Analysis of the MAGIC data 1 - Description of the images by the Hillas parameters 2 - What do we expect to see 3 - g/h separation and signal extraction by using the Supercuts ON-OFF classes 4 - Suggestions to the analyzers

1 - Description of the images by the Hillas parameters Parameters first proposed by M. Hillas in 1985 (Proceedings 19th ICRC, Vol 3, pag. 445-448) Hillas params lead to the 1st significant (9 sigmas) detection with an IACT (Whipple); the Crab nebula (Weekes et al, Astrophysical Journal, Vol 342, pag. 379-395, 1989) Also 1st significant detection (6 sigmas) of an extragalactic source: Mkn 421 (Punch et al, Nature, Vol 358, pag 477-478, 1992) More about Hillas param can be found at TDAS 02-03

Calculation of the Hillas parameters

1 - Description of the images by the Hillas parameters 00 ≤ ALPHA ≤ 900 More about Hillas param can be found at TDAS 02-03

2 - What do we expect to see We can obtain this info by playing with the Monte Carlo simulations I used the “standard MAGIC MC” - Same conversion efficiency for inner/outer pixels - Optical point spread function is produced by a 2D Gaussian with 0.7 cm sigma - Ped RMS 7 photons - etc, etc…

2 - What do we expect to see 2.1 - Distribution of the basic Hillas parameters Size > 2000 photons

2 - What do we expect to see 2.1 - Distribution of the basic Hillas parameters Size > 1000 photons

2 - What do we expect to see 2.1 - Distribution of the basic Hillas parameters No SIZE cut

2 - What do we expect to see 2.2 - Relation SIZE-ENERGY Clear correlation between SIZE parameter and E Gammas Protons Helium nuclei At a given E, gamma images have (in average) a larger light content As E decreases the probability for Proton and He showers to trigger also decreases; see change of slopes in profile histograms

The quality of the cuts is usually quantified (in MC) by using the Q factor This is a natural definition; directly related to the significance of the detected signal E > 150 GeV Q  8 E > 30 GeV Q  3 (values from TDAS 03-01) However, sometimes people (also) use it to quantify the hadron rejection of the telescope, and this is not FEAR; the hadron rejection due to the trigger and image cleaning is NOT included.

2 - What do we expect to see 2.2 - Relation SIZE-ENERGY Clear correlation between SIZE parameter and E Gammas Protons Helium nuclei At a given E, gamma images have (in average) a larger light content in the camera As E decreases the probability for Proton and He showers to trigger also decreases; see change of slopes in profile histograms At very low E (< 300 GeV) contribution of isolated muons become important

2 - What do we expect to see 2.2 - Relation SIZE-ENERGY Fraction of Cherenkov light produced by muons (in proton showers that trigger the telescope) increases as the E of the primary proton decreases

2 - What do we expect to see 2.2 - Relation SIZE-ENERGY Gamma images Proton images with 90% light from muons; ISOLATED MUONS Rest of proton images Mean SIZE of images from isolated muons is almost constant at 1000 photons Thing to be considered when decreasing Eth of the analysis

2 - What do we expect to see 2.2 - Dependence on the shower characteristics Gammas Protons Helium nuclei Strong dependence of the parameters on the energy of the primary Soft dependence on the impact parameter, except for gammas

2 - What do we expect to see 2.3 - Dependence on the SIZE and DIST Gammas Protons Helium nuclei Strong dependence of the parameters on the SIZE Soft dependence on the DIST, except at DIST > 0.90 Consequence of the limited trigger region of the camera of MAGIC. At large DIST values (>10), only EXTENDED images can trigger the telescope; thus sample is biased to extended images

2 - What do we expect to see 2.3 - Dependence on the SIZE and DIST SIZE > 2000 photons Bias to extended images produced by the limited trigger region is washed out when dealing ONLY with large images (SIZE > 2000 ph). Thing to be considered when decreasing Eth of the analysis

DIST parameter of the showers (Standard MC) After Cuts ( = 57% ;  = 7%) Before cuts (Filter cuts with Size > 2000 photons)

Impact parameter of the showers (Standard MC) After Cuts ( = 57% ;  = 7%) Before cuts (Filter cuts with Size > 2000 photons) Random Forest also keeps about 50% of gammas with impact > 125 m

Impact and DIST parameter After Dyn cuts with fixed Dist cut (0. 60-1 Impact After Cuts ( = 50% ;  = 6%) DIST After Cuts ( = 50% ;  = 6%) Events with large impact parameters CANNOT be removed simply by cutting in DIST

Explanation for the difference with respect to “old” Cherenkov telescopes Coulomb scattering and Cherenkov angle 200m 300m 400m

Large collecting mirror High sensitivity and FINE PIXELIZED camera Explanation for the difference with respect to “old” Cherenkov telescopes 80m 160m Large collecting mirror High sensitivity and FINE PIXELIZED camera Gamma/hadron separation possible even at large (>150m) impact parameters

Consequences of this effect Energy resolution will worsen (somewhat) due to the larger fluctuations in the light yield Head-tail asymetry will move towards tail for hadron showers We see that in MC and experimental data Additional contribution might come from aberration of the images as one moves away from camera center Reduction in the effectiveness of the LENGTH/WIDTH cut

A personal remark concerning this effect I was strongly criticized in Tuorla (June 2004) when presenting this effect. I DID NOT create these guys. Do not blame me for them !!!!! Gammas with large impact parameters are included in all the MAGIC sensitivity and E resolution calculations since 1.5 years (new MC) I just realized they were there… They might not be that bad if we can handle the situation… they increase our collection area by 2

2 - What do we expect to see 2.4 - Dependence of ALPHA on the SIZE CUT ALPHA reconstruction worsens as E decreases <E>  50 GeV <E>  100 GeV <E>  180 GeV <E>  350 GeV At SIZE > 2000 photons: fraction of gamma events in ALPHA range 150-300 is 7.5% of the events contained < 150

3 - g/h separation and signal extraction by using the Supercuts ON-OFF classes a) Definition of set of INITIAL SUPERCUTS on a chosen set of parameters Significance, Nex, ratio of efficiencies, signal/background or a function F(Significance, Nex, signal/background,)… e) Variation of the SUPERCUTS 3.1 - Basic algorithm b) Application of the set of cuts to the data c) Extraction of the gamma signal d) Estimation of the “quality” (Qestimator) of the signal f) Continuation with step b) as long as Qestimator is not optimum

3.2- Implementation in the MARS environment 3.2.1- Introduction Done by Wolfgang almost a year ago (only ON data used). Structure follows steps of the analysis of Daniel Kranich New classes done by myself to use ON-OFF data (finished at mid February) Basic tools (polynomial fits, Minuit interface for the minimization… ) are the same, and were/are working reliably. A small set of new functionalities were added Possibility to optimize in different bins in zenith angle, and combine (a posteriori) the results Storage of all info (alpha plots, normalization factors, shower parameters, cuts… ) in a root file Usage or static/dynamical cuts (use/not use theta, dist…)

3.2- Implementation in the MARS environment 3.2.2- Working principle Within class MFindSupercutsONOFF 1) INITIAL SET of CUTS are given (by user) and stored in container MSupercuts Cuts are computed (in case of dynamical cuts) and applied to data through class MSupercutsCalc 3) Signal extraction and significance calculation using class MHFindSignificanceONOFF Non is obtained by counting, Noff by fitting with a second order palimony ON-OFF normalization computed after cuts in background region (defined by user). But possibility to normalize ON-OFF before cuts (Data padding needed) Significance calculation: LiMa (1984) formula 17

3.2- Implementation in the MARS environment 3.2.2- Working principle 4) Parameters are modified to maximize significance using the class MMinuitInterface Final (optimized) parameters are written into container MSupercuts, and then applied to the data (MFindSupercutsONOFF) to produce (MHFindSignificanceONOFF) the alpha plots and compute final significance and Nex 6) Alpha Plots stored in postscript files and inside a root file together with other information (normalization factors, significance, Nex, hillas parameters, cuts…) FOR A LATER STUDY Class MFindSupeructsONOFFThetaloop used to run MFindSupercutsONOFF separately in different ZENITH angle bins (eventually also SIZE bins), and combining results (if desired) at the end. Besides, this is also the class defining names of root data files, histograms were to store info (Signfinicance, Nex…) and saving all those guys into single root file

3.2- Implementation in the MARS environment 3.2.2- How to use these classes Only 3 steps required 1) Download current Mars version from the CVS 2) Add the line “mtemp/mmpi/SupercutsONOFFClasses” in the SUBDIR field of the general Makefile, and compile it 3) Define some variables in a silly macro specifying how you want to run the analysis

4 - Suggestions to the analyzers 4.1 - Data Reconstruction 4.1.1 - Spend time INSPECTING calibration and pedestal runs. If they are bad you will spoil the data (which might be good) CheckConversionFactorsEvolution2.C TestCalibrationEvolutionForPixelSoftId.C TestCalibrationParameterForAllPixels.C 4.1.2 - Use always the closest (possible) calibration and (specially) pedestal run to calibrate and clean the data 4.1.3 - Inspect images in the camera display using different image cleanings. It is also interesting to apply some “simple” cuts to select the fraction of the data to be displayed SeeMagicCalibratedEventsBeforeAfterCuts2.C 4.1.4 - After computing the image parameters, inspect their distributions AND compare these distributions for ON-OFF MacroToPlotHillasBeforeAfterCuts.C

4 - Suggestions to the analyzers 4.2 - Supercuts optimization 4.2.1 - Compare ALWAYS the results obtained for the TRAIN and the TEST sample. They MUST be statistically compatible TRAIN TEST

4 - Suggestions to the analyzers 4.2 - Supercuts optimization 4.2.1 - Compare ALWAYS the results obtained for the TRAIN and the TEST sample. They MUST be statistically compatible Default when optimizing cuts with these classes 4.2.2 - Try ALWAYS to understand the SUPERCUTS applied from a physics point of view. SCDynamicalSupercutsApplied.C

4 - Suggestions to the analyzers 4.2 - Supercuts optimization 4.2.1 - Compare ALWAYS the results obtained for the TRAIN and the TEST sample. They MUST be statistically compatible Default when optimizing cuts with these classes 4.2.2 - Try ALWAYS to understand the SUPERCUTS applied from a physics point of view. SCDynamicalSupercutsApplied.C 4.2.3 - Compare with Monte Carlo simulations. a) Compare parameter distributions before/after cuts b) efficiency of the cuts, Q factor calculation, collection area calculation ComputeCollectionArea.C

4 - Suggestions to the analyzers 4.3 - Some other suggestions 4.3.1 - Transfer knowledge to the collaboration and (specially) to the other members of the group I feel partially responsible for a non (totally) successful transfer of the know how… sorry… a bit stressed in the last stage of my thesis… 4.3.2 - Be very CRITIC; specially SELF-CRITIC If you find some UNEXPECTED behaviour (calibration, Hillas distribution, cuts applied…), STOP and SPEND time trying to UNDERSTAND it. Most probably a problem in the analysis, but may be a new feature/discovery. In BOTH cases you gain It always bring good results in a medium-long time

4.3.2 - Be very CRITIC; specially SELF-CRITIC 4.3.2.1 - From my personal experience Analysis Noticed problems in pedestals, calibration, image cleaning procedures in both software and quality of data at the source hunting period (end February beginning March) On Sat Feb 28, 2004 05:01:46 I sent an e-mail (to the muc-group) announcing the detection (7-10 sigmas) of the Crab (Jan 27). The day after I noticed that the data was biased by a low cleaning level, which allowed some noisy pixels to show up in many images biasing the alpha values of the ON data towards low values. Is this effect familiar to you ????? I rectified the next day. - In the next days we got the 5.5 sigmas detection of Crab January 27th, first gamma source detection with MAGIC

4.3.2 - Be very CRITIC; specially SELF-CRITIC 4.3.2.1 - From my personal experience Analysis Discrepancies with other groups in several issues, which at end it was shown that the analysis performed at Munich was right: calibration with MExtSignal1 and MExtSignal2, light curve of Mkn 421 at February Big Barcelona flare (6 Hz) of Mkn421 in April (Daniel, Hendrik) …. Noticing about some new/unexpected features in MC data: 50% of triggered proton showers below 100 GeV are muons Bias to extended images produced by limited trigger region Large impact parameters of half of the accepted gammas

4.3.2 - Be very CRITIC; specially SELF-CRITIC 4.3.2.1 - From my personal experience Hardware Discovery of the MAGIC coating. It was the result of a very small (2-3%) NON-UNDERSTOOD increase in QE at visible wavelengths when working with a WLS

Increase in the U.V.

Sensitivity increased by a scattering surface attached to the PhC

4.3.2 - Be very CRITIC; specially SELF-CRITIC 4.3.2.1 - From my personal experience Hardware Discovery of the MAGIC coating. It was the result of a very small (2-3%) NON-UNDERSTOOD increase in QE at visible wavelengths when working with a WLS Low collection efficiency of the PMTs in the PhC periphery Discovery of the remaining of the Sb pill inside the ET9116 PMTs. This Sb grains could produce a short in the PhC-D1, preventing the PMT to work. Finding correlation between VCSEL noise and bias current, as well as identifying the fluctuations in the light produced by the bias current itself as the leading term in the VCSEL noise. Finally VCSEL drivers could be used…

4.3.2 - Be very CRITIC; specially SELF-CRITIC 4.3.2.2 - From the history of physics. Many Nobel Prizes are the result of the careful study of UNEXPECTED effects Radiactivity discovery Becquerel 1897 Pulsar discovery in 1967 Binary pulsar discovery CMB discovery