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Accelerating the Search for Dark Matter with Machine Learning

15–19 Jan 2018
Lorentz Center@Oort
Europe/Zurich timezone

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Contribution List

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  1. 2. Arrival / Registration
    15/01/2018, 09:00
  2. 58. Welcome from Lorentz Center
    15/01/2018, 10:00
  3. 47. Welcome, Workshop Structure and Objectives
    Sascha Caron (Nikhef National institute for subatomic physics (NL))
    15/01/2018, 10:10
  4. 3. Dark Matter overview
    Nicolao Fornengo (University of Torino and INFN)
    15/01/2018, 10:20
  5. 4. Introduction into Deep Learning and Image Analysis
    Max Welling
    15/01/2018, 11:05
  6. 5. First ideas to connect Astronomical data, Deep Learning and Image Analysis
    German Arturo Gomez Vargas (Universidad Autonoma de Madrid), German Gomez-Vargas (Pontifical Catholic University of Chile)
    15/01/2018, 11:50
  7. 6. Discussion: First connections ?
    15/01/2018, 12:15
  8. 7. Cross link: Deep Learning in High Energy Physics
    Amir Farbin (University of Texas at Arlington (US))
    15/01/2018, 14:00
  9. 8. Discussion: New ideas ?
    15/01/2018, 14:45

    Bring up projects --> Bring up a discussion ...

    We can vote which one we should open.

    Decide on the projects (darksurvey.com)

    Suggest new application of Deep Learning / Image Analysis.
    Suggest topic for brainstorming session.

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  10. 9. Work in subgroups : Finding new projects in Deep Learning / Image Analysis
    15/01/2018, 15:35

    Person proposing the subject + Organiser + Participants
    split up in 2-4 rooms.

    Work on 5 min presentation about the topic.

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  11. 10. The hunt for stellar-mass DM clumps: applying the statistical machine learning techniques to strong microlensing events
    Elena Fedorova
    15/01/2018, 16:30

    Strong gravitational microlensing (GM) events give us a possibility to determine some characteristics of both microlens and microlensed source. As the role of microlens can be played by a DM clump, GM can give us an important clue to understand the nature of dark matter on comparably small spatial/mass scales. In the same time, fitting the lightcurves of microlensed sources is quite...

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  12. 11. Fast model discrimination with Euclideanized signals
    Christoph Weniger (University of Amsterdam)
    15/01/2018, 16:45
  13. 13. Results of yesterdays workgroups on Deep Learning, Image Analysis and astronomical Dark Matter data
    16/01/2018, 09:00
  14. 48. Astronomical Dark Matter measurements and Challenges
    David Richard Harvey (EPFL - EPF Lausanne)
    16/01/2018, 09:30
  15. 14. Introduction into direct and indirect Dark Matter searches and their challenges
    Marco Regis, Dr Marco Regis (INFN - National Institute for Nuclear Physics)
    16/01/2018, 10:35
  16. 15. Introduction into unsupervised learning
    Erzsébet Merényi
    16/01/2018, 11:20
  17. 49. Discussion Unsupervised Leaning for Dark Matter
    16/01/2018, 12:05
  18. 16. First ideas to use Machine Learning in direct Dark Matter searches
    Andrew Brown (MIT), Andrew Brown (Nikhef), Dr Christopher Tunnell (University of Chicago), Christopher Tunnell (Enrico Fermi Institute-University of Chicago-Unknown)
    16/01/2018, 13:55
  19. 17. Discussion: New ideas ?
    16/01/2018, 14:25
  20. 18. First ideas to use Machine Learning in indirect detection
    Luc Hendriks (Nikhef)
    16/01/2018, 14:40
  21. 31. Discussion: New ideas?
    16/01/2018, 15:10
  22. 32. Work in subgroups
    16/01/2018, 15:50
  23. 56. Characterization of the Local Universe via angular cross-correlations
    Simone Ammazzalorso (University of Turin)
    16/01/2018, 16:50
  24. 57. Dark matter searches in dwarf irregular galaxies
    Viviana Gammaldi (SISSA)
    16/01/2018, 17:05
  25. 19. Results of yesterdays workgroups on unsupervised learning, direct and indirect DM searches
    17/01/2018, 09:15
  26. 21. New approaches in semi-supervised learning
    Kristiaan Pelckmans
    17/01/2018, 09:45
  27. 34. First ideas: Supervised DNNs for reconstruction in LHC data
    Markus Stoye (CERN)
    17/01/2018, 11:00
  28. 54. Recent results : Dark Matter searches at LHC
    Renjie Wang (LPNHE-Paris CNRS/IN2P3 (FR)), Renjie Wang (LPNHE-Paris, CNRS/IN2P3 (FR))
    17/01/2018, 11:45
  29. 55. Machine Learning for SHiP and NEWS experiments
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
    17/01/2018, 12:00

    Emulsion-based detectors such as ones used for OPERA experiment or planned for SHiP and NEWS experiments may reveal important characteristics of WIMP-like particles. However due to the nature of the emulsion, the signal to noise ratio tend to be rather small and hence might require special reconstruction techniques. Thus advanced data analysis approaches based on machine learning approaches...

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  30. 38. Miscellaneous thoughts on Machine Learning & Dark Matter
    Kyle Stuart Cranmer (New York University (US))
    17/01/2018, 14:00
  31. 39. Discussion: New ideas? Reinforcement Learning?
    17/01/2018, 14:45
  32. 40. Work on projects in subgroups
    17/01/2018, 15:25
  33. 51. Data-driven constraints on dark matter from dwarf galaxies
    Mr Bryan Zaldivar (LAPTh, Annecy)
    17/01/2018, 16:10
  34. 50. Fast Forecasting for Counting Experiments
    Tom Edwards
    17/01/2018, 16:25
  35. 59. Estimating the parameters of gravitational lenses with deep learning
    laurence perreault levasseur (Stanford University)
    17/01/2018, 16:40

    Machine learning methods have seen a rapid expansion in the last few years. In particular, deep learning has made several breakthroughs, including beating a champion of game of Go and outperforming practicing dermatologists in the visual diagnosis of skin cancer. Although in most applications these networks have been used for classification tasks, they can also be made to predict real-valued...

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  36. 22. Results of yesterdays workgroups on supervised learning and DM searches at the LHC
    18/01/2018, 09:00
  37. 23. Introduction into theory models for Dark Matter particles
    Andrea de Simone
    18/01/2018, 09:30
  38. 24. OpenML/AutoML: Organizing machine learning data and learning to learn better models
    Joaquin Vanschoren
    18/01/2018, 10:35
  39. 36. Adversarial Games for Particle Physics
    Gilles Louppe (New York University (US))
    18/01/2018, 11:20
  40. 37. Discussion: Further ideas?
    18/01/2018, 12:05
  41. 42. Dark Matter model exploration and first ML ideas
    Martin John White (University of Adelaide (AU))
    18/01/2018, 13:55
  42. 45. How to find Natural Supersymmetric Dark Matter?
    Melissa van Beekveld (R)
    18/01/2018, 14:40

    Supersymmetry (SUSY) is able to solve the hierarchy problem and it can provide a perfect dark matter candidate. The non-observation of SUSY particles at the LHC and dark matter particles at dedicated experiments drives the SUSY particles to be heavier and heavier, which is assumed to make it more and more difficult for SUSY to solve the hierarchy problem as it gives rise to the need of...

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  43. 52. BSM-AI (SUSY-AI) and iDarkSurvey: Learning (from) high-dimensional models
    Bob Stienen (Radboud University)
    18/01/2018, 14:55

    Although the standard model of particle physics is successful in describing physics as we know it, it is known to be incomplete. Many models have been developed to extend the standard model, none of which have been experimentally verified. One of the main hurdles in this effort is the dimensionality of these models, yielding problems in analysing, visualising and communicating results. Because...

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  44. 53. Using Deep Learning to predict Electroweakino production cross-sections at the LHC
    Sydney Otten (RWTH Aachen)
    18/01/2018, 15:10
  45. 43. Discussion: New ideas?
    18/01/2018, 15:45
  46. 44. Work in subgroups
    18/01/2018, 16:05
  47. 28. Open discussion: what have we leart? What next?
    19/01/2018, 09:30
  48. 35. Discussion: New connections?
  49. 12. Drinks/reception with informal discussions
  50. 29. Follow-up Workshop, Webpage, Mailing lists...
  51. 33. Hot topics
  52. 41. Hot topics
  53. 1. Introduction into Astronomical Dark Matter measurements and challenges
  54. 20. Introduction into Large Hadron Collider searches
  55. 25. New projects: Astronomical DM + ML
  56. 26. New projects: Direct and Indirect DM + ML
  57. 27. New projects: LHC searches + ML
  58. 46. Results of yesterdays workgroups on Active Learning and DM models
  59. 30. Summary of workshop and Good Bye
  60. 60. White paper preparation