Techniques in Organic Chemistry Summer 2016

Thin Layer Chromatography

            Thin layer chromatography is a method used to determine the purity of a solution or monitor a reaction progress. A TLC sheet is comprised of a silica gel. Based on the polarity of the molecule and the solvent used, each molecule will move a different distance on the gel. After the solvent moves the entire length of the sheet, the spots from the each molecule can be viewed under UV light.



A rotovap is a device used for the efficient and gentle removal of solvents by evaporation. A sample is attached to a vacuum while rotating in a heated water bath. The solvent then evaporates at a lower temperature than usual and recondenses on the cold finger which is filled with dry ice.



Column Chromatography

Column Chromatography is a way to purify a mixture of compounds based on the polarity of the individual molecules. The glass tube is filled with a silica gel that allows less polar molecules to travel through the column more quickly. Multiple fractions can be collected and then TLC analysis can be used to determine whether each fractions is completely pure.




H NMR is a method to determine whether we made the correct compound. Each hydrogen atom in the compound shows up as a different peak. Each peak can be compared to literature values.

h nmr


Enzyme Kinetics

Part of our research this summer was to determine whether the compounds we made were inhibitors of the enzyme beta-galactosidase using a standard procedure to test the rate of an enzyme. We added the enzyme to a substrate solution and, after a certain period of time, the solution turned yellow and the absorbance was able to measured. From there, we could determine the rate of the enzyme.



Members of Jameson’s Research lab for the Summer 2016:

Julia Harper

Alex Delenko

Cordell Boggs

Ryan Moran








Exploring the role of dnaQ in the bacterial response to quinolones and understanding the role of plasmid DNA in biofilm formation

Hello everyone! My name is Sarah DiDomenico and I work in Dr. Whatley’s lab! This summer, I am exploring the role of dnaQ in the bacterial response to quinolones. Quinolones are a class of antimicrobials that prevent DNA from unwinding by inhibiting topoisomerases. We are using the quinolone nalidixic acid. This particular type of drug causes cytotoxicity (cell death) by binding to the specific topoisomerase, gyrase. Gyrase is a protein found ahead of the replication fork that allows DNA to unwind for duplication. Once the quinolone is bound to gyrase, a stable cleavage complex is formed. Ultimately, this causes double strand breaks, and the accumulation of double strand breaks lead to cell death. However, the mechanism of double strand break generation is not clearly understood.

We propose the replication run-off model as a mechanism of double strand break generation. The beta and epsilon subunits are proteins found at the replication fork. We want to know how epsilon is involved in creating double strand breaks. We believe the epsilon-beta interaction directly affects the amount of double strand breaks created when bacteria are treated with quinolones. I am testing dnaQ mutants to identify its role in creating double strand breaks. The mutants I am using have varying interactions with the beta clamp. I am also using survival assays to test which mutants are sensitive to nalidixic acid. I am excited to see results over the coming weeks!

In the meantime, I am also contributing to Nene’s main biofilm project by using SEM to image biofilm formation. I optimized the imaging protocol in order to get a clear image of the Microbacterium and Chryseobacterium biofilm structure using Scanning Electron Microscopy.

Microbacterium and Chryseobacterium

SEM image of a biofilm formed by Microbacterium and Chryseobacterium. 

Microbacterium & Chryseobacterium Biofilm _Jun 30_5

SEM image of a biofilm formed by Microbacterium and Chryseobacterium. 


Hello! My name is Nene Sy and I am a rising junior here at Gettysburg College. Along with Sarah, I work in Dr. Whatley’s lab. I’m continuing the biofilm project I started in the fall of 2015, and now I’m getting some really exciting results! You might be asking yourself, “What is a biofilm?” Well, biofilms are communities of microbial cells that adhere to surfaces. These bacterial cells surround themselves with a self-produced extracellular matrix, supporting its functional and structural integrity.

In the lab, our research aims to determine which factors drive biofilm formation. For this project, I am determining whether environmental isolates Chryseobacterium hispalense and Microbacterium oxydans have plasmids. Plasmids are extrachromosomal DNA that have the ability to replicate independent of chromosomal DNA. Plasmids are vehicles of gene sharing between the same species. They can also be shared between different species of the same or different genera. Additionally, there are studies that have found biofilm-promoting genes on plasmid. Understanding the role of plasmids in biofilm may help prevent certain infectious biofilm-related diseases.

Starting this new part of the project marked the start of a war between the plasmids and me. I did a standard plasmid extraction on M. oxydans and C. hispalense and ran a standard gel. Images of the gel showed that both isolates harbored plasmids but because the plasmids were so large, I was not able to determine their sizes.


Screen Shot 2016-07-23 at 10.25.10 PM

WFO= C. hispalense;      WFW= M. oxydans.

To characterize these large plasmid, I decided to use a pulsed-field gel electrophoresis (PFG), a method that resolves large DNA fragments. I lysed the samples in agarose plugs, which decreased the chance of contaminating our plasmid samples with sheared chromosomal DNA.

After running multiple PFGs of M. oxydans, we were certain that there was a plasmid present. Unfortunately, we were getting regions of smearing. We began to troubleshoot. My protocol was initially for Gram-negative banter, like E. coli, which are easier to lyse. M. oxydans, on the other hand, is a Gram-positive bacterium; its cell walls consist of a thick peptidoglycan layer, which makes it harder to lyse than Gram-negative bacteria.

After reading PFG protocols for Gram-positive bacteria, Dr. Whatley helped me think about ways to modify my plug making method. After multiples iterations, we settled on a method, and I immediately saw less smearing. There was still incomplete lysis of M. oxydans, which made it clear that there was at least one other variable we needed to tweak.

In addition to lysis conditions, literature on PFGs consistently spoke about fresh vs. old cultures, the number of washes, staining of the gel, and the size of the plugs loaded into the gel. After considering this information, Dr. Whatley and I decided that I would test these variables out. I ended up increasing the lysis duration, increasing the number of washes, changing the staining of the gel, and loading the gels with 66% less sample. Immediately, my results were cleaner.


In lanes 2 and 3 there is a lot of DNA trapped in the well, suggesting that M. oxydans cells were not completely lysed under 1.5hr and 4hr lysis conditions.

The changes to the protocol really helped. In lane 4, there is not as much DNA trapped in the well, and you can see my plasmids!!! This is extremely exciting! All of the troubleshooting finally paid off!

There is minimal smearing in lane 4, and I think this means there are still nucleases in the sample. In the next plug prep, I will increase the proteinase K concentration. Proteinase K digests and inactivates DNases and RNases, which are enzymes that chew up nucleic acids (DNA).

Now that I have optimized my protocol, my next steps are to isolate the C. hispalense plasmid and determine whether M. oxydans’ plasmid is linear of supercoiled. I believe that the M. oxydans plasmid is supercoiled because of my recent PFG results. However, linear plasmids have been detected in species that are closely related to M. oxydans. 

Sarah and I are looking forward to our future experiments and we hope that you all enjoy our blog!








Sea Turtles, Sound, and Sun Block

Hello, fellow STEM enthusiasts! My name is Emily Waddell and I’m working with Dr. Wendy Piniak, from the Environmental Studies Department, this summer. Dr. Piniak and I are spending eight full weeks off of Gettysburg’s campus conducting research in Bahia de Los Angeles, Baja California, Mexico and at the Duke University Marine Lab (DUML) in Beaufort, NC.


Me and Dr. Piniak

Collaborations in Bahia de Los Angeles

When Dr. Piniak first explained the Bahia trip to me, I was beyond excited and could not contain my enthusiasm to get to travel to a new country, conduct field research with her, and meet other scientists and students from around the country. Our field site host is Ocean Discovery Institute (ODI), an impressive non-profit organization dedicated to helping and empowering underserved youth from the San Diego area through science and leadership opportunities. They recently won the 2016 CNN Hero Award! Even with Dr. Piniak’s numerous warnings of lack of down-time, draining heat, and early mornings, I was eager to hop on the plane mid-June. We flew to San Diego, CA then drove 12 hours south to Bahia de Los Angeles, a small coastal town on the west side of the Gulf of California.

Dr. Piniak’s lab focuses on sensory ecology which is the study of how animals receive and respond to sensory cues in their environment. Our specific focus is on how sea turtles and other marine animals respond to acoustic cues. While in Baja we worked with a collaborative team made up of Ocean Discovery Institute students and staff, NOAA Fisheries scientists, engineers from FlyWire Cameras, and local fishermen to study how we can use sound to prevent sea turtles from becoming fisheries bycatch (any organism that is unintentionally caught in fishing gear), which can cause injury or death to the sea turtle. Reducing sea turtle bycatch is crucial for the success of sea turtle protection and conservation because fishing gear/nets are a large threat to sea turtle populations globally. Our team’s goal is to develop sensory-based bycatch reduction tools that will reduce sea turtle bycatch but maintain target catch. Dr. Piniak’s previous research shows that sea turtles can hear low frequency sounds (<2000 Hz), and previous research in Bahia has shown that putting low-frequency sound on nets reduces sea turtle interactions with nets. With the help of FlyWire this year, we tested new underwater Acoustic Deterrent Devices (ADDs), which emit low frequency sounds. Our hope is that when these ADDs are attached to fishing nets, the sea turtles will change their behavior and move away from the net, preventing sea turtles from becoming bycatch.


The new underwater ADD

ADDs in Action

On my second day in Bahia, we went into the field with these new ADDs. Dr. Piniak and I set out to test the impact of ADDs on sea turtle catch with Jorge, an Ocean Discovery Institute Research Fellow, Tony, a FlyWire technician/representative, Joel, the Director of Research at Ocean Discovery Institute, and Ricardo and Guero, two local fishermen who are part of a local sea turtle monitoring group (Grupo Marino). We set a pair of 90 meter gillnets, an experimental net and a control net, at La Gringa Bay. We attached five ADDs on the experimental net placed 20 meters apart. Each ADD played tones between 200 and 500 Hz (the best hearing range of sea turtles).  On the control net we attached flower pots that resembled the shape and size of the ADDs but did not emit any sound. We then checked the nets every 75 minutes to see if any sea turtles were caught. If a sea turtle was caught, we brought the turtle on board the boat and recorded the species, time it was found, and which net it was caught in. As part of the local sea turtle monitoring program, we also collected size measurements (curved carapace length and width), made note of any previous tags, and if no tags were present, tagged the turtles using iconel flipper tags. I got to tag and release my first green sea turtle that day. It was absolutely incredible! We ended up catching two green sea turtles in the experimental net and one in the control net.


A green sea turtle (una tortuga)

Over the next two and a half weeks, we went out on two more turtle ADD days, setting nets at Punta Arena and Guadalupe. Both days lacked turtles, but were great experiences and opportunities for me. On these subsequent turtle ADD days, I got to meet Dr. John Wang, a fisheries ecologist, who works for NOAA, Jacob Isaac-Lowry and Sarah Alessi, the founders of FlyWire, and Christina Fahy, a fishery biologist from the NOAA Fisheries. I received lots of graduate school advice from them, learned about their careers, and got to know them on a personal level. Turns out Dr. Wang did similar research in undergrad as I did last summer at DUML (how fiddler crab megalopae respond to adult conspecific chemical cues) and Sarah is from the same hometown that my mother went to college in and has done work in Panama, where I studied abroad last fall.


Left to right: Jacob, Tony (attaching an ADD to the experimental net), and Christina

Team Bycatch

This summer was Dr. Piniak’s fourth year collaborating with this team and the third year she has brought a student from Gettysburg to Bahia with her. I’m so privileged to have worked with such a wonderful professor and team. The people definitely made the experience for me. During our second week, we were joined by nineteen high school students, Ocean Discovery Institute’s Ocean Leaders. Each student was a member of one of three research teams: Team Bycatch, Team Spatial Subsidy, and Team Photobiology.


Two Ocean Discovery Institute students and Dr. Piniak

Our group, Team Bycatch, has two research projects going on simultaneously this summer. They want to see if ADDs affect not only sea turtle catch rates, but also the local target fish catch. It would be great if the ADDs decrease sea turtle bycatch; however, how useful would they be to local fishermen if their target catch (ex. halibut, trigger fish, etc.) can also hear the ADDs and swim away from their nets? Therefore, it’s important to also examine how ADDs affect target catch. To examine fish catch we attach the ADDs to nets fished by local fishermen, mimicking the pair of control and experimental nets we used to observe turtle catch. We set nets in the morning and when we pulled nets in the afternoon, we recorded fish species caught in each net and these catches are compared to see if the ADDs alter the target catch.

Team Bycatch is also investigating if electronic monitoring (EM) is a better, more cost effective, and accurate alternative to having an onboard observer record catch data. Two gillnets are set every evening around 19:00 and then pulled the following morning. Two students and a fisherman haul in the net, recording every species caught in the net, the sex of the fish, its state (if it is alive, dead, or injured), if there are signs of depredation, its value (if the fish is a target species to be sold for money, kept as bait or food, is bycatch (incidental), or would normally be kept but has to be thrown back because of the catch restriction (veda)), and measure it. Meanwhile, a camera, designed by FlyWire, is recording the entire time and set right above where the net is being pulled in, so that it can capture the entire view and species brought up. The students then enter their onboard data into Excel and then watch a video from the boat they were not on and record any fish they see. The onboard list is then compared to the list created after scoring (watching) each video to see how effective and accurate EM can be.


A FlyWire camera attached to a boom


Dr. Wang and a student measuring a sharpnose

I had the opportunity to help with both research projects. I especially enjoyed pulling nets and measuring the fish! We caught sharpnose sharks (Spanish name: purito), manta rays (manata raya), smooth butterfly rays (mariposa), halibut (lenguado), guitarfish/shovelnose (guitarra), electric guitarfish (guitarra electrica), catfish (bagre), and cownose rays.


Measuring a smooth butterfly ray

Highlights of Bahia

One of my favorite parts about Bahia was working and bonding with the students. They were all so eager to learn and would dive right into the research. However, I also got to bond with the students outside of a scientific atmosphere doing Zumba with them and playing soccer in town. It was so much fun and all the students called me “Speedy Gonzalez.”

I can also check “swim with a whale shark” off my bucket list. At the end of my first week in Bahia, after hours of calibrating the ADDs (making sure they were playing the sound/frequency they were supposed to at the correct volume), Dr. Piniak, Tony, Ricardo, and I went to the whale shark zone and within minutes of boating along, Ricardo spotted one. I was so excited that I grabbed my snorkel and camera, and just jumped in (not even bothering to take off my shirt and shorts). Swimming next to such an elegant creature was breathtaking. It was just me and this gentle giant swimming next to each other for a couple minutes.


A whale shark!

Every night we slept on cots under the stars and right next to the water. Because we were in such a remote area under a cloudless sky, the stars were so bright and a shooting star could be seen each night. And every morning, I was woken up by the rising sun at 5:30 am. I’ve never been a morning person, but the beautiful sunrises made each morning worth getting up early.


The view from my cot every morning

I want to give a huge thank you to Dr. Piniak for letting me join her research team this summer and bringing me down to Bahia and all the scientists and ODI students and staff I met for making Bahia a memorable, fun, exciting experience!


Team Bycatch

Now, Dr. Piniak and I are at the DUML, where Dr. Piniak is teaching a sea turtle biology and conservation course and I am working on my honors thesis with Dr. Piniak and Drs. Kathy Reinsel and Jim Welch from Wittenberg University (and my REU advisors from last year). Stay tuned for a DUML post!


*All research described in this post was conducted with appropriate Mexican research permits and Gettysburg College IACUC protocols.

A Walk in a Panamanian Rainforest


The research crew analyzing data (From left to right; Brendan Dula, Dr. Alex Trillo, Meghan Brady, and Sarah Smith)


We are conducting research at the Smithsonian Tropical Research Institute (STRI) in Gamboa, Panama.  Gamboa is a small town that was originally built to house the US workers of the Panama Canal. Today, it houses many STRI researchers who conduct studies nearby in Soberanía National Park or Barro Colorado Island. Our research is focused on the relationships between two species of frog (The túngara frog and the hourglass treefrog), a species of frog eating bat (the fringe-lipped bat), and a genus of frog-biting midges (Corethrella). Before we explain our projects, here is a little more information on the cool organisms that form part of this story.


The túngara frog, Engystomops pustulosus, likes to breed in large puddles or ponds, where they call to attract females. These frogs are an important part of the food chain, as a lot of predators, such as snakes, herons, bats, and other frogs, eat them regularly. Hence why Dr. Trillo, our advisor, calls them “the popcorn of the jungle.” Túngara males can produce simple or complex calls. Simple calls consists of a single “whine” that decreases in frequency. In complex calls, the whine is followed by another note call the “chuck.” Females choose a male based on its call, preferring males that produce complex calls. These calls, however, are also highly attractive to predators and parasites. The fringe-lipped bat, Trachops cirrhosis, is a leaf-nosed bat found in tropical dry and wet forests that uses echolocation to hunt insects and frogs in the early hours of the night. Fringe-lipped bats are highly attracted to the complex calls produced by túngara males. Frog-biting midges in the Corethrella genus are small flies that parasitize frogs. Adult female flies of this genus are attracted to the mating calls of male frogs and they also parasitize males using complex calls to a greater degree than those making simple calls. Finally, the hourglass treefrog, Dendropsophus ebraccatus, has large breeding habitats and shares breeding ponds and swamps with túngara frogs.


Many frog species call from the same pond during a given night, forming mixed species choruses. In such aggregations, individuals of one species could be affected by the presence of frogs of other species calling around them. The question that we are trying to answer is whether hourglass treefrogs are affected by calling near individuals of another frog species that is highly attractive to predators and parasites, such as the túngara frog. Our advisor, Dr. Trillo, came up with two alternative hypotheses: (1) Hourglass treefrogs suffer more predation and parasitism when calling near a calling túngara frog than when calling near members of the same species (Collateral Damage Hypothesis) because both bats and flies will sometimes make foraging errors, attacking the neighbors of frogs they originally targeted; (2) Hourglass treefrogs suffer less predation and parasitism when calling near a túngara frog than when calling near members of their own species (Shadow of Safety Hypothesis) because both bats and flies will preferentially choose the more attractive individual. Previous research by Dr. Trillo showed that although there is no difference in bat predation on hourglass treefrogs when calling near túngara frogs versus those calling near members of their own species, frog-biting midges were much more attracted to hourglass treefrogs whenever they called next to túngara frogs.

The questions that came up after these findings were: Does this increase in parasitism vary depending on the type of túngara call (simple versus complex)? In other words, do whine-chuck calls cause higher levels of collateral damage on nearby calling hourglass treefrogs than the simple whine calls? and do you see the same effect on the hourglass tree frogs when túngara frogs are highly abundant versus when there is only one túngara calling? So, these are the questions we are trying to answer this summer!

Brendan and Meghan after setting up the equipment at one of the field sites


So, how do we answer these questions? Everyday around 5:30 pm we go to Dr. Trillo’s house and gather everything that we’ll need for the night. We have seven sites, and for each site, we alternate between four different treatments: (A) A speaker with a calling hourglass treefrog next to another speaker with a calling hourglass treefrog, (B) A speaker with a calling hourglass treefrog next to a speaker with a túngara simple call, (C) A speaker with a calling hourglass treefrog next to a speaker with a túngara complex call, (D) A speaker with a calling hourglass treefrog next to five speakers with túngara complex calls.

160630 Cam1 E 01.MP4_snapshot_17.49_[2016.07.21_16.14.20].jpg

A still image of a bat predatory visit to one of our speakers. It was attracted by the frog call and is now attempting to echolocate the frog.

Once we arrive at the first site of the night we set up either 2 or 6 speakers depending on which of the treatments we’ll be doing. We set up video cameras and IR lights to film the bats that visit our speakers. We also apply sticky goo to plastic sheeting placed over two of the speakers in order to capture frog biting midges. Then we get to hang out in the forest for 80 minutes and sometimes we get to see really cool forest critters like glass frogs or caimans!!! (see picture below). We repeat this same process one more time, later in the night, to get as many data points as we can. We are only here for 2 months! The next morning we count the frog biting midges that we caught the night before and watch bat videos and score the number of predator visits, normally bats. Every now and again we also get to head to Panama City for project supplies, groceries, or lectures given by other STRI researchers, which are super interesting!


A baby caiman we came across one night


We’re also collaborating with Dr. Ximena Bernal on another project, which we’ve unofficially dubbed the Poolito Project. That’s because we’ve set up a modified circular kiddie pool, with 24 upside down terra cotta pots in it for hourglass treefrogs to call from. We assembled this incredibly scientific set up to test if hourglass treefrogs choose different calling locations in response to the collateral damage they experience near túngara frogs. We go out and catch male hourglass treefrogs, transport them in super high tech Tupperware containers or Ziploc Baggies, and place them in the center of our kiddie pool/arena. After 3 minutes of settling time below a funnel we let them loose and play them either hourglass treefrog calls, tungara calls, or both together, and record whether they choose to call away from or close to the speaker for each treatment.


Life in Gamboa is definitely not like life at Gettysburg. It’s not unusual to run into fellow researchers in the forest carrying machetes or weighed down with gigantic bags of scientific equipment. Every day there seem to be new creatures to see – from the family of capybara crossing the road in the early morning to the lizards that have taken up residence in our bathroom. The animal diversity here is incredible! We’ve learned a lot about field work trouble shooting, too. There was one night where we had to make our own camera tripods out of palm fronds and another where we walked all over town searching for a building with working wifi (ours goes out when it rains) so that we could post a certain blog before its deadline…. The most important thing we’ve learned? If you want to be a field researcher, be prepared for a lifetime of adventure.


Sarah Smith investigating a tree near a field site


A toucan that likes to visit Dr. Trillo’s yard


We also have had opportunities to work with other researchers by helping them to capture bats using mist nets!

Summer Living in Woods Hole, MA: MBL and the Grass Lab

The primary focus of research in the Kittelberger lab is the singing behavior of the plainfin midshipman fish. The plainfin midshipman uses this singing in order to attract females for courtship; using their swim bladder muscles, the midshipman creates its song by vibrating its inflated swim bladder and producing a humming sound. In the past, Dr. Kittelberger has looked at things such as the role of periaqueductal gray, a midbrain structure involved in vocal production across vertebrates, in vocal patterning, and the potential role of dopamine in shaping vocal and social behaviors in midshipman. Currently, we’re investigating the possible involvement of a region in the far back of the brain and along the spinal chord, the supra-medullary nucleus, in this behavior. We’re currently doing immunohistochemistry stains in order to identify which neurons are active during vocalization. What this means is that we react brain slices with specific antibodies that bind to a protein that is expressed in active neurons. Through this series of reactions, we end up with fluorescent proteins bound to the areas of the brain sections that were active during singing, indicating their involvement in vocalization. Our hope is that these supra-medullary neurons will ‘light up’ when fish hum, but not when they are engaged in other behaviors, such as listening to other fish. We’ll thus be able to have some indication that these neurons are involved in vocalization. This is interesting because of the elusive nature of the supra-medullary neurons in fish. Little is known about this region of the fish brain, and even less is known about its function. Previous work in other fish species has suggested that the supra-medullary nucleus may be involved in the production of protective mucus on the skin surface, and the sensation of stimuli – such as touch – that trigger mucus production. Thus a role for this brain area in vocalization would be novel, and could, for example, be related to the presence of pheromones in mucus being released when the fish sing. This is obviously an intriguing idea, as pheromones could play an interesting role in the olfactory side of mate attraction and courtship.

In addition to the research being done in the Kittelberger lab, there is plenty of excitement around the Grass Lab, our home for the summer. Dr. Kittelberger is Co-director of the Grass Lab at the Marine Biology Lab in Woods Hole, MA. Founded by Ellen Grass, the Grass foundation is responsible for the creation and funding of the Grass Lab, where early-career investigators are provided an unparalleled opportunity to develop and conduct independent neuroscience research projects within the expansive and prestigious scientific community of the Marine Biological Laboratory, Woods Hole, MA. This year, there are eleven Grass Fellows who were afforded this opportunity. Some of them graduate students, some postdocs; the Grass fellows come from various backgrounds and fields within neuroscience. From innovating the research method of optogenetics to further understanding the inherent properties of hair cells, the projects being done in the Grass lab provide a great atmosphere of well-rounded diversity. In addition to all that can be learned from the research and fellows in the Grass lab, the MBL offers graduate courses year round, which include weekly public talks, socials, and other events. No only is this a great way to network and meet many accomplished scientists, but it is also a way to have fun and enjoy life here at the MBL.
*A special thanks to the Grass Foundation and the MBL for hosting us, and for providing me with this great opportunity to experience academic and professional diversity and excellence. *

Coding for Dummies

Sabrina Marell

When I first came here this summer, I had zero coding experience. I saw computers only as a tool to look at Facebook and binge watch many shows on Netflix. In the past few weeks, this has changed drastically. As Mikayla stated, our research is focused on looking at data that was obtained from running different simulations of galaxy cluster mergers, and from that data calculating different statistics so that we will be able to gain a better understanding of what is occurring before, during, and after the merger. We are doing this by creating a code that allows us to manipulate the data how we see fit. Personally, what I have been focusing on is calculating different statistics that help to define the substructure of a galaxy cluster. I have been focusing on calculating the skewness and kurtosis of the velocity of the galaxy clusters over time. Skewness is defined as:

Screen Shot 2016-07-14 at 3.23.47 PM

Where is the average value of the data, and is their standard deviation, and N is the number of data points. Skewness is a measure of the asymmetry of the distribution. For a normal distribution, skewness will be zero and any data that is symmetric should have a skewness near zero. If the skewness is negative that means that the data is skewed left and positive values mean that the data is skewed to the right. When I say skewed to the left, I mean that the left tail of the distribution is longer than the right tail and the opposite goes for when I say the data is skewed to the right.


Fig 1: What the distribution looks like when skewness is positive, zero, and negative respectively.

Another diagnostic for substructure is kurtosis, which is defined as:

Screen Shot 2016-07-14 at 3.24.07 PM

Where again is the average value of the data, and is their standard deviation, and N is the number of data points. If a distribution is normal, the kurtosis will equal 3. If the distribution has a sharper peak and/or the tails of the distribution are heavier, then it will have a kurtosis greater than three. If the peak is flatter and/or the tails are much lighter, then the kurtosis will be less than three.


Fig 2: What the distribution looks like with negative and positive kurtosis

To calculate the skewness and kurtosis, I wrote a code in Python that sampled data from the simulations over time and then from this I was able to perform the calculations on it. For each time step there are about 100,000 data points to choose from. For my program I make it so that each time the program runs through it resamples about 100 data points randomly from the large pool of data we have in the file. The data that we have is data for velocity and position in the x, y and z direction for halo1 and halo2, which represent the 2 different clusters merging.


Fig 3: Distribution of velocity x, y, and z for halo1 and halo2 at time=0

Fig 4: Distribution of velocity x, y, and z for halo1 and halo2 at time=70

Fig 5: Distribution of velocity x, y, and z for halo1 and halo2 at time=110

After I calculated the skewness and kurtosis, I was then able to plot them over time with error bars that are the standard deviation, which is calculated over how many resamples we did at each time step.


Fig 6: Skewness of velocity x over time


Fig 7: Skewness of velocity y over time


Fig 8: Skewness of velocity z over time


Fig 9: Kurtosis of velocity x over time


Fig 10: Kurtosis of velocity y over time


Fig 11: Kurtosis of velocity z over time

On the plots for skewness, you can see for velocities y and z that the skewness for this particular merger is almost zero for both halos. We would expect both halos in this particular case to act very similar since they are the same mass and colliding head on. However, for velocity x, halo1 and halo2 are seen to be behaving differently, we are going to be looking into why this is occurring and what this is telling us about the merger. In the future I am going to be looking at these plots of skewness and kurtosis over time not just for this particular simulation, but also for simulations of mergers where the clusters are either of different mass or the collisions are not head on. I will then be analyzing the plots, seeing how they differ from each other, and what these plots are telling us about the merger itself. This week, I am also going to be writing a program for the Lee Statistic, which is another statistic that is a diagnostic for substructure.

Looking at a computer screen for 8 hours a day can get very mentally exhausting so in order to give our minds a break Dr. Johnson, Ross, Mikayla, and I all go out and have a hack break. We like to find a nice shady spot and hacky sack with each other for about 30 min to an hour. This hack break not only allows us to give our minds a break for a little bit and recharge, but I feel like it has also made the environment we work in feel like a relaxed and comfortable one. It also is really great physical activity; even if it doesn’t seem like it, you can really work up a sweat! We also sometimes go out to the observatory here on campus to take images of different things in space.

In addition to working with Dr. Johnson I also had the opportunity to travel to Flagstaff, Arizona with Professor Milingo and work with her and two other students, Mikayla and Ross, in gathering data on the star cluster NGC 6811. We were able to do this because Gettysburg College is part of a Consortium called NURO. NURO allows us to operate a 31” telescope at the Lowell Observatory in Flagstaff for four nights so Dr. Milingo is able to gather data.


Fig 12: 31’’ NURO telescope

Now these nights that we went to gather data were a little different than usual because, as Mikayla so accurately described it on Facebook, it was the week our sleep schedules were backwards. A typical night would be us heading out to the observatory at around 6:15pm.

IMG_1539 Fig.13: Us outside the observatory with Dr. Milingo

We would arrive at the observatory armed with a lot of ramen, and at times even a toaster and some avocadoes if we felt like being extra fancy later on in the night. Then after we set up camp we would go into the warm room and turn on all of the computers and start taking some bias shots (taking a picture with 0 second exposure) and flats (taking a picture of a blank, evenly colored part of the sky) with the telescope. Both are used to subtract any noise or other false data from our images. Then after the sun had finally set, we would begin to take images of the star cluster. We would take 6 images at once, which would take 30 minutes, and in between we got to play games like Cards Against Humanity (Milingo loved that one), go look at the night sky (which was breathtaking especially because we were able to see the Milky Way and you know space is cool and all that jazz), or watch a lot of Netflix (I got through an entire season of the show Bones). Then once the images were done we would check the focus and then just start the process again. This went on until around 4am. Though this trip was filled with sleep deprivation, it also was able to teach each of us how to operate the telescope (who we lovingly call Steve now) and gave us the opportunity to experience what observing is like and what a life as an observational astronomer could include. Also, besides all of the educational aspects of the trip we were also able to tour Flagstaff and Sedona, experience eating a burger at In N’ Out, and we got to go the Grand Canyon one day; which is a sight I feel as though everyone needs to see at least once in their lifetime, even those who are terrified of heights like me. This experience was so unbelievable and was truly unforgettable.


Fig 14: Seen while driving to Flagstaff, Arizona


Fig 15: Our beautiful lunch we had at In N’ Out


Fig 16: South Rim of the Grand Canyon

Galaxy Clusters and the Grand Canyon

Mikayla Cleaver

Dr. Johnson’s Lab

Both Sabrina Marell and I are working on the understanding of galaxy cluster mergers (the combining of two galaxy clusters).  Galaxy clusters are some of the largest structures in the universe and are formed through gravity acting upon galaxies, pulling them together.  Now, if gravity were the only force acting upon the galaxies it would be expected that the clusters would form in spheres.  In reality, the galaxies flow along filaments and, in between, there are voids of empty space, creating an almost cobweb-esque look (Figure 1).  This leads scientists to believe that there is a repelling force acting upon the galaxies that we can’t see- this is what is called Dark Energy. It is believed to be the cause of the expansion of the universe.  Simulations were run, such as the one below, to create data sets to work with in research, like Sabrina and I are doing.  The simulations we used were run with two groups of galaxies, either each the same mass or one ten times as massive as the other.  The collisions were then either head on, offset by half, or just missing each other.  By using these combinations of data sets, we are able to get a good picture of what is actually happening in the universe.

millenium simulation

Figure 1: Image of universe structure from Millennium-II simulation. The dark areas represent the voids, or empty space, and the lighter areas represent where normal matter, such as galaxies is found.

My Research:

My research focuses on the methods used to measure the masses of these large galaxy clusters.  The method I am most focused on is using the Virial Theorem to calculate the mass using the average velocity of the cluster.  The Virial Theorem states that to keep an object in orbit its gravitational potential energy must be equal to two times its kinetic energy (this is shown through a really long proof that I’m not going to show here).   This equation (in the simplest form), solved to find the mass of an object, should then read:

equation           (1)

Where, in our case, M is the total mass of the galaxy cluster, R is the radius of the galaxy cluster, v is the mean of the velocities of all the galaxies within the cluster, and G is the gravitational constant.

Now, you would think that using this equation wouldn’t be a problem when estimating the mass of these galaxy clusters since we are able to find an average velocity for galaxy clusters.  But, what I am trying to show in my research is that the velocities of these galaxy clusters are actually changing during a merger, which causes a problem when trying to use the Virial Theorem to solve for the mass. If the velocity does change during a merger of galaxy clusters, this could cause mass values to be widely over- or under-estimated depending on when the measurements are being taken.

To calculate this potential difference in velocity, or the velocity dispersion, I took data from the computer simulations depicting galaxy cluster mergers over time.  Using Python to write a code to read the data in from these files and do calculations on it, I was able to create graphs of sigma over time, with error bars.  There are graphs for X, Y, and Z velocities, where X and Y are the plane on the sky (so up/down and left/right) and Z is the recessional velocity, or the velocity where the galaxies are moving away or toward you.  In the simulations, there are 100,000 particles to choose from each time step (the time steps are every five million years).  In my code, I use around 100 to 1,000 particles, each representing a galaxy, from each galaxy cluster involved in the merger.  The particles are chosen at random by my program, each time it runs through.  This is referred to as “Monte Carlo sampling”.  The more particles needed from each cluster, the longer my program takes to run.

millenium sim2millenium sim3millem 4

Figures 2-4: The three plots above are 3D position plots at three important times.  The starting position (time = 0 Myr), when the clusters first cross over each other completely (time = 65 Myr), and when they are the farthest apart until they start to move together again (time = 170 Myr).

Using the velocities of these particles, my code then takes the standard deviation of this list of either X, Y, or Z velocities.  This is the velocity dispersion, or sigma (σ).  By then calculating the standard error of sigma, I am able to start to graph the values in Python. For each time step, sigma and the standard deviation of sigma is calculated and graphed versus the time step (Figures 2, 3, 4).   The error bars are the standard deviation of sigma.  As it can be seen, the velocity does change over time during a merger. Black vertical lines indicate when the clusters are first fully over top of one another, when they have passed through each other and are the farthest apart before they move towards each other again, and then when they are once again right on top of each other.  The blue and red indicate the different clusters.

x disp

Figure 5: Velocity dispersion in the X direction over time is shown here.

 y disp

Figure 6: Velocity dispersion in the Y direction over time.

 z dips

Figure 7: Velocity dispersion in the Z direction over time.

 In the next couple weeks, I am going to work on two things.  First, plotting the velocity dispersion over time for all of the simulations run and then taking that data and projecting it onto different axes to try to get a different perspective.  The second project I hope to attempt is more of a visual one.  I am Physics major, but I am also a Studio Art major.  Dr. Johnson and I are hoping to create 3D position graphs of the simulation data that are more interesting and visually appealing.  One specific graph I hope to create is actually interactive- it will be a 3D position graph, but with a slider to move the data through time and have the viewer witness they merger.

xy disp

Figure 8: XY projection of velocity dispersion over time.  See how it is a combined image of the X and Y graphs? That’s what it means to project onto the XY plane.

 I was also lucky enough to work with Dr. Milingo, another astrophysicist in the Physics department here, for a week in Arizona.  I worked alongside her, as well as Sabrina Marell and Ross Silver, in gathering data for Dr. Milingo’s research on spotted stars.  Gettysburg is part of a consortium called “NURO”, or the National Undergraduate Research Observatory.  With this consortium, Dr. Milingo gets allotted time to use the 31” telescope in Flagstaff.  We were able to learn to work and use the programs at the telescope to help take pictures of the star cluster NGC 6811, which Dr. Milingo then uses to analyze the sun spot cycles of these stars.  Out of our four allotted nights, we were able to observe for three, as it was cloudy the last night.


Figure 9: This is the NURO telescope, a 31” diameter telescope that we used to take pictures of the star cluster, NGC 6811.


Figure 10: (From left to right) Ross working on logging the images, Mikayla (myself) working on taking the images, and Sabrina working on moving the telescope and tracking our star cluster (yay Autoguider!).

Of course, we also had the chance to do a lot of fun activities while in Arizona!  The four of us were able to visit Sedona, a supposed “vortex”, see a movie, and visit the Grand Canyon.  It was truly an amazing trip.  Here are some of the sights from the trip:


Figure 11: The sun setting behind NURO.


Figure 12: The red rock formations of Sedona.

grand canyon

Figure 13: The Grand Canyon.

It’s not all just work in our lab- we also do some fun activities as a lab! Every day we take a “hack break” where Sabrina, Ross, Dr. Johnson, and myself (and sometimes Julia Giannini) go outside and hacky sack all together.  The point behind this is to take a break from staring at a computer screen all day and get some physical activity in.  Trust me, it’s WAY more physical than you think!  I once tracked my fitbit steps during an approximately 30-minute hack sesh, and it was about 1,500 steps! It’s also nice to activate a different part of your brain, and it sometimes helps me think through problems I have when trying to figure out how to code something.  We also sometimes go out to the Gettysburg Observatory and take images of star stuff, like planetary nebulae.  Then, we take our images and create a complete image of the object we were taking pictures of.


Figure 14: Ross, myself, and Sabrina hacking on the edge of the Grand Canyon.  (Don’t worry, it was posed!)

ring nebula

Figure 15: Our image of the Ring Nebula, taken with a CCD at the observatory on an 8” telescope.