1. Fiducial Cuts for the CLAS E5 Data Set K. Greenholt (G.P. Gilfoyle) Department of Physics University of Richmond, Virginia Goal: To generate electron fiducial cuts on data from the CLAS detector, so as to focus on regions of CLAS in which the acceptance of the detector is well understood. INTRODUCTION The purpose of the Thomas Jefferson National Accelerator Facility (JLab) is to understand the fundamental properties of matter in terms of quarks and gluons. We describe here how data is collected at Jefferson Lab and how we determine the electron fiducial volume of one of the end station detectors called CLAS (CEBAF Large Acceptance Spectrometer). We do this by focusing on data where the efficiency of the detector is well understood. CEBAF The Continuous Electron Beam Accelerating Facility (CEBAF) at JLab in Newport News, Virginia, is used to study the properties of quark matter. CEBAF is capable of producing electron beams of 2-6 GeV. The accelerator is about 7/8 of a mile around and is 25 feet underground. The electron beam is accelerated through the straight sections and magnets are used to make the beam travel around the bends [See Fig. 1]. An electron beam can travel around the accelerator up to five times near the speed of light. The beam is sent to one of three halls where the beam collides with a target and the debris is measured. These data were collected in Hall B with CLAS [Fig. 2]. CLAS CLAS is located in Hall B and is used to detect pions, electrons, protons and other subatomic particles. The detector is able to detect most particles created in a nuclear reaction, because of its unique nearly-full-solid- angle structure. There are six different layers of CLAS [see Fig. 2] which produce electrical signals, providing us with information on velocity, momentum, and energy, and allow us to identify different subatomic particles. The drift chambers make up the first three layers, and determine the paths of different particles. The next layer is the Cerenkov counters which separate electrons from pions. The following layer is made of the time of flight scintillators to determine time of flight and hence velocity. The calorimeters, used to measure the energy of the particles, make up the final layer. Also in CLAS is a toroidal magnet that causes charged particles to bend as they pass through the drift chambers. This bending is used to determine momentum. This magnetic interaction is of particular interest to us, as we attempt to define the fiducial volume of the detector, because it affects the regions of stable efficiency. Fig. 1 JLab Accelerator and Halls A, B, and C Fig. 2 CLAS Event Display(CED), displays signals received from each layer of CLAS. Acceptance (often referred to as efficiency) is the ratio of the events measured in the detector versus the actual events produced in the nuclear reaction. In plain English, “how much of the good stuff do we actually catch in the detector?” Where do we Start? The red line is what we might expect to measure from an ideal or perfect detector; the black line is what we actually measure. Stable Acceptance: focus on the flat, smooth regions What is Acceptance? PROCEDURES: Stage 1: First Generation Fit  We plot the number of events versus the  angle for a particular momentum bin and  angle bin. We then use a CERN program called Minuit to fit a trapezoidal curve to the data points. The fiducial cut is defined as the edge of the plateau in Fig. 4. Stage 2: Second Generation Fit  We fit the upper and lower sector edges defined by the first generation fits, and plots them against the  electron angle. We then use Minuit to fit another curve to these data points. While often this fit is symmetric, the procedure does not require symmetry. Stage 3: Third Generation Fit  We plot the results generated by the first and second generation fits against the momentum of the electron (ascertained when the particle passes through the toroidal magnet), and fit these data with a polynomial function. Stage 1: First Generation Fits (1) Direct Observation: How well does the fiducial graph fit the actual data plot? In other words, are we cutting out good data, or including events that should be excluded? The left- hand plot shows a fit where MINUIT failed to find the correct low  edge. The right-hand panel shows the same data with an improved fit. This was done by choosing a better starting position. s1_p08_plot44 (2) Minimized  2 : Based on a numerical fit, is the  2 small enough? The left-hand panel shows a fit with a high  . Again in the right-hand panel we show the same data with an improved   achieved with a better starting point for the fit. s6_p08_plot34 s5_p08_plot35 (3) Reasonable Fit Parameter Uncertainties: We observed uncertainties (  2 ) on some fit parameters that were orders of magnitude smaller than expected (~10 -3 ). This would cause the point to be weighted more, distorting the second generation fits. The fit uncertainties were more reasonable after using the results from the original fit in the left-hand panel as a new starting point. The right hand panel shows the final results. Stage 2: Second Generation Fits Stage 4: Conclusions We seek to define a function which effectively limits the data analyzed to focus on regions of CLAS in which the acceptance of the detector is well understood. The left-hand panel shows the  versus  fit for the original first and second generation fits. The right-hand panel shows the data with the improved fit obtained by excluding low-statistics points. This fit defines the regions of CLAS in which we can well understand the acceptance. WHAT’S THE CHALLENGE? In regions of the azimuthal electron scattering angle  near the current-carrying coils that produce the CLAS magnetic field  the efficiency, or acceptance, of CLAS is not well known. To prevent the inclusion of these events in our sample, we generate constraints [fiducial cuts] on electron scattering angles to exclude the regions of the magnetic field near the coils. Hall B Stage 3: Third Generation Fits Fig. 4. Fiducial cut in terms of events plotted against  angle, showing the region of stable efficiency in the  distribution for the electrons in the labeled momentum and  bin. We plot the different fit parameters against the momentum of the electron and fit the curves with polynomials. We note that there should be some symmetry between the upper and lower edges. Fig. 5 (sector 1) and Fig. 6 (sector 3) show this behavior. Upper Edges Lower Edges Upper Edges Lower Edges Hall C Hall A Fig 3. Data plot from CLAS showing  versus  for the electron. Note: six sector orientation. Fig. 5.Fig. 6. Most of the fits we made were high quality, but a few required intervention. Here we discuss how we identified and fixed those fits. 1)We have fitted more than 10,560 distributions of  for the E5 data sets at 2.56GeV normal torus polarity and reversed torus polarity (first generation fits). 2)Some distributions required new starting positions to find acceptable fits. 3)We fitted the edges measured in the first generation fits successfully and excluded low statistics regions. 4)We observed smooth dependence on electron momentum for all fit parameters. References: 'Fiducial Cuts for electrons in the CLAS/E2 data at 4.4 GeV', D. Protopopescu, F. W. Hersman, M. Holtrop, UNH, S. Stepanyan, CNU, and CLAS/E2 run group, CLAS-Note 2000-007, November 27, 2000.