CS & Physics Meet-Up by Lamarr & B3D

Session

Coffee break with poster session

CBP

30 Nov 2023, 10:40

Joseph-von-Fraunhofer Strasse 25, Floor 3, Room 302 - Lamarr Co-Working Space (Lamarr Institut, TU Dortmund University)

50. Angular unfolding of the neutrino flux

Leonora Kardum

Poster

IceCube Neutrino Observatory, the cubic kilometer detector embedded in ice of the geographic South Pole, is capable of detecting particles from several GeV up to PeV energies enabling precise neutrino spectrum measurement. The diffuse neutrino flux can be subdivided into three components: astrophysical, from extraterrestrial sources; conventional, from pion and kaon decays in atmospheric...

54. Approaching PDEs with Physics-Driven Deep Learning

Nils Wandel (University of Bonn)

Poster

Partial Differential Equations (PDEs) play an important role in describing continuous physical systems such as fluids, waves, cloth and many more. Thus, by solving these equations, one can simulate for example fluids or garment in computer graphics or analyse lift and drag coefficients in engineering applications. However, in most scenarios, analytic solutions of PDEs are not available and...

51. Efficient Light Source Placement using Quantum Computing

Sascha Mücke (Lamarr Institute, TU Dortmund)

Poster

NP-hard problems regularly come up in video games, with interesting connections to real-world problems. In the game Minecraft, players place torches on the ground to light up dark areas. Placing them in a way that minimizes the total number of torches to save resources is far from trivial. We use Quantum Computing to approach this problem. To this end, we derive a QUBO formulation of the torch...

47. Flavour Tagging at the LHCb experiment

Jonah Blank (TU Dortmund)

Poster

One of the prominent questions in particle physics is the apparent assymetry of matter and antimatter, which in the early universe led to an abundance of the former, resulting in our cosmos as we see it today. One important part in understanding this asymmetry is the so called Charge and Parity violation (CPV), which describes the different behaviour under the combination of charge conjugation...

48. How to learn from real IACT data

Mirko Bunse (Artificial Intelligence Unit, Computer Science VIII, TU Dortmund University)

Poster

Imaging atmospheric Cherenkov telescopes (IACTs) typically require simulations to obtain labeled training data for reconstruction tasks. For the specific task of gamma hadron classification, we show that no simulations are needed if the direction of each event is employed as a so-called "noisy label". Machine learning research on the theory of class-conditional label noise provides us with...

52. Identification and Classification of Young Star Clusters

Maik Sowinski (Forschungszentrum Jülich )

Poster

The GAIA DR3 presents scientists with new opportunities to investigate star clusters due to the increased number of light sources (~1.6 billion) and improved precision of the data. A natural ansatz is now to attempt to create a comprehensive catalogue of star clusters using Machine Learning tools. The specific tools to be used are clustering algorithms, such as DBSCAN, HDBSCAN, and OPTICS....

53. Pulsarnet - Accelerating the Discovery of Binary Pulsars through Machine Learning

Abhinav Tyagi (MPIfR Bonn)

Poster

In the realm of radio astronomy, the discovery and analysis of binary pulsars present a unique opportunity to test the theories of gravity, particularly General Relativity, in the strong-field limit. PulsarNet introduces an advanced machine learning pipeline specifically tailored for this purpose. This pipeline uniquely processes the Fourier amplitude spectrum to identify binary pulsar...

49. Unfolding from a Computer Science perspective

Mirko Bunse (Artificial Intelligence Unit, Computer Science VIII, TU Dortmund University)

Poster

Unfolding corrects measurements of distributions if these measurements are affected by distortions and limited acceptance of the detector. We show that Computer Science knows unfolding under a different term, as "quantification learning" or as "class prior estimation". Through this connection, we use advancements made in Computer Science, both concerning the theoretical understanding of the...

37. Very Fast Submodular Function Maximization Optimization

Dr Sebastian Buschjäger (Lamarr Institute, TU Dortmund)

Coffee break with poster session

Poster

Submodular Function Maximization arises in many different applications fields in Machine Learning and Data Science, especially in Data Summarization. In Data Summarization, we want to select a meaningful subset of the data points for human inspection so the user can get a quick overview of meaningful data points. We contributed a novel Submodular Function Maximization algorithm called...