Physics of Collective Animal Behaviour: Making a leap from inanimate particles and atoms to conscious social animals.

– Aawaz Pokhrel and Pranav Kayastha

A Day in the Life of Puckett Lab:

I think it is rather difficult to write about a typical day in our lab, mainly because everyday is at least somewhat different from the other. I guess it is the same smell of fish everyday, and the same flavour of coffee every morning, but what we do here is less predictable and more open ended. One reason why our days are not well scheduled and planned is because not a lot people have looked into collective animal behaviour from the lens of physics, and no one has done projects like that of ours. This means that we will not know what stumbling blocks are patiently waiting for us and that we will have to (try to) solve them on our own. I like to think that we are like an explorer venturing her way into the unknown and the uncharted, even though she knows the journey is going to be frustrating and tedious, because she is confident that when she gets there, her journey will shed light to her fellow travellers and the rest of mankind.


Figure 1. Collective behaviour in different species.

Throughout the summer so far, we have been facing multiple hurdles on our journey to gather and analyse data. Most of them are very open-ended with more than one solutions. How to keep the water temperature constant but without inducing a current? How do we decide on what parameters to focus on? How can we better handle and transport the fish to minimize stress?  So we get together, brainstorm ideas about how to solve a problem, arrive on a consensus, and then start working on the solution. The whole process is really exuberant and fun, even though there is no manual or guide to tell us what to do. In fact, I think it is this part of our research that makes it a valuable learning experience. In the words of Julia Giannini, who worked in the Puckett Lab last summer, “it takes some of the mystery out of the science.” Each day is different from the other but they are all tied in together by the overarching goal of our project – to examine the material and thermodynamic properties of fish schools to provide a more robust and testable benchmark for modelling collective animal behaviour.  

Our Research:

Collective behaviour are ubiquitous in social animals. Standard models of collective behaviour generally use self-propelled particles that interact with neighbouring individuals. While canonical models successfully describe qualitative features of collective structures in animal behaviour, they do not capture the dynamical behaviour of these systems in response to perturbations. The failure of models is due primarily to inaccurate interactions between individuals. However, determining these interactions from experimental data is a challenging if not impossible inverse problem as interactions are complex and stochastic.

Figure 2. All three of us at some point in our research.

In other words, collective behaviour is hard to understand, and we know very little about it. Or, in the words of our supervisor Dr. James Puckett, “we know a lot about the atom, and about stars, but then somewhere in the middle of these two scales—life, basically—it gets messy.” Our aim, broadly speaking, is to try to make sense of this mess. How does a flock of birds decide which exact turn to make? How does a school of fish arrive at a consensus and make decisions? How do people decide which way to run in case of emergencies like fire in the building where there is no way of knowing the shortest or the safest route? Most of the answers to these questions rely on how individuals interact with each other and how these interactions lead to emergent properties. In our lab, we use physics and physical laws to understand these individual level interactions. More specifically, we explore the group behaviour through the lens of physics and material science, like stretching, shearing, compressing the school of fish.

Figure2. Experimental tank with our fish school.

We observe the response of laboratory schools of negatively phototaxic (meaning they like to be in the dark spot) freshwater fish (Rummy nose tetra) to projected light fields using a high-speed camera and particle tracking set-up. Rummy nose tetras are particularly suitable for our purpose because they are extremely social, but without any social hierarchy within the group (which makes it less complicated). For our experiment, we use a light field which consist of two black boxes. Being negatively phototaxic, fish are naturally attracted towards these black boxes. Once they are in the box, we try to stretch the school by splitting the box from the center in two opposite directions.  We explored different velocities and distance of separation to see the effect on stretch parameter of the school.

Here is our sample data. The two boxes are where the dark spots are, which can be seen by the fishes but not by our infra-red camera.

Video1. Stretching a fish school by splitting the dark spot.

By now, we have collected enough data to start the analysis and calculate the school’s material properties. The analysis mainly consist of establishing that we were indeed able to stretch the group. Then we will analyse the acceleration of  the individual fish during the stretching and see if it behaves anything like what we see in physical materials like a rubber band.

Figure 4. Dr. Puckett and Aawaz.

What’s more exciting than corn?

Corn can be found all around us from our food to our fuel. Corn is the plant behind the scenes running the show. If you find yourself fascinated with this plant good news for you, read on to find out more about this a-maize-ing plant! Sorry, if that was a bit corny but I promise that was the last joke.

So this summer I have been investigating two different mutant genes found in corn known as teopod 1 (tp1)  and teopod 2 (tp2) with Professor Wills. Teopod 1 is a mutation that affects both vegetative and reproductive structures. Plants with this gene will have the following phenotypes: many tillers, narrow leaves, and partially podded ears. Plants with the teopod 2 phenotype will have many of the same phenotypes: many tillers, narrow leaves, and many small partially podded ears. But tp2 differs from tp1 in that it has a more severe effect on vegetative and tassel phenotypes and has less of an effect on the ear phenotypes.

The following is an image of an ear of corn we grew in the greenhouse with the tp1 phenotype. Teopod gets it name because some kernels are podded meaning they have extra husk leaves that cover individual kernels not just the whole ear. In the following image below, this is an ear of corn with those extra husk leaves. The next image below shows one of the extra husk leaves from that ear. Notice how the leaf is attached to a single kernel this is a phenotype associated with the teopod gene and does not occur in plants without this gene.

Image 1A blog

The following is an image of the corn we had growing in the greenhouse from  a few weeks ago. There are 5 different plant lines growing here, either W22 or B73, two of the main plant lines with lots of genetic information already mapped out. W22 and B73 do not contain the mutant genes. Another plant line, X contains tp1 gene and plant lines Y and Z contain tp2 gene. We pollinate plants so there will be a cross from a W22 or B73 plant to one of the X,Y,or Z plants that contains the mutant gene. So the F1 generation will be heterozygous for the mutant gene if the X,Y, and Z plants were homozygous for the mutant gene.

image 2 blog

Our goal is to find the specific location of these genes in the genome through genetic mapping. Based on other research we know tp1 is on chromosome 7 and tp2 is on chromosome 10 so now we just need to find the exact location by developing genetic markers.

So what are genetic markers? They are basically locations where there are mutations to be found between two different plant lines. The plant lines we examined were W22 and B73, they did not contain our mutant genes but have a ton of genetic information available so we are able to find useful mutations. We tried to design markers for the two ends where we believed the teopod genes were located. We looked for mutations in either the W22 or B73 lines that have huge insertions or deletions that are easy to score. Hopefully, these markers can be used as well in our other plant lines but it will be lots of trial and error before we can find good genetic markers.

Image 4

The genetic markers are indicators on specific regions of the genome. So each bar would represent a different plant that either has the mutation or does not. Yellow would be representative of the mutant gene DNA and green will be representative of DNA without the mutation. If the gene is say located between M1.5 and M1.6 we would be able to narrow it down to that location based on whether or not the plant shows the tp phenotype.

Image 5

So basically these markers help us narrow down exactly where the gene is located through process of elimination. All the bars above the red line are green representing plants without the mutation and the bars below the red line are yellow representing plants with the mutation. This is why it is important to have markers to identify the 5 plant lines a part. If it a segment has B73 or W22 DNA in between the two markers then the gene is not located in that region but the plant has the tp phenotype the gene is located somewhere in the entire region.

Finally, here is a picture of some plant leaves we took for our DNA extraction.

Image 1 blog

We ground up these leaves and extracted DNA to use for polymerase chain reactions (PCR) which is a method to amplify DNA. Primers are included in this reaction to amplify the region that contains the insertions or deletions that we designed primers against. If the PCR goes smoothly then we should see a gel that has an obvious difference in the number of base pairs in the W22 and B73 plant samples. Successful primers meant to amplify DNA regions for tp1 should have bands for W22, B73, and X plant lines while successful primers meant to amplify DNA regions for tp 2 should have bands for W22, B73, Y, and Z plant lines.

Algae: A Salamander Embryo’s Best Friend

Maggie DeBell, Yan Zhou, and Evan Czulada



At one point in our lives, we were all embryos growing inside of our mother’s womb. Eventually, this little ball of life became full-sized human beings. Our lab studies how embryos grow and develop over time. We observe this interesting part of biology through the lens of our organismal model, the salamander. Salamander embryos develop outside of their parents’ bodies, which allows us to study them in great detail without being invasive to the parent. Furthermore, these amphibians are abundant in this region, providing us with easy access to them and their embryos.


Yet not all salamanders were created equal. Ambystoma maculatum, also known as the spotted salamander, is unique among all vertebrates. Found in this salamander’s egg capsules and tissues is a type of algae called Oophila amblystomatis. This endosymbiotic relationship–the entrance of one organism into the tissues and cells of another–is the only occurrence of its kind of all vertebrates. Learning more about this relationship allows us to understand, evolutionarily, how this type of symbiosis can impact the members of two different species. Moreover, connections can be made between the route of algal entry and a similar mechanism of pathogen invasion into human cells. This natural phenomenon has led to multiple experiments being conducted to further our knowledge on this subject matter, from several types of imaging to studying the organisms inhabiting the egg capsules of the embryos.




Whole Mount Immunochemistry: Antibody Staining MicroCT Imaging

Evan Czulada:

To observe how this relationship affects the limb and organ development of Ambystoma maculatum, I have been working to establish a protocol of antibody staining these embryos for later use in a microCT machine. Antibodies are naturally produced via the immune system to protect the host against invasion, but they can also be used to bind to a specific antigen–a very specific protein, pathogen, or other part of the cell–and illuminate said antigen when positioned under a microscope. Using this technique, it is possible to show the expression of proteins vital to the development of the salamander embryos.

The specific protein that my antibodies target is called type II collagen. Type II collagen is an integral protein in the formation of cartilage, which primarily composes the skeleton of developing embryos. (Later, this cartilaginous skeleton becomes bone as the embryos become adults.) Therefore, using this technique, I am able to see where collagen-II is most abundant in the salamander embryo. Moreover, caspase 3, a marker for cell apoptosis (programmed cell death), is another antibody added to the embryos that shows where the salamander cells have terminated their use of the collagen-II protein.

Once the right protocol is finally developed, I can then proceed with other chemical treatments to permit imaging through a microCT machine. This type of imaging allows me to analyze the developing limb patterns of the young salamander in a three-dimensional context. The issues I have had with the protocol are based primarily on antibodies that are not properly binding to their selected antigens. Having obtained new antibodies, I hope to have a working mechanism for this kind of imaging in the near future.


Episcopic Imaging

Maggie DeBell:

To enhance our understanding of this symbiotic relationship, we will be collecting data on the placement and quantity of algae within salamander embryos. This can be done using episcopic imaging. The embryos are embedded in a material called JB-4, which allows us to preserve the autofluorescence of the algae. These blocks are sliced on a microtome, taking a photo after each cut. Eventually, these images will be put together to form 3-D reconstructions of the embryos, showing us exactly where the algae is.

To get a clear image of the embryos as they are being cut on the microtome, we took apart an old microscope and mounted it sideways. The apparatus holding it up allows us to focus the image.


We are currently working through issues with magnification, alignment, and knife angle optimization. There will be other details to work through, but we hope to start collecting data soon!


Bacteria Diversity

Yan Zhou:

There is a multitude of bacteria that inhabit the egg capsule of a salamander embryo. After identifying these specific bacteria using 16s rRNA gene sequencing, we can further study these microorganisms to determine how they inhibit a type of mold that is known to kill salamander embryos.

To accomplish this feat, the intracapsular fluid surrounding the embryo was first extracted and swabbed on plates and incubated for bacteria growth. Next, those bacteria can be isolated morphologically through separation into different Petri dishes. DNA was then extracted from the bacteria and amplified through a technique called PCR using a 16s primer. PCR is a method used to find specific genes or pieces of DNA and make many more copies of it for evaluation. Through analysis of the gel and its subsequent purification, the product can be sent away for Sanger sequencing (a way to find out exactly what each bacterium is based upon their DNA). This allows us to make determinations about these types of bacteria and permit further investigation.


Beets, Boulders, Battlestar Galactica

The Basics of Boulders:

For the past four weeks, we (Abby Rec and Ilana Sobel) have been investigating boulder fields in south-central Pennsylvania and Northern Maryland. Boulder fields are exactly what they sound like…


…a field of boulders. Our advisor, Dr. Sarah Principato, decided to begin studying them because of how little is known about their origin and process. Boulder fields are anomalous in the field of geomorphology but carry important information about ancient climate change. These fields are examples of how the topography changed in response to the last glacial maximum 20,000 years ago when the Laurentide Ice Sheet (blue) extended into most of Northeastern U.S. (below):


Map of ice margins of the last glacial maximum.

This summer’s research sought to identify whether the boulder fields were created by periglacial activity, or if their distribution and orientation is more heavily impacted by gravity. The term ‘periglacial’ refers to areas adjacent to glaciers/ice sheets that undergo periods of freezing and thawing due to extreme cold. When water enters fractures in bedrock and freezes, the water expands and cracks the rock, resulting in macrofractures such as in this boulder (bottom left):

While macrofractures caused by freeze-thaw action are good indicators of cold climate and possible periglacial activity, it doesn’t completely explain the orientation of the boulder field. In order to quantitatively investigate the features of boulder fields, we chose to focus on two boulder fields (Hawk Mountain and Raven Rock Hollow) and two talus slopes (Thurmont Vista and Waggoner’s Gap) in order to distinguish any trends in geomorphological process. *Talus slopes are bedrock piles that collect at the base of a slope or cliff—caused by gravity pushing the rock downslope* (see image top right).

We expected that boulder field area will increase approaching the ice margin. In addition to size changes, we also compared and contrasted boulder fields and talus slopes. We hypothesized that talus slopes would have a stronger fabric (orientation of long axes) than block fields.

Getting the data…

Throughout this study, Ilana and I have certainly learned that field work does not come without its fair share of trials (no pun intended), laughs, and brief flickers of frustration when moving at one transect per hour. Being a pilot study, we were eager to organize and implement an efficient method for measuring these boulder fields. Surprisingly, all it took was two tape-measures and a Brunton compass. Hiking boots recommended.

At our first site at Hawk Mountain, Sarah, Ilana and I quickly learned why so few studies have been conducted on boulder fields. They are NOT easy to walk on. But, braving the deep crevices, snakes and spiders, we got to work laying transects in 5 meter intervals and measuring axis length, orientation (direction of long axis), parallel dip (angle of the rock face along the long axis) and perpendicular dip (angle of rock perpendicular to the rock face) with a Brunton compass.

Ilana was absolutely in her element, navigating the boulders with the skill and agility of a mountain goat. I, however, was ill-equipped for the rough terrain and required much hand-holding as I dodged large spider webs and sought refuge on intermittent flat rocks. We divided the responsibilities so that Ilana was in charge of the tape-measures (transects and axis length) and that I was responsible for the temperamental, but very useful, Brunton compass.

Transect and dip measuring requires some athleticism (see above)—as you can see, we have crouched in some very unnatural positions. I have also had to place my hand into several dark crevices of a boulder field nicknamed ‘Devil’s Racecourse’ in order to achieve a perfect perpendicular dip. No pain, no gain, ladies and gentlemen.

Having since made my peace with the Brunton compass, Ilana, Dr. Principato and I managed to get a good sample size from Hawk Mountain, Raven Rock Hollow, Thurmont Vista and Waggoner’s Gap. Our team suffered only one fatality: our single field-pencil, which fell—I swear, in slow motion—from Ilana’s hand into a dark, cavernous hole between boulders. We continue to mourn the loss. (Later that same day, Ilana suffered her only fall of our field experience. She was fine.)

Above: Ilana and I on a foggy, mysterious day at Raven Rock. We were a little unsettled by a noise in the woods with an uncanny resemblance to an Ewok from Star Wars. Neither of us would’ve been surprised if this lil guy marched out of the trees with a tiny hunting spear.

Data Analysis:

After collecting the data, it was time to work on forming conclusions. After a bunch of t-tests and correlation analyses, we came out with some interesting results that compels further investigation. We found that long axis length of the rocks in boulder fields were statistically significantly larger than rocks in talus slopes. Axis length of rocks in Hawk Mountain boulder field were found to be significantly similar to axis length in Raven Rock Hollow boulder field. There was also a significant difference in perpendicular dip between boulder fields and talus slopes—boulder fields having a larger dip than talus slopes. These results suggest that there is something special going on with boulder fields: they’re different from talus slopes which are influenced by gravity, and their erratic dip angles indicates that the fabric of the fields were not created merely by falling rocks. There were no significant correlations between axis length and parallel/perpendicular dip in both boulder fields and talus slopes, which suggests that process greatly impacts the distribution and orientation of the boulders, as rock angle is highly variable in each site and shows no distribution trends.

We’re planning on continuing our study to investigate boulder fields further north, approaching the ice margins using GoogleEarth in order to gain a better understanding of boulder field area in relation to geographical location. Stay tuned.


My project

Computer Aided Instruction (CAI) uses tools to assist learning concepts.  The Computer Organization course is the first course CS that introduces hardware, and so is out of the realm of experience for most students.  This makes learning of the circuits hard, particularly as the diagrams used to introduce the circuits are static.  The circuits themselves are dynamic and require the use of a clock and some logic that is controlled by sets of wires connecting to inputs on the various components, and much of this control logic is not presented in the static diagram.

My project is to create these circuits, including the dynamic behavior and all control wires. I will first produce these circuits using a circuit design tool named Logisim.  I will then implement these circuits into hardware using a Field Programmable Gate Array (FPGA) and other hardware components.  I have implemented two versions of multipliers and dividers in Logisim and am preparing to transfer these designs to the FPGA using the Very High Speed Integrate Circuit (VHSIC) Hardware Description Language (VHDL).

An example of the circuits I am creating is from Chapter 3 of Patterson and Hennessey


This diagram illustrates the basic components of the multiplier and the overall logic, but does not implement the steps needed to make it actual work.  This diagram corresponds to the following circuit I have implemented in Logisim:


This is an example of the multiplication circuit.

Now that I have finished the circuit designs in Logisim, I will begin using the Mercury FPGA development board to implement the multiplication circuit.


Fly Away with Us

Screen Shot 2018-06-14 at 2.12.13 PM

          Drosophila melanogaster: the common fruit fly and the bane of human existence.  Here in the Hiraizumi Lab, we make the best out of these pesky creatures by using them as a model system to investigate regulation of dipeptidase enzymes.  But what is dipeptidase? Great question! Dipeptidase is an enzyme capable of breaking down small peptides into their amino acid components. By studying dipeptidase in D. melanogaster, we potentially can relate our findings to dipeptidase enzymatic activity in humans.  Low levels of specific dipeptidase activity in humans has been associated with diseases such as Alzheimer’s Disease and Huntington’s Disease, just to name a few.

Screen Shot 2018-06-14 at 2.10.41 PM

          Our research focus is on the dipeptidase B gene (Dip-B) in D. melanogaster.  Currently, we are using two different strains of D. melanogaster, NC25III and CL55, to study biochemical and molecular differences in DIP-B enzyme.  The NC25III strain of D. melanogaster exhibits a nine-fold decrease in DIP-B enzymatic activity when compared to the CL55 strain.  What causes this difference in DIP-B enzymatic activity between the two strains of D. melanogaster?  We continue to address two possible explanations:

  1. The NC25III strain of D. melanogaster produces fewer number molecules of DIP-B enzyme than does the CL55 strain.
  2. The two strains of D. melanogaster produce the same quantity of DIP-B enzyme; however, the DIP-B enzyme of the NC25III strain of D. melanogaster has less catalytic activity than does the CL55 strain.

          Former lab member, Rachel Wigmore, conducted western analysis the year prior to compare the relative quantity of DIP-B enzyme produced between the two strains of D. melanogaster.  Western Analysis is a technique that utilises antibodies to detect specific protein of interest and their relative quantities in samples.

Screen Shot 2018-06-14 at 5.45.44 PM

          The quantity of protein present in each strain of D. melanogaster can be determined by the density of the protein bands (i.e., how dark the bands are).  Looking at the Western blot, you can see that there is no apparent difference in the band intensity of the DIP-B enzyme between the CL55 strain of D. melanogaster and the NC25III strain.  After standardizing the banding intensities using the 58kD band of the ladder, we conducted a two-way ANOVA on Rachel’s Western blot.  We found no significant difference between the two strains in mean band intensity. This finding suggests that the two strains of D. melanogaster produce the same number of DIP-B enzyme; thus, the DIP-B protein of the NC25III strain of D. melanogaster appears to be less catalytically active than does the CL55 strain.

Rachel conducted her Western analysis using only male flies.  We decided to expand on her study to see if there could be any difference in protein quantity between male and female D. melanogaster of the two strains.  But how do you determine the sex of the flies?  Another great question! Male D. melanogaster have prominent black dots, known as sex combs, on their front legs.  Females do not have sex combs. Furthermore, females have an elongated abdomen (presence of ovipositor) when compared to males.

Screen Shot 2018-06-14 at 2.09.54 PM

          Our western analysis appeared very similar to Rachel’s, where there was no detectable difference in DIP-B enzyme between strains; furthermore, there appeared to be no detectable difference in DIP-B enzyme between the two sexes.  

Screen Shot 2018-06-14 at 2.11.54 PM

          We conducted a three-way ANOVA to determine whether or not there were differences in band intensity.  To do this, we put our computer science skills to use and wrote a program that could carry out such an analysis using the SAS Operational Quantification Tool.  Our three-way ANOVA revealed that there was no significant difference in the quantity of DIP-B protein between different strains and different genders.

Screen Shot 2018-06-15 at 9.43.58 AM

          Another objective is the comparison of Dip-B gene sequence between CL55 and NC25III strains of D. melanogaster.  Dip-B coding sequences (exons only) of both strains have been sequenced and can be compared against the National Center for Biotechnology Information (NCBI) to characterize any differences between the CL55 and NC25III strains. Differences in the coding sequence could be responsible for amino acid sequence differences associated with catalytic activity.  In order to confirm the coding sequence data, we are replicating sequence analysis with more samples from each strain. Although we do have information about coding sequences, we still lack sequence information of introns and the 5’UTR.  Previous lab members have designed forward and reverse primers for PCR to amplify the 5’UTR and the coding sequence of the CL55 and NC25III strains of D. melanogaster. The size of PCR products can be confirmed by gel electrophoresis.

Screen Shot 2018-06-15 at 10.07.20 AM

          Although the primers have been designed, the work on 5’UTR and intron sequences remains unfinished.  During the summer we will be amplifying the 5’UTR of both strains from their genomic DNA and also produce cDNA from their mRNA.  Once the sizes of PCR products of 5’ UTR and coding sequence are verified, the bands of correct size will be excised and extracted from the gel.

          We will be designing new primers to sequence this 5’UTR and the entire Dip-B gene.  The sequences of 5’UTR and coding sequence will be compared with each other to see if any difference exists. The sequence data can then be used to look for differences in coding sequence between the two strains as well as differences in signals for initiation of transcription. The amino acid sequence can be predicted based on coding sequences and the structure of protein can be predicted as well.  These studies will help us to understand the biochemical and molecular basis of DIP-B enzyme activity differences.

Nicolas Stauffer (2020) & Yifei Duan (2020)

Two-phase flow experiment and results


In this research project, we concentrate on a one-dimensional partial differential equation that models the development of the interface between two fluids with different viscosities (usually taken to be water and oil) which has applications to water contamination and oil production. We conduct an experiment similar to a previous study from DiCarlo, D.A(2004), Experimental measurements of saturation overshoot on infiltration, Water Resour. Res.,40, W04215, doi:10.1029/2003WR002670, where water is pumped through long thin tubes containing sand saturated with oil. Our experiment is not exactly the same as DiCarlo’s. DiCarlo’s experiment has only the water and the sand, such that water infiltrates and exudes through sand, as time goes forward. However, in our experiment, as time evolves, the water infiltrates and displaces the oil. We track the water saturation at the leading edge with diffusive light spectroscopy using a digital camera and recording the amount of light from a light box transmitted through the tube. Based on analytical and numerical analysis of the partial differential equation, we have an idea of what to expect from the experiment.

So far, we have conducted and refined this experiment, the next step is to check whether the experimental results fit the results predicted by the mathematical model. We will investigate which parameter values in the partial differential equation provide a good fit with the data and check if these values are physically realistic for the materials used.


As we mentioned before, the experiment that we conducted is similar to the previous study from DiCarlo. Instead of pumping the water through the tube containing sand saturated with oil, we used two fluids with different viscosities but close densities. The solution A that we used is a mixture of 25% glycerine and 75% pure water, the solution B is a mixture of green-dyed water and glycerine with varied ratios. In the experiment, the solution B is pumped through a 30 cm glass tube containing 1mm diameter glass beads saturated with the solution A. The set-up for the experiment is shown below.

For processing the experiment, the solution B is pumped into the glass tubes with a fixed flow rate by the syringe pump which is the red machine in the figure. The digital camera will take the snapshot of the whole process with a 5s time interval. The bright light which is located at the back of the glass tubes will advance the color contrasts.

All the snapshots are collected and produced into a video and a graph of saturation relative to position. The following video and the graph for saturation show the process that the green-dyed water is pumped into the glass tube with the flow rate of 1mL/min.




This overall behavior matches the predictions from the mathematical model. In the graph, we can see there is a decrease between the position of 0.4 and 0.6, then form a plateau between the position at 0.6 and 0.8. Finally, there is an abrupt drop in the position around 0.8 and 0.9, which is a particularly interesting feature called a shock. We hope to see more shocks when we vary the amount of water and glycerine in solution A.

The Fifth Force Is Strong With This One

Sam Infanger

This summer, I am working with Professor Crawford. We are working on a simulation of two fundamental force experiments. An hypothesized fifth force would arise from near-contact interactions a particle has with substances. This fifth force would be dependent on the particle’s spin. The first experiment involves shooting a neutron through an apparatus. In this apparatus, there are glass and copper sheets in each of its four quadrants. By manipulating and measuring the spins of the neutrons before they enter the apparatus and after they exit the apparatus, it is possible to detect if a fifth force was in play. The second experiment is similar in its execution but the rotation of the neutron would be due to the weak interaction, one of the four known forces. The main difference between the first experiment and the second experiment is that the target is filled with liquid helium instead of copper and glass sheets. The process of shooting the neutron through and measuring the spins is mostly the same as it was in the first experiment.

We are now working on the simulation portion of the experiments. Codes already exist for these two experiments. The code contains all the parts of the real-life apparatus and has many parameters that represent the different factors that can play a role in the experiments. Basically the code follows neutrons through the apparatus and lets them interact with the different pieces, like materials in the beam, neutron waveguides, and magnetic fields. One of the problems these codes face is that in order to get the data to within desired uncertainty, one must run the codes for millions or even billions of neutrons. However, doing so requires a lot of time. Therefore, it would be nice if we could speed up the codes and decrease their run times. One reason the codes are so slow is that at each step the code loops through a long data file to find the magnetic field at the point of interest and interpolate between points in the data file. We have found a way to eliminate the need for these loops by creating an array that works like a reverse phone number lookup table. It has so far been effective at decreasing our runtime. However, there are still a few bugs in the code that need to be ironed out. So far this anti-loop code is implemented in only the code for the fifth-force experiment. I will later incorporate this style and other improvements into the code for the liquid-helium experiment.

Fifth force apparatus

Neutron Beam and Lifetime

Hello all! My name is Jose Negron and I work with Professor Crawford in finding the precision of a neutron lifetime to the precision of 0.1% or better! One might say that this is a perfectionist’s goal, but in reality we need the uncertainty of the neutron life time to be this good if we want to fully explore the properties of a neutron and the important theories in particle physics involving it. This requires that we take tons of data and then analyze this data to make note of trends and comparisons between data taken in the past. There are three tools needed to achieve this task: GEANT4, ROOT, and C++. Geant and Root help me gather and analyze data and both require understanding the use of C++. Thankfully the codes that are used to make these runs of data have already been created and I simply must vary them whenever I want to change a run to see how it affects the data itself. Using certain commands, I can create simulations of the experiment and recreate the apparatus in a 3D space! I can create a neutron beam, which is one of the most important parts of the apparatus itself, and shoot neutrons and observe its decaying process and count the number of protons vs neutrons. My research is a little different from last year’s in that I am taking data using a different model of physics used in the codes made to create the runs itself. Essentially, I am gathering new data and analyzing them while comparing them to the data taken from last year. Sometimes there is questioning data that I receive after analyzing it and I ask myself, did I make a mistake? Was there a problem in the code? Is there something I am not seeing? Why is this data so different from what I expected? As scientists, we are prone to observe errors and receive data that we do not expect to see, but in my opinion, working on understanding these errors and correcting them is what makes our research interesting!