Optimization of Pulsar Analysis with the Large Area Space Telescope

1. Gamma-ray Large Area Space Telescope Pulsars in DC2preliminary results from an “optimized” analysis Massimiliano Razzano (INFN - Pisa) LAT Collaboration Meeting 2006 (Stockholm, Aug. 28-31 2006) M.Razzano - DC II Closeout Meeting

2. Analysis overview • At the DC2 closeout I presented an analysis on pulsars with radio counterparts with fixed cuts (E>100MeV, r= 3.0° & E>1GeV,r=0.5) and a single periodicity test; • Now we know the DC2 truth so we can extend our analysis procedures to all simulated pulsars in order to have better population statistics • A new automatic analysis on RL pulsar have been started and here are shown some of the preliminary results (still work in progress) from this optimized analysis. Optimized analysis: • This new analysis is an improvement of the basic analysis presented at DC2 Closeout • No fixed cuts in energy or ROI, but search for the “optimal” cuts that maximize the detection chance probability; • More robust and organized Python scripts and classes; • Not only the c2 test but also Z2n and H test; • Change analysis strategy;

3. The 3 ingredients of the analysis • Science Tools: • Basic for pulsar data analysis ; • (gtselect, gtbary, gtpphase, gtpsearch, gtephcomp; • The pyPulsar suite: • Python classes and scripts for interfacing ScienceTools; • File I/O and results organization; • Extension (with FITS, ROOT, etc..) • Plotting • The SLAC Computing Farm: • Set of bash and python scripts for running analysis; • Much more operations on a single pulsar in order to find optimal cuts Parallel analysis on each pulsar (instead of serial)

4. pyAnScripts (scripts for various pulsar-related analysis) pyPulsar (class for managing data and analysis of a pulsar) Analysis results LAT Pulsar Tools Python scripting: pyPulsar pyPulsar is a set of Python classes and scripts for doing pulsar analysis • pyGtTools • (classes for interfacing with LAT Pulsar Science Tools) • pyGtBary • pyGtPsearch • pyGtEphcomp • pyGtPhase • pyGtSelect

5. PyPulsar mini-tutorial • pyPulsar can be found in the CVS under /users/razzano/pyPulsar • (compatibility with latest ST release is under test…); • Create a List of pulsars to be analyzed, i.e. using FindCoincPulsars.py(it can create a list of pulsar coincident with LAT catalog sources of from the whole D4 database): 2 analysis mode: standard and optimized. • Run the script SingleRadioAnalysis.py, giving in input the pulsar parameters • Optional, you can run a multiple analysis (MultipleRadioAnalysis.py) on a pulsar list. At the end you will have a summary that contains list of pulsars analyzed together with analysis results

6. Thresholds and Optimized Cuts • Instead of fixed minimum energy Emin and radius of the ROI rROI for the analysis, we span a range of trial energies and radii, and for every couple Emin and rROI we compute the chance probability CP, in order to find the optimal couple Emin,rROI that minimize P in the periodicity test • Emin(MeV) = {20,100,300,500,1000,5000,10000,20000} • 0.5° < rROI < 3.5° What is the threshold for pulsar detection? After some tests and some discussions I decided to use a more “conservative” detection limits, according to the EGRET pulsars papers

7. Pulsars in the LAT source catalog? As first step a search for coincidence between LAT sources and pulsars in D4 Radius dependent on energy (LAT_Cat_v2) 50 pulsars found

8. Example: List of Pulsars coincident with LAT Catalog Input are the LAT Catalog and the pulsar database… This list have been obtained by scanning D4 with LAT Catalog (FindPulsarCoinc.py)

9. The Chance Probability Map For each pulsar a “Chance Probability Map” is computed and the minimum is found, Corresponding to this minimum there are the “optimal” Emin and rROI e.g. here Emin=10 GeV and rROI=1.5° More fine cuts can be tried to better find the optimal cuts. Here only an example is shown

10. The Probability profile In the same way it is possible to show the chance probability change with energy for different rROI. Here are shown profiles for rROI=0.5 (dotted), rROI=3.0 (dashed) and rROI=1.5 (solid), where the minimum is For some bright pulsars optimal cuts are for high Emin,but this is not too realistic since the DC2 pulsars are “ideal” and does not include timing noise. A more careful search excluding high Emin should be more realistic

11. The final reports For every pulsar a summary report with the optimized cuts is written, then a Python script creates a general report of all analyzed pulsars This contains pulsar position, potential LAT counterpart, f0,f1, optimal cuts and informations about periodicity test

12. Pulsars in the LAT catalog: c2-Test The analysis with Chi2 shows an increment with respect to the previous method Δ High Confidence □ Low Confidence Chi-2 detections of pulsars coincident with the LAT Catalog

13. Low confidence High confidence Pulsars in the LAT catalog: DC fluxes Distribution of pulsar DC flux (from the LAT Catalog) with optimized analysis compared with normal analysis (left)

14. Work in progress… • From the preliminary results the optimized cut analysis seems to provide better results than the basic analysis: many “discovered” pulsars and lower fluxes. • The Python scripts have been upgraded and refined to be more robust; • The parallel analysis approach is much more efficient than the serial one (at least 50 analyses for every pulsars must be done for the optimized cut analysis) • Unfortunately some problems in accessing fits data files from different parallel processes give some conflicts, so a definitive analysis is not yet complete. This have been solved and new analysis runs go to run. • Such type of analysis will be performed also on the whole DC2 pulsar population, in order to refine our pulsar studies with known ephemerides (i.e. use the RQ as RL);