Spectrum Reconstruction of Atmospheric Neutrinos with Unfolding Techniques

1. Spectrum Reconstruction of Atmospheric Neutrinos with Unfolding Techniques Juande Zornoza UW Madison

2. Introduction • We will review different approaches for the reconstruction of the energy spectrum of atmospheric neutrinos: • Blobel / Singular Value Decomposition • actually, both methods are basically the same, with only differences in issues not directly related with the unfolding but with the regularization and so on • Iterative Method based on Bayes’ theorem

3. Spectrum reconstruction • In principle, energy spectra can be obtained using the reconstructed energy of each event. • However, this is not efficient in our case because of the combination of two factors: • Fast decrease (power-law) of the flux. • Large fluctuations in the energy deposition. • For this reason, an alternative way has to be used: unfolding techniques.

4. Spectrum unfolding • Quantity to obtain: y, which follows pdf→ftrue(y) • Measured quantity: b, which follows pdf→fmeas(b) • Both are related by the Fredholm integral equation of first kind: Matrix notation • The response matrix takes into account three factors: -Limited acceptance -Finite resolution -Transformation • The response matrix inversion gives useless solutions, due to the effect of statistical fluctuations

5. Dealing with instabilities • Regularization: • Solution with minimum curvature • Principle of maximum entropy • Iterative procedure, leading asymptotically to the unfolded distribution

6. Single Value Decomposition1 • The response matrix is decomposed as: U, V: orthogonal matrices S: non-negative diagonal matrix (si, singular values) • This can be also seen as a minimization problem: • Or, introducing the covariance matrix to take into account errors: 1 A. Hoecker, Nucl. Inst. Meth. in Phys. Res. A 372:469 (1996)

7. SVD: normalization • Actually, it is convenient to normalize the unknowns • Aij contains the number of events, not the probability. • Advantages: • the vector w should be smooth, with small bin-to-bin variations • avoid weighting too much the cases of 100% probability when only one event is in the bin

8. SVD: Rescaling • Rotation of matrices: allows to rewrite the system with a covariance matrix equal to I, more convenient to work with:

9. Regularization • Several methods have been proposed for the regularization. The most common is to add a curvature term add a curvature term • Other option: principle of maximum entropy

10. Regularization • We have transformed the problem in the optimization of the value of , which tunes how much regularization we include: •  too large: physical information lost •  too small: statistical fluctuations spoil the result • In order to optimize the value of : • Evaluation using MC information • Maximum curvature of the L-curve • Components of vector L-curve

11. Solution to the system • Actually, the solution to the system with the curvature term can be expressed as a function of the solution without curvature: where (Tikhonov factors)

12. k Tikhonov factors • The non-zero tau is equivalent to change di by • And this allows to find a criteria to find a good tau Components of d fun0 fun1 fun2  = sk2

13. Differences between SVD and Blobel • More simplified implementation • Possibility of different number of bins in y and b (non square A) • Different curvature term • Selection of optimum tau • B-splines used in the standard Blobel implementation

14. prior guess: iterative approach smearing matrix: MC experimental data (simulated) Bayesian Iterative Method2 • If there are several causes (Ei) which can produce an effect Xj and we know the initial probability of the causes P(Ei), the conditional probability of the cause to be Ei when X is observed is: • The expected number of events to be assigned to each of the causes is: • The dependence on the initial probability P0(Ei) can be overcome by an iterative process. 2 G. D'Agostini NIM A362(1995) 487-498

15. Iterative algorithm • Choose the initial distribution P0(E). For instance, a good guess could be the atmospheric flux (without either prompt neutrinos or signal). • Calculate and . • Compare to . • Replace by and by . • Go to step 2. P(Xj|Ei) Smearing matrix (MC) Reconsructed spectrum P(Ei|Xj) n(Ei) P(Ei) no(E) Po(E) Initial guess n(Xj) Experimental data

16. For IceCube • Several parameters can be investigated: • Number of channels • Number of NPEs • Reconstructed energy • Neural network output… • With IceCube, we will have much better statistics than with AMANDA • But first, reconstruction with 9 strings will be the priority

17. Remarks • First, a good agreement between data and MC is necessary • Different unfolding methods will be compared (several internal parameters to tune in each method) • Several regularization techniques are also available in the literature • Also an investigation on the best variable for unfolding has to be done • Maybe several variables can be used in a multi-D analysis

18. This is the end

19. B-splines • Spline: piecewise continuous and differentiable function that connects two neighbor points by a cubic polynomial: from H. Greene PhD. • B-spline: spline functions can be expressed by a finite superposition of base functions (B-spilines). (first order) (higher orders)

20. Maximum entropy • In general, we want to minimise • S can be the spikeness of the solution, or, the entropy of the system: • Probability of any event of being in bin i is fi/N. • Then, following the maximum entropy principle, we will minimize: • Useful for sharp gradients, i.e. when the solution it is not expected to be very smooth