Interpreting extragalactic backgrounds via angular ... - CERN Indico

Introduction into direct and indirect Dark Matter searches and their challenges

Interpreting extragalactic backgrounds via angular cross-correlations

Marco Regis  

(Torino)  

Two big challenges “… the moment of truth has come for WIMPs: either we will discover them in the

next five to ten years, or we will witness the inevitable decline of WIMP paradigm.”

G.Bertone, Nature, 2010

The period we have been witnessing is very relevant for WIMPs. What are the lessons we learned?

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Two big challenges “… the moment of truth has come for WIMPs: either we will discover them in the

next five to ten years, or we will witness the inevitable decline of WIMP paradigm.”

G.Bertone, Nature, 2010

The period we have been witnessing is very relevant for WIMPs. What are the lessons we learned?

1) We do not know what to search for 2) There is no (totally) clean observable  

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Two big challenges We do not know what to search for From modified gravity

(i.e. DM does not exist) … ... to non-particle/baryonic DM (e.g. primordial black holes) ...

ALL

potentially VIABLE

… to very

specialized

DM models

Credit: M. Weiss/CfA

There is no (totally)

Credit: Simon Swordy (U. Chicago), NASA

clean observable

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Two big challenges We do not know what to search for Unknown fundamental (microscopic) nature not only impacts the

type/strength of non-gravitational signal but implies also macroscopic uncertainties:

- Clustering (especially at small-scales)

→

Buckley&Peter 2017

- DM phase-space distribution in the Galaxy

see talks by Nicolao Fornengo, David Harvey and Andrea de Simone

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Two big challenges There is no (totally) clean observable Direct/indirect detection target cosmo signals, where there are many other players, “backgrounds” are typically poorly known

J.  Monroe Credit: Simon Swordy (U. Chicago), NASA

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Direct

detection  

 

Direct detection Scattering of WIMPs off target atomic nuclei Recoil energy measured by light, charge or phonons.

CDMS image

Undagoitia&Rauch 2017

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Characteristic WIMP signatures



Annual modulation: DAMA/LIBRA, ANAIS, KIMS, DM-Ice, PICO-LON, SABRE



Diurnal modulation: DAMA with larger mass could likely access it



Directionality: Nuclear emulsion (NEWS), Anysotropic crystals

(ADAMO), Liquid Ar TPC, Negative Ion Time Expansion Chamber (NITEC), Carbon nanotubes, DRIFT, MIMAC, DMTPC, NEWAGE,

  D3,

…

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

DAMA/LIBRA NaI(Tl) scintillation detectors located at LN Gran Sasso

DAMA/LIBRA

  o  tw e s c e n i d s ca de

annual modulation

Detection at ~ 9 C.L. PROPER MODULATION FEATURES: cosine like, T~1 year, t0 ~ 2nd of June

NO MODULATION above 6 keV and in the 2-6 keV multiple-hits residual rate. Not easy to reconcile it with other sources of background.  

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

CoGeNT Coherent Germanium Neutrino Technology

2   n i

(Soudan Underground Laboratory, Minnesota) Ge-detectors

0 1 0

Aalseth+ 2010

Large background contamination, but low threshold

2   n i

 

1 1 0

Aalseth+ 2012

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

CDMS Cryogenic Dark Matter Search experiment

(Soudan Underground Laboratory, Minnesota)

Ge-Si detectors measuring phonons and ionization

2   n i

9 0 0

Ahmed+ Science2010

2009: 2 events on Ge (expected background = 0.8 events, P=23%) 2013: 3 events on Si (expected background = 0.4 events, P=5.4%)

Agnese+ PRL2013

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Direct detection - status Undagoitia&Rauch 2017

Under the hypothesis of contact-type spin-independent interactions

Roszkowski+ 2017

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Direct detection Beyond the easiest case (contact scalar interactions) 

Full set of effective operators for DM – nucleus scattering Relevant for GeV-TeV DM

Fitzpatrick et al., JCAP 1302 (2013) 004





Interactions on (bound) electrons

+ interferences

Relevant for keV – MeV DM

DM phase space distribution in the Galaxy (esp. the high-v tail) - simulations - observations (GAIA)

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Indirect

detection  

 

Indirect detection Annihilations (or decays) of DM particles in astrophysical objects

generate fluxes of “standard” detectable particles.

 

Energy of the process set by the   DM mass → WIMPs ~ GeV-TeV

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Indirect detection channels

WIMPs are a primary source of

high-energy charged cosmic-rays, Hillas 2006

gamma-rays and

neutrinos

NASA image

Other wavelengths for

candidates different from WIMPs (e.g. X-rays for sterile neutrinos)

Plus radiative emission in the radio, X and  

gamma bands from WIMP induced e+-e 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Positron fraction pre-PAMELA AMS­01 Collab. PLB2007

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Positron fraction with PAMELA Cirelli+2008

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Positron fraction after AMS-02 AMS­02 Collab. PRL2014

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Positron fraction interpretations DiMauro+ 2014

Boudaud+ 2015

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Anti-matter excesses Evoli+,2015

DAMPE spectrum of e+ and e-

Yuan+,2017

AMS-02

antiprotons

Cuoco+,2016

Tentative anti-He events in AMS-02 Coogan&Profumo, 2017

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Galactic Center pre-Fermi LAT “… it is possible to successfully identify dark matter

annihilation radiation, even in the presence of significant astrophysical backgrounds.” Dodelson, Hooper, Serpico, PRD2008

MR&Ullio,2008

Crowdy place at other

wavelengths

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Galactic Center with Fermi LAT Daylan+ 2014

First analyses by

Fermi-LAT Collaboration, 2017

Goodenough&Hooper (2009) and Vitale+ (2009)

Calore+ 2014

de Boer+ (2016) → molecular clouds distribution

Yang & Aharonian (2016) and Macias+ (2016) → bi-lobed structure

 Bartels+ (2017) → traces stellar mass in the boxy and nuclear bulge 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Galactic Center interpretations Dark Matter

Difficult to make strong statements Daylan+ 2014

Different explanations:

●

Source of CR derived from observed level of star formation

rate and supernova explosion at GC ●

Population of millisecond pulsars

(e.g., Gaggero+2015, Carlson+2016)

(e.g., Bartels+2016, Lee+2015)

Fermi-LAT Collaboration, 2017

Fermi-LAT Collaboration, 2017

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Dwarf spheroidal galaxies Many dSph discovered with SDSS and DES, and

likely many more to come from near-future surveys.

Low level of star formation Hernandez et al., 2000 Carina dSph Inferred from colour­magnitude diagram 

Metal-poor stellar systems McConnachie, 2013

→ The non-thermal

emission related to star-formation is expected to be extremely faint.

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Dark matter in dSph McConnachie, 2013

Very large mass-to-light ratio

→ Mass budget largely dominated by DM

MAGIC+Fermi­LAT Collab., 2016

Very promising target for particle DM

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Prediction for dSph The detection of -ray emission from the direction of a dSph is guaranteed!

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Prediction for dSph The detection of -ray emission from the direction of a dSph is guaranteed! Plenty of background sources in the Universe!

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Prediction for dSph The detection of -ray emission from the direction of a dSph is guaranteed! Plenty of background sources in the Universe! Are we already witnessing this situation?

Geringer­Sameth+, 2015

 

DES+Fermi­LAT Collab., 2016

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Already observed in ReticulumII ?

Radio counterpart for possible ­ray excess

 > -1

MR+, JCAP 2017

~ 1.5 → BL Lac?

S25 ~ 10-12 erg cm-2s-1  

Ackermann et al. 2015

-rays:

 

    Barolo Astroparticle Meeting,  Barolo, 05/09/2017                                                                                                                         Marco Regis 

Lesson We have been discovering new properties of the “backgrounds” as we try to discover DM

→ “Backgrounds” are known up to an O(1) factor.

Supervised vs unsupervised algorithm?

Deep learning is mandatory, but is it enough?

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Statistical techniques The indirect Dark Matter signal is typically predicted to be feeble but extended → statistical correlations can be a powerful tool

Recently adopted techniques in gamma-rays (to infer unresolved sources): - 1-point statistics

(Malyshev&Hogg 2011,Zechlin+2015, Lee+2015, Feyereisen+2015, Zechlin,MR+2016, Lisanti+2016)

- Wavelet decomposition - Stacking

(Bartels+ 2015)

(Branchini,MR+2017, Lisanti+2017, ...)

- Information field theory

(D3PO, Selig+ 2013,2015)

- Brock-Dechert-Scheinkman statistic

(Baxter&Dodelson2011)

- Poisson ordered-pixel method (Campbell+ 2017)

- Multi-Scale Variance Stabilizing Transform (Schmitt+2010)

- Convolutional neural networks with mock images (Caron+2017) - 2-point auto-correlation

(Fermi-LAT 2012, Fornasa+2016, Fornasa,MR+ 2017)

- 2-point cross-correlation (MR+2012-2017, Ando+2014-2017, Shirasaki+2014-2017)

 

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Angular cross-correlations N O

SI S U C ISbelieve a certain Map where we D   e h source tpopulation is present as y bunresolved   component D E P WIMP DM in the Fermi-LAT map) O (e.g. O C S

Tracer of the sources we are looking for: - Catalog of the same sources but at different wavelength

- Gravitational tracer (e.g. lensing or galaxy/cluster catalog)

 → CCFab(|-) → Clab correlation in

 

 

physical space

correlation in

harmonic space

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

Tomography Cuoco,MR+, ApJS 2015

MR+, PRL 2015

DM peaks at low z, whilst

TOMOGRAPHIC APPROACH

astrophysical sources peak at z > 0.5  

 

  Accelerating the Search for Dark Matter with Machine Learning,  Leiden, 16/01/2018                                                                    Marco Regis 

3D Power Spectrum Typically obtained from Simulations or Halo model Cuoco+, ApJS 2015

Camera, Fornasa, Fornengo, MR, ApJ 2013

It is (roughly speaking) mapped in the multipole range 100 < l < 1000

 

 

  European Physical Society Conference on High Energy Physics 2017,  Venice, 06/07/2017                                                                    Marco Regis 

Angular cross-correlation outcomes Cuoco,MR+, ApJS 2015

→ Competitive technique

→ Significant degeneracies are still present in current analyses

 

 

  European Physical Society Conference on High Energy Physics 2017,  Venice, 06/07/2017                                                                    Marco Regis 

How to combine different pieces? - Redshift distribution - Angular spectrum - Energy spectrum - Temporal dependence?

→ Machine Learning can help!  

 

  European Physical Society Conference on High Energy Physics 2017,  Venice, 06/07/2017                                                                    Marco Regis