using the fetch_sdss_S82standards() function. Some of the data in this catalog is visualized below:


Stars in the Cellar: CLASSES LOST AND FOUND.

For more than a century, the familiar spectral sequence OBAFGKM has stood firm. It is now being extended, testimony to both modern technology and the sequence's amazing adaptability to the discovery of new stellar types.

"M!" the students thundered. I was teaching one of my favorite subjects, stellar spectra, to a rather large upper-level introductory astronomy class. The task was to get them to learn the spectral sequence -- the basic stellar categories -- without resorting to tired mnemonics about kissing fine girls. In a fit of creativity (or so it seemed), I burst into the classroom, and throwing my arms in the air like a college cheerleader, I called out: "Give me an O!" A dribble of "O"s followed. "Give me a B!" produced somewhat better results. As we worked our way through A, F, G, and K the students got into the act and called out louder. By the end, M, we could have been in the stadium cheering the Illini on to victory. Spectral-type M stars, I told the students, lie at the end of the sequence, populating the bottom of the temperature-pile of stars. For a hundred years they've been considered the coolest stars known.

When something has endured for a century, you assume it will never change -- much like a historic building. But if the edifice is strong, you can surely add to it, and -- remarkably -- that is what has happened to the famed spectral sequence. No longer is it OBAFGKM (with side branches R, N, and S). Now it is OBAFGKM with newcomers L and T. Really!

What happened? Technology happened. New observational and analytical tools came into use and, as a result, new classes were found. The story of the expanding spectral sequence is one of fresh discovery, with profound implications for understanding the galaxy, the stars, and even the planets.

The modern tale of the stellar sequence begins back in the quiet time of 1890s Harvard, but it had a long prelude. The birth of the sequence took place literally in a ray of sunshine.

Break sunlight with a prism or raindrop and it splashes into an array of colors from red through violet. Stretch this spectrum carefully, with great color separation, and out pop myriad fine (and some not-so-fine) gaps in the colors. These "spectrum lines," discovered in 1802 by British chemist William H. Wollaston, are superimposed on the solar spectrum by the actions of atoms, their electron-stripped ions, and by simple molecules in the outer layers of the Sun's bright surface. Each element, ion, or molecule has its own unique pattern of light and dark lines that form when the electrons bound to atoms absorb radiation at particular wavelengths. Chemists create these patterns in the lab by igniting pure substances and recording the resulting patterns through spectroscopes. Comparison of laboratory spectra with the spectrum of sunlight allowed solar lines to be identified. They turned out to be the lines for the common elements of Earth: hydrogen, carbon, oxygen, iron, and so on.

From an absorption line's strength (the amount of energy it extracts from the spectrum) and an application of atomic theory, we can determine the composition of the Sun's light-emitting surface. Measured by the number of atoms, it turns out to be 92 percent hydrogen. The rest is mostly helium and a tiny smattering of everything else, led by oxygen, carbon, neon, and nitrogen. Take away the light stuff, the hydrogen and helium, and the "everything else" is apportioned about as it is in the Earth's crust, powerful evidence that we and the Sun were both born at about the same time from the same dusty cloud of interstellar gas. Our familiar landscapes are made from the solar distillate.

Now go to the stars. What a surprise they gave to the pioneers in spectroscopy. The first stellar spectra, observed in 1817 by the German physicist Joseph Fraunhofer, did not look anything like that of the Sun. By the 1860s, stellar rainbows were being studied in bulk by William Huggins in England and Angelo Secchi of the Roman College Observatory. In spite of Huggins's discovery that the Earth, Sun, and stars contain the same chemicals, the observational evidence stood firm: a great many stars did not have Sun-like patterns. Although the Sun is mostly hydrogen, its spectrum is actually dominated by absorptions from sodium and ionized calcium. However, hydrogen absorptions quite overwhelm the spectra of Vega, Altair, and Sirius. In other stars -- Betelgeuse and its reddish kin, for example -- hydrogen is effectively absent, while we see complex bands produced by such molecules as titanium oxide. Why the differences? Although all stars are made of the same basic stuff, their spectra make it seem as if their actual chemical compositions vary all over the place. How best to understand what's going on?

The first step, as in any science, is to classify. But how? In 1863 Secchi invented a system that would act as the prototype for future developments. It divided stars into five groups based on similar line patterns and colors. Roman numerals I through V identified the following classes, respectively: blue-white stars with simple hydrogen spectra (Vega, Sirius), stars with more complex spectra like the Sun (Aldebaran and Arcturus), orange-red stars with more complex bands of lines (Betelgeuse), red stars with different kinds of complex bands (such as 19 Piscium), and finally those containing both emissions (bright lines) and absorptions (Gamma Cassiopeiae and Beta Lyrae).

Secchi's scheme was oversimplified, since stars within any one class could be quite different from one another. Help came when Henry Draper, a physician and amateur astronomer in New York, and Edward C. Pickering, professor of astronomy at Harvard University, focused their attention on the problem of sorting stars. In 1872 Draper photographed Vega's stellar spectrum, just beating out Huggins for the honor of making the first permanent recording. (Huggins was the first to photograph a nebula's spectrum.) Upon Draper's death, his widow gave his recorded spectra, his telescope, and a memorial fund to Harvard. Pickering took up Draper's work and built a spectrographic telescope of novel design. Draper and Huggins had used a dispersing prism at the telescope's focus to view one spectrum at a time. Pickering, however, placed the prism in front of the lens, so he could image spectra of all the stars in a field at once, allowing rapid classification by his assistant, Williamina Fleming.

With all those spectra available, a simple scheme that could handle all the detail was needed. Beginning in 1890 Pickering and Fleming expanded Secchi's groups with Roman letters A through O, based primarily on the strengths of the observed hydrogen lines (P and Q were used for those that did not fit). Further observation showed that some classes were erroneously assigned, unneeded, or could be merged with others. Two other assistants, Antonia Maury and Annie Jump Cannon, saw that absorption lines other than hydrogen fit better from one class to another if B were placed before A and O classified before B. The result was the astronomer's basic spectral alphabet: the first three of Secchi's groups morphing into OBAFGKM his deep red fourth group became class N. Cannon further refined the system by decimalizing the classes, making B9 merge into A0 and so on through M.

The classes are distinctive and precise. Hydrogen first strengthens from O to A, then weakens steadily toward M, where it disappears. O stars display absorptions of ionized helium, B stars of neutral helium. From A through M various metal lines (those from elements heavier than helium) and their ions change their strengths, ionized calcium becoming strong through class G then weakening as neutral calcium and sodium begin to dominate. The M stars (Secchi's type III) show off the molecules in their atmospheres, chiefly titanium oxide (TiO). Secchi IV, class N, does not fit. Its stars are red like those of class M, but they harbor carbon lines rather than TiO.

The great memorial to Draper and his work was the Henry Draper Catalogue, published between 1918 and 1924 by Cannon and Pickering. It contains Cannon's remarkable solo classification of 225,300 stars, which she later extended to 359,082. In spite of all the new developments and advances in astronomy, the original Draper catalog is still consulted -- a testimony to its greatness. Moreover, the most commonly used name for a telescopic star brighter than 10th or 11th magnitude is still its "HD number."

The key to understanding the Draper sequence is color. The colors of spectrally ordered stars vary smoothly, from blue (class O) through white (A), yellow (G), orange (K), and orange-red (M). Star color is an indicator of stellar temperature. The spectral sequence is the result of temperatures that range from a high of around 50,000[degrees] Kelvin for the hottest O stars to about 2,000[degrees] K at the end of M. The sequence responds not to chemical change but to temperature-dependent ionization and efficiency of absorption (see page 42).

A new dimension -- luminosity -- was added in 1943 with An Atlas of Stellar Spectra by William W. Morgan, Paul C. Keenan, and Edith Kellman (the "MKK" atlas). Stars on the "main sequence" (or "dwarfs," ordinary stars like the Sun) have a wide range of visual luminosity: from a million times that of the Sun for O stars, down to a millionth of the Sun's brightness for stars near M9. To these add the evolved dying stars -- those cool giants and supergiants that have ballooned to huge proportions some of them have diameters approaching the size of Saturn's orbit. Their large size leads to low density and subtle spectral changes. All of these differences in stars, ranging from main sequence through subgiant, giant, bright giant, and supergiant are labeled V to I (not related to Secchi's Roman numerals) in the MKK.

Astronomy students have always learned their OBAFGKM and sometimes their RNS. And for years, that was the end of it, with no star cooler than M or N. For purposes of our discussion of stars beyond M, however, the dying, carbon-rich stars -- the R, N, and S types -- can be removed from consideration. We'll stick to main-sequence dwarfs. These stars generate their energy by converting hydrogen to helium deep in their cores. At the cool end are M9.5 stars, with masses around 8 percent that of the Sun. Below the 8 percent limit, the internal temperatures are too low for full hydrogen fusion to be sustained.

Theoreticians, however, have long predicted that there should be lower-mass bodies, or "substars." They would not be running the full fusion reactions seen in hotter stars but would glow dimly from heat generated by gravitational contraction and the fusion of deuterium (a heavy form of hydrogen). Called "brown dwarfs," they were avidly sought, but none were confirmed until a few years ago when Gliese 229B (a dim companion to the red dwarf Gliese 229A) was discovered. Its mass is not yet directly known, but 229B has a temperature so low that it displays methane absorptions, a substance no real star can have. However, one brown dwarf, plus a large number of candidates, hardly compares with the vast number of real M dwarfs out there.

Substars are so cool that to find them in any number astronomers had to break from traditional optical methods and observe in the infrared, where low-temperature bodies radiate most of their light. There are several search programs for such substars, including the Deep Near Infrared Survey ofthe Southern Sky (DENIS) being done by the Paris Institute of Astrophysics, and the Two Micron All Sky Survey (2MASS) by the University of Massachusetts and Caltech's Infrared Processing and Analysis Center. These have led the way, turning up hosts of faint, dim red "stars" that lie off the end of the classic sequence. Their spectra look nothing like class M. Instead of oxides, these stars are dominated by hydrides (metal atoms with hydrogen attached) and by the raw metals themselves.

For the first time in 110 years, astronomers needed another letter to describe ordinary stellar dwarfs. Picking a letter sounds trivial until you try. R and S had already been added R and N were then combined into "C" (for carbon). Most remaining letters, including those recycled from unused Draper classes, could be confused with other kinds of objects. "D," for instance, is part of white-dwarf nomenclature, "Q" is a quasar designation, and so on. Only four letters remained: H, L, T, and Y. A key characteristic of the classic sequence (class C excepted) is that the letters do not imply spectral characteristics. Thus, "H" might be construed as standing for "hydrides." "L" seemed like a logical choice, and thus the sequence expanded to OBAFGKML.

Spectral observations are difficult because these red stars are faint. But, as with the other classes, L contains a range of properties, so it too must be subdivided. In cooler M stars, vanadium oxide (VO) is very strong, and its absorptions reach a maximum at L0. Proceeding down the sequence, VO disappears at L4, and TiO is hardly there at L7.

As the temperature falls further, absorptions of the alkali metals potassium, rubidium, and cesium strengthen. Near L8, the last subclass (leaving room for a potential L9), the hydrides are weakened and the potassium absorption becomes incredibly strong and broad, dominating the near-infrared spectrum. Comparison with line-formation and solid-precipitation theory suggests a temperature range from around 2,000[degrees] K at L0 to as low as 1,300[degrees] K at L8. Total luminosities (as opposed to those seen by eye) are comparably low, ranging downward from 0.0003 times that of the Sun.

We learn in astronomy class that stars are supposed to be entirely gaseous. However, in stars cooler than around L2, the temperature becomes so low that titanium actually precipitates into a solid form, into a mineral called perovskite (CaTiO3). At various other cool temperatures VO precipitates as a solid, as does lithium chloride. These and other chemicals produce a grainy stellar fog (and the term "rock star" takes on a whole new meaning).

Assessment of the content of class L is difficult. The coolest stars should be thoroughly mixed by convection. Above the "real star" mass limit, the internal temperature is so high that an old low-mass star's natural lithium is destroyed by nuclear reactions. Thus, unless it is very young, a star must be a brown dwarf if it has lithium absorptions. About a third of the L stars qualify as brown dwarfs. The low luminosities and temperatures of the L dwarfs, which indicate very low mass, confirm that estimate. This suggests that the class contains a mixture of real stars and brown dwarfs.

As rich and useful as it is, class L still cannot do all the work. Methane-rich Gliese 229B has a temperature of only 1,000[degrees] K.

The Sloan Digital Sky Survey and 2MASS have found many more such stars, recognizable by their odd colors and horribly complex spectra. For these stars the term "T dwarf" is now catching on, along with "L dwarf." Observations are difficult, and there are insufficient data to warrant subdivision of this class. But at the end, here are OBAFGKMLT.

It is not enough just to know of the existence of the L and T dwarfs. How do these new classes affect what we know of stars and about the galaxy? The data are sparse, but that has never stopped astronomers from drawing conclusions, sometimes even correct ones.

For example, we now may be able to put to rest a long-standing speculation about the composition of the mysterious dark matter that dominates the galaxy. M dwarfs are numerous and constitute 70 percent of all stars along the main sequence from M to O. The L and T star count, sparse as it is, suggests that there are twice as many brown dwarfs as there are "real stars." Nevertheless, their combined mass is so low that it makes no significant impact on the dark-matter problem. So we must look elsewhere for dark matter.

The sheer number and faintness of brown dwarfs raise the question of what is really the nearest "star." A brown dwarf could easily be hiding closer than Proxima Centauri, an M5 dwarf long touted -- at 4 light-years -- as being the closest star to us. Knowing is more than an academic exercise. A dim L or T star might even now be stirring up the Oort comet cloud and sending a rain of deadly comets Earthward. And this is only one way that these new additions to the spectral sequence might reach out and touch us.

Where will these little bodies reach their end? We have no idea of the lower limit to substellar masses. Brown dwarfs are, like ordinary stars, presumably made "in place" by collapsing interstellar clouds. Planets, on the other hand, are built up from solids in dusty disks that circulate around new stars. Perhaps the masses of brown dwarfs and planets can actually overlap each other! There may, therefore, still be new spectral classes to develop as observations descend the temperature scale. If so, the old Harvard classification system, the century-old creation of Pickering, Fleming, Cannon, and Maury, can keep up with it as we head into the new millennium. Keep your eye on letter "Y."

Back to my cheering astronomy class. My then 13-year-old daughter, who had a day off, was visiting to see Dad at work. After the cheers were over, a young man sitting next to her asked, "How would you like to have to live with that guy?" Imagine how annoyed he'd been if I'd given them L and T to cheer about.

James B. Kaler is professor of astronomy at the University of Illinois. His latest book is Cosmic Clouds, written for Scientific American Library. He outlines a "star of the week" on his Web site at http://www.astro.uiuc.edu/

RELATED ARTICLE: It's All in the Temperature

How can the spectra of stars all made of the same stuff be so very different? Why are there so many spectral classes? Temperature, which controls the state of the atoms in a gas, is the source of the differences. A spectrum is created within a star's thin, partially transparent outer layer, from which radiation escapes into space. The continuous background of color comes from the star's warmer depths, while the cooler, upper gases superimpose the absorptions. These dark gaps are produced when electrons attached to atoms collect individual photons of light and jump to higher-energy orbits. Since the orbits for any given atom are structured -- that is, they involve specific energies -- the electrons can capture only photons having specific energies, which in turn correspond to specific wavelengths.

But to absorb a photon, an atom and its electrons must be in the right state. If they are not, absorptions will not appear, no matter how much of a substance is present. The major factor is ionization. Remove an electron from an atom to create an ion with positive charge and you change the spectrum completely.

Collisions among the atoms in a gas are primarily responsible for setting the ionization level. At low temperature (even in the Sun) all the hydrogen is neutral. But around 10,000[degrees] Kelvin, where class A meets class B, hydrogen begins to ionize, and the hydrogen lines must weaken. In cool stars, like class M, we see neutral calcium, but increase the temperature into K and then G, and the calcium ionizes to produce a different spectrum. At higher temperatures yet, the calcium is stripped of another electron and the doubly ionized form takes over, weakening the lines of the singly ionized form. Molecules behave similarly. They are fragile things that can exist only at low temperature, so we see titanium oxide in M stars and hydrides in the L stars. Increase the temperature and collisions blow the molecules apart. Only the hardiest ones survive even solar-surface temperatures.

Superimposed on the ionization process is electron excitation. Think of the atom as a stairway. Each atom or ion has a different set of steps, each with different risers. Electrons can be on any of the steps but not in between. It takes energy to climb the stairs. As a rule, electrons (like people) love their lowest-energy states. They can be kicked upstairs by collisions but immediately fall back down. But while on one of the steps, they can get hit by another photon and jump even higher. The act destroys the photon and produces an absorption line.

The visible hydrogen lines come from electrons that are temporarily on their second steps. If the temperature is too low, hydrogen sits sulkily on the first step and refuses to accept any optical photons. The result: no hydrogen absorption in cool class M or L, even though the stars are almost totally hydrogen. As temperatures climb, more hydrogen atoms move onto step two. The absorptions intensify, right through class A until the beast of ionization begins to eat them all up. Since each kind of atom has its own electronic structure and stairway, each behaves differently. Neutral helium does not appear until class B, and ionized helium requires the great heat of class O.


Digital Discovery

At the CfA, the custom-built DASCH scanner is busy scanning plates. The device is massive, resembling a large microscope attached to a futuristic table saw. It can handle two plates at a time, taking 60 mini-images of each in 90 seconds and knitting them together. The entire process of loading, scanning, and unloading a plate takes about two minutes. Cleaning each plate before the scanning process eats up more time. Now at the rate of roughly 300 plates per day, DASCH has successfully scanned close to 80,000 plates since its inception in 2006. Still, that’s only about 20% of the sky. To accomplish the task, the sky has been split up into 12 sections of 15˚ of galactic latitude each, and the data is released upon completion. In June 2014, the team released the third set of data. So far, the data that has been scanned from the three sets contains nearly 3.5 billion magnitudes.

Alison Doane inspects a plate of the Small Magellanic Cloud taken on a 24

The process doesn’t stop at just creating a digital copy of the photographic images on the plates. The software behind the scanner, which was written from scratch, calculates magnitudes of stellar variability within the plates and compares it with calibrated data from preexisting catalogs of stellar variability. Already, Grindlay and other astronomers analyzing the data have discovered several new objects and new kinds of stellar variables, the existence of which were previously unknown.

“There are anywhere from 30-50 thousand stars in every plate that we scan, sometimes even 100,000,” Doane says. “That’s a lot of information that can be tapped into to learn about our universe.”

Alison Doane places a positive copy of the Small Magellanic Cloud on top of a negative of the same image. The Computers would pore over these composites to discover new variable stars.

For instance, Grindlay and his team have been studying the data from DASCH for stellar mass black holes, which are black holes formed by the collapse of massive stars. Their formation is often preceded by a sudden flare-up, from a nova or supernova, so Grindlay began to look for these stellar black holes by looking for outbursts. “We started looking at the ones in regions of the sky we had already scanned and, lo and behold, we found four of these objects that have had previous outbursts,” he says. “They had occurred 50 to even 100 years before the outbursts that occurred in modern times.” One of these objects was only discovered because, in 1999, a modern X-ray telescope detected an outburst—but Grindlay was able to identify its 1901 outburst using DASCH data. Another similar object, which was discovered in 1999, seems to have had an outburst in 1928, according to Grindlay’s findings.

“It’s not just on one plate—each of these has maybe three or four—in some cases even eight—observations that allow us to measure the duration of the outburst,” added Grindlay. Given that these are rare events, “if you only see them 1% of the time, and you see a few that are very similar in their outburst duration and intervals between outbursts, then you can immediately infer that there are many, many more than you would have otherwise thought.”

Given that less than a fifth of the sky has been scanned at DASCH, it remains to be seen what else might emerge from the plates. And while the DASCH project holds tremendous promise for the discovery and better understanding of astronomical phenomena, it’s not yet a success story. Astronomers have been trying, largely unsuccessfully, to preserve historical plates ever since they went out of use in the late 1980s.

For Griffin, the Dominion astrophysicist, “It’s been a lifelong involvement.” “When I started my career in the ’60s, these were the only way to record such images.” Griffin, who studies stellar spectra and the evolution of stars, really began to champion the preservation of these plates since much of her work also depended on looking at data from the past. “I often found myself thinking, wouldn’t it be great if I didn’t have to track down these plates, then make the trip to get them, and then scan them before I even got to analyzing the data.”


Any freely available large stellar spectra catalog? - Astronomy

Context. Measuring stellar inclinations is fundamental to understanding planetary formation and dynamics as well as the physical conditions during star formation. Oscillation spectra of red giant stars exhibit mixed modes that have both a gravity component from the radiative interior and a pressure component from the convective envelope. Gravity-dominated (g-m) mixed modes split by rotation are well separated inside frequency spectra, allowing accurate measurement of stellar inclinations.
Aims: We aim to develop an automated and general approach to measuring stellar inclinations that can be applied to any solar-type pulsator for which oscillation modes are identified. We also aim to validate this approach using red giant branch stars observed by Kepler.
Methods: Stellar inclination impacts the visibility of oscillation modes with azimuthal orders m = < - 1, 0, +1>. We used the mean height-to-background ratio of dipole mixed modes with different azimuthal orders to measure stellar inclinations. We recovered the underlying statistical distribution of inclinations in an unbiased way using a probability density function for the stellar inclination angle.
Results: We derive stellar inclination measurements for 1139 stars on the red giant branch for which Gehan et al. (2018, A&A, 616, A24) identified the azimuthal order of dipole g-m mixed modes. Raw measured inclinations exhibit strong deviation with respect to isotropy which is expected for random inclinations over the sky. When taking uncertainties into account, the reconstructed distribution of inclinations actually follows the expected isotropic distribution of the rotational axis.
Conclusions: This work highlights the biases that affect inclination measurements and provides a way to infer their underlying statistical distribution. When a star is seen either pole on or equator on, measurements are challenging and result in a biased distribution. Correcting biases that appear in low- and high-inclination regimes allows us to recover the underlying inclination distribution.


Any freely available large stellar spectra catalog? - Astronomy

Below are the courses available from the ASTRO subject code. Select a course to view the available classes, additional class notes, and class times

ASTRO 101 - Black Holes View Available Classes

An introduction to the science of black holes and its connection to how black holes are portrayed in popular culture and news. Topics include: properties of light introduction to gravity, relativity and quantum physics life cycle of stars measurements of black hole properties observed features of black holes interacting with their environment event horizons, the ergosphere, and singularities quantum black holes, information, and Hawking radiation gravitational lensing gravitational radiation. Prerequisites: Math 30-1. Credit may be obtained for only one of ASTRO 101 or ASTRO 122.

ASTRO 101 - Black Holes

An introduction to the science of black holes and its connection to how black holes are portrayed in popular culture and news. Topics include: properties of light introduction to gravity, relativity and quantum physics life cycle of stars measurements of black hole properties observed features of black holes interacting with their environment event horizons, the ergosphere, and singularities quantum black holes, information, and Hawking radiation gravitational lensing gravitational radiation. Prerequisites: Math 30-1. This course may not be taken for credit if credit has been obtained in ASTRO 122.

ASTRO 120 - Astronomy of the Solar System View Available Classes

The development of astronomy and astronomical techniques, including results obtained from the latest orbiting observatories. The origin, evolution and nature of the Earth, the other planets and non-planetary bodies will be discussed. Viewing experience will be available using the campus observatory. Prerequisites: Mathematics 30-1 and Physics 30.

ASTRO 122 - Astronomy of Stars and Galaxies View Available Classes

The development of our understanding of the universe, including current models of stellar evolution and cosmology. Emphasis on understanding the physical processes underlying astronomical phenomena. Viewing experience will be available using the campus observatory. Prerequisites: Mathematics 30-1 and Physics 30.

ASTRO 122 - Astronomy of Stars and Galaxies

The development of our understanding of the universe, including current models of stellar evolution and cosmology. Emphasis on understanding the physical processes underlying astronomical phenomena. Viewing experience will be available using the campus observatory. Prerequisites: Mathematics 30-1 and Physics 30. This course may not be taken for credit if credit has been obtained in ASTRO 101.

ASTRO 320 - Stellar Astrophysics I View Available Classes

Application of physics to stellar formation and stellar evolution theoretical models and observational comparisons of main sequence stars, white dwarf stars, neutron stars, supernovae, black holes binary star systems, stellar atmospheres and stellar spectra. Prerequisites: MATH 115, 118, or 146, and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130. Pre or corequisite: any 200-level PHYS course. Some additional knowledge of astronomy (ASTRO 120 and/or 122) is advantageous.

ASTRO 320 - Stellar Astrophysics I

Application of physics to stellar formation and stellar evolution theoretical models and observational comparisons of main sequence stars, white dwarf stars, neutron stars, supernovae, black holes binary star systems, stellar atmospheres and stellar spectra. Prerequisites: MATH 115, 118, 136, 146 or 156, and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130. Pre or corequisite: any 200-level PHYS course. Some additional knowledge of astronomy (ASTRO 120 and/or 122) is advantageous.

ASTRO 322 - Galactic and Extragalactic Astrophysics View Available Classes

The interstellar medium and interstellar reddening galactic structure kinematics and dynamics of stars in galaxies quasars introduction of cosmology. Prerequisites: MATH 115, 118, or 146, and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130, and PHYS 208 or 271. Previous knowledge of astronomy is advantageous. ASTRO 320 is strongly recommended.

ASTRO 322 - Galactic and Extragalactic Astrophysics

The interstellar medium and interstellar reddening galactic structure kinematics and dynamics of stars in galaxies quasars introduction of cosmology. Prerequisites: MATH 115, 118, 136, 146, or 156 and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130, and PHYS 208 or 271. Previous knowledge of astronomy is advantageous. ASTRO 320 is strongly recommended.

ASTRO 429 - Upper Atmosphere and Space Physics View Available Classes

Basic space plasma phenomena: the Earth's plasma and field environment the solar cycle generation of the solar wind the interplanetary plasma and field environment the solar-terrestrial interaction magnetospheric substorms the aurora borealis magnetosphere-ionosphere interactions effects of magnetospheric storms on man-made systems use of natural electromagnetic fields for geophysical exploration. Pre- or corequisite: PHYS 381.

ASTRO 430 - Physical Cosmology View Available Classes

Observational cosmology geometry and matter content of the Universe physical processes in the early stages of the Universe inflation, Big Bang nucleosynthesis and the cosmic microwave background radiation cosmological aspects of galaxy formation and the growth of large-scale structure. Prerequisites: PHYS 310, MATH 334. Pre- or corequisite: PHYS 458.

ASTRO 465 - Stellar Astrophysics II View Available Classes

Stellar interiors and nuclear transformations energy transport model stars variable stars stellar evolution. Prerequisites: PHYS 310, 271, ASTRO 320, MATH 334. Note: Credit may be obtained for only one of ASTRO 465 or ASTRO 565.

ASTRO 565 - Stellar Astrophysics II View Available Classes

Stellar interiors and nuclear transformations energy transport model stars variable stars stellar evolution. Prerequisites: Consent of Instructor. Note: Credit may be obtained for only one of ASTRO 465 or ASTRO 565.


Any freely available large stellar spectra catalog? - Astronomy

Below are the courses available from the ASTRO subject code. Select a course to view the available classes, additional class notes, and class times

ASTRO 101 - Black Holes View Available Classes

An introduction to the science of black holes and its connection to how black holes are portrayed in popular culture and news. Topics include: properties of light introduction to gravity, relativity and quantum physics life cycle of stars measurements of black hole properties observed features of black holes interacting with their environment event horizons, the ergosphere, and singularities quantum black holes, information, and Hawking radiation gravitational lensing gravitational radiation. Prerequisites: Math 30-1. Credit may be obtained for only one of ASTRO 101 or ASTRO 122.

ASTRO 101 - Black Holes

An introduction to the science of black holes and its connection to how black holes are portrayed in popular culture and news. Topics include: properties of light introduction to gravity, relativity and quantum physics life cycle of stars measurements of black hole properties observed features of black holes interacting with their environment event horizons, the ergosphere, and singularities quantum black holes, information, and Hawking radiation gravitational lensing gravitational radiation. Prerequisites: Math 30-1. This course may not be taken for credit if credit has been obtained in ASTRO 122.

ASTRO 120 - Astronomy of the Solar System View Available Classes

The development of astronomy and astronomical techniques, including results obtained from the latest orbiting observatories. The origin, evolution and nature of the Earth, the other planets and non-planetary bodies will be discussed. Viewing experience will be available using the campus observatory. Prerequisites: Mathematics 30-1 and Physics 30.

ASTRO 122 - Astronomy of Stars and Galaxies View Available Classes

The development of our understanding of the universe, including current models of stellar evolution and cosmology. Emphasis on understanding the physical processes underlying astronomical phenomena. Viewing experience will be available using the campus observatory. Prerequisites: Mathematics 30-1 and Physics 30.

ASTRO 122 - Astronomy of Stars and Galaxies

The development of our understanding of the universe, including current models of stellar evolution and cosmology. Emphasis on understanding the physical processes underlying astronomical phenomena. Viewing experience will be available using the campus observatory. Prerequisites: Mathematics 30-1 and Physics 30. This course may not be taken for credit if credit has been obtained in ASTRO 101.

ASTRO 320 - Stellar Astrophysics I View Available Classes

Application of physics to stellar formation and stellar evolution theoretical models and observational comparisons of main sequence stars, white dwarf stars, neutron stars, supernovae, black holes binary star systems, stellar atmospheres and stellar spectra. Prerequisites: MATH 115, 118, or 146, and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130. Pre or corequisite: any 200-level PHYS course. Some additional knowledge of astronomy (ASTRO 120 and/or 122) is advantageous.

ASTRO 320 - Stellar Astrophysics I

Application of physics to stellar formation and stellar evolution theoretical models and observational comparisons of main sequence stars, white dwarf stars, neutron stars, supernovae, black holes binary star systems, stellar atmospheres and stellar spectra. Prerequisites: MATH 115, 118, 136, 146 or 156, and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130. Pre or corequisite: any 200-level PHYS course. Some additional knowledge of astronomy (ASTRO 120 and/or 122) is advantageous.

ASTRO 322 - Galactic and Extragalactic Astrophysics View Available Classes

The interstellar medium and interstellar reddening galactic structure kinematics and dynamics of stars in galaxies quasars introduction of cosmology. Prerequisites: MATH 115, 118, or 146, and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130, and PHYS 208 or 271. Previous knowledge of astronomy is advantageous. ASTRO 320 is strongly recommended.

ASTRO 322 - Galactic and Extragalactic Astrophysics

The interstellar medium and interstellar reddening galactic structure kinematics and dynamics of stars in galaxies quasars introduction of cosmology. Prerequisites: MATH 115, 118, 136, 146, or 156 and one of PHYS 124, PHYS 144, or EN PH 131, and one of PHYS 126, PHYS 146, or PHYS 130, and PHYS 208 or 271. Previous knowledge of astronomy is advantageous. ASTRO 320 is strongly recommended.

ASTRO 429 - Upper Atmosphere and Space Physics View Available Classes

Basic space plasma phenomena: the Earth's plasma and field environment the solar cycle generation of the solar wind the interplanetary plasma and field environment the solar-terrestrial interaction magnetospheric substorms the aurora borealis magnetosphere-ionosphere interactions effects of magnetospheric storms on man-made systems use of natural electromagnetic fields for geophysical exploration. Pre- or corequisite: PHYS 381.

ASTRO 430 - Physical Cosmology View Available Classes

Observational cosmology geometry and matter content of the Universe physical processes in the early stages of the Universe inflation, Big Bang nucleosynthesis and the cosmic microwave background radiation cosmological aspects of galaxy formation and the growth of large-scale structure. Prerequisites: PHYS 310, MATH 334. Pre- or corequisite: PHYS 458.

ASTRO 465 - Stellar Astrophysics II View Available Classes

Stellar interiors and nuclear transformations energy transport model stars variable stars stellar evolution. Prerequisites: PHYS 310, 271, ASTRO 320, MATH 334. Note: Credit may be obtained for only one of ASTRO 465 or ASTRO 565.

ASTRO 565 - Stellar Astrophysics II View Available Classes

Stellar interiors and nuclear transformations energy transport model stars variable stars stellar evolution. Prerequisites: Consent of Instructor. Note: Credit may be obtained for only one of ASTRO 465 or ASTRO 565.


2. Methods

We first discuss our analysis of the THN-PSL data and show how we identified contaminated data in detail, then discuss how we derived spectra and albedo from the uncontaminated data. Finally we discuss spectra and albedo from other data sources for our catalog.

2.1. Calibrating the spectra of solar system bodies from the THN-PSL

The THN-PSL is a collection of observations of 38 spectra for 18 solar system objects observed over the course of several months in 2008. The spectra of one of the objects, Callisto, were contaminated and could not be reobserved, whereas the spectrum for Pluto in the database is a composite spectrum of both Pluto and Charon. We analyzed the data for the 16 remaining solar system objects for additional contamination and found 6 apparently contaminated objects among them, leaving 10 objects in the database that do not appear contaminated. Their albedos are similar to published values in the literature for the wavelength range such data are available for. We show the derived albedos for both contaminated and uncontaminated data from the database in Fig. 3, compared with available values from the literature for these bodies.

FIG. 3. A comparison of geometric albedos for the solar system bodies in our catalog between published values and the albedo calculated from the THN-PSL data. THN-PSL data-based albedos are denoted with solid lines for uncontaminated data and with an asterisk and gray line if contaminated. References for comparison albedos: a (Spencer, 1987), b (Reddy et al., 2015), c (Noll et al., 1997), d (Kaltenegger et al., 2010), e (Meadows, 2006), f (Fanale et al., 1974), g (Karkoschka, 1998), h (Lane and Irvine, 1973), i (McCord and Westphal, 1971), j (Mallama, 2017), k (Fink and Larson, 1979), l (Protopapa et al., 2008 Lorenzi et al., 2016), m (Pollack et al., 1978), and n (Cassini VIMS—NASA PDS). Items are arranged by body type then by distance from the Sun. THN-PSL, Tohoku-Hiroshima-Nagoya Planetary Spectral Library.

The THN-PSL data were taken in 2008 using the TRISPEC instrument while on the Kanata Telescope at the Higashi-Hiroshima observatory. TRISPEC (Watanabe et al., 2005) splits light into one visible channel and two near-infrared channels, giving a wavelength range of 0.45–2.5 μm. The optical band covered 0.45–0.9 μm and had a resolution of . The first infrared (IR) channel has a coverage from 0.9 to 1.85 μm and had a resolution of . The second IR channel has a coverage from 1.85 to 2.5 μm and had a resolution of . Note that the slit subtends 4.5 arcseconds by 7 arcmin, meaning that spectra for larger bodies such as Saturn and Jupiter were not disk integrated (Section 4). As discussed in the original article, all spectra are unreliable <0.47 μm and between 0.9 and 1.0 μm from a dichroic coating problem with the beam splitters. Near 1.4 and 1.8 μm, Earth's water absorption degrades the quality and >2.4 μm thermal contamination is an issue. These wavelength regions are grayed out in all relevant figures in our article but do not influence our color analysis, due to the choice of filters. The raw data available for download include all data points.

The THN-PSL article discusses several initial observations of moons that were contaminated with light from their host planet, rendering their spectra inaccurate (080505 Callisto, 081125 Dione, 080506 Io, and 080506 Rhea). These objects (with the exception of Callisto) were observed again and the extra light was removed in a different manner to more accurately correct the spectra (Lundock et al., 2009). Callisto was not reobserved and, therefore, the THN-PSL Calisto data remained contaminated (Fig. 3).

The fluxes of the published THN-PSL observations were not calibrated but arbitrary normalized to the value of 1 at 0.7 μm. This makes the data set generally useful to compare the colors of the uncontaminated objects, as shown in the original article, but limits the data's usefulness as reference for extrasolar planet observations because geometric albedos can only be derived from calibrated spectra. The conversion factors used in the original publication were not available (Ramsey Lundock, pers. comm.).

However, in addition to the V magnitude, the THN-PSL gives the color differences: V-R, R-I, R-J, J-K, and H-Ks for each observation, providing the R, I, and J magnitudes. Therefore, we used the published V, R, I, and J magnitudes to derive the conversion factor for each spectrum to match the published color magnitudes and to calibrate the THN-PSL observations.

We define the conversion factor k such that , where and f are the normalized and absolute spectra, respectively. Adapting the method outlined in Fukugita et al. (1995), the magnitude in a single band using the filter response, V, and the spectrum of Vega, , are given by Equation 1. The spectrum of Vega (Bohlin, 2014) 2 and the filter responses are the same as in the THN-PSL publication and shown in Figs. 4 and 5, respectively. The filters we used are V (Johnson and Morgan, 1953) R and I (Cousins, 1976 Bessell, 1979) and J, H, K, and Ks (Tokunaga et al., 2002). Since the THN-PSL article recorded the V, R, I, and J color magnitudes for each object, we derive the conversion factor to obtain each magnitude and average them to obtain k. For example, we substitute in Equation 2 and isolate kV as shown in Equation 3 to calculate the conversion factors for the V band. The conversion factors for each band for a single object were averaged and used to calibrate the normalized spectra.

FIG. 4. Reference spectra used for calibration (Sun and Vega) and model spectra used for host stars at 1 AU (F0V, G0V, K0V, M0V, M9V). Vega was multiplied by 1013 to fit on the same plot.

FIG. 5. Standard filters used for flux calibration and color calculations. Gray bands show the wavelength range where the observed fluxes from the THN-PSL are not reliable.

We used this method to calibrate the THN-PSL data for each object. When comparing the coefficient of variation (CV) of the conversion factors for each body, we found that the data showed two distinct groups, one with a CV >14% and another with a CV <6%. We use that distinction to set the level of the conversion factor for uncontaminated spectra to (CV >6%) over the different filter bins. If the CV value was in the second group (CV >14%), the data are flagged as contaminated and not used in our catalog. The nature of this contamination is unclear, it could be photometric error during the observation, excess light from the host planet, or other effects that influenced the observations. The values calculated for kV, , , , and , and the CV for each observation are given in Appendix Table A1.

2.2. Albedos of solar system bodies

We then derive the geometric albedo from the calibrated spectra as a second part of our analysis (see Tables 1 and 2 for references) by dividing the observed flux of the solar system bodies by the solar flux and accounting for the observation geometry as given in Equation 5 (de Vaucouleurs, 1964).

Table 1. Parameters for the 10 Solar System Bodies from the THN-PSL We Used to Calculate the Calibrated Flux and Albedos

References used for phase function and albedo.

a To obtain the proper geometric albedo for this Earthshine observation, a factor of 2.38E5 is needed.

b The Pluto–Charon spectrum is added for completeness.

THN-PSL = Tohoku-Hiroshima-Nagoya Planetary Spectral Library.

Table 2. Data for the Solar System Bodies from the THN-PSL Data Set That Were Contaminated Based on the Shape of Their Calculated Geometric Albedo

a Note that the authors state that the Callisto data are contaminated.

where d is the separation between Earth and the body, ab the distance between the Sun and the body at the time of observation, and the semimajor axis of Earth. and f are the fluxes from the Sun seen from Earth and the body seen from Earth, respectively, Rb is the radius of the body being observed, and is the value of the phase function at the point in time the observation was taken. For , we used the standard Space Telescope Imaging Spectrograph Sun spectrum (Bohlin et al., 2001) 3 shown in Fig. 4. If the geometric albedo exceeds 1, the data are flagged as contaminated and not used in our catalog.

Note that we also compared the spectra that were flagged as contaminated in this two-step analysis with the available data and models from other groups (Fig. 3). All flagged spectra show a strong difference in albedo for these bodies observed by other teams, supporting our analysis method (Fig. 3).

2.3. Using colors to characterize planets

We use a standard astronomy tool, a color–color diagram, to analyze whether we can distinguish solar system bodies based on their colors and what effect resolution and filter choice has on this analysis. Several teams have shown that photometric colors of planetary bodies can be used to initially distinguish between icy, rocky, and gaseous surface types (Traub, 2003 Lundock et al., 2009 Cahoy et al., 2010 Krissansen-Totton et al., 2016). We calculated the colors from high- and low-resolution spectra to mimic early results from exoplanet observations as well as explored the effect of spectral resolution on the colors and their interpretation. The error for colors derived from the THN-PSL data was calculated by adding the errors used by Lundock et al. (2009) and the error accumulated through the conversion process of 6% in the k value. This gives and for the error values. We reduce the high-resolution data of to to mimic colors that are generated from low-resolution spectra as shown in Fig. 6. The colors at high resolutions were used to determine the best color–color combination for surface and atmospheric characterization, a process that was repeated for colors derived from low-resolution spectra.

FIG. 6. An example of a reduced resolution spectrum compared with its high-resolution observations.

We also explored how to characterize solar system analog planets around other host stars using their colors by placing the bodies at an equivalent orbital distance around different host stars (F0V, G0V, K0V, M0V, and M9V). We used stellar spectra for the host stars from the Castelli and Kurucz Atlas (Castelli and Kurucz, 2004) 4 and the PHOENIX library (Husser et al., 2013) 5 (Fig. 4). As a first-order approximation, we have assumed that the albedo of the object would not change under this new incoming stellar flux (Section 4).


RATIONALE AND SCOPE

Observational constraints to stellar astronomy generally come from medium or low resolution spectra obtained in few hundred of nm bands, or high-resolution spectra in few tens of nm spectral regions. There is a lack of high-quality observational data in the field of stellar astronomy that satisfies the criteria of large wavelength range, high S/N and high resolution. Making use of UVES mounted on Kueyen (VLT UT2), the project is aimed at filling this gap, with a minimum, if non-zero impact on the normal observational activities.

Open cluster stars

The UVES Paranal Observatory Program was started in February 2001 (end of P66).Director Discretionary Time was requested in order to obtain UVES spectra of stars belonging to the open clusterIC 2391 (Omi Vel Cluster). This is a bright, rich, and young open cluster (40 Myr?), chosen so that we could obtain spectroscopic data even for the faintest known members.

A similar exercise was repeated toward the end of P67, in August 2001, and theselected target was NGC 6475 (Messier 7). Compared to P66, less telescope time was available and it was not possible to observe extensively the cooler regions of the main sequence.

In total 48 stars of IC 2391 and 31 stars of NGC 6475 have been observed so far.

Field stars

In contrast to other VLT instruments, very little useof twilight time is made with UVES. It has been decided to occasionally use this time to perform observations of bright stars. These early and low-priority observations can also be used for checking the instrument health,performing some quick technical test, or even for training purpose. Thus,twilight observations of bright stars have benefits from the operational point of view. At the same time, the POP is gathering a library of high-resolution,high S/N ratio spectra of (field) stars across the H-R diagram, which all stellar astronomers will be strongly interested in.


Welcome to the ESO Science Archive Facility

The ESO Science Archive Facility contains data from ESO telescopes at La Silla Paranal Observatory, including the APEX submillimeter telescope on Llano de Chajnantor. In addition, the raw UKIDSS/WFCAM data obtained at the UK Infrared Telescope facility in Hawaii are available.

The Principal Investigators of successful proposals for time on ESO telescopes have exclusive access to their scientific data for the duration of a proprietary period, normally of one year, after which the data becomes available to the community at large. Please read the ESO Data Access Policy statement for more information, along with the relevant FAQs.

Browsing the archive does not require authentication. Please acknowledge the use of archive data in any publication.

There are three main ways to access the archive, varying for content and presentation/interface: the usual Raw Data query form, the innovative Science Portal to browse and access the processed data, and the novel Programmatic and Tools access which permits direct database access to both raw and processed data, and to the ambient condition measurements, also in a scriptable and VO manner. Other query forms are available in the table at the bottom of this page.