Bootcamp: Computational tools for physics

Session

Invited talks

19 Mar 2025, 11:15

2048 (Malott Hall)

Conveners

Invited talks: Day 1

• Sanyukta Agarwal (University of Kansas)

Invited talks: Day 2

• Anna Binoy (University of Kansas)

Invited talks: Day 3

• Anna Binoy (University of Kansas)

10. Interferometric Reconstruction

Aishwarya Vijai

19/03/2025, 11:15

Invited talk

The Radio Neutrino Observatory in Greenland (RNO-G) is located at Summit Station and is designed to detect Askaryan emission from ultra-high energy (UHE) neutrinos above 100 PeV. The detector is made up of an array of antennas buried at a depth of 100 meters with the purpose of triggering on and reconstructing neutrino-like signals in the radio regime. Interferometry can be used to find the...

3. Statistical Tools for Neutrino Signal Search

Pawan Giri (University of Nebraska Lincoln)

19/03/2025, 11:35

Invited talk

Neutrinos are vital messengers for understanding the most extreme astrophysical processes, capable of traveling across cosmic distances without deflection. The Askaryan Radio Array (ARA), deployed at the South Pole, searches for these elusive particles by detecting radio pulses generated when neutrinos interact with Antarctic ice. However, identifying these rare signals is particularly...

8. Source Modeling in Large Astronomical Imaging Surveys

Viraj Manwadkar (Stanford University)

19/03/2025, 11:55

Invited talk

Source modeling is the process of modeling the flux distribution of astronomical objects such as galaxies and stars to enable accurate photometric measurements. However, conventional source modeling pipelines applied on wide imaging surveys often struggle with large, nearby galaxies, which are frequently "shredded" into multiple smaller components. This fragmentation leads to inaccurate flux...

4. Singular Value Decomposition for RFI Denoising

Curtis McLennan (University of Kansas)

20/03/2025, 11:15

Invited talk

Radio-based physics experiments like the Radar Echo Telescope frequently require analysis techniques that can recover signals at or below the level of noise. Singular Value Decomposition is a versatile tool for signal processing, allowing complex signals to be efficiently analyzed, denoised, and reconstructed, even when signal characteristics are not known. This talk highlights the methodology...

2. Monte Carlo Simulations

Christoph Welling (University of Chicago)

20/03/2025, 11:35

Invited talk

Monte Carlo methods are a tool to estimate probability distributions or simulate physical processes, that is commonly used in physics as well as other fields like chemistry, biology, finance and artificial intelligence.
This talk will give a general introduction to the use of Monte Carlo methods for physics simulations, with some example and practical considerations.

1. Offline Neutrino Filtering using a Convolutional Neural Network at the Radio Neutrino Observatory Greenland

Ruben Camphyn

21/03/2025, 11:15

Invited talk

Neutrino astronomy is a vibrant field of study in astrophysics, offering unique
insights into the Universe’s most energetic phenomena. The combination of a low cross
section and zero electromagnetic charge ensure that a neutrino retains most information
about its original source while traversing the universe. On the other hand, these low
cross sections, combined with a reduced flux at...

11. Machine Learning for Applications in Condensed Matter Physics Research

Gabriella Townsley

21/03/2025, 11:35

Invited talk

How can you use a machine learning model in your research? This talk will outline when and where a model is most useful in materials science, how it’s created with different methods, and some of the advantages and disadvantages of each. I will also discuss my own research on phase change materials and its machine learning model as a more in-depth example.

5. Artificial Intelligence at the High Energy Frontier

Margaret Rose Lazarovits (The University of Kansas (US))

21/03/2025, 11:55

Invited talk

Whether searching for dark matter or measuring Standard Model parameters, analyzing data from the Large Hadron Collider is no easy task. Leveraging machine learning in high energy physics (HEP) is not a new idea, but recent AI advancements have accelerated analysis efforts. Methods like transformer models, variational auto-encoders, and graph neural networks have strengthened HEP analysis...