Bootcamp: Computational tools for physics

19 Mar 2025, 09:00 → 21 Mar 2025, 15:00 US/Central

2048 (Malott Hall)

Anna Binoy (University of Kansas), Kristin Rennells (KU Physics & Astronomy), Sanyukta Agarwal (University of Kansas)

Participant registration link: https://tinyurl.com/ComputationalBootcamp

In-person and remote attendance available, please register by 13th March. 

Light refreshments will be provided 9:00-9:45 am, check-in starting from 9:30 am. All sessions will be located in MAL 2048, lunch in MAL 3005.  Laptops required for the workshop practice session, wifi connectivity available through eduroam. 

Malott Hall located between Wescoe Hall (Jayhawk Blvd side - 2nd floor entry) and Haworth Hall (Sunnyside Ave side - ground floor entry). 

Public transport in Lawrence: All buses are free, use Passio GO! or Transit app on your mobile devices to check bus route and schedule info. Bus routes 4, 8, 11 and 42 serve campus stops. Refer to https://lawrencetransit.org/routes/ for further information.

Dinner options: Lawrence downtown with various restraunt options is walking distance from campus (~15 mins) and bus route 4 runs between campus and downtown. 

Travel to Lawrence: Refer to this guide. Nearest airport is MCI in Kansas City. Cabs/taxi readily available from airport to Lawrence. Please contact organizers for any other travel related questions. 

Github link for the Practice session.

Please fill the anonymous post-survey for feedback and comments.

Wednesday 19 March

09:30 → 10:00 Check in and setup: Check in and system setup

10:00 → 11:15 Overview lecture: Day 1 lecture - Github, shell scripting and code structuring

10:00 Version Control, Shell Scripting and Remote Computing for Academic Research

The increased computational research approach demands efficient management of code, automation of tasks, and access to high-performance computing resources. Acquiring such skill sets can have a positive impact on the productivity of researchers and increase throughput. This talk will provide an introduction to three fundamental tools: version control, shell scripting, and remote computing. Version control systems, such as Git and GitHub, help researchers track their code, monitor code changes, collaborate seamlessly, and maintain reproducibility in research projects. Meanwhile, shell scripting allows automation of repetitive tasks, data processing, and job scheduling. Remote computing facilitates large-scale simulations, data analysis, and access to cloud-based resources. This talk aims to provide participants with practical knowledge of these tools, demonstrating their applications in academic research. Attendees will gain hands-on insights into repository management, scripting fundamentals, and remote computing workflows, empowering them to streamline their research processes effectively.

Speaker: Pramil Paudel (University of Kansas)

Version_Control_handsOn.pdf

Version Control, Shell Scripting and Remote Computing.pdf

Version Control, Shell Scripting and Remote Computing.pdf

Version Control, Shell Scripting and Remote Computing.pptx

11:15 → 12:15 Invited talks: Day 1

Convener: Sanyukta Agarwal (University of Kansas)

11:15 Interferometric Reconstruction

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 source of these radio signals as received by an array of antennas. This talk will outline how interferometric reconstruction works and a python-based implementation of the technique which is used by the RNO-G. This technique is broadly used in other neutrino detection experiments and radio astronomy as well.

Speaker: Aishwarya Vijai

11:35 Statistical Tools for Neutrino Signal Search

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 challenging due to the overwhelming presence of background noise.

To isolate potential neutrino events, ARA employs a suite of signal characterization techniques, including signal-to-noise ratio (SNR), root power ratio (RPR), correlation, impulsivity, and more. These statistical tools help distinguish neutrino-like signals from both thermal noise and anthropogenic interference.

In this presentation, we will explore how different analysis variables are calculated and implemented in the search for neutrino signals.

Speaker: Pawan Giri (University of Nebraska Lincoln)

video1943396329.mp4

11:55 Source Modeling in Large Astronomical Imaging Surveys

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 measurements and misclassification of sources. In this talk, I will discuss this problem and present two methods one can use to improve photometry for such galaxies: (1) aperture photometry, which provides a straightforward way to measure total flux, and (2) scarlet, a novel tool introduced by Melchior et al. 2018, that enables source deblending and non-parametric modeling of sources.

Speaker: Viraj Manwadkar (Stanford University)

12:15 → 13:00 Lunch

13:00 → 14:30 Practice session: Day 1 workshop

Convener: Anna Binoy (University of Kansas)

Thursday 20 March

09:30 → 10:00 Check in and setup: Day 2 - setup and questions from prev day

10:00 → 11:15 Overview lecture: Day 2 - Data Sceince lecture

10:00 Modeling Data: Fitting, Inference, and Machine Learning

This lecture will cover the relationship between "models" and "data", such that "learning" corresponds to making inferences about the parameters of our model. We will see that this paradigm covers everything from curve fitting to contemporary machine learning with neural networks. The theory of general model fitting will be explored with practical examples, with some discussion of the concepts of "goodness-of-fit" and model selection.

Speaker: Christopher Rogan (The University of Kansas (US))

Rogan_KUBootcamp_20_03_25_annotated.pdf

11:15 → 12:15 Invited talks: Day 2

Convener: Anna Binoy (University of Kansas)

11:15 Singular Value Decomposition for RFI Denoising

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 of singular value decomposition and presents a practical example of using SVD to accomplish weak signal extraction.

Speaker: Curtis McLennan (University of Kansas)

11:35 Monte Carlo Simulations

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.

Speaker: Christoph Welling (University of Chicago)

12:15 → 13:00 Lunch

13:00 → 14:30 Practice session: Day 3 - Data Science workshop

Convener: Sanyukta Agarwal (University of Kansas)

Friday 21 March

09:30 → 10:00 Check in and setup: Day 3 - setup and questions from prev day

10:00 → 11:15 Overview lecture: Day 3 - Machine Learning lecture

Convener: Sanyukta Agarwal (University of Kansas)

10:00 Introduction to machine learning

These days, machines often perform tasks that were previously far beyond their capabilities, and they are increasingly outperforming humans too! Machine learning has been around for a long time, however, so what has changed? Since, ~2006 we have been in the “third wave” of deep learning, and it has become possible for machines to learn complex “nested representations”, and this has revolutionized their capabilities. The impact on science has been transformative, and it is hard to say where the limit is! This talk will give a conceptual overview of machine learning and deep learning, give an introduction to some of the core concepts, and cover examples of machine learning in action.

Speaker: Prof. Elliot Reynolds (University of Kansas)

11:15 → 12:15 Invited talks: Day 3

Convener: Anna Binoy (University of Kansas)

11:15 Offline Neutrino Filtering using a Convolutional Neural Network at the Radio Neutrino Observatory Greenland

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 higher energies, make the neutrino
one of the most elusive particles to detect in the standard model. The Radio Neutrino
Observatory in Greenland (RNO-G) aims to detect sporadic neutrino interactions in the
Greenlandic ice sheet by means of electromagnetic signals in the radio frequency range,
induced by the produced charged secondary particles. The low incoming neutrino flux
forces the detector to set a low trigger threshold, leading to the measured data being
overwhelmed by thermal noise fluctuations. Hence, a sophisticated and robust filter is
needed to differentiate between neutrino-like signals and noise. In this talk we
present the application of a convolutional neural network to identify noise events. The network employed uses real RNO-G data and simulated
neutrino signals to categorize measured data as noise or neutrino-like events

Speaker: Ruben Camphyn

neutrino_filter.pdf

11:35 Machine Learning for Applications in Condensed Matter Physics Research

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.

Speaker: Gabriella Townsley

11:55 Artificial Intelligence at the High Energy Frontier

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 workflows. Further, domain-specific approaches, like Lorentz-invariant networks and embedded inductive biases, have tailored these approaches to this field. These techniques and other powerful machine learning analysis methods are currently being successfully deployed in a range of contexts, including at the high energy frontier.

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

lazarovits_KUPhysCompBootcamp_3.21.25.pdf

12:15 → 13:00 Lunch

13:00 → 14:30 Practice session: Day 3 - ML workshop

Convener: Anna Binoy (University of Kansas)