Department of Physics and Astronomy : University …dmw/ast142/Projects/Project.pdfdata, and one of the astronomy-lab computers on which the programs CCDStack and IDL reside. Find

Astronomy 142 Project Manual Spring 2017

© 2016, University of Rochester 1 All rights reserved

1 RR Lyrae stars and the distance to the globular cluster M 3

1.1 Introduction

In the following we repeat a classic experiment: detect the periodically-varying stars in a globular cluster, and thereby measure its distance, by considering the stars to be standard candles. Specifically we will measure the time-average luminosity of the nearby star RR Lyrae, and the time-average flux of stars in M 3 that vary with the same period as RR Lyr, combining these measurements to determine M 3’s distance.

This observing project will potentially involve visits to the telescope over the course of a week, hopefully including consecutive nights, with different groups visiting each night. Everybody who opts for this project will share their data, receive everyone else’s in return, and join in the data reduction. Each student will perform their own analysis and write an independent report on the results.

Stars in the instability strip of the H-R diagram have been fundamental in the determination of the distance scale of the Universe. These stars pulsate radially, often in their fundamental acoustic mode. As we have noted in class, their pulsation periods Π vary inversely with the mass density ρ of the star:

0

64 (uniform density star).R

s

drv G

πγ ρ

Π = ≅∫

Lower average density leads not only to longer periods; it also leads to larger luminosity L, in part because lower-density stars are much larger than higher-density stars of the same mass. Thus a given type of pulsating star exhibits a strict relation, often linear, between Π and L. Such relations can be determined by observation of the periods and fluxes for stars whose distances are accurately known, for example, by trig parallax. Once the Π -L relation is determined, it can be used as a standard candle to measure much greater distances to faint stars of the same type and period. The measured period Π tells us what the star’s luminosity L is, and the measurement of the star’s flux f yields the distance r, via:

4r L f .π=

Henrietta Leavitt invented standard candles in the course of her observations, during 1908-1912, of thousands of periodic variables in the Magellanic Clouds. Her variables are classical Cepheids, the highest-luminosity Population I inhabitants of the instability strip; the Π -L relation for classical Cepheids is called Leavitt’s Law in her honor.

The instability-strip variables in globular clusters include supergiants, giants, and stars close to the main sequence: W Vir stars, RR Lyr stars, and SX Phe stars, respectively. RR Lyrs are by far the most numerous. Few of these stars lie in the solar neighborhood; it has been possible to measure trig parallax for only a handful of RR Lyr stars.1 One of these is RR Lyrae itself, for which such variability was discovered by Williamina Fleming in 1901. Taking RR Lyrae to be a standard candle, we can measure accurate distances to globular clusters, the distribution of which is a good tracer of the global structure of the Milky Way.

1 Soon the ESA Gaia mission will provide parallaxes for many more field RR Lyr stars. It won’t, however, be able to measure parallax for stars in many globular clusters; the RR Lyr standard-candle technique used in this project will remain the best way to measure globular-cluster distances for a long time.

http://www.pas.rochester.edu/%7Edmw/ast142/Lectures/Lect_07b.pdf

https://en.wikipedia.org/wiki/Harvard_Computers

http://www.pas.rochester.edu/%7Edmw/ast142/Lectures/Lect_19b.pdf

https://en.wikipedia.org/wiki/Harvard_Computers



1.2 Experimental procedure

1.2.1 Planning

1. Form, or join, a team of three. Members of each team must have compatible schedules; make sure each of you can generally go observing on the same set of nights of the week.

2. As a stretch of clear weather approaches, choose your observing night, in coordination with other teams. Look up or calculate the times of sunset and sunrise, and the length of the night. Also look up or calculate the times during which M 3 and RR Lyrae will be high enough in the sky to observe (> 30° elevation), and the times at which each object transits.

3. If other groups are also interested in this experiment, agree with them on the details of the observations: basically, adherence to the procedure described below, and on how you will name data files. There are virtues, and will be extra credit, in compiling data from multiple nights. If yours is not the first group to attempt the M 3 – RR Lyr experiment, find out from the first group, or from the instructors, all the details of their experience: how long their observations lasted, what the seeing was, how stable the autoguiding was, etc.

4. Unlike the observing projects in AST 111, this one takes all night. Therefore you will be required to sleep at the Gannett House, after your observations are done and before returning to campus. Plan accordingly by packing for an overnight stay, and discussing with your team and the instructors the all-important requirement of packing sufficient Food. (Mees is unfortunately beyond the range of pizza delivery.) Familiarize yourself with the rules of the observatory sleeping quarters at the Gannett House.

5. Bring your notebooks, and bring a thumbdrive with at least a few GB of free space. You would do well also to bring your personal laptop or tablet computer. Paper copies of the experiment procedure (i.e. this document), telescope checklist, and CCD-camera observing guide will also prove useful, though these will also be accessible to you on line at Mees.

6. Arrive well before sunset. You have one important task to carry out before the sky gets dark: the acquisition of flat-field data. The telescope and camera must be ready to observe, and your team ready to work, no later than sunset.

1.2.2 At the telescope

The observing jobs divide into three parts: telescope operation, camera operation, and reading the instructions to the two operators. Rotate among the jobs frequently enough that nobody gets bored.

1. Start the telescope and initialize its pointing, by following the steps in Section I of the Mees Observatory telescope checklist. This leaves you with DFMTCS, FrameGrab, and TheSky running on the TCS computer.

2. Start the CCD camera, its computer, and CCDSoft, and focus the telescope, by following the steps in Section I of the Mees Observatory CCD camera checklist. This includes taking flat-field data, following the steps listed in Section V of the camera checklist. You now have CCDSoft running on the CCD camera computer, and both camera and telescope ready to observe.

3. On TheSky, search for M 3 (Cntl-F for the search window), click on it to bring up its information window, and check to see if it is up ( elevation > 30° ). If so, you’re ready to start observing. (If not, observe something else, perhaps through the eyepiece, til M 3 rises; it won’t be long, if it is currently Spring semester.)

http://www.pas.rochester.edu/%7Edmw/ast142/Projects/Gannett_house_rules.htm

http://www.pas.rochester.edu/%7Edmw/Mees/telescope.pdf


http://www.pas.rochester.edu/%7Edmw/Mees/camera.pdf



4. As a guide star, we recommend a 12.5 magnitude star which is known to TheSky as GSC 2004:639. Find it by bringing up the search window (Cntl-F) and entering its name. Follow the steps in Section II of the camera checklist to commence autoguiding.

5. As in Section III of the camera checklist, Step 3: set up for R, G and B observations of M 3: 5-minute exposures, 2×2 binning, set Take to 1 for R and G, and 2 for B. Active status should be checked for, R G and B, and unchecked for L. This four-image, approximately 20-minute sequence will be repeated over and over throughout the experiment.

6. Observe M 3 continuously until RR Lyrae rises into view, about 3-4 hours after M 3 did. On Camera Control > Color, set Series Of: accordingly e.g. a series of 6 for two hours. Then push the Take Color button.

7. Relax for a while. Watch the imaging results attentively for the first cycle, to make sure the autoguiding remains stable through the filter changes. After that, check every several minutes to make sure that the guide star is staying in the middle of the box. And check on TheSky now and then toward the end of this segment, to see when RR Lyrae will become available. RR Lyr is known to TheSky as SAO 48421. While relaxing: please do not stream movies at the observatory, unless it’s after midnight. We get unlimited bandwidth between midnight and 6 AM, but are restricted at other hours and pay extra for exceeding our limit.

8. As soon as RR Lyrae is viewable, let the current M 3 RGB color cycle finish – pushing Abort after the second B if necessary. Back on Camera Control > Autoguide, push Abort to turn off the autoguider. Then point the telescope at RR Lyr = SAO 48421. On Camera Control > AutoSave, change to the appropriate filename prefix. (Do not reset the starting number. CCDSoft does not ask permission before overwriting files!) On Camera Control > Take Image, take a 2-second light frame to make sure the star is reasonably close to the center of the CCD; move it and take another image if necessary.

9. Observe RR Lyr. The parameters in Camera Control > Color should be the same as before except for exposure time: 2 seconds in each image for RR Lyr. Set Series of: to 8, and push the Take Color button. This will be done in seconds; there’s no need to autoguide.

10. From now until dawn, alternate between four-frame, 20-minute M 3 sequences as in Step 5, and bursts of short images of RR Lyr as in Step 9. Don’t forget to keep changing the filename prefix as you do.

11. When all is done, copy the working directory to your thumbdrive(s).

12. Exit CCDSoft, and shut down the CCD camera computer. Disconnect the power supply and cables from the camera, coil them, and return them to the black nylon briefcase.

13. Shut down the telescope and the TCS computer, by following the steps in the Telescope Shutdown section of the telescope checklist.

14. Go to the Gannett House and get some sleep. You may not return to campus without at least the driver(s) sleeping, for at least four hours. This safety rule will be enforced strictly.

1.3 Data reduction

Each team will carry out the first stages of processing on their data, before we merge data from all the teams. These stages will include construction of your master flat-field images, and calibration of your M 3 and RR Lyr data: bias and dark subtraction, flat fielding, and bad-pixel correction. This work breaks into






three roughly equal size parts, one for each filter. Make sure your team divides the data-reduction workload equitably among the members.

Set aside a few hours for your team to meet in 203H B&L, in the company of the thumbdrive carrying your data, and one of the astronomy-lab computers on which the programs CCDStack and IDL reside. Find the directory on this computer’s desktop in which the Master Dark and Bias frames are kept. Then:

1. Copy your working directory from the thumbdrive to the desktop of the lab computer. Tell the instructors where it can be found. (We will keep copies.)

2. Start CCDStack, using the icon in the Windows taskbar.

3. If you haven’t done so already, follow the procedure in the camera checklist, Section V, to create master flat fields from your flat-field observations. Use the master flats you create, in the calibration of the M 3 and RR Lyr data.

4. Open all of the R images of M 3, using File > Open Selected… and navigating to your working directory (Figure 1). This will present you with the full stack of these images, which you can page through and examine with the tools on the left side of the window (Figure 2).

Figure 1: CCDStack File > Open Selected… Note the tool for searching for strings among the filename, useful in filtering out the data that belong.



Figure 2: upper left corner of the CCDStack window, and the tools along the left edge.

5. Calibrate these images. Choosing Process > Calibrate evokes the Calibration Manager dialog. Using the two tabs and three buttons, specify the master dark frame, bias frame, and flat field frame appropriate to CCD temperature (-20 C), binning (2×2) and, for the flat, filter (R). Then click Apply to all.

6. Next, remove hot pixels: those with (permanently) high and variable dark current that doesn’t subtract out in calibration. Choose Process > Data Reject > Procedures. In the resulting dialog choose reject hot pixels from the dropdown, specify a strength of 50, check clear before apply, then click Apply to All. Then choose interpolate rejected pixels from the dropdown, and again click Apply to All.

7. Then cold pixels: also with permanent dark-current problems, if not completely dead. Repeat Step 6, substituting cold for hot in the first dialog.

8. Align all of these images so the stars are in precisely the same places. First, page through the images until the one taken when M 3 was closest to transit is on top. Choose Stack > Register, which brings up the Registration dialog.

9. On the Star Snap tab, click remove all reference stars, then select/remove reference stars. Double-click to select 6-8 stars which don’t have any particularly close neighbors, aren’t close to the edge of the image, don’t include extremely bright looking stars, and are spread uniformly around the cluster. Page through the stack of images to see how close to being in alignment they already are. Drag any misaligned ones into closer alignment. Then click align all to shift the frames into rough alignment.

10. Switch to the Apply tab, choose Bicubic B-Spline from the dropdown box, and click Align All to shift the frames very accurately into alignment. Page through the images to make sure it worked. It should now be hard to tell when you page from one image to the next.

11. Adjust all the images to remove atmospheric extinction. Again page through the images until the one taken when M 3 was closest to transit is on top. Choose Stack > Normalize > Control > Both. In response to the subsequent instructions, drag a rectangle well off the cluster’s center to represent background, and click OK. Then drag a rectangle encompassing the densest part of the cluster to represent the highlights, again clicking OK. Copy and save the results then presented in the Information window.

12. Now that the stack of images is registered and normalized, it can be corrected for cosmic-ray hits and satellite trails. Choose Stack > Data Reject > Procedures. In the resulting dialog pick STD sigma reject from the dropdown box, specify a factor of 2.2 (sigma) and an iteration limit of 8, and click Apply to All. Then select interpolate rejected pixels from the dropdown, and again click Apply to All.

Navigate backward Navigate forward

Blink Image manager

Adjust display Magnification

Histogram File header

Information window Full screen



13. Choose File > Save Data > All. In the File name box of the resulting dialog, enter a suffix indicating the degree of processing the images have had, e.g. CalRegNormFix. Then click Save.

14. The Red images are now tidied up; choose File > Remove all images – Clear.

15. Repeat Steps 4-14 for the G images of M 3. Then repeat them for the B images, except: do not clear the stack at the end of the B image reduction.

16. Use the stack of B images in a quick search for variable stars. Page to the first M 3 image of the night, then use the Navigate Back and Navigate Forward buttons to toggle between the first and last blue images. How many stars can you find, which changed brightness substantially between the beginning and end of the night? Keep a list of their pixel coordinates, noting that what’s under the cursor is displayed at bottom right of the CCDStack window. Repeat this with image pairs separated by different time intervals, such as beginning/halfway and halfway/end. Note that the Blink button results in continuously and automatically paging through the contents of the stack with approximately half-second intervals between images.

17. Now clear the stack and repeat this procedure on the RR Lyr images, with two differences:

a. Do not normalize the images to correct for atmospheric extinction (Step 11), and do not fix cosmic ray hits (Step 12) just yet.

b. Instead, save the stack after registration, with filename suffix CalReg, and clear the stack. Then open, one at a time, each series of eight consecutive two-second exposures. Then, as in Step 12, choose Stack > Data Reject > Procedures, pick STD sigma reject with a factor of 2.2 (sigma) and an iteration limit of 8, and click Apply to All. Then select interpolate rejected pixels from the dropdown, and again click Apply to All. Save the series with suffix Fix. Choose Stack > Combine > Mean to produce an average of the series. Save this new image, accepting CCDStack’s suggested prefix of Mean. Then clear the stack and proceed to the next series.

c. After processing all the individual series, clear the stack and load all of the blue RR Lyr Mean images. Put them on Blink, and note that the brightness of RR Lyr changes by quite a bit from the beginning to the end, while the rest of the stars in the field change by much less. Thus RR Lyr varies like many of the variables in M 3. The small changes in the rest of the stars turn out to be the systematic effect of atmospheric transmission; this was taken out of the M 3 data in the Normalize step, but in the case of RR Lyr, which is the brightest object in a frame with few stars, normalization has to be done more carefully, in the next set of tasks.

1.4 Analysis

Next you will measure the fluxes of the M 3 variable stars and of RR Lyr, as functions of time through your observing night. Using these data you will measure the distance to M 3.

1. In CCDStack, open the blue M 3 frames again (File > Open Selected…), with File Strings blue and CalRegNormFix, and by blinking frames taken at least a few hours apart, identify at least 16 variable stars that are not in particularly crowded neighborhoods and have no close companions. Make a list of their pixel coordinates.

2. Run IDL, using the icon in the Windows taskbar. Then type ATV <Enter> at IDL’s command prompt, at the bottom of the window. The ATV window will appear (Figure 3). On its Mouse Mode dropdown, choose ImExam.



3. It’s easier to spot the variable stars in B images, but to compare stellar fluxes to other people’s measurements it’s better to measure them in G images, as G is essentially the same as the Johnson V band. So open the first of your G images of M 3 in ATV, using File > ReadFITS.

4. Unlike CCDSoft and CCDStack, IDL displays images with the pixel coordinates at lower left instead of upper left, so your images will look inverted top to bottom. (The coordinates of a given star are always the same, though.) If this will be too confusing, choose Rotate/Zoom > Invert Y to set it right.

5. Click on any star to bring up ATV’s aperture photometry window (Figure 3). In this window, click the Show Radial Profile button, which displays the flux per pixel of your star as a function of distance away from its centroid. This window tells you the centroid position and the flux of the star, given by the total flux within the aperture radius minus the flux in the sky annulus, between the inner and outer radii. By entering new values in the boxes provided, adjust these parameters so that the star’s flux is essentially completely contained in the aperture, as in the profile shown in Figure 3. Aperture/Inner/Outer = 8, 10, 16 usually works in the less-crowded regions.

Figure 3: ATV (top right) and ATV’s aperture photometry window (top left).



6. In ImageInfo, display the current image’s ImageHeader, and copy the UT date and time the image was taken, recorded in the header as DATE-OBS.

7. In the aperture photometry window, click Write results to file… and enter a file name in which to store star fluxes. It will be useful to have the observation date and time as all or part of the file name. While this file is open, a click on a star will write its centroid position, flux (as “counts”), and FWHM diameter in pixels.

8. Click on each of your variable stars in this image. Note any cases in which the radial profile indicates that nearby stars are influencing the measurement of the variable. Throw the star off the list and choose a different one if there isn’t a good way to exclude too-bright stars from the sky annulus. When you’re finished, click Close photometry file.

9. Repeat Steps 3-8 for each of the G images of M 3 you acquired.

10. Repeat Steps 3-8 for each of the Mean G images of RR Lyr you acquired. In this case there’s only one variable star, RR Lyr itself, but click the next four brightest stars as well, to enable the correction for atmospheric extinction that we postponed from Data Reduction, Step 17.

11. From the information in your photometry files, make a spreadsheet which lists the flux, in Counts, as a function of time for each of your M 3 variables. Use this to calculate the average and standard deviation for the flux of each variable star, and to plot the flux vs time – i.e. the light curve – for each of the variables you selected. Traditionally, these plots use the Day as the unit of time. Have these plots and the table of averages available for use in your lab report.

12. Repeat Step 11 for your RR Lyr data, with one difference. For each image, calculate the ratio of fluxes for the non-RR Lyr stars, to the fluxes these stars have in the image taken closest to transit. Average these ratios for each image. Then create new columns containing stellar fluxes divided by the average ratio for their images. Use these new columns to calculate RR Lyr’s average flux and standard deviation, and plot the light curve for RR Lyr. Again, have these data available for use in your lab report.

13. Discuss the light curves for M 3’s variables and RR Lyr. Which M 3 light curves resemble RR Lyr’s most closely? Are there light curves in which you see clear minima and maxima? Are there light curves in which you see two maxima or minima, and can thereby measure the pulsation period?

14. Assume that RR Lyr, and the M 3 variables with similar light curves, all have the same luminosity. The distance to RR Lyr has been measured by trig parallax; it is 260 10 pcr = ± (Catelan & Cortés 2008). So what is the distance to M 3, and what is the uncertainty in this distance? Use the average fluxes and uncertainties, obtained in Steps 11 and 12, in this determination.

15. Before Gaia, the largest stellar distance we knew from trig parallax was about 500 pc. By what factor is the distance to M 3 larger than this?

16. (Optional) From your M 3 data you could make a color image of the cluster, and thus a superb ornament for your website or Facebook page. Here’s how to make it, back in CCDStack:

a. For each filter, open all of the CalRegNormFix files, as in Step 1, and create an average of them, with Stack > Combine > Mean. Save the Mean image and clear the stack before proceeding to the next filter’s CalRegNormFix files.

http://adsabs.harvard.edu/abs/2008ApJ...676L.135C



b. Clear the stack if it isn’t already; then open the three Mean images you just created. Choose Color > Create. CCDStack will magically identify your R, G and B images, and offer the filter scaling factors for adjustment. Enter Filter Factors of 1.00, 1.16, and 1.58 for R, G, and B respectively, which will scale the colors so that an A0V star looks white. Then click Create. You will be prompted to drag a rectangle on the image to identify a background area well away from the cluster center; do so, check Desaturate background, and click OK. Then click Yes on the next dialog, and Apply to this in the one after that. Save this image under some suitable name, e.g. M3.RGB.FITS.

c. Use Adjust Display to tweak the image for display of the faintest stars, while keeping from “overexposing” the brightest stars and the cluster center. When you’re satisfied, choose File > Save Scaled Bitmap to make a JPEG image, or Save Scaled Data to make a TIFF image, the better to be imported into Photoshop.

17. (Optional) If other teams have M 3 observations, ask for their light curve data in return for yours. For each of the variable stars in common, plot flux as a function of time since the earliest of all the observations. For how many stars can you see complete pulsation cycles?

1.5 Lab reports

Each student must write their own report, in which they explain the purpose, methods, and outcomes of the observations, and answer all of the questions raised in the sections above. We offer a sample lab report on the AST 142 Web site, to give an idea of the length and level of detail expected.

http://www.pas.rochester.edu/%7Edmw/ast142/Projects/Reports.pdf



2 The Messier Marathon

2.1 Introduction

The first interesting catalogue of non-stellar objects that do not belong to the Solar system was that compiled by Charles Messier in the late eighteenth century. In the era of Messier’s activity, solar-system astronomy seemed the most exciting frontier, exemplified by Herschel’s (1781) discovery of Uranus and the realization that the orbital anomalies of the new planet could be explained most easily by another planet, or planets, further away. Within this rubric was the study of minor solar system bodies, which in those days meant comets, as asteroids weren’t discovered until 1801. Messier was a hunter of comets. In his hunt, he and his assistant Pierre Méchain also discovered many fuzzy, extended objects that could be mistaken for comets at a glance, but which seemed not to move with respect to the fixed stars. Messier published this list (1781) as a warning to fellow comet-hunters. Originally containing 103 objects2, the catalogue expanded slightly during the 20th century as astronomers, notably Camille Flammarion and Helen Sawyer Hogg, re-read additional notes and descriptions of observations kept by Messier and Méchain. There are now officially 110 Messier objects. The catalogue includes most of the brightest examples of stellar clusters, gaseous nebulae and galaxies visible from the northern hemisphere. Very influential in its day, the M catalogue spawned many more collections of extended celestial objects, starting with the General Catalogue (GC; 1864), the New General Catalogue (NGC; 1888) and its appendices the Index Catalogues (IC; 1896, 1905), compiled by John Herschel and John Dreyer, from observations by William, Caroline and John Herschel. Professional and amateur astronomers alike remain fascinated by the wide variety of astrophysical processes which can be studied in detail in the Messier objects.

The Messier catalogue has two features that many find particularly interesting. First, there are two brief intervals each year in which every object in the catalogue could in principle be observed between sunset and sunrise. Second, the number of objects is sufficiently small that a good observer can point a telescope at each object during the course of one clear dark night, but sufficiently large to make it a challenge. This observing program – all, or at least nearly all, the M objects in a single night – is called the Messier Marathon. The Marathon is very popular among amateur astronomers. The longer of the two Messier-catalogue visibility windows lies in the last couple of weeks of March and the first couple of weeks of April, and thus falls within the prime AST 142 observing season. In this experiment we will run the Messier Marathon, recording CCD images of the M objects, or at least their centers.3

2 Some of the objects were known to be non-stellar and non-planetary for a long time before Messier. For example, of the first 45 catalogue objects he published, the last four are naked-eye nebulosities known to the ancients, which Messier added for completeness: the middle “star “ of Orion’s sword (M 42 and M 43), the Beehive Cluster in Cancer (M 44) and the Pleiades in Taurus (M 45). The Andromeda Nebula (M 31), also visible to the naked eye, was known at least 800 years before Messier’s time. Several other M objects were discovered within the generation or two before Messier: for instance, the Great Hercules Cluster (M 13), which is barely visible to the naked eye under perfect conditions, was described by Halley, and the Crab Nebula (M 1) by Bevis, early in the 1700s. But Messier was the first to collect these scattered observations, add many other examples systematically, and publish the results, so his work had more impact than that of his predecessors. 3 Serious amateur astronomers usually consider the use of computer-controlled telescopes to be against the rules of the Messier Marathon. Beware of thinking that completion of this project entitles you to stand in the august company of those who have done it with Dobsonians pointed by hand; they’ll consider you a cheater. On the other hand, most of them don’t record images of the targets while they’re Running the Marathon, and you will.



2.2 Experimental procedure

2.2.1 Planning your Messier Marathon

There are 110 objects in the catalogue, and less than 12 hours of darkness in which to observe them, so each object needs to be acquired and imaged in less than six and a half minutes. Thus nobody succeeds at the Messier Marathon without planning carefully the sequence of observations. One doesn’t simply go through the list in right-ascension or catalogue-number order. Instead one accounts the times at which objects set and rise, noting the constraints imposed by the southernmost objects that aren’t above the horizon for very long, and using the flexibility of the long observing windows for the northernmost objects.

During the past few weeks of Learn Your Way Around The Sky lessons in recitation, you have learned everything you need to know to calculate the rising and setting times of each object and the Sun, to the required, better-than-a-few-minutes accuracy. First there’s the relation between zenith angle ZA and hour angle HA, in terms of an object’s declination δ and the observer’s latitude λ :

( )δ λ δ λ= +arccos cos cos cos sin sinZA HA

which you derived in Recitation #5. Then there’s local sidereal time, ST, which depends upon the observer’s longitude L, time t (in UT) since the Vernal equinox (at 1t ), longitude 1L at which it was noon at the Vernal equinox, and Earth’s orbital parameters, including the sidereal rotation period ⊕P , the length of the day, the semimajor axis length a, eccentricity ε , and ecliptic obliquity ψ . In Recitations 4, 6 and 8 you derived the components of sidereal time, which expressed in sidereal days is

( ) ( ) ( )εθ θπ⊕

− = − − + − + ∆ + ∆ +

1

1 11 1 1 mod ,1

day 2 dayot tST t t L L t t

P ,

where by ( )mod ,1x we mean the part of x in excess of the largest integer smaller than x, or in this case time of day on a 24-hour clock, with the integer number of previous days subtracted off, and where

( ) ( ) ( ) ( )εω ψθ ω θ ωω∆ −

∆ = − ∆ = −00 1

1 cossin , sin 22ot t t t t t .

Here ω is the angular frequency of the Mean Sun, ω∆ 0 the difference in Solar angular frequency between perihelion and aphelion, 0t the time of perihelion, and ψ the obliquity of the ecliptic. Finally, there is the celestial position of the Sun, for which you obtained the declination in Recitation #9, and for which the right ascension is only slightly different from sidereal time:

( ) ( ) ( )

( ) ( ) ( )

1

2

1 1mod 2 ,2 ,day

cos .

o

o

t t t tP

t t t t

ε

ε

α π θ θ π

δ ψ ω θ θ

⊕

= − − + ∆ + ∆

= − − − ∆ −∆

Here the results would be in radians, 2t is the time of the last winter equinox, and ( )πmod ,2x again means that the largest integer multiple of π2 is subtracted off, that can still leave a positive result.

“All” one would have to do, then, to obtain the rising and setting times of each Messier object and the Sun for any arbitrary night of the year, is to solve for the times t on that night which satisfy



( )( )α δ λ

δ λ

= −

+maxcos cos , cos cos

sin sinZA ST t L

for each target’s α δ and . Then one takes the rising and setting times and arranges the order of observation, such that the average pace of the observations – say, one every five or six minutes – can accommodate observation of each object after it rises and before it sets. In Figure 4, the results of such a calculation are rearranged into one practical realization of the Messier Marathon. (Code supplied on request.)

Fortunately, you will not have to go to this much trouble. The popularity of Messier Marathons among amateur astronomers has led to the presence on the Web of several Messier Marathon planners which can be customized for any observing date: just ask Google for “Messier Marathon” and you’ll find a bunch. But you do know how to do the calculations which these planners do, and you could do them if you wanted to. We will not ask for them in this project, because they involve the use of numerical root-finding routines which are not easily implemented in spreadsheets like Excel. If for some reason you ever want to write your own program which calculates accurately when any given celestial objects are up, you may start with the code Dan used to generate Figure 4, perhaps translated into your favorite math spreadsheet (e.g. Mathematica) or programming language.

Figure 4 (right): plan for a Messier Marathon on the night of 31 March – 1 April. The blue bars indicate the times during which the objects are above the effective horizon at Mees Observatory; the black bars indicate the times during which the Sun is below the real horizon.. The targets would be observed in order from the top down; they are sorted in a manner that allows observation at a constant pace and avoids large telescope moves as much as possible. Note that a few of the objects “set” before sunset (M 74, M 79, M 77) or “rise” after sunrise (M 30), and are not really going to be observable on that night. For the first three an earlier date would be necessary; for M 30 a later date might work.

M 104

M 61M 49

M 98M 99

M 84M 86M 87

M 100M 58

M 89M 88

M 59M 90

M 60M 91

M 85M 68

M 102M 64

M 3M 53

M 13M 92

M 5M 57M 12

M 10M 56M 107

M 14M 29

M 39M 52

M 27M 80M 71

M 9M 11M 16M 26

M 23M 17

M 4M 18M 24

M 25M 19M 21M 20

M 15M 8

M 22M 28

M 2M 72M 73

M 75M 30

Night

M 81M 82

M 108M 97

M 40M 109

M 106M 101

M 94M 51

M 63

Night

M 74M 79M 77

M 32M 110M 31

M 33M 41

M 93M 76M 43M 42M 45M 34

M 78M 103M 47M 50M 46

M 1M 35M 38M 36M 48M 37

M 67M 44

M 95M 96M 105M 104

M 65M 66

M 61M 49

M 98M 99M 84M 86M 87M 100M 58M 89M 88M 59M 90M 60M 91M 85

M 68

M

M 4

M

24 34

Time (hours, EDT)

2000 2200 0 0200 0400 0600



With all this in mind, here is the pre-observing procedure to be followed.

1. Form or join a team. As you will see, there is very little down time for anybody on the observing team during the course of this project. Accordingly a larger-than-usual team of four or five is recommended. There are three tasks that must be performed constantly through the night, and we presume that team members will rotate periodically so that everybody gets to enjoy each different task. Members 4 and 5 can relax until rejoining the cycle of tasks. This is really the only way to get a break during these observations.

2. Download the Messier catalogue, available as an Excel spreadsheet on the AST 142 Projects page.

3. Calculate the amount of time each object is over the effective horizon, ( )max2HA ZA . Identify the objects which never get above the horizon, and should be left out of the observing list. Identify circumpolar objects and prepare to keep them handy: one must always be observing something, and these may serve if you’d otherwise have to wait for an object to rise.

4. Choose the observing night. Look up or calculate the times of sunset and sunrise, and the length of the night.

5. Refine your observing list by identifying the objects which rise too late or set too soon. Determine thereby your time budget: the number of seconds available for each object on the average. By consulting online Messier Marathon plans and Figure 1, determine the order in which the list should be observed. Make a spreadsheet list of targets and times at which you should begin observing each target. Hint: Note the position of M 68 in Figure 1. Consider how this, and other objects with very narrow windows of observability, constrain the schedule.

6. Plan to take at least 2 minutes of imaging data per object, with the L filter. The imaging should be in the form of a short exposure (20-30 sec) followed by centering if necessary, and either a longer exposure or a series of short ones. For faint objects (e.g. most galaxies) in a dark sky, it would be best to take one long exposure; for bright objects (e.g. M 42) or bright sky conditions (twilight or moonlight) it works better to take a stack of short exposures and average them afterwards.

7. Learn enough about the Messier objects to know which are unlikely to have their interesting parts fit in the 15.4 arcminute wide camera field. Flag these objects on your observing plan spreadsheet. For these targets you will record images of the center with the camera but will supplement these images with descriptions of the view through the finder telescope or the image-stabilized binoculars. (A good job for those currently relaxing.)

8. Present your list, with calculations and order appropriate for the observing night, to the instructors for review. No group gets to go to the telescope without presenting a blow-by-blow plan in advance.

9. Unlike the observing projects in AST 111, this one takes all night. Therefore you will be required to sleep at the Observatory after your observations are done and before returning to campus. Plan accordingly by packing for an overnight stay, and discussing with your team and the instructors the all-important requirement of packing sufficient Food. (Mees is unfortunately beyond the range of pizza delivery; the nearest restaurants and grocery stores in Naples, and the nearest restaurants on your way home that are likely to be open are in Victor.) Familiarize yourself with the rules of the observatory sleeping quarters at the Gannett House.

10. Bring your notebooks, and bring a thumbdrive with at least a few GB of free space. You would do well also to bring your personal laptop or tablet computer. Paper copies of the experiment procedure (i.e.



this document), telescope checklist, and CCD-camera observing guide will also prove useful, though these will also be accessible to you on line at Mees.

11. Arrive at least 45 minutes before sunset. The telescope and camera must be ready to go, and all team members must be at their stations, before the sun goes down.

2.2.2 Observations

Three team members should be stationed at three specific jobs: telescope operator, CCD camera operator, and Scribe:

• The Scribe orders the observations, enforces the schedule, and maintains a spreadsheet in which they record the correspondence between file name and object being observed, exposure times, etc.

• The camera operator takes the data, and is in charge of quality control: making sure images aren’t saturated, making sure the target is centered properly, etc.

• The telescope operator moves the telescope to acquire the targets; offsets the telescope at the request of the camera operator, in the process of centering targets, using DFMTCS Telescope > Movement (Figure 5); and looks for opportunities to sync the telescope coordinates whenever a star happens to be centered in the camera, thus to keep the pointing sharp.

Rotate the team members among the jobs at least once an hour. Here is the recipe they should follow:

1. Start the telescope and initialize its pointing, by following the steps in Section I of the Mees Observatory telescope checklist. This leaves you with DFMTCS, FrameGrab, and TheSky running on the TCS computer.

2. Start the CCD camera, its computer, and CCDSoft, and focus the telescope, by following the steps in Section I of the Mees Observatory CCD camera checklist. Notable differences from ordinary observations:

a. You should take flat-field data at sunset, but only for the L filter and unusually with 2×2 binning, following the steps listed in Section V of the camera checklist. Don’t stop to create the Master flat; just take the data and get on to the M objects.

b. Note that you can find stars near the zenith, and sometimes even focus the telescope adequately, before sunset. Give it a try.

c. You won’t have time to change the filename prefix for every object. Rely on a generic prefix and the serial numbers, and the spreadsheet maintained by the Scribe, to keep your targets and data sorted.

d. No time, or need, to autoguide.

e. Because you will be using only one filter, but changing targets and exposure times so frequently, it will be more convenient to take data with CCDSoft’s Camera Control > Take Image tools instead of the usual Camera Control > Color. Take all images with 2×2 binning, rather than the finer binning we usually use with L.

You now have CCDSoft running on the CCD camera computer, and both camera and telescope ready to observe and to autosave the data.






Figure 5: DFMTCS Telescope > Movement > Offset/Other, with the telescope about to offset 5 arcminutes west and 3 arcminutes north.

3. Begin the Marathon. The Scribe is in command, enforces the following of the plan, and maintains the spreadsheet of file names and targets. Illustrations of the routine: Scribe: Move to M ___. Telescope: We’re there. Camera: Looks like a galaxy nucleus in the 20 second image, please move us 5 minutes west and 3 minutes north. Telescope: Done. Camera: Two-minute exposure in progress. By the way, the 20 second image was number 147 and the current one is 148. Scribe: Duly noted. Get ready for M ____ next. Telescope: Ready. Camera: Image 148 done and ready for next.

4. Occasionally: Camera: 20 second image number 230 already beautiful, brightest stars barely on scale. Can we do 16 20-second images? Scribe: No time for that; make it 9 20-second images. Camera: Oh, all right. 230 through 239 in progress.

-300

180



5. When necessary: Camera: Not much in Image 86; what does it look like in the finder? Off-duty observer (after running up the stairs and peeking through the 6” finder telescope): Open cluster, big on the scale of the finder’s field, no nebulosity. No wonder you’re not seeing much. Scribe: Duly noted.

6. The non-routine: whenever an object is not clearly seen in the camera or finder, quickly to point to a nearby bright star, center it in the camera and update the TCS telescope position, and then point back to the target. You’ll have to learn how to do this in no more than a couple of minutes.

7. When all is done or when the Sun comes up – whichever comes first – cover and stow the telescope, list all the exposure times used during the night (and temperatures, if it had been necessary to change temperature), and take a dark frame for each exposure time.

8. When all is done, copy the working directory to your thumbdrive(s).

9. Exit CCDSoft, and shut down the CCD camera computer. Disconnect the power supply and cables from the camera, coil them, and return them to the black nylon briefcase.

10. Shut down the telescope and the TCS computer, by following the steps in the Telescope Shutdown section of the telescope checklist.

11. Go to the Gannett House and get some sleep. You may not return to campus without at least the driver(s) sleeping, for at least four hours. This safety rule will be enforced strictly.

2.3 Data reduction

Now we calibrate your Messier Marathon images. Since the images differ only in target and integration time, this task benefits greatly from CCDStack’s automation. Make sure your team divides the data-reduction workload equitably among the members.

Set aside a few hours for your team to meet in 203H B&L, in the company of the thumbdrive carrying your data, and one of the astronomy-lab computers on which the programs CCDStack and IDL reside. Find the directory on this computer’s desktop in which the Master Dark and Bias frames are kept. Then:

1. Copy your working directory from the thumbdrive to the desktop of the lab computer. Tell the instructors where it can be found. (We will keep copies.)

2. Start CCDStack, using the icon in the Windows taskbar.

3. If you haven’t done so already, follow the procedure in the camera checklist, Section V, to create a master flat field from your L flat-field observations. Use the master flat you create, in the calibration of your images.

4. Open all your Messier Marathon images, using either File > Open… or File > Open Selected… and navigating to your working directory. This will present you with the full stack of these images, which you can page through and examine with the tools on the left side of the window (Figure 2).




Figure 6: upper left corner of the CCDStack window, and the tools along the left edge.

5. Calibrate the images en masse. Choosing Process > Calibrate evokes the Calibration Manager dialog. Using the two tabs and three buttons, specify the master dark frame, bias frame, and flat field frame appropriate to CCD temperature (-20 C), binning (2×2) and, for the flat, filter (L). Then click Apply to all.

6. Next, remove hot pixels: those with (permanently) high and variable dark current that doesn’t subtract out in calibration. Choose Process > Data Reject > Procedures. In the resulting dialog choose reject hot pixels from the dropdown, specify a strength of 50, check clear before apply, then click Apply to All. Then choose interpolate rejected pixels from the dropdown, and again click Apply to All.

7. Then cold pixels: also with permanent dark-current problems, if not completely dead. Repeat Step 6, substituting cold for hot in the first dialog.

8. Choose File > Save Data > All. In the File name box of the resulting dialog, enter a suffix indicating the degree of processing the images have had, e.g. Cal. Then click Save.

9. Done with the whole stack; choose File > Remove all images – Clear.

10. Now, using the Scribe’s spreadsheet of observations, identify all multiple images of given objects, such as those described in Section 2.2.2, step 4. Use File > Open… to open the first set.

11. Align all of these images so the stars are in precisely the same places. Choose Stack > Register, which brings up the Registration dialog.

12. On the Star Snap tab, click remove all reference stars, then select/remove reference stars. Double-click to select 6-8 stars which don’t have any particularly close neighbors, aren’t close to the edge of the image, don’t include extremely bright looking stars, and are spread uniformly around the image. Page through the stack of images to see how close to being in alignment they already are. Drag any misaligned ones into closer alignment. Then click align all to shift the frames into rough alignment.

13. Switch to the Apply tab, choose Bicubic B-Spline from the dropdown box, and click Align All to shift the frames very accurately into alignment. Page through the images to make sure it worked. It should now be hard to tell when you page from one image to the next.

14. Choose Stack > Normalize > Control > Both. In response to the subsequent instructions, drag a rectangle well off the cluster’s center to represent background, and click OK. Then drag a rectangle encompassing the densest part of the cluster to represent the highlights, again clicking OK.

Navigate backward Navigate forward

Blink Image manager

Adjust display Magnification

Histogram File header

Information window Full screen



15. This registered, normalized stack of images can be corrected for any cosmic-ray hits and satellite trails. Choose Stack > Data Reject > Procedures. In the resulting dialog pick STD sigma reject from the dropdown box, specify a factor of 2.2 (sigma) and an iteration limit of 8, and click Apply to All. Then select interpolate rejected pixels from the dropdown, and again click Apply to All.

16. Choose File > Save Data > All. In the File name box of the resulting dialog, enter a suffix indicating the degree of processing the images have had, e.g. CalRegNormFix. Then click Save.

17. Average the stack of images, using Stack > Combine > Mean. Save this image too (File > Save Data > This), using the first file name in the stack as a suffix, and Mean as a prefix.

18. Choose File > Remove all images – Clear.

19. Repeat Steps 10-18 for each multiple observation.

20. Using File > Open Selected…, open all of the calibrated images (Cal suffix).

21. Page through the images and remove from the stack (Ctrl-R) all of the images which went into an average: those with suffix CalRegNormFix.

22. Using File > Open Selected… again, open all of the averages of multiple images (Mean prefix).

23. Now comes the time consuming part. You now have as many as 110 images in the stack: your best try on each of the Messier objects you observed. Go through them one by one, and make each look as good as possible by use of the Adjust Display tools, launched with the button shown in Figure 6. By “good”, we should mean that the image stretch (Gamma and DDP parameters) is adjusted to show all details of the object from brightest to dimmest, that Maximum is chosen not to saturate too much of an image’s brightest parts, and that Background is chosen so that the image is black at levels below the dimmest detected part of the object: thus blotting out any sky background, but none of the stars brighter than that.

24. When you’re done beautifying the images, save them all in scaled form: File > Save display Bitmap > All, with JPEG as the Save as Type. Leave copies of all images in the working directory you created on the lab computer.

2.4 Analysis

As your main result, you have digital images and sometimes eyepiece descriptions of the vast majority of the Messier objects.

1. Make a master table of final images and object descriptions, with the objects listed in the order you actually observed them. Include in the descriptions all comments collected while looking through the eyepiece or binoculars, and a classification of each object made in accordance with its appearance. For each galaxy, this should include your estimate of the Hubble type as is consistent with the image you took. This portion of your report should be produced in a form suitable for posting on the AST 142 Web site: self-contained in a pdf document, or html with all the image files posted separately.

2. Make an additional cross-reference table in which the objects are sorted according to their classification: open cluster, globular cluster, planetary nebula, H II region/reflection nebula, supernova remnant, elliptical galaxy, spiral galaxy. Comment on the ranges of brightness and angular size observed under each of these classes.



3. Pay particular attention to the identification and classification of three objects: M 40, M 73, and M 102.

2.5 Lab reports

Each student must write their own report, in which they explain the purpose, methods, and outcomes of the observations, and your complete set of images, object descriptions and cross references. We offer a sample lab report on the AST 142 Web site, to give an idea of the length and level of detail expected.

http://www.pas.rochester.edu/%7Edmw/ast142/Projects/Reports.pdf

Documents

Department of Physics and Astronomy : University …dmw/ast142/Projects/Project.pdfdata, and one of the astronomy-lab computers on which the programs CCDStack and IDL reside. Find