IC-22 Point Source Analysis with Unbinned Maximum Likelihood

IC-22 Point Source Analysis withUnbinned Maximum Likelihood

C. Finley, J. Dumm, T. Montaruli2008 May 2

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Overview

1. Basic Set of Cuts for point-source quality sample using Level 2 processing

• 14 days ( 12.6 days livetime) of Level 2 Filtered data (Sept. 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29)

• 1000 files of Level 2, muon-filter Nugen E-1 (optimal for E-2 source sim.) Dataset 753

• 1000 files of Level 2, muon-filter Nugen E-2 (optimal for high statistics AtmNu) Dataset 768

2. First look at adding energy term to the likelihood in IC-22 (similar to method of J. Braun for AMANDA-II)

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Muon Filter + Pandel Zenith>90 Cut

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Paraboloid Sigma Cut

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Pandel Reduced Log Likelihood Cut

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Split Hit Series Cut: Pandel zen1>80 and zen2>80

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NDirC Cut

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Pandel SDir Cut

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Maximum Likelihood Analysis Part I: Point Spread Function

By now the maximum likelihood expression is familiar for point source searches:

where the source PDF is a Gaussian with width

given by the paraboloid sigma uncertainty estimate:

and the background PDF depends only on zenith angle:

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Sensitivity with Analysis-Level Cuts

E-2 average sensitivity 0 = 1.710-11 TeV-1 cm-2 s-1

Preliminary IC-22 Sensitivity for 250 d

( ~ 20 atmNu events per day )

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Sensitivity with Analysis-Level Cuts

E-2 average sensitivity 0 = 1.710-11 TeV-1 cm-2 s-1

IC-9 Sensitivity

IC22 Sensitivity

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Sensitivity with Analysis-Level Cuts

E-2

Sensitivity est. from Gent meeting, based on atmNu background simulation only

E-3

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Maximum Likelihood Analysis Part II: Energy

Now, want to add energy term to the Likelihood function, to weight higher energy events with greater significance:

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Maximum Likelihood Analysis Part II: Energy

Now, want to add energy term to the Likelihood function, to weight higher energy events with greater significance:

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Energy PDFs

Start with simplest energy estimator: NChan

P (Ei | =2)

P (Ei | =3)

Patm (Ei)

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Maximum Likelihood fit to nSrc and Gamma: Examples

+

+

+

+

gam

ma

gam

ma

gam

ma

gam

ma

nSrc nSrc

nSrc nSrc

Examples: Simulated E-2 source at declination +30°

Left: 15 events injected (cross)

Right: 30 events injected (cross)

1-sigma and 2-sigma contours are shown for best fit to number of source events nSrc and spectral index gamma

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Maximum Likelihood fit to nSrc and Gamma: Examples

+

+

+

+

gam

ma

gam

ma

gam

ma

gam

ma

nSrc nSrc

nSrc nSrc

Below: Simulated E-3 source at declination +30°

Left: 15 events injected; Right: 30 events injected

1-sigma and 2-sigma contours are shown for best fit to number of source events nSrc and spectral index gamma

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Effect of Energy Term on Discovery Potential

Simulated E-2 source at declination +30°:

5-sigma Discovery potential (Power 50%): without energy term in likelihood: 6.1 10-8 GeV-1 cm-2 s-1 (E/GeV)-2

(mean number of source events: 15) with energy term in likelihood: 4.2 10-8 GeV-1 cm-2 s-1 (E/GeV)-2

(mean number of source events: 10.5)

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Summary

Basic cuts on Level 2 parameters and basic maximum likelihood analysis

yield point source sensitivity ≈ 7x better from IC-9.

Many significant improvements coming soon:• Level 3 reconstructions• MPE reconstruction: better high energy efficiency, better angular resolution (see

talk by J. Dumm)• Time residuals of MPE should improve efficiency of NDirect cut

(or we may find an alternative cut for best high energy efficiency)

Adding energy term to likelihood: • Test with new energy estimators in level 3 (better than Nch estimate)• Reasonable to expect at least 30% improvement in discovery potential for hard

spectra.