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University of Nevada, Reno

A Photometric Survey and Analysis of the M29 and M52 Open Star Clusters at the University of Nevada, Reno

A thesis submitted in partial fulfillment of the requirements for the degree of

Bachelor of Science in Physics

by

Matthew N. Tooth

Dr. Melodi Rodrigue, Ph.D., Thesis Advisor

December, 2012 UNIVERSITY OF NEVADA THE HONORS PROGRAM RENO

We recommend that the thesis prepared under our supervision by

Matthew N. Tooth

entitled

A Photometric Survey and Analysis of the M29 and M52 Open Star Clusters at the University of Nevada, Reno

be accepted in partial fulfillment of the requirements for the degree of

Bachelor of Science, Physics

______Melodi Rodrigue, Ph.D., Thesis Advisor

______David Bennum, Ph.D., Thesis Reader

______Tamara Valentine, Ph.D., Director, Honors Program

December, 2012 ! i!

Abstract

One of the many tools at an astronomer’s disposal is photometry. By measuring the magnitudes of stars in star clusters in various wavelength filters we can obtain data that can provide important pieces of information about groups of stars. Conclusions can be obtained through photometry through the production of a color magnitude diagram, which can provide information about star clusters specifically. Given the expense involved in astronomy missions, obtaining inexpensive photometric measurements is of great interest to the scientific community to develop cheap, efficient, and accurate methods to obtain photometric data. This thesis aims to determine if these measurements can be taken at the university level, and if the measurements taken can be used to obtain the ages of the Messier 29 and Messier 52 open star clusters. I developed and implemented a method of obtaining color magnitude diagrams for M29 and M52, but I could not make strong conclusions about the ages of the clusters.

! ii!

Acknowledgements

I would like to thank my thesis advisor, Dr. Melodi Rodrigue for being there for me when I needed advice the most. She has always been supportive, and was a better advisor than I could’ve hoped for. I would also like to thank the University of Nevada,

Reno Physics Department and Honors Program for assisting me in completing this thesis.

Dr. Tamara Valentine and Dr. David Bennum have furnished me with a great deal of help and advice that has helped me to perform this research. I would also like to thank my father, Paul Tooth, and the rest of my family for encouraging me to explore the world around me and to always ask, “Why?”

Some other people who have helped me along the way:

Teddy Rodrigue, Tony Berensen, Ryan Berensen, Emil Petkov, Steve Keim, Kyle

Swanson, and many others.

“The cosmos is full beyond measure of elegant truths; of exquisite interrelationships; of the awesome machinery of nature”

-Carl Sagan, Cosmos

! iii!

Table of Contents

Abstract ...... i

Acknowledgments ...... ii

Table of Contents ...... iii

List of Figures ...... v

Chapter 1: Introduction ...... 1

Photometry ...... 1

Motivation and Purpose ...... 2

Chapter 2: Theory ...... 3

Stellar Evolution and the Hertzsprung-Russell Diagram ...... 3

Star Clusters ...... 4

Photometry ...... 6

Chapter 3: Observational Methodology ...... 8

Necessary Equipment and Computer Programs ...... 8

Observational Procedure ...... 8

Chapter 4: Data Analysis ...... 10

Analysis of Raw Data ...... 10

Obtainment of Values from Data ...... 10

B-V and Magnitude Calculations ...... 14

Sources of Error ...... 14

! iv!

Chapter 5: Results ...... 16

M29 Magnitude Diagram and Analysis ...... 16

M52 Magnitude Diagram and Analysis ...... 18

Summary of Results ...... 19

Future Work ...... 20

Bibliography ...... 22

Appendix A: Raw Data ...... 24

Appendix B: Data Reduction ...... 30

! v!

List of Figures

Figure 1: Hertzsprung Russell Diagram ...... 4

Figure 2: Hubble Telescope Image of Messier 10 ...... 5

Figure 3: UVBRI System ...... 7

Figure 4: Color Magnitude Diagram ...... 7

Figure 5: Dark Frame Image ...... 11

Figure 6: M52R Photo ...... 13

Figure 7: M52B Photo ...... 13

Figure 8: Color Magnitude Diagram for M29 ...... 16

Figure 9: Color Magnitude Diagram for M29 From Literature ...... 17

Figure 10: Color Magnitude Diagram for M52 ...... 18

! 1!

Chapter 1: Introduction

Photometry

The study of the stars and space has piqued the human race’s interest since before the ability to study them through scientific inquiry emerged. Humanity’s ancestors sought to understand what they observed in the night sky, and how celestial objects may affect them. One of the early methods for studying the stars was called photometry, which is the study of the relative brightness of stars. Photometry has been in use since 137 CE, and was used by scientists such as Ptolemy to observe the stars. Some historians believe that

Hipparchus first developed photometry in 130 BCE, but Hipparchus’s work on the subject was not referenced by Ptolemy and the original catalog was lost [1].

Early photometric observations were crude, and used a night-adjusted human eye instead of modern telescopes. The magnitudes of stars obtained by these early measurements were off by a fraction of a magnitude, and remained above one-half magnitude of error until the first visual photometers were developed [1]. The first optical/mechanical system developed to perform photometric measurements used crossed polarizers to determine magnitudes, and was created by Freidrich Zollner in 1861 [1]. In the 19th century scientists began to use films to obtain useful and accurate measurements of the brightness of the stars. Instruments for performing photometry developed significantly through the 20th century, which featured the development of the Charge

Coupled Device camera. With the ability to obtain reliable data scientists have turned photometry into a reliable and accurate tool for the study of stars that is still used in modern scientific studies.

! 2!

Motivation and Purpose

Photometry is still relevant in modern observational astronomy. Astronomers can infer many properties of astronomical objects through the use of various mathematical techniques. A few recent applications of photometry include characterizing star clusters

[2], determining galactic structure [3][4][5], verifying theoretical models [6], and finding exoplanets in projects like the National Aeronautics and Space Administration’s

(NASA’s) Kepler mission [7]. Obtaining data through photometry provides astronomers with an easily obtained basis for determining meaningful information about astronomical objects of interest.

Many missions and projects that are funded through NASA and the European

Space Agency (ESA) are often prohibitively expensive, and require long-term investments into diagnostic instruments that may not be capable of performing every task that scientists planned within budget limitations. Given that these projects are very expensive, and that the scientific community still wishes to obtain accurate data that can provide insight on the stars, less expensive ways to obtain data of a similar quality have been developed [8]. At the University of Nevada, Reno the Physics Department possess several small telescopes and Charge Coupled Device cameras along with appropriate filters that can be used to obtain photometric measurements, which can be used to support future NASA and ESA missions. This thesis will verify that photometric observations can be taken on the university scale. Furthermore, these observations will be used to obtain the approximate ages of the Messier 29 and 52 open star clusters.

! 3!

Chapter 2: Theory

Stellar Evolution and the Hertzsprung-Russell Diagram

While there are many different ways of representing data obtained in stellar observations, one of the more common tools used to describe a group of stars is the

Hertzsprung-Russell diagram [Figure 1]. The Hertzsprung-Russell Diagram (HR diagram) is an intensity versus temperature graph that provides data about what stage of development a star is in. On the x-axis in Figure 1 the classification letters for stars are shown, which are determined by examining each star’s spectra. Spectra are the wavelength bands that are absorbed or emitted by an excited atom/molecule, and the classifications based on spectra are OBAFGKM, where each letter represents a different type of star (Our sun is a G star)[9].

A star spends most of its life on what is known as the main sequence while the star fuses hydrogen in its core [9]. Once a star begins to run low on hydrogen, it begins to fuse helium and heavier elements (“metallic elements”). As a star fuses heavier elements its outer layers expand and cool as a result of shell fusion and other factors. At this stage in its evolution the star moves into the giant phase, which is represented by the upper- right field of Figure 1. After moving into the red giant phase, a star continues fusing its core material until it lacks the proper mass to fuse heavier elements, or until it reaches iron (Fe), which cannot be fused without the addition of more energy due to its high binding energy [9]. Once a star with sufficient mass reaches this cutoff it will generate a supernova and form either neutron star or black hole [9]. Smaller stars that reach their respective cutoffs will form degenerate cores and shed their outer layers to form a white dwarf [9]. White dwarfs are shown on the lower left field of Figure 1, but neutron stars ! 4! are not featured in this figure due to their extremely high temperatures and low magnitudes. By measuring the brightness of a star and its temperature, scientists can produce HR diagrams such as the one shown in Figure 1. Using HR diagrams, scientists can observe overall trends in stellar populations.

Figure'1:'' Hertzsprung0 Russell'Diagram! This!HR!diagram! displays!stellar! classifications! along!with! locations!of!the! main!sequence,! giants,!and!dwarfs! [10]!

Star Clusters

One of the objects that astronomers wish to study is known as a star cluster. Star clusters are groups of stars that fall into two categories: open clusters or globular clusters.

Globular clusters are typically made up of several thousand to several million old, low- metallic stars [9]. The high number-density of these clusters makes it difficult to resolve individual stars in images, and the clusters are found outside of a galaxy’s stellar disk, which makes globular clusters poor candidates for low-budget and ground-based observations. An example of a globular cluster is shown in Figure 2, which demonstrates ! 5! the difficulty of resolving individual stars in such a densely populated cluster. Due to these shortcomings, globular clusters are not considered as candidates for the ground- based observations performed in this thesis.

Figure'2:' Hubble' Telescope' image'of' Messier'10' [11]!M10!is! located!in! Ophiuchus!,! and!is!15,000! light!years! away!and!80! light!years! across.!!

The second major type of cluster is called an open cluster. An open cluster is a group of fifty to one thousand stars that form in the same region. These stars are young and made up of metallic stars that have not overcome a crossing timescale [9][12].

Crossing timescale is the time it takes for a star to cross the span of an open cluster. If an open cluster is not gravitationally bound it will disperse once the crossing timescale is reached [12]. In light of the fact that these clusters have smaller groups of stars that can be individually resolved, and they are typically located in the stellar disk of our galaxy, they make excellent objects for this photometric study. My list of candidate objects includes the Pleiades, the Hyades, Messier 26 (M26), M29, M34, and M52. The open clusters M29 and M52 are chosen for my thesis observations due to their relatively high ! 6! apparent magnitudes – 7.1 and 5 respectively - and their small angular sizes – 7 arcminutes and 13 arcminutes respectively [13] (Where apparent magnitude is a measure of brightness and one arcminute is one sixtieth of a degree of a circle). It is important to note that smaller magnitude numbers correspond to brighter objects, and larger magnitudes correspond to dimmer objects. In addition to their good magnitudes and angular sizes, M29 and M52 are also relatively high in the sky, which minimizes atmospheric interference while taking observations. Higher objects experience less interference from the atmosphere because there is less atmosphere for the light to travel through to reach the observer than if the object were just above the horizon. The Pleiades and Hyades both possess high apparent magnitudes, but are not chosen due to their large angular size.

Photometry

I chose to do a photometric survey of M29 and M52 so that I could determine their ages through the use of a color-magnitude diagram such as the one shown in Figure

3. A color magnitude diagram is similar to a HR diagram in that it allows astronomers to compare large numbers of stars, and also helps astronomers to determine the relative age of a star cluster through observing the distribution of stars in the diagram. However, the color magnitude diagram differs from the HR diagram in that it compares the magnitudes of stars in one visual wavelength filter to the difference between their B and V filter magnitude measurements [12]. The B and V filters are part of the UVBRI filter scheme, which splits light measurements into different wavelength sections that can provide different pieces of information about stars. The ranges for each of the UVBRI filters are shown in Figure 3. The B and V filters measure in the 350-550nm and 475-700nm ! 7! ranges, respectively [1]. A color-magnitude diagram can be constructed using these data, which can provide similar information to a HR diagram, but can also provide a cluster’s age. A color magnitude diagram of the Pleiades star cluster is shown in Figure 4. The oldest stars in a cluster “branch-out” of the main sequence once they move into the giant phase (Refer back to “giants” in Figure 1), so by producing color-magnitude diagrams astronomers can estimate a cluster’s age by observing the branching point on the graph and determining the age at which a larger star could transition into that stage of development [9].

Figure'3:' UBVRI'System' [1]!This!graph! shows!the! sensitivities!of! each!filter!in!the! UBVRI!scheme.! The!A0!and!G2! curves!are! example!stellar! curves!

Figure 4: Color Magnitude Diagram [12] This is a color magnitude diagram with a line superimposed on the data to highlight the main sequence. The giants are located on the upper right side of the graph. The majority of stars in this image are still on the main sequence, while a few stars are beginning to move into the giant stage.

! 8!

Chapter 3: Observational Methodology

Necessary Equipment and Computer Programs

The telescope used during observations was a Celestron C11 reflecting telescope.

The C11 features a focal length of 2.8 meters, a diameter of 0.28 meters, and an obstruction of 0.092 meters. The C11 was used in combination with a Santa Barbara

Instrument Group (SBIG) ST-7 Charged Coupled Device (CCD) and a Color Filter

Wheel (CFW). The ST-7 CCD was 765 by 510 pixels and had a 9 micrometer square pixel area. The CFW was attached to the ST-7 so that the B, V, and R filters could be used during observations. Under this configuration, the C11 had a field of view of 8.5 by

5.6 arc minutes and the ST-7 and CFW were controlled by the CCDOps camera control program [14]. The obtained data were analyzed using the photo-analyzing program

ImageJ [15]. An astronomy plugin that can be found at reference [16] was added to

ImageJ to increase its functionality.

Observational Procedure

Observations were taken on the University of Nevada, Reno campus on the

Leifson Physics lawn over several days in October 2012. The data analyzed for this thesis were taken under clear skies on October 26th with an ambient temperature of ~15C during the 10:00 PM hour. We began by leveling the telescope mount and attaching the telescope to the mount. We then linked the telescope to GPS satellites, and proceeded to align the telescope to known stars in the sky. The C11 features several methods of alignment, but the two used during observations were the two star alignment and three star sky alignment. In two star alignment I chose two known stars, one in the East and one in the West, to align the telescope’s GPS to objects in the sky. The three star ! 9! alignment took advantage of known star positions to allow me to align the telescope on any three bright stars. Through several practice trials I found that three star alignment produced better alignment results and afforded better telescope tracking during observations.

Upon finishing alignment, we attached the ST-7 camera and CFW to the telescope and linked the ST-7 to the CCDOps program. We allowed the ST-7 to reduce its temperature to thirty degrees below ambient temperature, and proceeded to use the bright star Vega to focus the camera and verify that the telescope tracked objects successfully.

The camera’s temperature was decreased to thirty degrees below ambient temperature so as to minimize the thermal response in the CCD (Which can contribute to error in the obtained data). After these preliminary steps were completed, we used the C11’s catalog feature to navigate to M29, M52, and Vega. Exposures of each object lasting fifteen to forty five seconds were taken in the B, V, and R filters. We chose to analyze the exposure of fifteen seconds for our data to prevent error from “blooming”, which occurs when one of the wells in the CCD is overexposed to the source [1]. After we obtained measurements of M29, M52, and Vega we took fifteen second dark and flat-field exposures for future data processing. The dark exposures utilized the dark function in the

CCDOps program, which made the ST-7 take an exposure (matching the data exposure lengths) with a closed shutter to account for the aforementioned thermal response of the

CCD. The flat field image required a uniform source for the picture so as to test the response differences between each pixel [1]. We covered a large lamp with a white sheet and illuminated a nearby projector screen to produce the desired uniform flat field for our images. ! 10!

Chapter 4: Data Analysis

Analysis of Raw Data

Upon finishing the observations, I proceeded to analyze my obtained data, which has been compiled in Appendix A. I converted the images from SBIG format to FITS format by the CCDOps program so that ImageJ could open and process them. I subtracted the dark exposure from the photos in order to account for dark current. Dark current is the CCD’s response over a time interval in the absence of a source due to thermal effects and other small sources of error [1]. Figure 5 shows the dark frame taken for this experiment, and features several response problems that were removed through subtraction from the raw images. The gradient that was present on the left side of Figure

5 and the prominent bright points are two examples of error that was introduced by dark current and faulty CCD wells. After the dark frame was taken into account, I considered using the flat field images to correct for differences in pixel responses, but the flat field images that I obtained during observations were not useable. The lack of flat field correction can contribute significantly to the error of results, and will be further discussed in the error section of this chapter. A representative selection of the images obtained during observations are shown in Appendix A.

Obtainment of Values from Data

I used the “Astronomy Tools Plugin” [16] in ImageJ to analyze the processed images. The plugin features an Aperture Photometry Tool, which I used to take measurements of the stars’ “count” values on my images. In order to obtain these

! 11!

Figure'5:' Dark'Frame' Image! The!points! shown!in!this! photo!and!the! brightness! gradient!on! the!left!side! show!the!dark! current!and! mechanical! error!present! in!our!photos!

data, I measured the radius (in pixels) of a bright star on each image by zooming in on the image and counting them by eye. I then specified the object radius at the measured radius, the background annulus inner radius as two pixels wider, and background annulus outer radius two pixels above the background inner radius. Background light from stars that are not visible in the image or from other sources can contaminate data taken from the image, so the Aperture Photometry Tool measures the background intensity around the star and subtracts the background intensity from the star’s measured intensity. The background annulus inner and outer radii were specified to be two and four pixels above the object radius respectively.

An example of an image in mid processing is displayed in Figure 6. In Figure 6, red circles represent the background annulus inner and outer circles. I obtained intensity values for a variety of stars in each cluster through the use of the Aperture Photometry

Tool, but I excluded some stars that did not appear in all of the images or were not easily seen in the images. An example of an excluded star can be seen in Figure 6 as the ! 12! leftmost highlighted star. This star was very bright in the R image, but was not included because it did not appear in the B or V images due to alignment issues or due to the star being more intense in different wavelength ranges. The partially analyzed B image of

M52 is shown in Figure 7, which demonstrates that the leftmost highlighted star in the R image is missing in the B image. The problem of misalignment arose from the telescope not being perfectly aligned, which left a small amount of human error in the catalog finding feature of the C11. The telescope would navigate to a particular object, but did not stay centered on the object for long periods of time. The telescope also required small adjustments by hand to center the desired objects. The telescope images are also limited in exposure time due to field rotation issues that arise during long exposures. In spite of these problems, the matching values obtained from the images were exported to Excel as a worksheet and combined into a single file for easy processing.

! 13!

Figure!6:! An!image!of! M52!showing! the!data! points!that! were!used.! The!red!circles! indicate!the! inner!and! outer! background! annulus!radii.!

!

Figure!7:! An!image!of! M52!showing! the!data! points!that! were!used.! The!red!circles! indicate!the! inner!and! outer! background! annulus!radii.!

! 14!

B-V and Magnitude Calculations

In order to obtain the B-V and single filter magnitudes I used the measurements of

Vega to provide a proper basis for measuring apparent magnitudes. I used a simple equation to calculate the magnitudes, which is given by:

1 !!!! = !"#$% + 2.5(log !"#$% − log !"#$% )

Where m was the magnitude of the star in the chosen filter, Ivega was the measured intensity of Vega, Istar term was the measured intensity of the star in the filter corresponding to the one chosen for the Ivega term. The Cvega term in the equation was the magnitude of Vega, which is zero. In order to obtain B-V values, I calculated mv and mb values by substituting them into equation 1. Once these values were obtained they were subtracted from each other to obtain B-V values:

2 !!!! − ! = !! − !!!

The B-V values were plotted against the mv values to produce Figure 8 and Figure 9.

These figures were the obtained color magnitude diagrams, and will be discussed further in chapter V.

Sources of Error

Possible sources of error that affected my results were found both in the observational stage and the data analysis stage. The error in the observations was a combination of atmospheric interference, light cloud cover that was not visible to the naked eye, temperature fluctuation over observations, and nearby sources of light that contaminated our images. Analysis error arose from possible oversaturation of some stars, the exclusion of some stars in different filters, and the lack of a proper flat field ! 15! exposure. Error that was present in observations was within acceptable tolerances, but the lack of a flat field may have contributed to improper calculations due to different pixel responses in the CCD cameras.

! 16!

Chapter 5: Results

M29 Magnitude Diagram and Analysis

Figure'8:'Color'Magnitude'Diagram'for' M29'

B0V' U0.8! U0.6! U0.4! U0.2! 0! 0.2! 0.4! 0!

1!

2!

3! Mv'

4!

5!

6!

Figure 8 displays the color magnitude diagram obtained through my measurements for Messier 29 where each point represents an observed star. Due to the use of Vega as the zero point in equation (1), any values that are equal to zero in Figure 8 are equal in magnitude to Vega’s magnitude in that particular filter. Stars that have a B-V value less than Vega are “more blue” than Vega, and are smaller and hotter than Vega.

The trend of this graph is not easily established due to the scarcity of data points and the scattered trend of the points [12]. However, starting at (0.3, 5.7) and extending a line to (-

0.55, 1.7) showcases a roughly linear trend present in the data. This trend line may be the main sequence line in this image, but without more data points with diverse values I ! 17! cannot say make strong conclusions about the presence of a main sequence line. If the indicated line is the main sequence line, the three points with B-V < -0.55 could be said to be outliers that may be stars that are in the background of the image, or are just natural outliers in the cluster. The data look potentially promising, but more observations would need to be taken to verify this possible main sequence trend. Given that the main sequence line was absent from my data I cannot estimate the age of M29.

Figure 9 is a B-V chart obtained in Joshi et al [17]. Figure 9 demonstrates the expected distribution of stars in this cluster, but does not look to align with my results.

Upon closer inspection, however, I found that the group of stars near (0, -.5) has a similar

Figure'9:' Color'Magnitude' Diagram'of'M29' From'Literature.! This!is!a!color! magnitude!diagram! of!the!M26!star! cluster!from!! literature![17]!

appearance to the group in Figure 8 at (-0.2, 3). This correlation may be a coincidence, but it warrants future measurements of M29 to possibly verify a connection.

! 18!

M52 Magnitude Diagram and Analysis

Figure'10:'Color'Magnitude'Diagram'for' M52'

B0V' U2! U1! 0! 1! 2! 3! 4! 0! 0.5! 1! 1.5! 2! 2.5! Mv' 3! 3.5! 4! 4.5! 5!

Figure 10 displays the color magnitude diagram obtained for M52. This diagram also fails to show a main sequence line, but it is possible that the line is along (0, 4.5) and

(-1.7, 2.5). If the main sequence line lies on those points the other stars to the right of this line are further along in their development as compared to Vega. These data lack a clear main sequence line, and thus cannot be used to obtain a relative age of M52. The data does seem to conform to a general curve, however, so this may represent the portion of the cluster that is branching off of the main sequence line. These stars are also “more red” than Vega, so they may need to be excluded as outliers in the data. The stars present in the lower left portion of the image may also be outlier stars that are not in the cluster, but

I would need to establish a better picture of where the main sequence line is to conclude ! 19! that they do not belong in the cluster. More observations need to be performed to verify the obtained values, and to expand the number of data points that are being considered in this analysis. Without a main sequence line, M52’s age was not calculated.

Summary of Results

The observations taken during the experiments prove that the concept of using inexpensive university-based telescopes can provide useful photometric data. The main sequence line may also be featured in both images, but it is not fully present and requires further research.

The data that were taken for this experiment could be incomplete or incorrect depending on several factors. The star clusters M29 and M52 were chosen over the

Pleiades and Hyades due to M29 and M52’s relatively small angular size. The Pleiades and Hyades are much brighter than M29 and M52, so they may have been better candidates for observation. Our current observational setup has a limited field of view, as discussed in Chapter III, so Pleiades and Hyades were not observed. The downside to observing M29 and M52 over Pleiades and Hyades is that M29 and M52 have lower magnitudes than Pleiades and Hyades, so observing all of the stars in these clusters require longer exposures with our current equipment. As was demonstrated in this experiment, the tracking established was not sufficient to obtain longer exposures, and the CCD camera was overexposed at higher exposures.

While I was unable to judge the ages of M29 and M52, I did develop a reliable and reproducible method for obtaining photometric measurements of star clusters at the

University of Nevada, Reno. With this experimental methodology future observations can ! 20! expand on my work to obtain supporting data that may lead to publication or collaboration with other universities or government institutions.

Future Work

This thesis has demonstrated that university based telescopes of this type can provide meaningful data that can be used to characterize astrophysical objects. While this particular experiment lacked a sufficient number of data points to make strong conclusions, the method for taking and analyzing photometric measurements has been developed through this project. Future work on this subject can be performed at UNR with the aim of characterizing larger clusters through the use of several exposures per cluster that cover different sections of the cluster. While this technique is hard to implement, I believe that it is an effective way to greatly increase the number of observable stars in a cluster, which allows for stronger conclusions to be drawn from the obtained data. The observations performed for this thesis show great promise in providing a tool for important astrophysics research at UNR that can be used to support larger projects around the country. It is my hope that another student will take what has been implemented in this thesis and expand on it to obtain specific values for star cluster ages.

Future experiments can take advantage of several pieces of equipment at UNR.

The Physics Department possesses a computer program that can use CCD tracking to prevent field rotation by fixing the image on several bright stars in an image during exposure. The department also has a wedge mount that will help reduce field rotation problems in images. Using these pieces of equipment, along with larger telescopes, future ! 21! students and faculty can further develop the observational procedures established in this thesis.

! 22!

Bibliography

1. Chromey, Frederick. To Measure the Sky – An Introduction to Observational

Astronomy. New York: Cambridge University Press, 2010.

2. Mucciarelli et al. "Near-Infrared Photometry of Four Stellar Clusters in the Small

Magellanic Cloud". The Astrophysical Journal. 690:288-294. 2009, January

3. Gorbikov, Brosch. "Grey Milky Way Extinction From SDSS Stellar Photometry".

Monthly Notices of the Royal Astronomical Society. 401,231-241. 2010.

4. Zoccali et al. "Age and Metallicity Distribution of the Galactic Bulge From Extensive

Optical and Near-IR Stellar Photometry". Astronomy & Astrophysics Manuscripts. 2008,

February 2.

5. Garcia-Barreto et al. "UBVRI Photometry of Stellar Structures Throughout The Disk of the Barred Galaxy NGC 3367". The Astronomical Journal. 134:142-157. 2007, July

6. Sichevskij, S. G. "A Method for Determining Stellar Parameters from Multicolor

Photometry". Astronomy Reports. Vol. 56, No 9:710-715. 2012

7. Ames Research Center Kepler Photometer and Spacecraft. NASA Ames Research

Center. Web. 15-Nov-2012. Retrieved from: http://kepler.nasa.gov/Mission/QuickGuide/MissionDesign/PhotometerAndSpacecraft/

8. NASA FY 2012 Budget. NASA. Web. 14-Nov-2012. Retrieved from: http://www.nasa.gov/pdf/516674main_FY12Budget_Estimates_Overview.pdf

9. Maoz, Dan. Astrophysics in a Nutshell. New Jersey: Princeton University Press, 2007.

10. Gene Smith's Astronomy Tutorial. University of California, San Diego. Web. 25-Oct-

2012. Retrieved from: http://casswww.ucsd.edu/archive/public/tutorial/HR.html ! 23!

11. Hubble Views the Globular Cluster M10. NASA Hubble Mission. Web. 12-Nov-

2012. Retrieved from: http://www.nasa.gov/mission_pages/hubble/science/cluster- m10.html

12. Bohm-Vitense, Erika. Introduction to Stellar Astrophysics – Volume 1. New York:

Cambridge University Press, 1989.

13. SIMBAD Astronomical Database. UDS/CNRS. Web. 2-Oct-2012. Retrieved From: http://simbad.u-strasbg.fr/simbad/

14. SBIG Software Page. Aplegen Inc. Web. 24-Sep-2012. Retrieved from: http://www.sbig.com/Software.html

15. ImageJ Software Download Page. National Institutes of Health. Web. 25-Oct-2012.

Retrieved from: http://rsb.info.nih.gov/ij/download.html

16. Frederic V. Hessman. ImageJ Astronomy Package Download Page. Web. 30-Oct-

2012. Retrieved from: http://www.uni-sw.gwdg.de/~hessman/ImageJ/Astronomy/

17. Kim, Seung-Lee, Lee, See-Woo. "Variable Stars in the Open Cluster M29". Journal of the Korean Astronomical Society. 29:31-41. 1996

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Appendix A: Raw Data

Brightness/contrast adjusted images taken during my observations and used in my analysis are contained in this appendix.

M29, B filter, 15s exposure, 26-October

! 25!

M29, R filter, 15s exposure, 26-October

M29, V filter, 15s exposure, 26-October

! 26!

M52, B filter, 15s exposure, 26-October

M52, R filter, 15s exposure, 26-October

! 27!

M52, V filter, 15s exposure, 26-October

Vega, B filter, 15s exposure, 26-October

! 28!

Vega, R filter, 15s exposure, 26-October

Vega, V filter, 15s exposure, 26-October

! 29!

Dark image, 15s exposure, 26-October

Flat field image, no filter, 5s exposure, 26-October

! 30!

Appendix B: Data Reduction

This appendix gives an outline on how I obtained intensity measurements from the data that I had.

Step 1: Converting images from “.ST7” to “.FITS” format

Images were opened in CCDops and the following path was used to export the images as “.FITS”: File - Export As

The “FITS Image” option was chosen from the export options, and the images were saved in a folder separate from the original images.

Step 2: Opening the image in ImageJ and loading the Astronomy Tools Plugin

Once the image was converted to “.FITS” I opened it in ImageJ and proceeded to load the Astronomy Tools Plugin. The plugin can be loaded using the following path:

Plugins – Macros – Install

I selected the astronomy tools plugin as the input for this path and proceeded to use the aperture photometry tool to obtain data. Before I took data from the image, I proceeded to load the dark image and subtract the dark from the image I was processing.

Step 3: The Aperture Photometry Tool and How to Use It

The aperture photometry tool is what I used to obtain data values from the stars in my images. In order to use the tool, it must be configured, so I configured the tool by ! 31! double-clicking on the tool to open its configuration menu.

Opening this menu displayed the following:

The options I selected are shown in the previous image, and the numbers displayed for the various radii were taken from each image. In order to obtain each number I first obtained a radius of a bright star (in pixels) by zooming-in on the image and measuring the radius by hand. The following image is of a zoomed-in example star: ! 32!

The values for the background radii were just incremented by two pixels each. After obtaining these values and inputting them into the program I proceeded to the second window of options for the Aperture Photometry Tool, but the options shown in that window were not changed. Once the configuration of the tool was finished, I proceeded to point and click on various stars in the image using this tool, and the data were summarized in a chart that was generated during the use of the tool. The following image is a cluster image that was in the middle of being processed. The red circles represent the points that the Aperture Photometry Tool analyzed:

! 33!

Once the data values were compiled into the Aperture Photometry Tool table, I saved them into excel format by selecting the table window and using the “Save As” function.

The excel sheet was then set up with the proper equations, and final values and graphs were obtained through simple excel commands and formulae.