Deep Learning in the Natural Sciences

Europe/Berlin
Hamburg

Hamburg

Description

A workshop to connect researchers working on Deep Learning in the natural sciences with a focus on astronomy, particle physics and photonics.

The program consists of invited speakers, submitted contributions and student talks with generous room for discussion.

The workshop will take place at:
Universität Hamburg
Jungiusstraße 9, Hörsaal I (Wolfgang Pauli-Hörsaal)
20 355 Hamburg

 

Participants
  • Abhilash Chandran
  • Alexander Froehlich
  • Anastasia Karavdina
  • Andrea Malara
  • Arne Christoph Reimers
  • Ashraf Mohamed
  • Atanu Nath
  • Auro Prasad Mohanty
  • Betty Calpas
  • Blake Forland
  • Christian Schneider
  • Christoph Rosemann
  • Christopher Behrens
  • Christopher Matthies
  • Claudio Gheller
  • David Francois Droz
  • Dennis Schwarz
  • Dirk Krucker
  • Dominik Elsaesser
  • Eric Metodiev
  • Erik Buhmann
  • Felix Beckmann
  • Filipe Maia
  • frank schluenzen
  • Frank-Dieter Gaede
  • Frederik Gerhard Faye
  • Gregor Kasieczka
  • Hamed Bakhshiansohi
  • Hans Fangohr
  • Harald Viemann
  • Hartmut Stadie
  • Henrik Jabusch
  • Henry Chapman
  • Jack Rolph
  • Jack Rolph
  • Jan Skottke
  • Janusz Malka
  • Jesse Thaler
  • Jochen Liske
  • Johannes Haller
  • Jonas Rübenach
  • Julian Moosmann
  • Julianna Carvalho Oliveira
  • Jun Zhu
  • Karla Pena
  • Kehinde Gbenga Tomiwa
  • Lars Lottermoser
  • Leonid Didukh
  • Lisa Benato
  • Lukas Dirks
  • Lukasz Kreczko
  • Marcel Mielach
  • Marcel Völschow
  • Marcus Brüggen
  • Martin Erdmann
  • Martin Gasthuber
  • María Teresa Núñez Pardo de Vera
  • Mehtap Arli
  • Melanie Margarete Eich
  • Mohammad Reza Heidari
  • Nino Ehlers
  • Nis Meinert
  • Ntsoko Phuti Rapheeha
  • Oleksii Turkot
  • Paolo Gunnellini
  • Patrick Komiske
  • Peter Schleper
  • Philipp Heuser
  • Ronja vom Schemm
  • Sakire Aytac
  • Sergii Mamedov
  • Sergius Dell
  • Severin Diederichs
  • Shafagh Dastjani farahani
  • Simon Schnake
  • Stefan Czesla
  • Thomas Stielow
  • Tilman Plehn
  • Tim Weber
  • Tim Wilksen
  • Tobias Lösche
  • Torben Ferber
  • Valerio Mariani
  • Varsha Ramachandran
  • Vladyslav Danilov
  • Won Sang Cho
  • Xiaogang Yang
  • Yaroslav Gevorkov
  • Yavar Taheri Yeganeh
  • Yves Kemp
  • Zhiyuan He
  • Thursday, February 28
    • Pre-Workshop introduction and arrival
      • 1
        Registration & Sign-Up
      • 2
        Deep Learning Basics I
        Speaker: Gregor Kasieczka (Hamburg University (DE))
      • 3
        Deep Learning Basics II
        Speaker: Gregor Kasieczka (Hamburg University (DE))
    • 12:00 PM
      Lunch
    • Talk: Talks I
      Convener: Marcus Brueggen
      • 4
        Opening
        Speakers: Gregor Kasieczka (Hamburg University (DE)) , Marcus Brueggen
      • 5
        Collision Course: Particle Physics as a Machine-Learning Testbed
        Speaker: Jesse Thaler (MIT)
      • 6
        Machine Learning Techniques in Cosmological Simulation
        Speaker: Claudio Gheller
      • 7
        Low-dose X-ray Imaging with Deep Neural Networks
        Speaker: Xiaogang Yang
    • 3:00 PM
      Coffee Break
    • Talk: Talks II
      • 8
        Machine Learning Techniques in Astroparticle Physics
        Speaker: Dominik Elsaesser
      • 9
        Rise of the Tagging Machines
        Speakers: Tilman Plehn (Heidelberg University) , Tilman Plehn
      • 10
        Machine Learning for Diffractive Imaging and Crystallography
        Speaker: Filipe Maia
    • Talk: Talks III
      Convener: Johannes Haller (CERN)
      • 11
        Deep Learning in Particle and Astroparticle Physics
        Speaker: Martin Erdmann (Rheinisch Westfaelische Tech. Hoch. (DE))
      • 12
        A metric for collider events

        When are two collider events similar? In this talk, I will answer this question by developing a metric between the radiation patterns of events. The metric is based on the well known earth mover’s distance, and intuitively is the minimum “work” required to rearrange the energy flow of one event into the other. With a metric in hand, I will discuss and demonstrate numerous tools for analyzing and visualizing the space of events for collider applications.

        Speaker: Patrick Komiske (Massachusetts Institute of Technology)
      • 13
        Radio Galaxy Classifications with Deep Learning
        Speaker: Vesna Lukic
      • 14
        Particle identification on the DAMPE experiment

        The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic rays observatory in operations since 2015, equipped with alongside other instruments a deep calorimeter able to detect electrons up to an energy of 10 TeV and cosmic rays up to 100 TeV. The large proton and ion background in orbit requires a powerful electron identification method. We explore a neural network based approach to an on-orbit particle identification problem. We present the issues that arise from the constraints of particle physics and our experiment, notably the difference between training set based on simulated (Monte Carlo) data, and the application set based on real unlabeled data, leading to a trade-off between performances and general usability.

        Speaker: David Francois Droz (Universite de Geneve (CH))
      • 15
        Machine learning with augmentation for boosting di-Higgs searches at the LHC
        Speaker: Won Sang Cho (Seoul National University)
    • 11:00 AM
      Coffee Break
    • Talk: Talks IV
      Convener: Gregor Kasieczka (Hamburg University (DE))
      • 16
        Application of Generative Models to Natural Science
        Speakers: Fedor Ratnikov, Fedor Ratnikov (Yandex School of Data Analysis (RU))
      • 17
        Towards data-driven particle physics classifiers

        Deep learning in particle physics often relies on imperfect simulations due to the lack of real labelled data, which risks learning mismodeling artifacts rather than the underlying physics. In this talk, I discuss the prospects for training classifiers directly on collider data using mixed samples, drawing from techniques in weak supervision and topic modeling. Using the example of quark versus gluon jet classification, I demonstrate how these ideas allow data-driven classifiers to be trained and actually provide an operational definition of the underlying categories.

        Speaker: Eric Metodiev (Massachusetts Institute of Technology)
      • 18
        Autoencoding New Physics
        Speakers: Jennifer Thompson (ITP Heidelberg) , Jennifer Thompson (ITP Heidelberg)
      • 19
        CNN Classification of X-ray Selected Clusters
        Speaker: Matej Kosiba
      • 20
        Closing Discussion
        Speaker: Peter Schleper (Hamburg University (DE))