DM-Stat: Statistical Challenges in the Search for Dark Matter (18w5095)

Arriving in Banff, Alberta Sunday, February 25 and departing Friday March 2, 2018

Organizers

Gianfranco Bertone (University of Amsterdam)

Roberto Ruiz de Austri (University of Valencia)

(Yale University)

(Imperial College London)

Objectives

The overarching objective of the DM-Stat workshop is to bring expert statisticians in techniques which may help or accelerate the eventual discovery of particle DM, together with leading physicists in areas relevant to the discovery of dark matter and its particle properties. The goal is to proceed via a set of distinct objectives: 1) Begin effective collaboration between the DM and astrostatistics communities; 2) Single out problems in the search for dark matter which can be effectively tackled with advanced statistical tools; 3) Introduce DM physicists to modern statistical tools, and provide them with resources to use them; and 4) Integrate statisticians and their expertise into teams looking for DM.



General statistical challenges include:
  1. Meta-analyses, data fusion, and combining data sets;
  2. Search for modes and peaks;
  3. Bayesian hierarchical modelling;
  4. Robust methods for separating data from background.
We aim to incorporate the latest developments in these fields into the search for DM.



Relevance, importance and timeliness

The next decade will present us with major advances in experiments designed to search for dark matter, as well as experiments with a broader focus on searches for physics beyond the standard model. As the LHC begins its second run at an unprecedented centre of mass collision energy of 13 TeV, we expect troves of new data to be presented to the particle physics community over the next few years. After the discovery of the Higgs boson in Run 1, a significant fraction of the focus of new physics searches at LHC relates to producing and characterizing dark matter, both on the theory and experimental sides, through new tools such as simplified models (see e.g. [Malik:2014ggr}). Simultaneously, an unparalleled quantity of astrophysical data will become available over the next 3-5 years. The Square Kilometre Array (SKA) radio telescope will allow us to map the distribution of matter in the dark ages before the formation of the first galaxies, via the 21 cm line of the hydrogen atom \cite{Lopez-Honorez:2016sur]; gamma ray telescopes such as the Cherenkov Telescope Array (CTA) will yield important information about the highest energies in the universe, while the next generation of galaxy surveys (eBOSS, DESI) will map the distribution of structure in the universe. Concurrently, space-based missions such as Gaia will map the distribution of dark matter in our own neighbourhood for the first time. The PINGU upgrade to the IceCube neutrino detector at the South Pole will be able to detect light DM candidates, as we embark on the first decade of neutrino astronomy. Meanwhile, DM-specific searches such as LZ, SuperCDMS and ALPS, will provide the strongest constraints, albeit each with a narrower focus.



The massive ranges both in data and parameter space highlight the importance of a concerted effort. At present, there are no agreed-upon approaches or tools in consolidating these disparate observations and models. Over the past few years, some individual studies have begun to outline and target these problems [Lyons:2014pta,Conrad:2014nna,Algeri:2015zpa], but the statistics employed in most DM searches remain relatively crude. It is clear that in the face of the above challenges, the statistical aspect of astroparticle and DM physics will be a major focus of research in the years to come. By bringing physicists into collaborations with experts on the search techniques and strategies that are most likely to lead to discovery, we hope to kick-start this programme.



Programme

DM-Stat will consist of 2-4 ``plenary'' talks per day, intended to bring the range of participants up to speed on each given topic. These talks will then be followed by a 1-2 hour guided discussion session, to be co-led by the plenary speaker and one of the organisers. The following topics have been chosen for individual sessions, although some modifications are possible depending on developments between now and 2018:
  • Status of DM searches;
  • Status of DM theory;
  • Bayesian Inference;
  • Statistical tools and machine learning;
  • Overview of known DM properties and simulation results;
  • Direct and indirect detection overview;
  • Statistics in indirect detection;
  • Statistics in LHC searches;
  • Signal searches and frequentist approaches;
  • Meta analyses, data fusion and combining data.
The overviews are meant to arm the audience with the required context to situate their own expertise, and to contribute meaningfully to the discussion.



Attendees

Participants in DM-Stat will be drawn from a selection of established experts, postdoctoral scientists and PhD students. The format will involve two to four keynote talks per day by a recognized expert, followed by a discussion period involving all participants. The keynote speakers are listed in the Participants section, and have all enthusiastically agreed to participate in DM-Stat. Remaining participants will range from expert faculty to postdocs and especially promising students, drawn from institutions in Canada, the US and Europe.



Bibliography

    .


    2014.



  1. [Lopez-Honorez:2016sur] Laura Lopez-Honorez, Olga Mena, Ángeles Moliné, Sergio Palomares-Ruiz, and Aaron~C. Vincent.


    {The 21 cm signal and the interplay between dark matter annihilations and astrophysical processes}.


    JCAP, 1608(08):004, 2016.

  2. [Lyons:2014pta] Louis Lyons.


    {Statistical Issues in Searches for New Physics}.


    In {\em {Proceedings, 2nd Conference on Large Hadron Collider Physics Conference (LHCP 2014)}}, 2014.

  3. [Conrad:2014nna] Jan Conrad.


    {Statistical Issues in Astrophysical Searches for Particle Dark Matter}.


    Astropart. Phys., 62:165--177, 2014.

  4. \bibitem{Algeri:2015zpa} Sara Algeri, Jan Conrad, and David~A. van Dyk.


    {Comparing non-nested models in the search for new physics}.


    2015.