Calculation of ( ,n) yields for low background experiments using - - PowerPoint PPT Presentation

Calculation of (α,n) yields for low background experiments using Geant4

Vicente Pesudo CIEMAT TAUP - Toyama, Sep 2019

Authors

Emilio Mendoza Daniel Cano Victor Alcayne Pablo Romojaro

2

Roberto Santorelli Pablo García Abia Vicente Pesudo (the guy talking)

• Combined effort of two groups at CIEMAT:

Nuclear innovation: & Basic research:

OUTLINE

• context
• optimization (arxiv: 1906.03903)
• validation (arxiv: 1906.03903)
• application to DM-detector materials

(DS-20k)

3

• Neutron recoils are irreducible background for WIMP

searches.

• In underground experiments, cosmogenic neutrons are

heavily suppressed and easily tagged with active vetos.

• The selection of radiopure materials is not enough to

neglect the production of neutrons after an (α,n), where the α comes mainly from the U-238 and Th-232 decay chains.

• Codes like TALYS (and hence TENDL) are useful for the
• verall picture, but differ from experimental values in

some relevant cases.

4

Experience from materials working groups:

• Lots of assays and a significant systematic uncertainty

is accumulated in the neutron yield. It may cancel out in average, it may not.

• Border effects very rarely taken into account (Kind of

“cross talk”, but for α coming from one material and producing the neutron in the adjacent one). This may be especially relevant for:

• surface contamination (Rn daughters plate-out, Rn

diffusion…).

• Small components with relatively high activity, high Z

[low (α,n) XS], but adjacent to a material with high (α,n) XS.

5

• Geant4 is the simulation code more commonly used by

particle physicists and in the low-E community. Versatile and detailed geometries.

• For the (α,n) case:
• Convenient for calculating the α tracks (EM

processes).

• Neutron production cross sections and energy

distributions can be read from ENDF-6 format data

• libraries. Note: this involved some patching to

Geant4.10.5

• It has particle and process biasing techniques:

artificially enhance a particular XS to make the simulation more computationally efficient, (α,n) here. We wanted to go beyond the short term approach and provide the community with a versatile tool, easy to implement for any case.

6

Optimization of simulation parameters:

• Biasing of 102 - 104 optimal [case of 13C(α,nX)Y @ 10 MeV]
• Step Length of 10-4 mm or below

7

Example of performance for:

• step size 10-4 mm.
• bias factor 104.

8

We present a comparison between:

• Different codes for (α,n) calculation.
• JENDL2005 and several TENDL libraries for our

code. Then, we present some applied case of interest for DM searches.

Note that the JENDL libraries are not complete (only those XS measured experimentally), so a combined JENDL2005 + TENDL2017 library was implemented to study our cases of interest.

9

10

Comparison between codes, calculated/experimental:

Comparison between codes, calculated/experimental:

11

12

Cross sections for different TALYS evaluated libraries vs JENDL one using

• ur code:

TENDL, in this case: · Lack of resonances · Overall

• verestimation

13

Cross sections for different TALYS evaluated libraries vs JENDL one using

• ur code:

TENDL, in this case: · Lack of resonances · Overall

• verestimation

14

Cross sections for different TALYS evaluated libraries vs JENDL one using

• ur code:

TENDL, in this case: · Lack of resonances · Mild slope · Massive

• verestimation

15

Cross sections for different TALYS evaluated libraries vs JENDL one using

• ur code:

TENDL, in this case: · Lack of resonances · Small

• verestimation

Applications 8 MeV α in a Cu source surrounded by: more Cu: 7.5 e-8 n/α vs LAr: 1.2 e-7 n/α Increase of factor 1.6

16

Applications 8 MeV α Solder in LAr: 1.2 e-7 n/α vs Solder on PCB in LAr: 8.8e-8 n/α. Reduction in PCB side. Way of studying passive shields for (α,n).

17

18

Applications 8 MeV α Solder in LAr: 1.2 e-7 n/α vs Solder on PCB in LAr: 8.8e-8 n/α. Reduction in low Z. Way of studying passive shields for (α,n).

Applications 8 MeV α in a Cu cable: 7.4 e-8 n/α vs with fluorine-based insulator: 4.5 e-7 n/α factor 6.1

19

Conclusions: We are providing a new tool to estimate (α,n) yields, based

• n Geant4. We validated its performace in different isotopes

and compared it with different codes. The improvement is two-fold:

• Using experimental data where available.
• Exploiting geometric, biasing and transportation

capabilities of Geant4. We expect an overall reduction in the uncertainty of the (α,n) yields. We proved that approaches like this one are fundamental for estimating properly the effects of:

• surface contamination.
• small dirty components with high-Z.

20

ANNOUNCEMENT: Workshop: “(α,n) background in dark matter experiments” 21-22 of November in Madrid If you are interested please contact any of us: Roberto Santorelli roberto.santorelli@ciemat.es Vicente Pesudo vicente.pesudo@ciemat.es

21

BACKUP

22

Copper disk lar

23

24