Baseline optimization studies
Download
1 / 14

reyna2baseline - PowerPoint PPT Presentation


  • 274 Views
  • Uploaded on

Baseline Optimization Studies D. Reyna Argonne National Lab Some Basic Questions Does sensitivity to θ 13 survive under complete 3 flavor mixing? How can we best use all of the information in the energy spectrum? What are the optimal locations for 2 identical detectors?

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'reyna2baseline' - emily


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Baseline optimization studies l.jpg

Baseline Optimization Studies

D. Reyna

Argonne National Lab


Some basic questions l.jpg
Some Basic Questions

  • Does sensitivity to θ13 survive under complete 3 flavor mixing?

  • How can we best use all of the information in the energy spectrum?

  • What are the optimal locations for 2 identical detectors?

  • Bottom line: Can we achieve the desired sensitivity?

D. Reyna – Argonne National Lab


Independence of m 2 l.jpg
Independence of ∆m2

solar

atmospheric

D. Reyna – Argonne National Lab


Total rate vs baseline l.jpg
Total Rate vs. Baseline

Eν= 3.5 MeV

Full Reactor

Spectrum

D. Reyna – Argonne National Lab


Shape comparison l.jpg
Shape Comparison

Make 2

Comparison

Of

Distributions

100 m

1 km

  • Each bin normalized to total at that location

  • Errors are statistical only

D. Reyna – Argonne National Lab


2 comparisons l.jpg

Shape Test:

  • Minimizes Detector Specific Systematics

  • Loss of overall rate information

2 Comparisons

Combined:

  • Assumes Identical Detectors

  • More Statistical Power

D. Reyna – Argonne National Lab


Optimizing 2 locations l.jpg

0.0015

0.002

0.0025

0.003

0.0035

Optimizing 2 Locations

  • 2 Identical Detectors

  • Fix 1 Detector Baseline

  • Sweep the Other

D. Reyna – Argonne National Lab


Optimizing 2 locations cont d l.jpg
Optimizing 2 Locations (cont’d)

100m

1km

500m

1.5km

2km

D. Reyna – Argonne National Lab


Statistical power of combined 2 test l.jpg
Statistical Power of Combined 2 Test

Combined (stat only)

Combined (+ 1% sys)

Shape Only (stat only)

Shape Only (+ 1% sys)

D. Reyna – Argonne National Lab


Secondary maxima l.jpg
Secondary Maxima?

Combined (stat only)

Combined (+ 1% sys)

Shape Only (stat only)

Shape Only (+ 1% sys)

D. Reyna – Argonne National Lab



90 confidence limits l.jpg
90% Confidence Limits

D. Reyna – Argonne National Lab


Short and long solutions l.jpg
Short and Long Solutions

D. Reyna – Argonne National Lab


Final thoughts l.jpg
Final Thoughts

  • It is possible to get the desired sensitivity

  • Optimal near detector location should be as close as possible

  • A Detector should be at the first oscillation minimum (900 – 1400m)

  • A farther location (~2.5km) yields almost as much statistical power

    • Could be effective if a very near detector is not possible

    • Possible 3rd detector?

D. Reyna – Argonne National Lab


ad