Current physics education research has demonstrated that, when not taught directly, students have a wide range of conceptual resources regarding the use of variables upon entering introductory physics. There is a growing body of work that characterizes students’ use of variables and how students connect variables to their physical meaning (Brahmia 2019). We build on this work by seeking to better understand how students are making sense of variables in introductory physics labs. Data was collected from students’ responses to lab curriculum on the online lab platform, Pivot Interactives, from the 2020-2021 academic year. By examining students’ variable choice when graphing experimental data over the course of a quarter, we are able to identify emerging commonalities in variable use and how the variables students choose correlates with the students’ broader understanding of quantitative reasoning. Preliminary data from student graphs of position versus time show a prevalence of students using math-like variables, such as y and x, instead of variables traditionally used to represent these quantities in physics, such as x to represent position and t to represent time. Use of math-like variables in a physical context suggests that these students’ may have not yet formed a strong association between the variable itself and the meaning of the physical quantity it represents. Insights into student variable use and its relationship to the students’ overall quantitative reasoning can help instructors consider effective methods that adapt curriculum to directly address the use and meaning of variables within physics. By doing so, instructors may have an opportunity to directly impact their students’ quantitative reasoning – a skill valued across all STEM disciplines.

Research in math education has shown that covariational reasoning – understanding how the change in one quantity affects another – is a vital skill for calculus and often not well developed in pre-calculus curriculum (Carlson et al. 2002). One important manifestation of covariational reasoning is interpreting the meaning of graphs, particularly the meaning of the slope, which represents the rate of change between two quantities. Prior physics education research has demonstrated that these conceptual mathematical skills do not directly translate from math to physics, in part because reasoning in physics is a blend of mathematical reasoning and making sense of physical quantities (Redish 2015). The purpose of this study is to contribute to this body of work by characterizing how introductory physics students are reasoning about the slope of a graph. To do this, we analyze student responses to questions which ask them to describe the meaning of the slope of graphs generated by students during data analysis in a lab course. Data was collected from 390 students in Pivot Interactives, software that was used to administer labs during online learning due to the Covid-19 pandemic. We use thematic analysis to determine trends in reasoning that students demonstrate throughout the quarter. Preliminary results show that in the beginning of the course, a large portion of students have not yet developed successful covariational skills. However, a second exposure to a graph of the same two quantities yields little to no errors in their reasoning. Even across new applications of slope, i.e. with two new quantities, student explanations appear to be improving significantly from the first two labs. These findings suggest implications for effective methods to emphasize covariational reasoning in the physics lab curriculum.

Trapped ions is an approach to quantum computing that proposes to store qubits in the stable electronic states of ions. These qubits transition from one state to another by absorbing or emitting photons. This process is known as quantum jumps. The absorption and emission of photons by individual ions become coherent processes at sufficiently small separations. The goal of our project was to observe these collective effects by observing the quantum jump rate in systems of multiple ions. We identified quantum jumps by observing sudden changes in the number of photons emitted by an ion. In our preliminary analysis we found a proportional relationship between the quantum jump rate and the number of ions in a chain. However, due to the presence of noise from neighboring ions, our results had significant errors. To minimize the randomness introduced by noise, we counted the number of photons in small intervals of “integration time” and evaluated the optimal transition rate by minimizing incorrect identification of jumps. We hypothesize that the transition rate of ions depends on the number of ions in our system. A deeper understanding of quantum jumps may possibly help us control them and eventually be used to correct errors in quantum computing involving trapped ions.

Semi-visible Jets may occur from strongly coupled hidden sectors produced at the Large Hadron Collider, as suggested in the Hidden Valley models. While dark hadrons interact strongly with each other, they interact only weakly with visible states through the portal, which will undergo a QCD-like shower and ultimately hadronize, producing collimated sprays of dark hadrons. These states are invisible to colliders’ detectors unless they are able to decay to the Standard Model. A portion of these states are likely to be stable, providing good dark-matter candidates. Yet, many of the hadrons should decay back to the visible sector through the portal coupling, which result in a spray of stable invisible dark matter along with unstable states that decay back to the Standard Model. The signature of such Semi-visible Jets is characterized by the missing energy aligned along the direction of one of the jets. In this research, we generated the Semi-visible Jets s-channel samples using standalone MadGraph5, Pythia8, and Delphes and conducted data analysis using uproot and pyjet package in Python. We analyzed the kinematics of Semi-visible Jets with different parameter settings such as event selection cuts and jet clustering algorithms by creating kinematic plots of physical quantities including jet momentum, invariant mass, transverse mass, and missing energy using Python. Also, we have created the ATLAS JobOption, which has already been used for sample generation in the CERN ATLAS framework. We have noticed differences in kinematic distribution with different event selection and jet clustering algorithms and we expect to find the parameter settings for them that will optimize the Semi-visible Jets signal. By applying optimized parameter settings, we can locate the possible region where Semi-visible Jets can be observed in the Large Hadron Collider, which is a significant step forward in the discovery of dark matter.

Detecting physics beyond the standard model is an important task that utilizes cutting edge new models. In the “Anomaly Detection Data Challenge 2021,” we develop a novel anomaly detection algorithm for the task of finding a priori unknown and rare New Physics data. The challenge uses simulated anomaly detection data that emulates the strict bandwidth, latency and resource constraints of the L1 trigger of the Large Hadron Collider whose dataset is composed of 10 particle jets, 4 muons, 4 electrons, and missing transverse energy. Autoencoders and variational autoencoders are powerful neural network models that are widely used to approximate input distributions and reduce latent dimensions. We reproduce simple autoencoders/variational autoencoders to detect anomalies within the dataset using Mean Squared Error (MSE) loss and Kullback- Leibler divergence (K-L Divergence) as the anomaly metrics respectively. Using these well-performing autoencoder models as a baseline, we develop and test novel, powerful, generative and autoencoder based models for the anomaly detection task.