CS & Physics Meet-Up by Lamarr & B3D

Session

Pitch Talks

T

30 Nov 2023, 09:50

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

Conveners

Pitch Talks: ML Methods and Application Examples (in Physics)

• Jens Buss (E5b, Lamarr)

Pitch Talks: Inverse Problems

• Jens Buss (E5b, Lamarr)

Pitch Talks: Problems and data sources

• There are no conveners in this block

26. ML Appl. Example Neutrino + Neutrino Map of the Universe

Dr Mirco Huennefeld

30/11/2023, 09:50

ML Methods and Application Examples (in Physics)

Pitch talk

36. Class-conditional label noise

Dr Mirko Bunse (Lamarr Dortmund)

30/11/2023, 10:00

ML Methods and Application Examples (in Physics)

Pitch talk

40. Deep Learning for real-time classification of astronomical radio signals

Andrei Kazansrev (Max Planck Institute for Radio Astronomy)

30/11/2023, 10:10

ML Methods and Application Examples (in Physics)

Pitch talk

Continuous developments in systems for recording radio astronomical signals lead to a natural increase in the volume of data acquired. This increase, in turn, exacerbates the challenges associated with the storage and processing of this information. A potential solution to this challenge is the development of systems underpinned by deep machine learning. These systems, at the stage of signal...

35. Innovative approaches for galaxy cluster mass determination via deep learning

Kaustuv Basu (Uni Bonn)

30/11/2023, 10:20

ML Methods and Application Examples (in Physics)

Pitch talk

Determination of galaxy cluster masses from astronomical data is one of the primary prerequisites for their cosmological analysis and various deep learning methods have been tested with simulated data sets. Although the attention of the community is moving towards more advanced architectures like GANs and vision transformers, improvements can still be made with the traditional convolutional...

55. Representing and rendering large noisy radio data cubes

Prof. André Hinkenjann (Hochschule Bonn-Rhein-Sieg, Institut für Visual Computing)

30/11/2023, 10:30

ML Methods and Application Examples (in Physics)

Pitch talk

To support visual analytics of large radio/volume data, we accelerated a source finding/filtering algorithm by employing GPUs. Also, we implemented a GPU based volume renderer that achieves real-time performance and can be used in interactive environments, like an Augmented Reality setup. When visually analysing unfiltered data the frame rates of the renderer drop significantly and data is...

21. Quantification

Dr Mirko Bunse (Lamarr Institut)

30/11/2023, 14:00

Inverse Problems

Pitch talk

22. Solving Inverse Problems with Deep Learning

Sebastian Konietzny (Informatik LS8, TU Dortmund University), Dr Tobias Uelwer (Informatik LS8, TU Dortmund University)

30/11/2023, 14:10

Inverse Problems

Pitch talk

Our work entails using generative models as priors for Fourier phase retrieval. We were successful in the Helsinki Tomography Challenge 2022 by employing a large synthetic dataset with end-to-end convolutional networks for limited-angle computer tomography. Additionally, our latest project focuses on extracting (ideally weak) earthquake signals from real, noisy Distributed Acoustic Sensing (DAS) data.

23. Unfolding in Astroparticle Physics

Leonora Kardum, Prof. Wolfgang Rhode

30/11/2023, 14:20

Inverse Problems

Pitch talk

32. Atmospheric Noise Removal for FYST: Current Methods and ML Prospects

Ankur Dev (Uni Bonn)

30/11/2023, 14:30

Inverse Problems

Pitch talk

The Epoch of Reionization Spectrometer (EoR-Spec) instrument on the Fred Young Submillimeter Telescope (FYST) will undertake a Line Intensity Mapping (LIM) survey targeting the [CII] line across redshifts 3.5 − 8.0. The observed frequency range for EoR-Spec, 210 to 420 GHz, is substantially influenced by atmospheric emissions that affect LIM power spectrum measurements. One of the challenges...

39. Leveraging Machine Learning for Next-Generation Radio Pulsar Searches

Dr Vishnu Balakrishnan (MPIfR)

30/11/2023, 14:40

ML Methods and Application Examples (in Physics)

Pitch talk

The discovery of new radio pulsars has significant implications for both Gravitational and Condensed Matter Physics. With the emergence of large datasets on the order of petabytes requiring quasi-real-time analysis, computational methods, particularly machine learning, have been increasingly important. This pitch will give a short ovreview of the progress made in applying machine learning...

16. Particle Shower Simulation

Dominik Baack (TU Dortmund)

01/12/2023, 09:30

Problems and data sources

Pitch talk

29. Radio Data Simulations

Felix Geyer, Kevin Schmidt

01/12/2023, 09:40

Problems and data sources

Pitch talk

38. Potential ML applications to accelarate ISM simulation

Tim-Eric Rathjen

01/12/2023, 09:50

Problems and data sources

Pitch talk

14. ML Solutions for Time Series Problems

Dr Amal Sadallah (Lamarr Institute Dortmund)

01/12/2023, 10:00

Problems and data sources

Pitch talk

33. Time Series Data - Machine Learning For Observatory Sites

Dr Ralf Timmermann (Uni Bonn)

01/12/2023, 10:10

Problems and data sources

Pitch talk

The CCAT observatory on Cerro Chajnantor, Chile, will be operated entirely remote - with no crew at site during observations. Hence, we require reliable Predictive Maintenance (Outlier-/Anomaly-Detection) methods on its critical infrastructure to guarantee continuous operations. Moreover, Time Series data is collected from various radiometers at Atacama representing the atmospheric...

43. P-adic numbers

Prof. Thomas Liebig (Lamarrr Institut, TU Dortmund)

01/12/2023, 10:20

Problems and data sources

Pitch talk

24. Inverse Problems in Radio solved with Deep Learning

Dr Kevin Schmidt

Inverse Problems

Pitch talk

27. ML Appl. Example Exp. Teilchenphysik: Flavour Tagging

ML Methods and Application Examples (in Physics)

Pitch talk