DIRECTIONAL DARK MATTERSEARCH WITH NEWSDM
Andrey ALEXANDROV (On behalf of the NEWSdm collaboration)
INFN and University of Naples, Italy
National University of Science and Technology 'MISiS', Moscow, Russia
Lebedev Physical Institute (LPI RAS), Moscow, Russia
24/08/2021

20th Lomonosov Conference on Elementary Particle Physics

1NEWSdm COLLABORATION
Nuclear Emulsion WIMP Search directional measurement

81 physicists
23 institutes
JAPANChiba, Nagoya, Toho, Tsukuba
RUSSIA
LPI RAS Moscow
JINR Dubna
SINP MSU Moscow
INR RAS Moscow
NUST MISiS Moscow
NRU HSE Moscow

Website:Letter of intent:
2

ITALY
LNGS, GSSIINFN: Napoli, Roma, Padova
Univ.: Napoli, Roma, Padova,Potenza, Benevento
SOUTH KOREA
Gyeongsang University

TURKEYMETU Ankara

news-dm.lngs.infn.it
https://arxiv.org/pdf/1604.04199.pdf

Outline■ Physics motivation■ Nano Imaging Trackers■ Super-resolution readout■ Machine learning techniques to nanometric image analysis■ Underground facility and current setup■ Conclusions: intermediate and final goals

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WIMP directional detection
Earth@Summer

Direction of solarsystem(220km/sec)

Nuclear emulsion
detector
Dark matter
windWIMP

Target nuclei
Earth@Winter• Strong correlation between the direction of WIMP and scattered nuclei → strong signature andunambiguous proof of the galactic DM origin• Unique possibility to overcome the “neutrino floor”, where coherent neutrino scattering creates
an irreducible background• Nuclear Emulsion is a high-density solid-state medium → large mass with a compact detector
4Detection principle
AgBrcrystal

1. Ionization induced by a particle–

2.6 eV band gap

2. Electrons trapped at a lattice defect on the ~40 nm
crystal surface––

Attract interstitial silver ions
Produce a “latent image” = Agn

3. Chemical amplification of signal––+ --- +-- +Ag+Ag++Ag+Agn Ag+ - -+Silver filament

Lattice defectLatentimage

Development → silver filaments
107 - 108 amplification

4. Dissolve crystals
5. Observe it at optical microscopes
5

Grain as seen by a sensor of an
optical microscope

NIT: Nano emulsion Imaging Trackers
A long history, from thediscovery of the Pion (1947) tothe discovery of νμ→ ντoscillation in appearance mode
(OPERA, PRL 115 (2015) 121802)• Nuclear emulsions: AgBr crystals in organic gelatine• Passage of charged particle produce latent image• Chemical treatment make Ag grains visible
SEM image6• New kind of emulsion forDM search• Smaller crystal sizeDirectional detection challenge
expected recoil length in the nuclear emulsion

Optical

Optical

Optical

X-ray

X-ray

X-ray

L = 380 nm

L = 265 nm

L = 160 nm

WIMP mass― : 100 GeV/c2― : 50 GeV/c2― : 20 GeV/c2― : 10 GeV/c2

3.2 g/cm3target : C,N,O, Ag, Br

10GeV/c2

20GeV/c2

100 GeV/c2
50 GeV/c2

Track length [nm]Inaccessible due to diffraction limit

Need super-resolution to measure tracks shorter than 200 nm
7

Shape analysis

PTEP (2019) 063H02• Elliptical fit to measure the shapeanisotropy

Correlation between track lengths measuredby X-ray microscopy and ellipticity
obtained with optical analysis

8Correlation between readout efficiencies
and track lengths for different ellipticity
thresholds

100 keV Carbon

Optical readout beyond the diffraction limit■ Super-resolution idea: use the plasmon resonance effect to overcome thediffraction limit:– generated by a light wave trapped within conductive nanoparticles smaller than thewavelength of light– resonant frequency strongly depends on the composition, size, geometry, dielectric
environment and distance between nanoparticles– occurs in the visible region for Ag and Au nanoparticles!
– improve resolution by analyzing scattered light polarization and spectrum

λlOscillation of e-cloud9

Original (all polarizations)

Modulated (rotating polarizations)

Modulate the intensity of each pixelin the image:𝐼𝜃 = 𝑎 cos 2(𝜃 − 𝜑) + 𝑏𝜃 – polarization angle
𝜑 – pixel “phase”,
𝑏 – pixel brightness mean,𝑎 – pixel brightness change
amplitude⨂𝑃𝑆𝐹
Microscope

1. Barycenter shift analysis

2. Joint image deconvolution
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Barycenter shift analysis
Sci. Rep. 10 (2020) 18773

NIM A 824 (2016) 600–602
Rotating polarizer

No polarizer
grain1grain21150 nm

1150 nm

Carbon ion
100 keV
11pol angle

Carbon ions
60 keVOriginal (all polarizations)

Modulated (rotating polarizations)

Modulate the intensity of each pixelin the image:𝐼𝜃 = 𝑎 cos 2(𝜃 − 𝜑) + 𝑏
𝜃 – polarization angle
𝜑 – pixel “phase”,
𝑏 – pixel brightness mean,𝑎 – pixel brightness change
amplitude⨂𝑃𝑆𝐹
Microscope

Reconstructed0𝜑

180 255
𝑎0

𝑎 + 𝑏 = 𝑝𝑖𝑥𝑒𝑙 𝑏𝑟𝑖𝑔ℎ𝑡𝑛𝑒𝑠𝑠
𝑎 = 𝑝𝑖𝑥𝑒𝑙 𝑐𝑜𝑙𝑜𝑟 𝑠𝑎𝑡𝑢𝑟𝑎𝑡𝑖𝑜𝑛
𝜑 = 𝑝𝑖𝑥𝑒𝑙 𝑐𝑜𝑙𝑜𝑟
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Joint image deconvolution
Long BS event (moving)

Reconstructed image

Sci. Rep. 10 (2020) 18773

200 nm

SEM image
BS = 48 nm

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Joint Image Deconvolution
Event Length comparison with SEM

Length accuracy: 28 nm ≈ pixel size (27.5 nm)
Spatial resolution: 80 nm (Nyquist theorem)
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Pearson Coefficient Matched Unmatched
Length0.912-0.009Width0.713-0.007

Super-resolution microscope
Sci. Rep. 10 (2020) 18773

Spatial Accuracy = 3 nm
Liquid Crystal Polarization Analyzer

15

Measurement in 3DA

International Patent No. WO/2018/122814

16

Two focal planes:
Horizontal: XY
Vertical: ZA

Plasmon resonancewavelength dependency*polarization rotating~45 nm : blue
~80 nm : green

100 nm*polarization rotating~45 nm : blue~120 nm :orange

1720 nm

60 nm

100 nm

100 nmPlasmon response for 𝛼 and C tracks
Alpha track

Image size 15 µm x 15 µm
18100keV C-ions

Image size 15 µm x 15 µm

Sense recognition with color
Machine Learning approach

0°180°

Carbon ion 100 keV

Sense prediction accuracy = 65%
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Realistic simulation of nanoparticle images• Generate a 3D model of the object to be simulated(filaments, nano-particles)• Use discrete dipole approximation to obtain optical images(ADDA, HoloPy)• Tune the parameters and check the simulation bycomparison with real samples
HoloPy: DDA for holography in Python https://github.com/manoharan-lab/holopy
Use ADDA for scattering calculations: https://github.com/adda-team/adda
Silver spheres

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Silver ellipsoidal nanoparticle

Simulation of two silver filaments

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Background reduction: Machine Learning approach• Fog/dust reduction factor and efficiency for different thresholds on ML
probability-like output on validation data

arXiv:2106.11995
Submitted to Comput. Phys. Commun.
Schematic view of the CNN architecture• Fixed threshold to obtain
efficiency similar to barshift• Fog reduction is compared22

Emulsion facility at LNGS Hall F• Work carried out in the facility:• Installation of containment vessels under the floor• Improvement of electric system• Installation of a thermostatic chamber• Emulsion production machine• Access to the emulsion facility since December 2020

Gel production room

23Development room

Gel production machine produced in Japanand certified compliant to EU safety

NEWSdm: current setup• Experimental setup in Hall C, close to Borexino• Assembly of the setup in March 2021
• Test measurements ongoing

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Mass

Exposure

Temp.

Shield~10g

40days-50ºC

40cm PE + 10cm Pb

Future facility for NEWSdm: 10kg and beyond
Emulsion facility and shielding with anequatorial telescope

10 m

2510 mNEWSdm intermediate and final goals• First directional dark matter detector with a 10 kg solid target• Explore the DAMA region with a completely different technique based on the visual observationof recoil tracks in emulsion• First high-sensitivity spin-independent measurement with a directional approach• First step in the application of the emulsion technology, scalable to larger masses• Longer term: overcome the neutrino floor

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90% C.L. upper limits for the NEWSdm detector with anexposure of 10 kg year in the zero-background hypothesis

90% C.L. upper limits for the NEWSdm detector with an
exposure of 10 ton year in the zero-background hypothesis

THANK YOU FOR ATTENTION!
Andrey ALEXANDROV (On behalf of the NEWSdm collaboration)
andrey.alexandrov@na.infn.it

24/08/2020

20th Lomonosov Conference on Elementary Particle Physics

27

BACKUP SLIDES

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Direct Dark Matter searches• Current status of searches for spin-independent elastic WIMP-nucleus scattering assuming thestandard parameters for an isothermal WIMP halo29

Direct Detection of Dark Matter – APPEC Committee Reporthttps://www.appec.org/wp-content/uploads/2021/04/appec_dm_report_2020_ga_approved.pdf
arXiv: 2104.07634

Importance of the directional detection
PHYSICAL REVIEW D 92 (2015) 063518

10 kg

1 ktondirect

10-44

10-48

directdirectional

1

directional

10

Need 3D with sense recognition for best results!
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Nuclear emulsion: detection principle
Electron trape☑ surface kink site
☑ artificially doped (e.g., AuS, S2, Fe, ・・)☑ crystal defect☑ Ag2 on P-center (positive kink site)

Conduction bandelectrontrap

2.6 eV

Excited by the ionization ofa charged particle

Ag+

Holetrap
Valence bandhInterstitial Silver Ions
AgBr is an ionic crystal. Crystal
defects generate interstitial silver
ions due to ionic conductivity

31AgBrAg+Hole trap

☑ Iodide doped☑ Ag2 on R-center (neutral kink site)

e- + Ag+ → Ag
Ag + e- + Ag+ → Ag2
Agn-1 + e- + Ag+ → Agn
Latent image specksn >4 In standard photographic theory

Directionality preservation of nuclear recoils••Performance in the measurement of the recoil direction and comparison with other techniques
Simulation of nuclear emulsion granularity: volume filled with AgBr crystals described as spheres ofdiameters 44±7 nm for NIT, 25±4 nm for U-NIT•Evaluation of energy-weighted
cosine distribution

Realistic distribution of mean values of weighted-cos𝜗 for NIT and U-NIT,
compared with other detectors

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A. Alexandrov, G. De Lellis, A. Di Crescenzo, A. Golovatiuk and V. Tioukov, «Directionality
preservation of nuclear recoils in an emulsion detector for directional dark matter search» JCAP 04(2021) 047

Optical readout beyond the diffraction limit

PTEP, Vol. 2019 Issue 62019, 063H02
33

Resolved event (npeaks)

Reconstructed image

200 nm
SEM image

BS = 37nm

Grain v1_g192
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Z reconstruction with two parallel planes
Biophys J. 95(12):6025-43 (2008)

Image pair 1-0.646

Image pair 2-0.4

Image pair 3-0.194

Image pair 40.106■

Near the focal plane the PSF changes little => bad Zlocalization■

Out of focus the PSF starts to change rapidly: new rings ofvarying radii => fit improves => good Z localization

Image pair 50.356■

Idea: with two focal planes separated by a small (~resolution)
distance the object will always be out-of-focus at one of theplanes

Image pair 6

0.606

Double plane images taken in Naples

35∆Z ≈ 500 nm
Focal Plane 2
Focal Plane 1

Z reconstruction with machine learning:
Convolutional Neural Network• Each event is a doublet of images plus the Z coordinate• 2 images (500 nm apart) are merged in a single larger one• The output is the estimated Z coordinate
Sampling step of 250 nm along Z
Original Z profile (all images)

Depth of Field:
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Predicted (2 images) Z profile≈ 400 𝑛𝑚Z difference

Achieved accuracy ∼ 46 nm
Improvement of one order of magnitude!

Shield simulation
Optimisation of the shield with Geant4 simulation to reduce:- neutrons from environmental radioactivity- neutrons produced by cosmic muon spallation in the surrounding rock and in the shield itself- Environmental gammas
Best configuration: 100 cm of polyethylene for a total
neutron rate of ∽ 1.4 for an exposure of 10 kg year

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RADIOACTIVITY FROM 14C
Given the carbon content in the emulsion and the 14C activity, beta-rays
amount to ~108 per kg*year
Strong reduction factor: NIT emulsions insensitive to MIP and largely
insensitive to electrons

Additional level arms:• Reduced sensitivity to electrons at lowtemperatures• Electron response to polarized lightscattering• Colour camera to distinguish nuclear
recoils from electrons• Replace the gelatine with synthetic
polymers (final choice)
NIM A 845 (2017) 37338