Indirect search for dark matter in the Galactic Centre with IceCube

(1)

Indirect search for dark matter in the

Galactic Centre with IceCube

(2)

Indirect DM Search with Neutrino Telescopes

²

Dark matter halo

Milky Way surrounded by dark matter halo

→ Highest DM density expected towards the Galactic Centre

Indirect search

Look for neutrinos produced by dark matter annihilation

𝝂

(3)

Coverage of the Galactic Centre

³

Up-going events

[arXiv:astro-ph.9907432]

IceCube background:

Dominated by atmospheric muons and neutrinos For up-going events:

Atmospheric muons stopped by Earth

Galactic Centre located at dec ~−29°

→ Neutrinos coming from the GC are seen as Down-going events by IceCube

→ Consider a veto

Reduce atmospheric muons background

(4)

Event Selection

⁴

Low energy event selection (oscNext)

§ Consider 8.03 years of IceCube data from 2012 to 2020

§ Developed for atmospheric neutrino oscillation measurements Vetoed volume

Consider only DeepCore events

𝝁

𝝂

_𝝁

(5)

Theory inputs

• Dark Matter Mass: 5 GeV and 1 TeV

• Neutrino spectra from dark matter annihilation

→ 𝝌aro𝝂 spectra [1]

→ Channels: 𝑊^!𝑊^", 𝑏(𝑏 , 𝜈_#𝜈*_#, 𝜏^!𝜏^", 𝜇^!𝜇^"

Signal Expectations

⁵

d𝜙_!

𝑑𝐸_! = 1 2

𝜎_"𝜐 4𝜋 𝑚_#^$

𝑑𝑁_!

𝑑𝐸_! .

%

&'

𝑑Ω .

(.*.+

𝜌_#^$ 𝑟(𝑠, Ψ, 𝜃) 𝑑𝑠

J-factor

NFW and Burkert halo profiles

→ Computed with Clumpy [2] with parameters from [3]

Dark matter self-annihilation cross section

Neutrino flux from dark matter annihilation in the GC

(6)

Probability Density Functions

⁶

3-dimensional PDFs

→ Event topology: PID (Particle ID)

→ Opening angle to the Galactic Centre: 𝚿

→ Reconstructed energy: 𝐥𝐨𝐠_𝟏𝟎(𝐄)

Binning choice

§ Binning in PID

Optimised separation of tracks and cascades

→ 3 bins with edges [0, 0.5, 0.85, 1]

§ Binning in 𝚿:

18 bins ranging from from 0° to 180°

§ Binning in 𝐥𝐨𝐠_𝟏𝟎 𝐄

50 bins ranging from 0 to 3

(7)

PDF Smoothing

⁷

Kernel Density Estimation (KDE)

§ Apply gaussian kernel implemented in Scipy [1]

§ Use KDE on 2D distributions

→ Applied on 𝚿_{𝐫𝐞𝐜𝐨} - 𝐥𝐨𝐠_𝟏𝟎(𝐄_{𝐫𝐞𝐜𝒐}) distributions

→ Done for each PID bin

KDE

(8)

KDE Edge Correction

⁸

correction Need to account for eventual boundary effects

Reflect the KDE at boundary

• Evaluate function for values outside of range and reflect it

• Applied in 1 dimension for Ψ_+,-.

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Background PDF

⁹

Simulations weighted with atmospheric flux

KDE

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Signal PDFs

¹⁰

Simulations weighted with

• Spectra: 𝜒aro𝜈 for annihilation through 𝝉^!𝝉^" for DM mass of 100 and 1000 GeV

• Source morphology: NFW halo model

(11)

Analysis Method

¹¹

Binned likelihood method

Likelihood formulations considered:

Poisson Likelihood

ℒ!"#$$"% 𝜉 = $

&

Poisson(n_"'$^& ; n_"'$^("( 𝑓(i; 𝜉))

where

𝑓 𝑖 ;

𝜉

=

𝜉

𝑓_/ 𝑖 + 1 −

𝜉

𝑓₀₁(𝑖) Interval construction methods:

Frequentist construction

(12)

Sensitivities

¹²

(13)

Comparison to previous results

¹³

Considerable improvement with respect to:

• IC86 3y GCWIMP search [1]

• Combined ANTARES/IceCube search [2]

Enhanced sensitivities due to:

• Improved event selection

• Energy and neutrino flavour considered

[1] Eur. Phys. J. C 77 (2017) 9, 627 [2] Phys. Rev. D 102 (2020) 8, 082002

(14)

Conclusion

¹⁴

§ Enhanced event selection with more years of data

→ OscNext event selection

§ Additionnal informations used in the PDFs

→ Energy and flavour information

§ Considerable improvement of the sensitivities

→ Up to ~ 2 orders of magnitude at 10 GeV

Outlooks

§ Unblinding of the analysis

(15)

15

Backup Slides

(16)

Sensitivities: Charon vs PPPC4

¹⁶

(17)

OscNext Event Selection

¹⁷

Compared to IC86 3y GCWIMP search sample [1]

→ Effective area ~1 order of magnitude better