Local refined Earth model for JUNO geo-neutrino analysis

Transcription

1 Local refined Earth model for JUNO geo-neutrino analysis Virginia Strati University of Ferrara & INFN arxiv: In collaboration with: Marica Baldoncini, Ivan Callegari,Yu Huang, Fabio Mantovani, William F. McDonough, Barbara Ricci, Roberta Rudnick, Steve B. Shirey, Gerti Xhixha Neutrino Geoscience June Paris

2 Outline o The JUNO experiment o Modeling local geoneutrino signal o A 3D model of the crust surrounding SNO+ o Modeling the crustal structure with Ordinary Kriging o Geoneutrino signal at JUNO o A focus on the 6 crustal tiles o The antineutrino reactor signal o Future perspectives

3 It is a 20 kton of LS detector (~ 20 times the volume of KamLAND) surrounded by ~20000 PMTs. The JUNO experiment The Jiangmen Underground Neutrino Observatory is a neutrino experiment under construction with different scientific goals (neutrino mass hierarchy, geoneutrinos, solar neutrinos, atmospheric neutrinos ) Location: South of China far about 53 km from 10 nuclear reactors cores under construction and far about 200 km from 2 operational nuclear cores. The laboratory is being built at some 700 m underground corresponding to about 2000 WME (Water Meter Equivalent) to compare with 2700 WME of KamLAND.

4 The expected geoneutrino signals For each site, the expected geo-neutrino signal S from U and Th distributed in the Earth can be calculated as the sum of three contributions: S S S S EXP LOC FFC M EXP = total expected signal LOC = crust of the region within some hundreds km from the detector FFC = Far Field Crust M = Mantle signal SNO + BOREXINO 2 KamLAND Huang et al. 2014, Geochemistry, Geophysics, Geosystems 15(10) arxiv: Fiorentini et al 2012, Physical Review D 86(3). arxiv:

5 Modeling local geoneutrino signal S X r a r X X 4 r r V 2 dr a = U and Th abundances ρ = rock density dv = volume of reservoir ε vx = antineutrino production rate UC U and Th contents are calculated using data from representative samples. Literature data: laboratory measurements and drill core information. Geological map Geological cross sections Interpreted seismic profiles. JUNO Geochemistry Upper Crust Middle Crust Lower Crust Φ (a ; ρ ; dv) Geophysics MC LC U and Th abundances calculated on the base of seismic arguments, rarely from direct sampling. Values inferred by seismic arguments and geophysical data Estimation of the depth of the surfaces on the base of seismic velocities.

6 Case study: a crustal 3D model surrounding SNO+ * Huang et al. 2014, Geochemistry, Geophysics, Geosystems 15(10) arxiv: SNO+ is a 1kton LS detector located in Ontario (Canada) in the Superior Province, one of the world s largest Archean cratons. We* modeled the crust of the six 2 x 2 crustal tiles (440 km x 460 km) for predicting geoneutrino signal. The goal was to define the geometry of LC, MC and 7 main reservoirs of the UC, assigning them U and Th abundances. We digitized velocity contours (6.6, 6.8 and 8.0 km/s) in order to extract depth of the top of MC (TMC), LC (TLC) and Moho Discontinuity (MD) Latitude Longitude Depth km km km UC MC LC Mantle

7 Modeling the geophysical discontinuities surfaces Inputs Depth-controlling points obtained by 15 refraction lines, 3 reflection lines and data from 32 seismographic stations. N points Top of MC (TMC) 343 Top of LC (TLC) 343 Moho discontinuty (MD) 392 ORDINARY KRIGING: a geostatistical estimator that infers the value of the depth in unobserved locations from input data points taking into account the spatial continuity of the variables. Output Estimated maps of TMC, TLC and MD depth with a 1 km 1 km resolution. Maps provides the Normalized Estimation Errors (NEE).

8 The Ordinary Kriging method GENERAL ESTIMATOR n z *( x0 ) i z( xi) x 0 zx ( i ) i i 1 target point measured samples weight assigned to the samples 1) Statistical analysis: description of the dataset 2) Study of the spatial variability: computation and modeling of the Experimental Semi- Variogram (ESV)

9 Modeling the Experimental Semi-Variogram (ESV) The model parameters are tested in order to adapt a theoretical model to the experimental tendency: the best fit is chosen. A priori variance Sill (20.2 km 2 ) ESV 1) NUGGET EFFECT the variance at distance= 0. It shows a good correlation between adjacent points from different seismic lines. Nugget effect (1.8 km 2 ) Moho Discontinuty N of pairs Model Range (269 km) 2) SPHERICAL STRUCTURE Range: beyond this distance, the data are not spatially correlated and the variance stabilizes at a value (Sill) similar to the a priori variance.

10 Masses of the reservoirs and their uncertainties For the first time the masses of the main crustal reservoirs containing U and Th are estimated together with their uncertainties in the region surrounding SNO+. CRUST 1.0* Huang et al M [10 18 kg] Volume [10 6 km 3 ] ρ [g/cm 3 ] M [10 18 kg] UC ± ± ± 0.6 MC ± ± ± 0.3 LC ± ± ± 0.6 Total ± ±1.6 The relative uncertainties of the reservoirs masses are of ~ 6%. Together with uncertainties of U and Th abundances these results are crucial for a reliable estimation of geoneutrino signal in SNO+. * Laske et al. [2013] at N E S

11 From SNO+ to JUNO The geoneutrino signal is predicted on the base of a global crustal model *. A special focus is dedicated to the 6 4 local crust surrounding the detector (600 x 400 km). * Huang et al. 2013, Geochemistry, Geophysics, Geosystems 14(6) arxiv:

12 Global crustal geophysical model and its uncertainties For each 1 1 voxel the thickness corresponds to the MEAN of 3 models: Refraction and Reflection seismic waves: CRUST Surface seismic waves dispersion (passive method): CUB Gravity filed data collected globally by GOCE satellite: GEMMA 3 The associated uncertainty on each voxel thickness is the HALF-RANGE of the 3 models. ~10% uncertainty in continents Larger uncertainty (~20%) in oceans and continental margins [1] Bassin et al [2] Shapiro and Ritzwoller [3] Negretti et al

13 Thicknesses of the crust surrounding JUNO The total crustal thicknesses ranges between 26.3 and 32.3 km with an uncertainty for each cell of approximately 7%. The sediments in the continent are very shallow (< 1 km) and reach a thickness of 4 km on the sea. In the cells close to the detector the UC has a relative high thickness (about 11 km).

14 Globally U and Th abundances of the lithosphere New compilations of geochemical data about OC, Seds, UC are included in Huang et al Continental Lithospheric Mantle (CLM) is included in the lithosphere. New approach in the evaluation of U and Th abundances (and their uncertainties) in MC and LC based on seismic arguments. a(u) [mg/g] a(th) [mg/g] Distribution OC 0.07 ± ± 0.06 Gaussian Sed 1.7 ± ± 0.6 Gaussian UC 2.7 ± ± 1.0 Gaussian MC_f Lognormal MC_m Lognormal LC_f Lognormal LC_m Lognormal CLM Lognormal C In MC and LC we can recognize two components on the basis of P and S waves velocities: felsic and mafic rocks. Ultrasonic velocity measurements of deep crustal rocks provide a link between seismic velocity and lithology. The fractions of felsic (f) and mafic (m) rocks in the MC and LC are: f (%) m (%) MC ~ 60 ~ 40 LC ~ 20 ~ 80

15 Towards a refined reference model for JUNO 50 km Upper Crust Middle Crust Lower Crust Continental Lithospheric Mantle Different U and Th abundances Different contribution to the signal The 50% of the total signal comes from the regional crust that lies within 550 km of the detector. The CRUST contributes for the ~ 70% of the total geoneutrino signal. At a distance of 100 km, the crust contribution can be considered the only one (~30%).

16 Expected signals from the LOC The main contribution (10.8 TNU) comes from T2, hosting JUNO, and it corresponds to the 27% of the total. The T2 has a shallow layer of Sed (<0.1 km) and a thick UC (~ 11 km) that has to be characterized in detail for more refined prediction of geoneutrino signal.

17 Antineutrino energy spectra expected at JUNO The reactor antineutrinos signals are calculated assuming two different scenarios: -R OFF : only 2013 operational data -R ON : operating Yangjiang and Taishan nuclear power plants. R ON Geoneutrinos R OFF In the geoneutrino energy windows ( MeV), the ratio between reactor antineutrinos and geoneutrinos varies between 8.9 (R ON scenario) and 0.7 (R OFF scenario). Possible experimental effects (energy resolution, background, detection efficiency ) will be discussed in Livia Ludhova s talk. * Baldoncini, M., et al. (2015). Physical Review D 91(6) arxiv:

18 What do we learn from this exploratory study? The total crustal thickness of the 6 tile surrounding JUNO ranges between 26.3 and 32.3 km with an uncertainty of ~ 7%. The expected regional signal is S LOC = TNU and the 62% come from tilet2, the tile hosting the detector. In the R OFF scenario, the ratio between reactor antineutrino and geoneutrino signal is 0.7 to compare with 0.6 of Borexino experiment. The signal from the mantle is strictly model-dependent and can vary from 2 to 19 TNU. Assuming a geochemical model of the Earth, it gives about 22% of the total expected signal S TOT = S LOC (TNU) S FFC (TNU) S LITH (TNU)

19 * ** * Courtesy from JUNO collaboration - Xiaonan Li Cecile Jollet ** H-Q. Huang, et al., J. Asian Earth Sci. 74, 280 (2013). Future perspectives The area is characterized by the presence of I-types calc-alkaline granites (~ 160 Ma)**. LOCAL EXPLORATORY SURVEY* High-K Jurassic granite ρ 2.7 g/cm 3 a(k) 5 % a(u) 9 mg/g a(th) 25 mg/g Future improvements: Tectonic and structural setting Reconnaissance of geophysical data (refraction and reflection data, teleseismic data, gravity data..) Reconnaissance of geochemical data (ICP-MS, gamma-ray measurements ).

20 and hopefully a sampling of the region!