HAMLET-PHYSICS 2024 Conference/Workshop

19 Aug 2024, 06:00 → 21 Aug 2024, 20:00 Europe/Copenhagen

Lundbeck Auditorium

We are looking forward to welcoming you to the first HAMLET-PHYSICS Conference/Workshop, to be held in sunny Copenhagen, August 19 - 21, 2024.   The workshop has three main goals: 1. To bring together Danish and international physicists using ML to meet, share ideas, and build community across location and physics specialty 2. To bring domain scientists into close contact with ML experts, to build community across the theory - application bridge 3. To provide a friendly environment for researchers to share best practices, for students to interact with experts, and for other sciences and industry to understand the state of ML in physics

Scientific Program

• Keynotes, plenaries and parallels
• Discussions and (AI-assisted) research speed-dating
• Beer talks and train-ride chats
• Hackathons and demonstrations from experts in high performance computing and machine learning

Confirmed speakers include Thea Aarrestad (ETH Zurich), Savannah Thais (Columbia University) and Petar Velickovic (DeepMind, remote).

Abstracts are open for contributions at the intersection of machine learning and

• Particle physics
• Astrophysics and cosmology
• Quantum physics
• Biophysics
• Climate science
• Geophysics
• Molecular physics
• Condensed matter

This is not an exhaustive list. We warmly welcome all submissions for talks, suggestions and ideas, and will strive to accommodate all submissions.

Important Dates

• Registration & abstract submission opens: May 10, 2024
• Abstract deadline: July 12, 2024
• Notification of talks: July 15, 2024
• Program online: July 17, 2024
• Registration deadline: August 2, 2024
• Scientific program of the conference begins August 19, 09.00
• Scientific program of the conference ends August 21, 16.00

Abstracts submitted after the deadline will be considered on a case-by-case basis.

Social Program

Monday August 19th will feature a reception event around Niels Bohr's Office.

On Tuesday evening August 20th, the workshop will take to the rails: A heritage 1950s Norwegian State Rail diesel locomotive will take workshop attendees from Copenhagen (Østerport Station) to Kronborg Castle in Helsingør (location of Shakespeare's Hamlet tale).

While on board, refreshments will be served, and breakout sessions will occur according to attendees research areas of interest. A visit of Kronborg will be included, along with an open-air conference dinner in Helsingør. 

Organization

International Advisory Committee

• Adriano Agnello (STFC)
• Benjamin Nachman (Berkeley)
• Daniel Whiteson (UC Irvine)
• David Rousseau (Paris XI)

National Advisory Committee

• Allan Peter Ensig-Karup (DTU)
• Mads Toudal Frandsen (SDU)
• Sofie Marie Koksbang (SDU)
• Manuel Meyer (SDU)
• Alessandro Lucantonio (Aarhus University)

Local Organizing Committee

• Daniel Murnane (NBI)
• Troels Petersen (NBI)
• Inar Timiryasov (NBI)
• Stefan Pollok (DTU)
• Oswin Krause (DIKU)
• Troels Haugbølle (NBI)

Sponsors

       

 

 

Monday, 19 August

1 Breakfast + Registration

Plenaries & Keynotes

2 Intro

3 Keynote - Tess Smidt (MIT)

4 Coffee

5 GraphNeT 2.0

GraphNeT, a deep learning framework for neutrino telescopes, provides a common library for deep learning experts and neutrino physicists in need of advanced deep learning techniques for their analyses. GraphNeT is a detector agnostic framework, making it easy and intuitive to apply deep learning techniques from one experiment to another, and to export models to the physicists that relies on them. GraphNeT lowers technical barriers, enabling physicists to utilize deep learning without extensive expertise, and sets the stage for inter-experimental collaboration. This poster presentation highlights the key developments of GraphNeT 2.0 and their impact on how they provide a way for neutrino telescopes to work together on advancing the state-of-the-art for deep learning based techniques in the field.

Speaker: Rasmus Ørsøe (TUM)

6 Enhancing Neutron Scattering Experimentation: A Data Science and Machine Learning Approach to Predict Background Scattering

In the field of neutron scattering experimentation, the use of complicated sample environments containing strong magnets introduces significant challenges. The inclusion of substantial material within the experimental structure is necessary in order to withstand the large magnetic forces, which eventually influences the experimental outcome through events of multiple neutron scattering. This project leverages data science and machine learning techniques to investigate and predict background scattering due to multiple scattering that takes place in the complex sample environment.

Utilizing the McStas neutron ray-trace simulation package, a detailed model of the 15 T magnet for the BIFROST spectrometer at the European Spallation Source (ESS) was developed. The model was parameterised to cover different experiment setups with a number of simulation parameters, generating a substantial amount of simulation results. A comprehensive database of 24000 simulation results was constructed, and analysed to uncover underlying patterns and relationships between the experimental setup parameters and the observed background scattering. Subsequently, machine learning models were trained, fine-tuned and tested, and their predictive accuracy was assessed.

This project contributes to the field of neutron scattering by providing a novel approach to addressing the challenges of background prediction due to multiple scattering in complex sample environments and can serve as an introduction to a new method of background recognition, paving the way for the development of automated background prediction tools that can be used within a wide range of instruments, with combinations of simulated and experimental data in the future. It also exemplifies the potential of data science in advancing experimental physics.

Speaker: Petroula Karakosta (Niels Bohr Institute, University of Copenhagen)

7 Talk

12:30 Lunch

Plenaries & Keynotes

8 Keynote 2 - Savannah Thais (Columbia)

9 Coffee

10 Identifying dwarf AGN candidates through novel machine learning techniques

While identification, characterisation, and triggering mechanisms of active galactic nuclei (AGN) have been since the 80's, the discussion has only been extended to include dwarf galaxies within the last decade.
This study aims to explore a novel AGN identification technique using a random forest (RF) classification technique, compare it to established identification methods, and investigate which set of properties/features constitute the best RF model.
Data is sourced from multiple catalogues: MaNGA (and its value added catalogue, Firefly) provides spatially resolved spectra of 10\,104 galaxies of which 1\,149 are dwarf galaxies. These galaxies constitute the base data set, and infrared (WISE) and X-ray (XMM) observations are matched to these. The NASA-Sloan Atlas is used for estimating environmental parameters.
The best model (from F1 score alone) is using internal features only of more massive galaxies. This model tends toward weighing fewer features higher and ignoring parameters that are less directly related to AGN ionisation. Conversely, this model disagrees the most with observations when it comes to dwarf galaxies, but provide twice as many dwarf AGN candidates as observations, and up to thrice as many compared to using intermediate mass galaxies as training set.
This approach provides a novel and interesting venue for identification of AGN in dwarf galaxies, but the method still requires fine tuning such as feature selection optimisation and validity assessment -- are the predicted AGN actually AGN? If so, RF can be used to increase the sample size of known dwarf AGN and to adjust observational diagnostic diagrams in the low mass regime.

Speaker: Mikkel Theiss Kristensen (University of Southern Denmark)

11 Data challenges for black-hole image reconstruction and feature identification

Black holes hold a tremendous discovery potential, and experiments such as the Event Horizon Telescope and its next generation upgrade could provide important cues about their structure. I will present a pedagogical discussion on the most relevant features in black-hole images (e.g. photon rings) and their detectability using realistic telescope arrays. I will focus on the structure of Fourier-plane data when expressed in terms of standard interferometric variables known as closure quantities, and motivate the use of machine learning to identify specific features and quantify observational uncertainties.

Speaker: Raúl Carballo-Rubio (Southern Denmark University (SDU))

12 Decoding the Early Universe: Machine Learning Applications in CMB Analysis

In this presentation, we will explore the application of advanced machine learning techniques in cosmology, focusing on the analysis of Cosmic Microwave Background (CMB) maps. Accurately calculating the tensor-to-scalar ratio from CMB data is a crucial yet challenging task, as it holds the key to understanding primordial gravitational waves and the early universe's inflationary period. I will discuss the use of deep learning and other sophisticated algorithms to extract meaningful features and parameters from CMB maps, which can be readily reapplied in other areas of cosmology and astrophysics studies. These methods offer robust tools for dealing with the complexities and high-dimensional nature of data. By leveraging machine learning, we can enhance our ability to simulate, analyze, and interpret CMB observations, providing deeper insights into the universe's fundamental properties. The versatility and potential of machine learning in advancing our understanding of the cosmos will be highlighted, by showing data analysis techniques applicable in all scientific disciplines.

Speaker: Leonora Kardum

Poster Session: Poster Session, Beer Talks, Confession Panel

Social Session: OPTIONAL: Dinner

Suggestion locations:
- Sunny: Broens Gadekøkken (Metro Line 3, stop Kongens Nytorv, walk 300m)
- Rainy: Meatpacking District (Metro Line 3, stop København H, walk 200m)

Tuesday, 20 August

Breakfast

Plenaries & Keynotes

13 Intro

14 Keynote 3 - Thea Aarrestad (ETH Zurich)

15 Coffee

16 Exploring Bottomonium Behaviour at Finite Temperatures: Machine Learning and Lattice QCD

We present findings from the FASTSUM collaboration on the behaviour of bottomonium particles (bound states of a bottom quark and its antiquark) at different temperatures. To analyze this, we used three methods: a Maximum Likelihood approach with a Gaussian function for the ground state, the Backus-Gilbert method, and a machine learning technique called Kernel Ridge Regression. Our study uses lattice simulations with 2+1 quark flavours, covering temperatures from 47 to 375 MeV. The findings help us understand how bottomonium particles behave in hot environments, which is important for studying the properties of quark-gluon plasma and the dynamics of heavy quarks in extreme conditions.

Speaker: Benjamin Jaeger (University of Southern Denmark)

17 Advancing Ultra-High Energy Neutrino Astronomy through Deep Learning and Differential Programming

Detection of neutrinos at ultra-high energies (UHE, E >$10^{17}$eV) would open a new window to the most violent phenomena in our universe. However, owing to the expected small flux of UHE neutrinos, the detection rate will be small, with just a handful of events per year, even for large future facilities like the IceCube-Gen2 neutrino observatory at the South Pole. In this contribution, I will discuss how to substantially enhance the science capabilities of UHE neutrino detectors by increasing the detection rate of neutrinos and improving the quality of each detected event, using recent advances in deep learning and differential programming. First, I will present neural networks replacing the threshold-based trigger foreseen for future detectors that increase the detection rate of UHE neutrinos by up to a factor of two. Second, I will outline and present preliminary results towards an end-to-end optimization of the detector layout using differential programming and deep learning, which will improve the neutrino direction and energy determination. I will present new results of a deep, ResNet-based neural network that, combined with Normalizing Flows, predicts the full Posterior PDF of neutrino direction and energy. The expected improvements are estimated to be equivalent to building an up to three times larger detector, accelerating the research field by a decade.

Speaker: Christian Glaser (Uppsala University)

18 Talk

19 Short Talk

12:00 Lunch

Interactive Session

Tutorials & Demos

Plenaries & Keynotes

20 Development of innovative methods for fission trigger construction

The development of innovative methods for fission trigger construction is part of the FRØZEN project which aims to get a better understanding of the angular momentum generation and the energy partition between fragments in the fission process. The reconstruction of the very first moments after the scission point is essential and requires correlated neutron and gamma detection as well as measuring the kinematic properties of fission fragments. Such a measurement could be achieved thanks to the last generation of hybrid $\gamma$-spectrometer named $\nu$-Ball2, coupled to a double Frisch-Grid Ionisation Chamber (dFGIC). In this experiment, a spontaneous ${}^{252}$Cf fission source was used. However, for other fissioning systems that require the use of a primary beam, fission could become a minor nuclear reaction compared to other processes. Additionally, with the increasing size of nuclear physics experimental setup, the need to recognize rarer reaction mechanism, one of the main challenges nowadays in nuclear physics is to develop more and more selective data analysis methods for more and more contaminated datasets.

AI models are being developed to replace the usual data analysis techniques for reconstructing the fission events, exploring the limits of AI implementation in such context. The first implementation is deeply motivated by the computationally expensive and time-consuming characteristics of more usual trace (sampled signal) analysis approaches, currently used to analyze the dFGIC response and tag fission for $\nu$-Ball2 setup. Some promising regression and convolutional neural network models have been tested to obtain precise fission tag time, the deposited energy, and the electron drift time from the dFGIC traces. The second implementation tackles the challenge of recognizing fission events from a polluted dataset by developing an AI-based algorithm to recognize fission solely based on the $\nu$-Ball2 response function. The fission fragments de-excitation process is reconstructed from the correlations between the individual fission fragments pairs and observables, such as gamma and neutron energies and multiplicities. AI models can be used to evaluate the impact of each observable into identifying fission. Once the algorithm is trained, it could be applied to various fissioning systems without the need for an ancillary fission tag detector, such as the dFGIC.

Speaker: Brigitte PERTILLE RITTER (Université Paris-Saclay / IJCLab)

21 AI for fusion, plasma simulations, and experiment control

We give an overview of the work in AI within a joint research endeavour between the UKAEA and the STFC Hartree Centre, the Fusion Computing Lab, to accelerate discovery and design of complex facilities in magnetic-confinement fusion.
We illustrate how we are enhancing the "classical" real-time control of tokamak shots with AI techniques of emulation and reinforcement learning, both for the shape of the confined plasma and for the cooling of the exhaust fusion byproducts in the divertor region, as well as the fast prediction of disruptive instabilities from heterogeneous and gappy measurements.
We also present ongoing work to accelerate demanding simulations in plasma physics, to quantify turbulent transport in different regimes across the tokamak, from the core to the scrape-off-layer region, highlighting the importance of domain-aware AI.
The techniques developed in this collaboration also have applications to the efficient planning of experiment campaigns, and optimal design of complex facilities, in other areas of physics.

Speaker: Adriano Agnello (STFC Hartree Centre)

15:00 From Lundback Aud to Østerport Station (Self transport)

Social Session: Heritage Train Ride from Østerport to Helsingør Station

Suggestion locations:
- Sunny: Broens Gadekøkken (Metro Line 3, stop Kongens Nytorv, walk 300m)
- Rainy: Meatpacking District (Metro Line 3, stop København H, walk 200m)

Social Session: Visit Kronborg

Suggestion locations:
- Sunny: Broens Gadekøkken (Metro Line 3, stop Kongens Nytorv, walk 300m)
- Rainy: Meatpacking District (Metro Line 3, stop København H, walk 200m)

Social Session: Conference Dinner

Suggestion locations:
- Sunny: Broens Gadekøkken (Metro Line 3, stop Kongens Nytorv, walk 300m)
- Rainy: Meatpacking District (Metro Line 3, stop København H, walk 200m)

Wednesday, 21 August

Plenaries & Keynotes

22 Symbolic regression for Science: challenges and opportunities.

Symbolic regression (SR) is a machine learning technique that seeks to identify mathematical expressions that best fit a given dataset. This method is particularly beneficial in scientific research, due to its ability to generate interpretable models that provide insight into the underlying mechanisms, in sharp contrast with purely data-driven, black-box models. Interpretable machine learning is gaining importance as models are increasingly deployed in critical societal applications like finance and medicine. Also, models discovered by SR are prone to analyses that allow to assess their out-of-distribution behavior. These beneficial properties have led to the application of SR across various fields, including physics, biology, climate modeling, finance, and various engineering disciplines. Despite these opportunities, the application of symbolic regression faces several challenges. One major issue is the computational complexity associated to the combinatorial search within the space of mathematical expressions, which requires the use of high-performance computing, scalable algorithms, and smart strategies or integration of domain-specific knowledge to guide the search and make it more efficient. Additionally, ensuring robustness and generalization of the discovered models can be difficult, as overfitting to noisy data or limited datasets may occur. In this talk, we wish to stimulate the debate among researchers about both opportunities and challenges concerning the use of SR for Science.

Speaker: Alessandro Lucantonio (Aarhus University)

23 cp3-bench: A tool for benchmarking symbolic regression algorithms tested with cosmology

In a recent paper we presented a new benchmark tool for symbolic regression and tested it with cosmology datasets. This new benchmark tool is easy to use, and we would like to spread the word and encourage other people to try it in their respective field where applicable. Out of the box it has ten different machine learning algorithms, but we also encourage the community to add their own methods to the framework and expand the capabilities. In this talk I will discuss how it works and the paper we published with a test application in cosmology. We find no indication that performance of algorithms on standardized datasets are good indications of performance on cosmological datasets. This suggests that it is not necessarily prudent to choose which symbolic regressions algorithm to use based on their performance on standardized data. Instead, a more prudent approach is to consider a variety of algorithms. Overall, we find that most of the benched algorithms do rather poorly in the benchmark and suggest possible ways to proceed with developing algorithms that will be better at identifying ground truth expressions for cosmological datasets. As part of this publication, we introduce our benchmark algorithm cp3-bench which we make publicly available at https://github.com/CP3-Origins/cp3-bench.

Speaker: Mattias Ermakov Thing (CP3, University of Southern Denmark)

24 Discovering interpretable physical models using Symbolic Regression and Discrete Exterior Calculus

Computer simulations aim to replicate physical phenomena as precisely as possible, but they often require extensive knowledge and rely on differential equations, which typically lack analytical solutions. The recent rise of Machine Learning, driven by large amounts of data, has offered an alternative to traditional human-designed methods. However, these data-driven approaches produce complex, black-box models that are hard to manage and interpret. To address this, Symbolic Regression (SR) has gained popularity for its ability to derive understandable mathematical equations from data. Nevertheless, most prior research only focused on re-discovering conventional physics equations or dynamical systems described by ordinary differential equations.
To further push the boundaries of the existing methods, we developed a framework that integrates SR with Discrete Exterior Calculus (DEC) for automated discovery of field theories. DEC provides a discrete geometric representation of physics, avoiding the need for differential formulations and employing a concise set of operators. This approach significantly reduces the SR search space, improving generalization even with limited data. We validated our framework by successfully re-discovering multiple Continuum Physics models using synthetic experimental data.

Speaker: Simone Manti (Aarhus University)

11:00 Coffee

25 Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory

We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply Transformers to predict these coefficients. The problem can be formulated in a language-like representation amenable to standard cross-entropy training objectives. We design two related experiments and show that the model achieves high accuracy (> 98%) on both tasks. Our work shows that Transformers can be applied successfully to problems in theoretical physics that require exact solutions. (Based on arXiv:2405.06107 [cs.LG])

Speaker: Dr Matthias Wilhelm (NBI)

26 Uncertainty estimation in magnetic field inference

Many physical problems are governed by Laplace's equation, to which there exists a unique solution when boundary constraints are fully defined. However, given only partial knowledge or discrete observation along a boundary, statistical methods are required to construct uncertainty estimates and likelihoods for possible solutions.
Within magnetism, obtaining the magnetic field or the magnetic scalar potential, $\Delta \phi = 0$, from incomplete boundary information is of fundamental interest for instance in robotics navigation and geophysics when estimating the geomagnetic field on the Earth’s surface.

Here we propose and demonstrate new statistical methods for estimating the magnetic scalar potential from an incomplete boundary-value problem: (1) By using Gaussian Processes (GP) or (2) by an ensemble of physics-informed neural networks (PINNs). Using either we obtain statistical information for the complete boundary. This information is then used to efficiently construct the statistical solutions using a basis of harmonic functions for the magnetic scalar potential, such that this is guaranteed to be physically correct, i.e. obey Maxwell’s equations.

We show that the obtained uncertainty estimates using the above two methods compare well to the ones obtained from Hamiltonian Monte Carlo (HMC) simulations, which extensively investigate the target probability distribution for all valid magnetic scalar potential matching given knowledge.
Further, we qualitatively verify that the uncertainty estimates are correct, i.e., being confident where boundary values are given while being uncertain far away from observations points.
This allows for improved subsequent decision-making with the found statistical solutions to Laplace's equation, e.g., where to measure next when only limited amount of resources is available.

Speaker: Stefan Pollok (Technical University of Denmark)

12:00 Lunch

Plenaries & Keynotes

27 Bayesian optimisation of ocean models

Accurately representing the tropical sea surface temperature (SST) remains a significant challenge for general circulation models. One of the largest sources of uncertainty is the vertical turbulent mixing. To accurately represent the distribution of ocean mixed layer depths (MLD), turbulence closure schemes necessitate careful tuning. This is commonly done manually by comparing with mixed layer depth climatologies. Advancements in machine learning research introduce a new strategy: automated tuning. VerOpt, an add-on to the Python-based ocean model Veros, uses Gaussian processes to emulate an objective function in a multi-dimensional parameter space. We demonstrate how VerOpt can be used to search the joint parameter space of the vertical & horizontal mixing and air-sea flux parameterisations. Furthermore, we discuss the technicalities and advantages associated with using a python-based ocean model that utilises JAX for GPU acceleration.

Speaker: Marta Mrozowska (University of Copenhagen)

28 Bayesian Model Selection of Inflationary Models Using the CONNECT Emulation Framework

Bayesian model selection is an invaluable tool when comparing different inflationary models. This is done by computing the Bayesian evidence for the different models using nested sampling, and then comparing them using the Bayes factor. Computing the Bayesian evidence of a model using an Einstein-Boltzmann solver code takes weeks, if not months for complex models, and it is therefore very rarely done. Using a neural network, e.g. from the CONNECT framework, to emulate cosmological observables, the time used to compute the Bayesian evidence of a model can be brought down from tens of thousands of CPU hours to about 100 CPU hours.
In this talk, I will present the setup and programs used to compute the Bayesian evidence using the neural network framework CONNECT. I will also show the Bayesian evidence for the cosmological model ΛCDM+ɑ_s+r computed with CLASS and CONNECT respectively. Furthermore, I will also present a comparison of different inflationary models using the Bayes factor.

Speaker: Camilla Theresia Grøn Sørensen (Department of Physics and Astronomy, Aarhus University)

29 Talk

30 Workshop Summary & Outro

15:00 Coffee & Beer