Name: KU Physics & Astronomy LOcally Organized Assembly (PALOOZA) 2023
Start: 2023-03-25T09:00:00-05:00
End: 2023-03-25T17:20:00-05:00
Location: Slawson Hall

KU Physics & Astronomy LOcally Organized Assembly (PALOOZA) 2023

Saturday, 25 March 2023 - 09:00

Monday, 20 March 2023
Tuesday, 21 March 2023
Wednesday, 22 March 2023
Thursday, 23 March 2023
Friday, 24 March 2023
Saturday, 25 March 2023

09:00 Welcome
Welcome
09:00 - 09:20
Room: G192
09:20 GUNS at Earth - Mohammad Ful Hossain Seikh (University of Kansas)
GUNS at Earth
- Mohammad Ful Hossain Seikh (University of Kansas)
09:20 - 09:40
Room: G192 Neutrinos are the fundamental particles which has promising potent to unveil the mysteries of our universe. Besides their importance in understanding fundamental Physics, a new era of multimessenger astronomy with neutrinos is outspreading which is evident from the large scale neutrino detectors around the world sensitive from meV to PeV energy scales. In this presentation, we discuss the grand unified neutrino spectrum (GUNS) at Earth from various sources. The original work was done by Vitagliano, Tamborra, and Raffelt in their review paper, DOI: 10.1103/RevModPhys.92.045006.
09:40 Virgo WISESize: Investigating Environmental Processes of Galaxies in the Virgo Cluster - Kim Conger (University of Kansas)
Virgo WISESize: Investigating Environmental Processes of Galaxies in the Virgo Cluster
- Kim Conger (University of Kansas)
09:40 - 10:00
Room: G192 The cosmic web of the Universe consists of a patchwork of dense clusters and empty voids, with strands of filamentary networks connecting and feeding these central clusters. Observations have demonstrated that these clusters harbor a lower fraction of star-forming galaxies relative to the isolated galaxies of the field, but astronomers have largely “washed out” filaments in their descriptions of cosmic environments, considering only the distance from the cluster center. However, recent large-scale hydrodynamic simulations have revealed filaments to be non-negligible sites of preprocessing as galaxies funnel through these networks into the high density clusters. The aim of the WISESize project is to directly probe galaxies in a more nuanced range of environments, using a combination of infrared and optical wavelength bands to quantify extrinsic galaxy quenching in the local Universe. In particular, we compare the spatial extent of obscured star formation to that of the stars, using the ratio to measure the processing of a galaxy’s gas reservoir in a given environment. With our results, we ultimately will identify the physical processes contributing to this quenching, and in so doing help improve prevailing models of galaxy evolution.
10:00 HARPSpec: Using Machine Learning to Find Chemical Compositions of Stars from Their Spectra - Joseph Hand
HARPSpec: Using Machine Learning to Find Chemical Compositions of Stars from Their Spectra
- Joseph Hand
10:00 - 10:20
Room: G192 Machine Learning is finding its way into an increasingly large number of scientific fields. In the field of stellar compositions, a new tool called the Cannon can analyze spectra using a regressive machine learning model to extract various properties of the stars, most notably their compositions. This tool is being used to construct HARPSpec, which analyzes publicly available spectra from the HARPS instrument located in the ESO. This talk covers the various steps that have been performed to make the Cannon work effectively with the spectra along with an analysis of its current performance. It will additionally cover possible future courses of action for further improvements.
10:20 Exploring Quantum Algorithms for Combinatorial Problems at Colliders - Jacob Scott
Exploring Quantum Algorithms for Combinatorial Problems at Colliders
- Jacob Scott
10:20 - 10:40
Room: G192 Collisions in colliders usually only create a pair of particles. But these particles can decay and create jets leading to many final state particles. It is a binary classification problem to determine which of the two mother particles these final state particles belong to. This becomes exponentially more complex as the number of final particles increases. Variational Quantum Algorithms (VQAs) are hybrid quantum algorithms in which a quantum circuit is parameterized and a classical optimizer is used to minimize the energy of the final state of the quantum circuit. Encoding a cost function as an ansatz for the circuit allows for the solving of this minimization problem. I explore a specific type of VQA called Quantum Approximation Optimization Algorithm (QAOA) applied to the decay of a top and antitop quark pair, in a simple example, and show how a cost function can be transformed into a Hamiltonian and applied as a time-evolution operator as a circuit.
10:40 Break
Break
10:40 - 11:00
Room: G192
11:00 Methods to Improving Monte Carlo - Brendon Madison (KU HEP , ILC)
Methods to Improving Monte Carlo
- Brendon Madison (KU HEP , ILC)
11:00 - 11:20
Room: G192 Brief presentation of methods to improving your Monte Carlo programming. To be covered: 0.) "Do you need that precision?" 1.) Sorting assisted random sampling 2.) Fit assisted random sampling 3.) A derivation of an analytic form of random sampling, its benefits 4.) Fits for verification 5.) Statistical measures for verification
11:20 Modeling the Variability of the Sun's Total Solar Irradiance through Supervised Machine Learning Techniques - Ashley Lieber (University of Kansas)
Modeling the Variability of the Sun's Total Solar Irradiance through Supervised Machine Learning Techniques
- Ashley Lieber (University of Kansas)
11:20 - 11:40
Room: G192 With the Sun as the Earth’s main source of energy and heat, it is critical to understand how and why it varies. The Sun’s irradiance changes over its 11-year solar cycle driven by variabilities in the magnetic field. The primary objective of this project was to create a machine learning model that predicts the total solar irradiance (TSI) and then analyze the contributions of each feature within the model in driving change of the Sun’s TSI. This improves upon the previous model by incorporating machine learning techniques on a wider array of data and features. Initially, we utilized the supervised machine learning technique of multiple linear regression to have the model predict an irradiance value for a given day based on given solar features. This model uses data of solar features visible in intensitygrams and line-of-sight magnetograms from the Helioseismic and Magnetic Imager (HMI) instrument aboard the Solar Dynamics Observatory (SDO). Additionally, this model utilized information about the active regions (AR) on the Sun’s surface and several space weather parameters. To ascertain the sunspot area, we performed image processing on the intensitygrams from the SDO HMI. Additionally, seventeen space weather parameters from the SHARP data were added to the model. Due to the number of features, a correlation matrix was created to eliminate features that were highly correlated and determine how features correlated with TSI. In order to quantify the success of this model, the TSI composite data produce was used as it is a historical TSI data record that can be compared to the model’s predictions. Preliminary results show that training the model on these features yields a root mean squared error (RMSE) of 0.0491 and a mean absolute error (MAE) of 0.1600 showing that the model is performing as expected in this early stage. A residual plot was also created to showcase the relation between the actual TSI values and the predicted TSI values. This work serves as the initial step towards continuing to understand the variabilities in the Sun’s irradiance along its solar cycle which will, in turn, aid in understanding how the Sun’s variabilities affect Earth.
11:40 Interlayer Electric Field Screening in Thin Films - Neema Rafizadeh
Interlayer Electric Field Screening in Thin Films
- Neema Rafizadeh
11:40 - 12:00
Room: G192 An overview of excitonic and eletcric field screening is given and then applied to a heterostructure of Graphene/WS$_2$. An optical setup is devised to quantify this effect within the heterostructure and preliminary results are shared. It is found that the excitonic screening effect can be quantified easily through this optical process through the change of the dielectric of the material. Though challenges will be faced in the future, the goal of quantifying this result continues currently with this project.
12:00 Lunch Break
Lunch Break
12:00 - 13:20
Room: G192
Poster Session
Poster Session
12:00 - 13:20
Room: G192
Contributions

12:00 Elucidating the Role of Autotaxin and Lysophosphatidic Acid in Myelination - Matt Zupan

12:00 Fluorescence-free EV-mRNA expression assay for breast cancer subtyping - Mattie Gordon (University of Kansas, Chemistry Department)

12:00 MULTI-CRITERIA OPTIMIZATION - Johnpaul Mbagwu (University of Kansas) Michael Chukwuka

12:00 Real Time Imaging of Autotaxin Activity in Oligodendrocyte Precursor Cells - Esther Holt
13:20 Thermal Noise Reduction for CMS Instrumentation Testing - Derek Grove (Physics Grad Student)
Thermal Noise Reduction for CMS Instrumentation Testing
- Derek Grove (Physics Grad Student)
13:20 - 13:40
Room: G192 Discussion of cooling equipment built here in the High Energy Physics (HEP) lab to cool particle detectors for experimentation. The cooling is necessary to reduce thermal noise, an ever present antagonist to a good room-temp experiment.
13:40 Vertex Reconstruction of a neutrino event In ARA - Shoukat Ali
Vertex Reconstruction of a neutrino event In ARA
- Shoukat Ali
13:40 - 14:00
Room: G192 The Askaryan Radio Array (ARA) is a large neutrino detector, aimed to detect ultra-high energy neutrinos. The installation of the detector has been started in 2011 and is still going on on the geographic south pole. One of the main motivations behind this experiment is to know the sources of the ultra-high energy neutrinos in the cosmos. It can be pointed out using the vertex reconstruction of the neutrino interaction in Ice. This work contains some results of the vertex reconstruction of the pulses that have been sent through the calibration antennas and received by the RF antennas of the ARA02. The reconstruction is also used for calibration purposes of the antennas of ARA.
14:00 How nanopatterns in organic molecules affect the interlayer exciton dynamics in organic-MoS2 heterostructures. - Kushal Rijal (Chan lab KU)
How nanopatterns in organic molecules affect the interlayer exciton dynamics in organic-MoS2 heterostructures.
- Kushal Rijal (Chan lab KU)
14:00 - 14:20
Room: G192 We investigated two organic molecules, PTCDI and PTCDA, which stack by making different molecular patterns on the MoS2 surface. Using photoemission spectroscopy, we observed a HOMO and LUMO splitting near the interface in PTCDI/MoS2 but not in PTCDA/MoS2. Density functional theory calculation showed that the splitting of the energy levels originates from the difference in the molecular patterns. Due to the splitting of energy levels, exciton dynamics in PTCDI/MoS2 is very different from that of PTCDA/MoS2. Time-resolved photoemission measurements show that electrons within the IX excitons are spatially localized near the interface in PTCDI/MoS2, whereas in PTCDA/MoS2, they are spatially delocalized across the molecular films. These results demonstrate that nano patterns of organic molecules can be used to control the exciton dynamics in organic/TMD heterostructures.
14:20 Break
Break
14:20 - 14:40
Room: G192
14:40 Quantum gates implementation for Reinforcement learning decision- making process - Aarash Maroufian (university of kansas)
Quantum gates implementation for Reinforcement learning decision- making process
- Aarash Maroufian (university of kansas)
14:40 - 15:00
Room: G192 This study introduces a method to improve the decision-making process of reinforcement learning agents via quantum information technology. In this approach, states |├ s⟩ of the system are replaced by quantum states (eigenfunctions) of the system |├ ψ⟩, in which a system can be in a superposition of states, and rewards of each step are calculated based on the calculated eigenvalues of the previous step. The agent decides the next step of the system based on the result of the quantum gate’s effect on the available options. The agent uses a probability-based approach to maximize the reward. Due to the high-speed, escalated performance of quantum algorithms, it is hoped that this method will improve the performance of reinforcement learning agents in unknown environments and complicated quantum systems. Finally, a theoretical basis of an experimental setup has been suggested to implement quantum gates for AI agents, using optical methods.
15:00 Multiwavelength Comparison of X-ray and Mid-Infrared Selected Active Galactic Nuclei - Thresa Kelly (University of Kansas)
Multiwavelength Comparison of X-ray and Mid-Infrared Selected Active Galactic Nuclei
- Thresa Kelly (University of Kansas)
15:00 - 15:20
Room: G192 Active galactic nuclei (AGN) have different emission spectra depending on their physical characteristics. Using only one wavelength region to identify an AGN may miss sources with unique characteristics. In this work, we compare the multiwavelength properties of X-ray and mid-infrared (MIR) selected AGN in the COSMOS field. Our X-ray-selected AGN sample includes all sources with a $0.5-10$ keV X-ray luminosity greater than $10^{43}$ erg s$^{-1}$. We define our MIR-selected AGN sample using the four Spitzer/IRAC channels. We find that 70% of X-ray selected AGNs are not identified as AGNs using their IR colors, and 40% of IR-selected AGNs do not meet the X-ray luminosity threshold. On average, X-ray-detected MIR-selected AGNs are more powerful than weakly MIR-emitting sources. X-ray selection captures the full range of emission properties, but misses a significant fraction of heavily obscured AGN. MIR-selected AGNs are more luminous in the MIR than X-ray selected AGNs, but have a similar range of ultraviolet (UV) emissions. AGNs that meet both X-ray and MIR selection criteria have the highest median relative energy emission in the UV; they are more luminous across their entire spectra, in general.
15:20 Memristor Tuning Through Layer Selection Using ALD - Berg Dodson (University of Kansas)
Memristor Tuning Through Layer Selection Using ALD
- Berg Dodson (University of Kansas)
15:20 - 15:40
Room: G192 Memristor fabrication techniques have been able to create memristors in the ultrathin regime (sub-2 nm). The fundamental paradox of memristors at this scale is that the number of defects in the active layer must be minimized to prevent undesirable leakage current but needs to include a sufficient and well distributed quantity of oxygen vacancies to promote conductive filament formation. Defects such as pin hole defects and defective metal-insulator interfacial layers can be eliminated through atomic layer deposition (ALD) of a conformal dielectric layer without breaking vacuum following the electrode deposition. ALD can also promote oxygen vacancy by mixing the primary dielectric with a material with a material that acts as an oxygen vacancy reservoir. Predicted by the density function theory simulation, MgO doping in Al2O3 can promote formation of oxygen vacancies while increases resistivity of Al2O3. Motivated by this, this work reports on ultrathin Al2O3 memristors with MgO atomic layers inserted at selected positions in the Al2O3 atomic layer stacks using an *in vacuo* ALD method to promote oxygen vacancy formation in a controlled manner. Memristors with different numbers of MgO atomic layers and MgO/Al2O3 atomic layer tacking sequences were evaluated to extract the correlation between the atomic structure and oxygen vacancy doping and the memristor performance. It was found that for our memristor thickness (1.8 nm) the number of MgO layers included in the memristor affected the memristor switching behavior and filament formation characteristics. Memristors with fewer MgO layers were found to have lower yield and more likely to undergo dielectric breakdown while memristors with more MgO layers were more likely to exhibit multiple conductive filament formation and variation in the conductive filament size.
15:40 Break
Break
15:40 - 16:00
Room: G192
16:00 Bremsstrahlung radiation - Abishai Mathai
Bremsstrahlung radiation
- Abishai Mathai
16:00 - 16:20
Room: G192 Relativistic heavy ions can produce tremendously strong can produce tremendously strong electro-magnetic fields. For Pb-208 nuclei at the LHC, the Electro-magnetic fields have a total energy of approximately 20% of the kinetic energy of the nuclei (1 nucleus ~ 100TEV and 1GEV ~ 1 proton) To consider a real-life example, let’s take a fly of mass 5 grams moving with a velocity of 10cm/sec. Then its kinetic energy would be 500 ergs. The K.E of the photon field collision would be comparable to one- third of a fly (~ 160 ergs). When charged fields decelerate the E-M fields must change and the object emits bremsstrahlung (Breaking radiation) photons. Collisions of Pb nuclei produce the most rapid decelerations in the universe (in the order of 10^22 g’s). We can measure the deceleration “a” using low energy data with a factor of ¾. This will help us figure out the number of gluons in a Pb nuclei.
16:20 Review of Mathematics, Numerical Factors, and Corrections for Dark Matter Experiments Based on Elastic Nuclear Recoil - Miguel Soto Alcaraz
Review of Mathematics, Numerical Factors, and Corrections for Dark Matter Experiments Based on Elastic Nuclear Recoil
- Miguel Soto Alcaraz
16:20 - 16:40
Room: G192 Many theories are postulated for what Dark Matter may be, one of them is a simple particle. A summary and derivation of nuclear recoil interactions are presented for weakly interacting dark matter particles. Modifications for the such interactions include Earth's motion, possibility of spin for the particles and nuclear form factors. Different elements are discussed and how it may impact on detectors.
16:40 Clouds and Clarity: Revisiting Atmospheric Feature Trends in Neptune-size Exoplanets - Yoni Brande
Clouds and Clarity: Revisiting Atmospheric Feature Trends in Neptune-size Exoplanets
- Yoni Brande
16:40 - 17:00
Room: G192 The last decade has seen the maturation of precise exoplanet transmission spectroscopy techniques, allowing for the identification of individual opacity sources in exoplanetary atmospheres. As more and more of these planets have been observed, tentative trends have been identified relating the strength of observed atmospheric features to other fundamental planetary and system parameters. However, small sample sizes and limited spectral sensitivity (primarily from the 1-1.8 micron G141 grism on Hubble's WFC3) make these identified trends uncertain. Previous work has, out of necessity, grouped dissimilar planets (massive Hot Jupiters as well as smaller, cooler gaseous planets) together in order to compile samples large enough to draw inferences from, but it is unclear that these planets exhibit all the same atmospheric physics which could affect their observed atmospheric spectra. We present a re-analysis of a smaller, yet more consistent sample of Neptune-size exoplanet atmospheric spectra, across a range of temperatures, and discuss whether these previously identified trends hold at the dawn of the JWST era.
17:00 Conclusion
Conclusion
17:00 - 17:20
Room: G192