Search for Dark Matter using Machine Learning

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Dark Machines

Search for Dark Matter using Machine Learning

Roberto Ruiz, Judita Mamužić

IFIC / CSIC - University of Valencia AI@IFIC, Valencia, 20 December 2019

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Dark Machines, AI@IFIC, 20 December 2019

Indirect (annihilation)

Collider (production)

Direct (scattering)

Dark Matter

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•Dark Matter (DM) could explain phenomena like galactic rotation curves.

•Only gravitational interaction observed, it is weakly interacting.

•It is estimated that dark matter accounts for ~27%

of all matter.

•DM can be explored in experiments:

•DM interacting with nucleus, e.g. when passing through Earth, detected by underground

experiments (direct, scattering).

•DM produces Standard Model (SM) particles, detected by telescopes and satellites (indirect, annihilation).

•DM produced in collisions of SM particles at high energies (collider, production).

•No significant sign of DM, using these methods, found so far, further studies required.

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Dark Machines

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•Answer cutting edge questions about Dark Matter, using most advanced techniques in data science, at darkmachines.org.

•Online research community:

•Physicists from different fields (astro-physics, astro-particle physics, particle physics, theory and experiment) and data scientists (machine learning experts).

•Network of ~300 members.

•Regular meetings online INDICO.

2018 Lorentz Center, Leiden, Netherlands

(founded)

2019 ICTP, Trieste, Italy

Next: 2020 CERN, Switzerland

•Yearly in-person meetings:

•Overview of advancements in machine learning and artificial intelligence, talks from machine learning experts.

•Overview of recent studies.

•Future ideas and discussions.

•Research lines (currently 8 challenges).

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Example 1: Unsupervised Collider Searches

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Relatively easy to find

Relatively difficult to

find

•New physics can be produced in collider experiments like ATLAS and CMS at the LHC in CERN.

•Aim to find new physics using techniques of unsupervised learning.

•Challenge: “Find the new physics.”

•Ongoing Standard Model and new physics models Monte Carlo generation ( events, concept

of open data).

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Example 2: High-dimensional Parameter Sampling

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Sampling for

optimisation

Sampling for posterior

estimation

•New physics can be studied by exploring large number of new physics models.

•Aim to have better sampling in the high-dimensional parameter space.

•Naive approach of random sampling is flawed, sophisticated algorithms are needed:

•Points concentrated near the edge of the parameter space for high dimensional problems.

•Easily misses high-likelihood regions in strongly peaked spaces.

•Challenge: “Implementation and comparison of different methods, give recommendations for different use-cases.”

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Example 3: Library of Trained Models

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•Facilitate easy sharing of models, so that trained models can be used by external researches.

•Currently in the pipeline:

•Electroweak production cross-section in the phenomenological Minimal Supersymmetry

•Loop integrals

•GAMBIT global fits

•Reconstruction efficiencies of long-lived particle searches Point from

19-dimensional new physics model

parameter space

ATLAS exclusion Fast exclusion done using ML training on

previous ATLAS measurements

DOI: 10.1140/epjc/s10052-017-4814-9

One example:

Gain in speed: from h to 𝒪 𝒪 μs

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Example 4: Inclusive Analysis of Fermi-LAT Point Sources

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•Aim to understand the -ray sky measurements.

•About 33% of point sources are unassociated.

•Challenge: “Use ML to both localise and classify point sources better than current state-of- the-art techniques.”

•We could go beyond and consider also the uncertainty on the measurement.

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Dark Machines

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•Exploit recent advances in machine learning to help the search for Dark Matter.

•Find more information at darkmachines.org/.

•Follow us on twitter @dark_machines.

•Get in contact, subscribe to the newsletter.

•Join some of our exciting challenges.

•Be a part of the network.

•Meet us in person, join the discussions in one of the next workshops.

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Backup

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Dark Matter

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Galactic rotation curves

Observations of visible stars (or galactic gas) rotation speeds around the galactic center and their radial distance from it (i.e. galaxy rotation curves), do not fall as the visible matter distribution

prediction.

Gravitational lensing

Observed galaxy mass is not sufficient to account for predicted gravitational lensing.

Bullet clusters

Dark matter is assumed to

interact only weakly. It bypasses the colliding gas, but is visible

by gravitational lensing (blue).

Search strategies:

• Direct: Signal from nuclei interacting with DM.

• Indirect: SM products are detected by telescope.

• Collider: DM produced in collisions.

Indirect

(annihilation)

Collider (production)

Direct (scattering)

Dark matter (DM) candidate:

•Dark (only gravitational interaction observed, weakly interacting)

•Massive (cold, non-relativistic)

•Stable (account for thermal relic density)

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