Phenomenological Análisis of Dark Matter Data

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Roberto Ruiz de Austri

Phenomenological Análisis of Dark Matter Data

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GAMBIT & Dark Machines

Roberto Ruiz de Austri 1

Outline

Global Fits: GAMBIT

Dark Machines

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Exploration of BSM models with Random scans

P

oints accepted/rejected in an in/out fashion (e.g., 2 -sigma cuts)

Non statistical measure attached to density of points: no probabilistic interpretation of results possible

Ineﬃcient in high dimensional parameter spaces (ie. D > 5 )

pMSSM (19D)

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Concentration of Measure Phenomenon

Random scans of a high parameter space only probe a very limited sub-volume: this is called the concentration of the

measurement phenomenon

Statistical fact: the norm of a vector in D dimensions drawn randomly from U[-1, 1] concentrates around with

constant variance.

(D/3)

^1/2

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Geometry in high D-spaces

Geometry fact: the volume inside the spherical core of D- dimensional cube is negligible.

Together, these two facts mean that random scans only explore a very small fraction of the available parameter space in high-dimensional models.

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Global Fits in BSM: recipe

Once a model is chosen

Construct the combine likelihood function including observables from collider physics, dark matter, flavour physics, …

Explore the likelihood function across the parameter space of the model with sampling techniques Test regions of the parameter space in a statistically sensible way (parameter estimation)

Test diﬀerent models the same way (model comparison)

L = L

_collider

L

_DM

L

_flavour

L

_EWPO

. . .

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SUSY DM Global Fits

CMSSM

Fittino, 1508.05951

SUSY SU(5) GUTs

MasterCode, 1610.10084

CMSSM

GAMBIT, 1705.07935

CMSSM

EasyScan_HEP, 1612.02296

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GAMBIT

Extensive model database - no just SUSY Entensive observable/data libraries

Many statistical and scanning options (Bayesian & frequentist) Fast LHC likelihood evaluator

Massively parallel Fully open-source

Fast deﬁnition of theories

Plug and play scanning, physics and likelihood packages

Members of:

ATLAS, Belle-II, CLiC, CMS, CTA, Fermi-LAT, DARWIN, IceCube, LHCb, SHiP, XENON

Authors of:

BubbleProﬁler, Capt'n General, Contur, DarkAges, DarkSUSY, DDCalc, DirectDM, Diver, EasyScanHEP, ExoCLASS, FlexibleSUSY, gamLike,

GM2Calc, HEPLike, IsaTools, MARTY, nuLike, PhaseTracer, PolyChord, Rivet, SOFTSUSY, Superlso, SUSY-AI, xsec, Vevacious, WIMPSim

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GAMBIT Structure

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GAMBIT Results Landscape

Scalar Higgs DM Portal:

1705.07931 Flavour EFT:

2006.03489

Axion-like particles:

2006.03489 Neutrinos and cosmo:

2009.0387

DM EFT:

2106.0256 MSSM7:

1705.07917

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SUSY Fits: Likelihoods

- Nuisance parameters:

SM, local DM density, nuclear matrix elements

- DM relic density:

Upper bound

- DM direct detection:

1. XENON100 (2012) 2. LUX (2016)

3. Panda-X (2016) 4. PICO (2015)

5. SuperCDMS 6. SIMPLE

- DM indirect detection:

1. Gamma-rays: Fermi-LAT 2. Neutrinos from DM annihilation in the Sun: IceCube79

- Electroweak precision observables:

^1.^{W mass}

2. Muon g-2

- 59 Flavour observables

- SUSY cross sections limit from LEP

-

SUSY searches at the LHC (full simulation)

1. Multi-jets searches (Run-I and II, ATLAS & CMS) 2. Stop searches (Run-I and II, ATLAS & CMS)

3. Two-three leptons searches (Run-I and II, ATLAS & CMS)

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MSSM-7

Hard problem to sample

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MSSM7 Results

[ 1705.07917 ]

Three neutrino scenarios: higgsino-dominated, higgsino/bino mix, bino dominated

~ 10^8 likelihood evaluations

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MSSM7 Results

Best fit point in co-annihilation region

Mass diﬀerence < 10 GeV (challenging for LHC) Under-abundant relic density at best fit point

Entire chargino co-ann. and light Higgs funnel regions will be probed by future DD

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Summary

We need global fits to do phenomenology

Global fits for any BSM model? GAMBIT is what you need !

GAMBIT v2.1 is out: gambit.hepforge.org

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Dark Machines

The goal is to exploit recent advances in Machine

Learning to help in the search for Dark Matter

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Who is in the Dark Machines community ?

Online research collective with scientist from many ﬁelds

Many are ML experts !!

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What Dark Machines does ?

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What Dark Machines does ?

Examples of Challenges

Data and software released in Zenodo and Github

Data and software released in the DarkMachines community space in Zenodo and Github repositories

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Dark Machines

The goal is to exploit recent advances in Machine Learning to help in the search for Dark Matter

http://darkmachines.org @dark_machines

Workshops Network

Exciting Challenges

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