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A search for dark matter in the center of the Earth with the IceCube neutrino detector

Jan Kunnen

Public PhD Defense

December 11th 2015

(2)

27%

(3)
(4)

Regular Matter

5%

(5)

Regular Matter 5%

Dark Energy

68%

(6)

Dark Matter 27%

Regular Matter 5%

Dark Energy

68%

(7)

The four questions I want to answer in this talk

What is Dark Matter?

How To Look for it?

Where to look for it?

How did I look for it?

(8)

��� = √ � (�)  

(9)

��� = √ � (�)  

(10)
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(12)
(13)

A popular DM candidate is the WIMP

W eakly

I nteracting

M assive

P article

(14)

A popular DM candidate is the WIMP

W eakly

I nteracting

M assive

P article

(15)

The four questions I want to answer in this talk

What is Dark Matter?

How To Look for it?

Where to look for it?

How did I look for it?

(16)

The four questions I want to answer in this talk

WIMPs!?

How To Look for it?

Where to look for it?

How did I look for it?

(17)

There are three types of detection,

which are complementary

(18)

There are three types of detection,

which are complementary

(19)

There are three types of detection,

which are complementary

(20)

There are three types of detection,

which are complementary

(21)

There are three types of detection,

which are complementary

(22)

There are three types of detection,

which are complementary

(23)

No indisputable evidence for particle dark

matter has been found so far

(24)

The four questions I want to answer in this talk

What is Dark Matter?

How To Look for it?

Where to look for it?

How did I look for it?

(25)

The four questions I want to answer in this talk

What is Dark Matter?

Directly, in colliders or indirectly Where to (indirectly) look for it?

How did I look for it?

(26)

One way to search for DM is

via indirect detection

(27)

One way to search for DM is

via indirect detection

(28)

One way to search for DM is

via indirect detection

(29)

One way to search for DM is

via indirect detection

(30)

Photons easily get absorbed on their trajectory

(31)

Photons easily get absorbed on their trajectory

Charged particles don’t point back to their source

(32)

Photons easily get absorbed on their trajectory

Charged particles don’t point back to their source

Neutrinos are good messengers!

(33)

Photons easily get absorbed on their trajectory Charged particles don’t point back to their source Neutrinos are good messengers!

But difficult to detect…

(34)

Indirect detection can be done with

the IceCube Neutrino Observatory.

(35)

IceCube can determine the muon and thus the neutrino direction

Detector completion In December 2010

5160 DOMs on 86 strings Central part : DeepCore

• Deployed in deepest, clearest ice

• Lowers energy threshold to ~10 GeV

• IceCube as active veto (muon shield)

Knowing the muon direction ≈

Knowing the neutrino direction

(36)

Many WIMP searches have been done,

such as : Galactic Center

(37)

Many WIMP searches have been done,

such as : Galactic Halo

(38)

Many WIMP searches have been done,

such as : Dwarf Galaxies

(39)

Many WIMP searches have been done,

such as : Galaxy Clusters

(40)

Many WIMP searches have been done,

such as : the Sun

(41)

Many indirect WIMP searches

have been done with IceCube

(42)

No Earth WIMP search has been done with IceCube

(43)

The four questions I want to answer in this talk

What is Dark Matter?

How To Look for it?

Where to look for it?

How did I look for it?

(44)

The four questions I want to answer in this talk

What is Dark Matter?

How To Look for it?

Let’s look for a signal from the center of the Earth

How did I look for it? (Now it gets technical…)

(45)

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

The last question I want

to answer in this talk

(46)

The Earth is an interesting source!

Signal neutrinos :

Coming from WIMP annihilations in the center of the Earth.

Maximum a few 10 3 events per year (more = excluded).

GeV to TeV energies.

(47)

The capture in the Earth depends

on the WIMP velocity

(48)

The existence of a Dark Disc could boost the muon flux.

— 68% contour --- 95% contour

— 68% contour

--- 95% contour

(49)

The capture in the Earth also depends

on the WIMP mass

(50)

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

The last question I want

to answer in this talk

(51)

The capture in the Earth also depends

on the WIMP mass

(52)

The highest capture (and thus signal) rate is expected

for WIMPs with masses of 50 GeV

(53)

To be sensitive to a big parameter space,

2 statistically independent analyses are done.

(54)

ν signal μ signal

A typical signal event if m χ = 1 TeV A typical signal event

if m χ = 50 GeV

The event topology will be different for different WIMP models

μ signal

ν signal

(55)

Noise has a bigger impact in the case of low energetic events

A typical signal event if m χ = 1 TeV A typical signal event

if m χ = 50 GeV

(56)

A lot of background is constantly coming from all directions

Background :

Produced in the atmosphere by cosmic rays

Few 10 9 muons and 10 5 neutrinos per year.

GeV to PeV energies.

(57)

Look at events to optimize

the Low Energy event selection.

A more difficult to catch atmospheric muon event

This muon sneaks in and makes some SLC hits on the outer strings, that are removed by hit cleaning.

ν signal

μ signal μ atmospheric

A typical signal event

if m χ = 50 GeV

(58)

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

The last question I want

to answer in this talk

(59)
(60)

Size (mm)

A m o u n t

Good stuf Bad stuf

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Size (mm)

A m o u n t

Good stuf Bad stuf

Diameter of filter holes

(62)

Size (mm)

A m o u n t

Good stuf Bad stuf

Diameter of filter holes

(63)

Level 1 Level 2 Level N

Some good stuf

Lots of bad stuf

(64)

Level 1 Level 2 Level N

Some good stuf Lots of bad stuf

Still some

good stuf

Less bad stuf

(65)

Level 1 Level 2 Level N

Some good stuf Lots of bad stuf

Still some good stuf Less bad stuf

Still enough

good stuf

Almost no bad stuf

(66)
(67)

#include <filterscripts/I3VEFFilter_13.h>

#include <filterscripts/I3FilterModule.h>

I3_MODULE(I3FilterModule<I3VEFFilter_13>);

#include <dataclasses/geometry/I3Geometry.h>

#include <dataclasses/physics/I3RecoPulse.h>

#include <dataclasses/physics/I3Particle.h>

#include <dataclasses/I3Double.h>

#include <icetray/I3Units.h>

I3VEFFilter_13::I3VEFFilter_13(const I3Context& context) : I3JEBFilter(context),

linefitcut_(2.9), muonllhcut_(2.6), toplayerDOMcut_(5),

allpulseskey_("InitialPulseSeriesReco"), poleMuonLlhFit_("PoleMuonLlhFit"), poleMuonLinefit_("PoleMuonLinefit"), singleStringReq_(false)

{

AddParameter("LinefitCut",

"Remove events that have linefit zenith angle less than this",

linefitcut_);

AddParameter("MuonLlhCut",

"Remove events that have MuonLlh zenith angle less than this",

muonllhcut_);

AddParameter("ToplayerDOMcut",

"How many layers of DOM's should be used in the veto cap", toplayerDOMcut_);

AddParameter("PoleMuonLlhFit",

"The standard log likelihood linefit performed by Muon Group",

poleMuonLlhFit_);

AddParameter("PoleMuonLinefit",

"The standard muon linefit", poleMuonLinefit_);

AddParameter("RecoPulsesKey",

"Key for all the reco pulses.", allpulseskey_);

AddParameter("SingleStringRequirement",

"Reject events with pulses on multiple strings.", singleStringReq_);

}

void I3VEFFilter_13::Configure() {

GetParameter("LinefitCut",linefitcut_);

GetParameter("MuonLlhCut",muonllhcut_);

GetParameter("ToplayerDOMcut",toplayerDOMcut_);

GetParameter("RecoPulsesKey",allpulseskey_);

GetParameter("PoleMuonLlhFit",poleMuonLlhFit_);

GetParameter("PoleMuonLinefit",poleMuonLinefit_);

GetParameter("SingleStringRequirement",singleStringReq_);

nRejNOmuonllhsrt=nRejNOpulses=nRejIsDownGoingLlh=nRejNOlinefit=

nRejIsDownGoingLineFit=

nRejNOgeo=nRejTopLayers=nRejMultipleStrings=nDOMs=0;

}

bool I3VEFFilter_13::KeepEvent(I3Frame& frame) {

I3RecoPulseSeriesMapConstPtr all =

frame.Get<I3RecoPulseSeriesMapConstPtr>(allpulseskey_);

if(!all) {a

log_debug("Pulses not found. Ignoring event.");

nRejNOpulses++;

return false;

}

// MuonLLH upgoing cut

I3ParticleConstPtr muonllhsrt =

frame.Get<I3ParticleConstPtr>(poleMuonLlhFit_);

if(!muonllhsrt) {

log_debug("Could not find the MuonSRTllh reco. Ignoring event.");

nRejNOmuonllhsrt++;

return false;

}

bool IsUpGoingLlh = (muonllhsrt->GetZenith())>muonllhcut_;

if(!IsUpGoingLlh) {

log_debug("The Event's MuonSRTllh reco is downgoing. Ignoring event.");

nRejIsDownGoingLlh++;

return false;

}

// Linefit upgoing cut I3ParticleConstPtr linefit =

frame.Get<I3ParticleConstPtr>(poleMuonLinefit_);

if(!linefit) {

log_debug("Could not find the linefit. Ignoring event.");

nRejNOlinefit++;

return false;

}

AND MUCH MORE OF THIS

(68)

θ θ

On-Source Off-Source

In this analysis we cannot just define an off- source region → need to rely on simulations!

Earth searches : Background estimated

by simulation Other searches :

Background estimated

by off-source data

(69)

In this analysis we cannot just define an off- source region → need to rely on simulations!

10% of 1 year of IceCube data

WimpSim : simulated signal Atmospheric muons &

atmospheric neutrinos

(70)

Blind region ν

atmospheric

μ

atmospheric

ν

signal

Exp. data ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

Before the event selection, the data is dominated by atmospheric muons (10 10 /year).

Remember, we look for max 10 3 signal events per year

The background is removed by

making event selections based

on direction, topology, etc.

(71)

Blind region

ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

In this filtering process a lot of effort went into the reduction of data-MC disagreement.

This effort was essential, as the background at final level cannot be estimated by

exp. data, but needs to be calculated from the MC.

During the event selection we minimize the data-MC discrepancy

ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

(72)

Blind region

ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

In this filtering process a lot of effort went into the reduction of data-MC disagreement.

This effort was essential, as the background at final level cannot be estimated by

exp. data, but needs to be calculated from the MC.

ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

During the event selection we minimize

the data-MC discrepancy

(73)

Blind region

ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

In a next step, Machine Learning Algorithms were used to make this selection as efficient as possible, i.e.

removing as much background as possible, without removing too much signal from the sample.

After some steps of filtering, the data rate is reduced to the mHz level (10 6 /year).

ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

(74)

Blind region ν

μ,atmo

μ

atmo

ν

signal

Exp. data ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

In a next step, Machine Learning Algorithms were used to make this selection as efficient as possible, i.e.

removing as much background as possible, without removing too much signal from the sample.

ν

e,atmo

ν

τ,atmo

Background-like Signal-like

After some steps of filtering, the data rate is reduced

to the mHz level (10 6 /year).

(75)

Blind region

ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

At final level, the data rate is reduced to about 10 4 /year

Remember, we look for max 10 3 signal events per year!

(76)

Blind region ν

atmospheric

μ

atmospheric

ν

signal

Exp. data ν

atmospheric

μ

atmospheric

ν

signal

Exp. data

The filtering process reduces the data from 10 10 /year to 10 4 /year

Remember, we look for max 10 3 signal events per year!

(77)

ν signal μ signal A typical signal event

if m χ = 1 TeV μ atmospheric μ atmospheric

A typical background event

Look at events to optimize the event selection.

(Work by Jan L. & Isabelle A.)

(78)

ν signal μ signal A typical signal event

if m χ = 1 TeV μ atmospheric μ atmospheric

A typical background event

Look at events to optimize the event selection.

(Work by Jan L. & Isabelle A.)

(79)

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

The last question I want

to answer in this talk

(80)
(81)

Look which hypothesis maximizes the

likelihood (based on FC ranks)

(82)

We hope to find signal!

Scenario 1 :

experimental data has higher rate than simulated background in the signal region

Some new phenomenon is going on!

Is it caused by WIMPs?

(83)

Scenario 1 :

experimental data has higher rate than simulated background in the signal region

Some new phenomenon is going on!

Is it caused by WIMPs?

Scenario 2 :

experimental data has same rate as

simulated background in the signal region

There's no muon flux caused by Earth WIMPs...

We hope to find signal!

But maybe there is no signal…

(84)

Scenario 1 :

experimental data has higher rate than simulated background in the signal region

Some new phenomenon is going on!

Is it caused by WIMPs?

Scenario 2 :

experimental data has same rate as

simulated background in the signal region

The muon flux caused

by Earth WIMPs is too small to observe.

What is the maximal allowed flux?

We hope to find signal!

If not we can always exclude models

(85)

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

The last questions I want

to answer in this talk

(86)
(87)

Neutrinos from

Earth WIMPs?

(88)

The zenith angle distributions of the exp. data agree with the BG-only hypothesis

Unblinded data : IceCube collaboration approved the analysis!

(89)

The zenith angle distributions of the exp. data agree with the BG-only hypothesis

Unfortunately no evidence for a WIMP signal has been found in the full data sample...

(90)

The zenith angle distributions of the exp. data agree with the BG-only hypothesis

Unfortunately no evidence for a WIMP signal has been found in the full data...

→ Set upperlimit on μ

s

(91)

The last question I want to answer in this talk

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

(92)

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

Exclude Models!

The last question I want

to answer in this talk

(93)
(94)

Unfortunately no evidence for a WIMP signal has been found in the

unblinded data→ Set upperlimit on μ s and related quantities

(95)

Unfortunately no evidence for a WIMP signal has been found in the unblinded data→ Set upperlimit on μ s and related quantities

The se m

ode ls a

re r ule d o ut

(96)

Interpretation of the result in a broader perspective

No equilibrium in the Earth This search is sensitive to both WIMP capture (σ SI )

WIMP annihilation (<σ A v>)

This makes this analysis unique, being

sensitive to both these quantities

(97)

Interpretation of the result in

a broader perspective

(98)

The four questions I discussed during this talk

What is Dark Matter? WIMPS?!

How To Look for it? As many ways as possible!

Where to look for it? As many places as possible!

How did I look for it? Neutrinos from Earth WIMPs?!

(99)

How did I look for it?

Investigate if the Earth is an interesting source Look at the event signature in IceCube

Filter the data

Set up a statistical analysis

Look if there is a signal in the data

Exclude Models!

The last question I discussed during this talk

→ My work

(100)

The first search for dark matter in the center of the Earth with IceCube has been performed.

As there was no good off-source region, simulation had to be used to estimate the background.

The dataset was split in 2 statistically independent sets, each optimized independently.

No evidence for a WIMP signal has been found in the final data set, so upperlimits have been set. An improvement of a factor 10 has been found w.r.t. the AMANDA search.

This result has been interpreted in the cross-section phase space.

Under the considered assumptions, this search is complementary to the IceCube Solar WIMP search.

Summary

(101)

27%

(102)

27%

(103)

27%

(104)
(105)

Backup slides

(106)

Interpretation of the result in a broader perspective

~

(107)

There are three types of WIMP detection,

which are complementary

(108)

There are three types of WIMP detection,

which are complementary

(109)

There are three types of detection,

which are complementary

(110)

Which WIMP masses are interesting?

Look at Earth Capture Rate.

(111)

Which WIMP masses are interesting?

Look at Earth Capture Rate.

(112)

Which WIMP masses are interesting?

Look at Earth Capture Rate.

(113)

Which WIMP masses are interesting?

Look at Earth Capture Rate.

(114)
(115)

Low energy optimization

High energy optimization

Focus of

my analysis

Isabelle A. &

Jan L.

(116)

How do we split the dataset?

By cutting on the reconstructed energy.

Reconstructed energy for 50GeV WIMPs

IceCube

preliminary

IceCube

preliminary

(117)

How do we split the dataset?

By cutting on the reconstructed energy.

Low energy optimization

High energy optimization Focus of

my analysis

Isabelle A. &

Jan L.

(118)

D ist rib u ti o n s b ef o re p re cu ts . D ist rib u ti o n s a ft e r p re cu ts .

Now the distributions that enter the BDT have

a good data-MC agreement!

(119)

1. OfflinePulsesHLC_z_pattern : 0.288587 2. partialCOG_r : 0.228153

3. MuEx4MPE_Sigma : 0.148708 4. z_travel : 0.107208

5. partialCOG_z : 0.079998 6. HiveSplitSplitCount : 0.076353

7. track_hits_distribution_smoothness : 0.053036 8. LineFit_zenith : 0.017892

9. LF_speed : 0.000064

(120)

BG correlation plot signal correlation plot

No overtraining!

No too much correlation

(121)

ν

μ,atmo x= 0.68

μ

atmo x= 1.00

ν

e,atmo x= 0.94

ν

τ,atmo x= 0.99

(122)

Before rescaling After rescaling

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