Identifying the Theory of Dark Matter with Direct Detection

Size: px

Start display at page:

Download "Identifying the Theory of Dark Matter with Direct Detection"

Loraine Gordon
8 years ago
Views:

1 Identifying the Theory of Dark Matter with Direct Detection Sam McDermott arxiv: and ascl.net/ with Vera Gluscevic, Moira Gresham, Annika H. G. Peter, and Kathryn Zurek

2 Motivation Dark matter exists Large ongoing experimental effort to discover its interactions with the Standard Model Imagine we detect dark matter: what information will we be able to extract? also, McDermott, Yu, Zurek , Gluscevic, Peter , Catena, ,

3 Parameter Space Direct detection experiments are probing several orders of magnitude in m DM Improving in σ DM about x2/yr σdm-sm [cm 2 ] current limit ν background Could make a m DM [GeV] discovery soon! adapted from Snowmass document

4 Parameter Space Direct detection experiments are probing several orders of magnitude in m DM Improving in σ DM about x2/yr Could make a discovery soon! σdm-sm [cm 2 ] m DM [GeV] current limit how to maximize what we can learn about theories here? ν background adapted from Snowmass document

5 Observables dr de R (E R )= m T m v esc,lab Z v min vf(v) d T de R (E R,v)d 3 v

6 Observables dr de R (E R )= m T m v esc,lab Z v min vf(v) d T de R (E R,v)d 3 v recoil spectrum

7 Observables astrophysics dr de R (E R )= m T m v esc,lab Z v min vf(v) d T de R (E R,v)d 3 v recoil spectrum

8 Observables astrophysics dr de R (E R )= m T m v esc,lab Z v min vf(v) d T de R (E R,v)d 3 v recoil spectrum nuclear / experimental physics

9 Observables astrophysics dr de R (E R )= m T m v esc,lab Z v min vf(v) d T de R (E R,v)d 3 v recoil spectrum particle physics nuclear / experimental physics

10 Requirements A set of models (hypotheses) & corresponding phenomenology of scattering off nuclei A statistical representation of experiments An analysis framework for evaluating how well a given hypothesis fits a single data realization

11 Requirements UV-inspired; cf. Gresham and Zurek A set of models (hypotheses) & corresponding phenomenology of scattering off nuclei A statistical representation of experiments An analysis framework for evaluating how well a given hypothesis fits a single data realization

12 Requirements UV-inspired; cf. Gresham and Zurek A set of models (hypotheses) & corresponding phenomenology of scattering off nuclei A statistical representation of experiments given by an EFT; cf. Haxton et al and An analysis framework for evaluating how well a given hypothesis fits a single data realization

13 Requirements UV-inspired; cf. Gresham and Zurek A set of models (hypotheses) & corresponding phenomenology of scattering off nuclei A statistical representation of experiments Bayesian model selection, cf. Gluscevic and Peter given by an EFT; cf. Haxton et al and An analysis framework for evaluating how well a given hypothesis fits a single data realization

14 EFT of DD d T de R (E R,v)= m T 2 v 2 X (N,N 0 ) X X R X E R,v,c (N) i,c (N 0 ) fw (N,N 0 ) j (y) X particle physics given a Lorentz-invariant theory, calculate the low energy, nonrelativistic cross section nuclear form factors given a Lorentz-invariant theory, nuclear physics measurements predict nuclear responses X = M, 0, 00, 00,,M 00, 0 (responses) y m T E R b 2 /2, (b/fm) /(45A 1/3 25A 2/3 )

15 Particle Physics: R X lowest-dimension, least-suppressed Lorentz-invt. products of DM fermion bilinears with SM fields standard SI (χχff), SD (χγ μ γ5χ fγμγ5f) photon-mediated millicharged (χγ μ χaμ), anapole (χγ μ γ5χ μ Fμν), magnetic dipole (χσ μν χfμν), electric dipole (χσ μν γ5χfμν) UV theory overall momentum and velocity dependence, triggered responses

16 Nuclear Physics : fw X form factor = how rate falls off at higher energy depends on target, response, and energy number of form factors = = number of targets number of responses

17 Statistical Methodology E({E R } M) = Z likelihood prior d L({E R }, M)p( M) observed (noisy) energy spectrum given model Pr(M j )= E({E R} M j ) P E({E R } M i ) i free parameters (σdd and mdm) What are the odds of extracting the true underlying model from the data?

18 Statistical Methodology E({E R } M) = Z likelihood prior d L({E R }, M)p( M) observed (noisy) energy spectrum given model Pr(M j )= E({E R} M j ) P E({E R } M i ) i free parameters (σdd and mdm) (Note: not maximizing a χ 2 statistic or finding a best fit)

19 Mock data need to make mock data sets {E R } simulate G2-like experiments Label A(Z) Energy window [kevnr] Exposure [kg-yr] Xe 131 (54) Ge 73 (32) I 127 (53) F 19 (9) Na 23 (11) Ar 40 (18) He 4(2) choose mdm, set σdd Xe(lo) 131 (54) just below current limits Xe(hi) 131 (54) Xe(wide) 131 (54) I(lo) 127 (53) XeG3 131 (54)

20 A Single Simulation 50 xt: noisy recoil-energy spectra Number of events SI electric dipole Nuclear recoil energy [kev] Context: noisy recoil-energy spe Number of events Context: noisy recoil-energy spe Number of events Nuclear recoil energy [kev] Nuclear recoil energy [kev]

21 Criterion for Success Pr(M j )= E({E R} M j ) P E({E R } M i ) i true underlying model is confidently selected if Pr(Mtrue)>90% this depends on the Poisson realization > 90% }create many Poisson realizations to test robustness

22 Results (example) 1.0 True model: SI (mass: 50 GeV) Simulations preferring true model (with >0.9 probability): 48% 100% Probability of true model Ge Xe Ge+Xe I Ge+Xe+I F Ge+Xe+F single elements not so good

23 Results (example) 1.0 True model: SI (mass: 50 GeV) Simulations preferring true model (with >0.9 probability): 48% 100% Probability of true model Ge Xe Ge+Xe I Ge+Xe+I F Ge+Xe+F Complementarity!

24 Conclusions A conclusive direct observation of DM will just be the beginning of the work Target complementarity will be a critical requirement for learning about DM physics

The Mainz LXe TPC MC simulations for a Compton scattering experiment

The Mainz LXe TPC MC simulations for a Compton scattering experiment Pierre Sissol Johannes Gutenberg Universität Mainz 12 November 2012 1 / 24 Outline 1 Dark Matter 2 Principle of a dual-phase LXe TPC