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Optimizing Artificial Deadtime for SN System: Correlated Noise Analysis

Explore the impact of artificial deadtime on correlated noise in the SN system, referencing afterpulses and radioactive decays. Analyze SN signal significance with different methodologies and evaluate its effectiveness across various depths and DOMs.

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Optimizing Artificial Deadtime for SN System: Correlated Noise Analysis

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  1. Optimizationofartificialdeadtimeforthe - SN-system Timo Griesel IceCube Collaboration Meeting 04/29-05/03/2008 Madison, USA

  2. Correlated Noise __________________________________________________ __________________________________________________ Time betweentwohits DOM 754dad1d3800 (Str49-1) Ideal poissoniannoise Correlatednoise, Afterpulses Shown fit : Page 1/12

  3. Afterpulse – originofthecorrelatednoise __________________________________________________ __________________________________________________ • Causes: • Afterpulses in the PM • <µs • Scintillations due to radioactive decays in the glas sphere (238U, 232Th )~ms Suppression of afterpulses due to artificial deadtime Hellwig et al. AMANDA-IR/20010301 Hellbing et. al. AMANDA-IR/20030701 O. Franzen Diplomathesis 2003 N. Sido Martin Diplomathesis 2003 Page 2/12

  4. The data __________________________________________________ __________________________________________________ Last Pole season Omicron ATWD hits data was taken (02/06/2008) by John J. and T.Griesel with the help of K.Hanson. I3deadtimeData.root Data: Format: Raw ATWD hits Position: String 39, String 49 – all DOMs Duration: 15min Data Size: ~2GB Contains: GMT Values Str49 Str39 49-60 Processing: Decryption of the OMICRON hits Sorting DOM wise Building a ROOT File with TTree objects 49-1 39-1 StrNo DOMPosition DOMHexName NoOfEntries RawHits Timestamps … … … 49-60 39-60 Page 3/12

  5. Whatwealreadyknewatthe Zeuthen meeting __________________________________________________ __________________________________________________ Paralyzing or Non-paralyzing deadtime  ? non-paralyzed  paralyzed  t t • The artificialdeadtimehastobe a non-paralyzingdeadtime Is there an upper limit for the deadtime ? • The artificial deadtime should not be higher than 400μs Deadtime ? • The optimized artificial deadtime based on „oxygen“ :  = 256μs Page 4/12

  6. First lookatthedata: Rates __________________________________________________ __________________________________________________ • Effects depending on the DOM depth ? Page 5/12

  7. The optimization method Generating SN Signal NSN Superpose NSN with measured background NB Apply artificial deadtime  to the sample NSN+B Apply artificial deadtime  to the sample NB Significance Increase artificial deadtime  __________________________________________________ __________________________________________________ • Is there a maximum in the significance plot ? Page 6/12

  8. The supernova signal __________________________________________________ __________________________________________________ Two different SN signals were used (e.g. @ 10kpc): (1.) Scaled SN1987A signal NKAM = 11ρIce = 917 kg/m³Veff = 51.6m³ * Efth = 0.8mKAM = 2.14 106kgDLMC = 52 kpc DSN = 10 kpc (2.) SN signal based on the „Mainz Simulation“ (A.Piégsa & T.Griesel) Page 7/12

  9. Significance single DOM __________________________________________________ __________________________________________________ Scaled SN1987A signal @ distance 10kpc, DOM 49-11 actual • Is this working out for all DOMs ? Page 8/12

  10. Significance DOMs Str49 __________________________________________________ __________________________________________________ Scaled SN1987A signal @ distance 10kpc, String 49 • What is that ? • Why is that ? Page 9/12

  11. Δt again… __________________________________________________ __________________________________________________ • Effects depending on the DOM depth ? Page 10/12

  12. Afterpulse vs Depth __________________________________________________ __________________________________________________ • Subtracting poissonian noise from background sample • Integration of the „afterpulse“ based correlation • Trend ? Why is that … Page 11/12

  13. Roadmap __________________________________________________ __________________________________________________ • Data for deadtime optimization processed • First look at the rates • First optimization attempt for Str49 was performed • First closer look at the correlated part Next steps… • Investigation of the background after deadtime application still poissonian distributed ? • Depth dependency studies • How is String 39 looking ? … other ideas ? Page 12/12

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