Performance studies of the Roman Pot timing detectors in the forward region of the IP5 at LHC

Transcription

1 TOTEM NOTE August 1, 2014 Performance studies of the Roman Pot timing detectors in the forward region of the IP5 at LHC M. Berretti (CERN) Abstract CERN-TOTEM-NOTE /08/2014 The detection of Central Diffractive (CD) events at high luminosity requires the measurement the time of flight of the leading protons in order to resolve the pileup contribution and reduce the beambeam background. The studies reported in this note concern the optimization of the timing detectors read-out geometry and considerations on the trigger strategy to reduce the rate and increase efficiency and purity in CD events selection. The background expected in the timing detectors is evaluated directly on data and introduced in a simulation tuned in order to take into account the relevant cross sections measured by TOTEM. The full simulation of the signal, pileup and background allows the assessment of the requirements of the timing detectors, in terms of resolution and geometry, such to optimize the reconstruction of the CD events thus that the protons can be assigned to the appropriate central vertex reconstructed by the CMS central detectors. The studies are performed in both vertical and horizontal Roman Pots which will operate at different LHC beam conditions. The conclusion is that by considering ad-hoc configurations of timing detectors a rejection of the main background sources (pileup and beam-beam) is achievable and CD processes can be measured even in hostile environment.

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3 Performance studies of the Roman Pot timing detectors 1 Contents 1 Introduction 2 2 Data samples and expected occupancies 2 3 Study of the background Low β optics High β optics Timing detectors performance Low β optics High β optics Physics performances Low β DPE trigger strategy High β DPE background reduction Conclusions 19 A Cross section values used in the simulation 19

4 2 M. Berretti(CERN) 1 Introduction The capability of the TOTEM-CMS detectors to tag the protons and to study the associated production at central rapidities provides a unique opportunity to study the dynamics of Pomeron-Pomeron interactions in a wide range of energies, essentially from the 1 GeV threshold to about 1 TeV [2]. Measuring the energy of the forward protons on each side determines the centre-of mass energy of the Pomeron-Pomeron interaction, allowing in particular the study of gg gg interactions in the colour-singlet channel with known energy of the initial state. The challenge is to disentangle a diffractive proton generated in one collision from protons generated in other collisions in the same interacting bunches. In Single Diffraction (SD) only one forward proton survives from the interaction and this process has high cross section, while in Central Diffraction (DPE) both protons survive and the cross section is one order of magnitude lower. This process is chosen to test the performance of the upgraded Roman Pot (RP) system with timing detectors. In high pileup configurations, relevant for the study of the DPE at large diffractive masses, many protons seen in the tracking stations at 220m are due to the background and to the SD interactions. Even in the special run with high-β =90 m and a low pileup probability of µ , SD and beam halo combinations could still contribute up to 20% of irreducible background when the DPE selection cut were applied. Therefore the introduction of the timing detectors in the vertical and horizontal RPs is mandatory in order to associate the proton tracks to the correct CMS vertex using the proton time of flight. The studies consider two optics configurations: low-β ( 0.55m), where the bunches are squeezed thus the luminosity and pile-up probability are high and high-β ( 90m) characterized by a lower pile-up probability. The first scenario is favourable to measure low cross sections processes and the large masses can be reconstructed in the horizontal RPs at m (M DPE >260 GeV assuming the RP window at 11σ from the beam center). The second scenario allows measurements of the diffraction processes at low masses which can been reconstructed in the vertical RPs with a good acceptance (M DPE > 0 for t >0.04 GeV 2, assuming the RP window at 11σ from the beam center). In Section 2 the data samples used in the analysis, the raw occupancy and trigger road multiplicities are presented. In Section 3 the background expected in the RP in the two optics configurations is measured directly from data at 8 TeV taken with β = 0.6 m and 90 m. A simulation including the physics protons signals and the background has been set up in order to characterise an optimal geometry of the timing detectors with a limited number of read out channels (Sec. 4). An implementation of a possible trigger algorithm for the DPE selection at high luminosity is proposed in Section 5.1. An estimation of the background reduction in the DPE analysis obtained in high-β runs by using timing information on the vertical RPs is reported in Section Data samples and expected occupancies Two data samples are considered for these studies. A dataset recorded during a low intensity fill (RPs alignment, April 2012) at s = 8 TeV, β = 0.6 m, µ 8, 2 bunches, 1.7e+11 protons per beam, σ 0.12mm, and the horizontal RPs inserted at 6σ is used for the low-β scenario. It is worth to note that the 6σ distance refers to the space between the X-position 2 of the LHC beam and the bottom part of the RP thin window. The distance of the active area of the RP detector from the Z-axis is actually 6σ+K+B: K is a constant offset taking into account the space between the bottom part of the RP thin window and the active area of the silicon; B is the beam X-coordinate. For the lowβ run considered in 1 Hereafter µ indicates the mean of the Poissonian distribution describing the number of inelastic pp interactions in one bunch crossing. 2 In this work a right-handed coordinate system is used, with the origin at the nominal interaction point (IP), the X-axis pointing to the centre of the LHC ring, the Y-axis pointing upwards, and the Z-axis pointing along the anticlockwise-beam direction.

5 Performance studies of the Roman Pot timing detectors 3 this work K=0.3±0.2 mm and B 1mm. The high-β scenario uses a datasample at β = 90 m, s = 8 TeV, µ =0.05, 112 bunches and 8.6e+12 protons per beam. The vertical RPs were inserted at 9.5σ from the beam, with K 0.3 mm and negligible B. Hereafter these samples will be referred as DSL and DSH respectively. The inelastic pile-up probability of the DSL sample was determined with two independent methods: a) by using the luminosity provided by CMS and b) by using a MC simulation where the value of the R 1T 2 parameter (the fraction of inelastic events with only one side of T2 ON) was firstly tuned to the one measured in a data sample with negligible pile-up; then the pile-up simulation is introduced and the value of the simulated µ which better reproduces the R 1T 2 of the DSL sample is used to estimate the pileup probability of the data. The two methods differs for about 8%. For the DSH sample the estimation of µ is instead obtained by measuring the probability per bunch crossing of having T2 OFF in a zero bias triggered (bunch crossing triggered) sample. In both samples bunch crossing trigger events have been considered to measure the occupancy in the detectors and extrapolate it to higher pile-up conditions by superimposing the events. The results are shown in Fig. 1(left) for the same condition as DSL but µ = 50 and Fig. 1(right) for the same condition as DSH but µ = 0.5. The assumption in the superposition of the events is that the collision debris background scales with the number of vertexes generated in the collision, while the beam halo scales with the beam current (see Sec 3). Fig. 1: Left: occupancy/bx*mm 2 in the horizontal RP (low-β, µ = 50, 6σ approach). Right: occupancy/bx*mm 2 in the vertical RP (β =90m, µ = 0.5, 9.5σ approach). Not included in the plot the corrections factor 2 (1.2) which accounts for multiple tracks inefficiency (see text). In the two configurations the measured RP track multiplicities has to be corrected to take into account the effects due to the ghost tracks, especially in events with showers, when the (multi)tracks reconstruction capability is limited. The occupancy values reported in Fig. 1 have to be multiplied by a factor 2 and 1.2 respectively for the low and high-β configurations 3. To get rid of this limitation, a more precise estimate of the multiplicity at high pile-up can be calculated using the number of clusters measured in the detectors. As the hits are not available in this case 4, the multiplicity can be represented by the trigger roads (1 mm 3 These correction factors have been deduced from the difference between the strip cluster multiplicity and the number of reconstructed tracks measured in the data. Hereafter the cluster multiplicity is defined as the average number of strip-cluster ON over the 10 planes of a RP. 4 For large multiplicities the conversion of the clusters channels to the (x,y) positions cannot be done due to the large number

6 4 M. Berretti(CERN) Fig. 2: Left: expected road multiplicity in the horizontal RPs (near and far) for a run with same condition as the DSL sample (β =0.6m, µ = 8.3, 6σ), for 500K bunch crossing triggered events. Right: expected multiplicity in the vertical RPs for a run with same condition as the DSH sample (β =90m, µ = 0.05, 9.5σ approach), for 1M bunch crossing triggered events. wide) 5. In Fig. 2 the trigger road multiplicity of the two horizontal RPs for the DSL sample is shown on the left, the one of the vertical RPs in the DSH sample is shown on the right where both the contributions of the top and bottom RPs are added. The tails of the distribution at high multiplicities represent events where most likely interactions with the material happen before or in the RP. Events with a smaller number of clusters have instead a similar amount of tracks. The probability of a single(double) 6 arm trigger to occur is found to be 43%(13%) in the horizontal RP and 1.4%(0.9%) in the verticals RPs. With the same low-β run conditions of the DSL sample but µ = 30(50), the trigger road multiplicity of the two horizontal RPs is reported in Fig. 3 for 40(20)K simulated bunch crossing events. The details on the simulation and on the background estimation are reported in chapter 3. The probability of a single(double) arm trigger to occur is found to be 31%(64%) at µ = 30 and 13%(86%) at µ = 50. The trigger road multiplicity of the vertical RPs, for high-β and µ = 0.5 is shown in Fig. 4, for 80K bunch crossing triggered events. Both the contributions of the top and the bottom RPs are included. The single(double) arm trigger probabilities are found to be 12%(9%) (See Table 1). of ghosts. 5 A trigger road consist of 16 silicon strips 66µm wide. 6 Hereafter with single arm trigger we refer to topologies where only one arm of the RPs has some road trigger reconstructed. The reported percentages refer to the sum of the single arm probabilities of the two configurations: (sector 45 ON and sector 56 OFF) and (sector 45 OFF and sector 56 ON).

7 Performance studies of the Roman Pot timing detectors 5 Fig. 3: Expected road multiplicity in the horizontal RPs (near and far) for a run with same condition as DSL (β =0.6m, 6σ) for µ = 30 (left) and µ = 50 (right). Fig. 4: Expected road multiplicity in the vertical RPs for a run with same condition as DSH (β =90m, 9.5σ) but µ = 0.5, for 80K bunch crossing triggered events.

8 6 M. Berretti(CERN) Table 1: Single/Double arm trigger probability and rates for two scenarios at high β (β =90m, 9.5σ approach) and low β (β =0.6m, 6σ approach) optics with different pile-up conditions. low-β high-β µ = 8 µ = 30 µ = 50 µ = 0.05 µ = 0.5 single arm probab rate/bunch (khz) double arm probab rate/bunch (khz)

9 Performance studies of the Roman Pot timing detectors 7 3 Study of the background In order to have a realistic estimation of the trigger rates and particles multiplicity expected in the RPs, the estimation of the background is mandatory and has to be performed for the different running conditions [1]. The study of the background, reported in Sec. 3.1 and 3.2 has been performed for low and high β running condition by using the data set DSL and DSH described in Sec. 2. Two main background sources are assumed to be important for this study: the collision debris and the beam halo. The first consists of non-leading protons that can be transported from the IP to the RP and of particles that generate signals in the RP by hitting apertures limitations in the accelerator. This part of the background is expected to scale with the number of vertexes generated in the bunch crossing. The second contribution is due to protons around the beam core which are enough distant from the beam axis to hit the RPs; this contribution is instead expected to scale with the beam current 7. In the next subsections all particles arriving in the RP which are not leading protons are considered as background. 3.1 Low β optics Different approaches have been used to understand how to extract the background component from the data and how to extrapolate it to higher pile-up conditions. The most direct approach is to calculate the multiplicity in the detectors using directly the zero bias triggered sample (DSL dataset) and subtract the leading proton signal using MC simulations. This would give unbiased results (except for the signal component which is MC dependent) but it wouldn t give details on the nature of the background. Another approach is to use a subsample of events in which the T2 detector can help in disentangling the signal-background components. Both methods have been used, the results have been compared to the data and the consistency between the two approaches gives confidence in the correctness of the conclusions. Biased method A subsample with only one active arm of the T2 detector has been selected on zero bias triggered events. In this topology, the number of tracks in the RPs placed on the same side of the active T2 arm ( RP T2 SAME ) is rich in background. The tracks in RPs placed on the opposite side of the active T2 arm ( RP T2 OPPOSITE ) are instead expected to contain mainly diffractive protons (see Fig. 5). With these assumptions, the following equations allow the determination of the background Fig. 5: Sketch of typical processes included in the RP T2 OPPOSITE (left) and RP T2 SAME (right) event samples. average rates in the RP T2 SAME and RP T2 OPPOSITE (respectively B 1 and B 2 ) subsamples: 7 I beam n bunch N proton while the pile-up is proportional to N 2 proton. In this study the background is calculated per bunch crossing and the effective scaling is done based on µ. Any effect which could be related to higher number of bunches cannot be taken into account.

10 8 M. Berretti(CERN) RP T2 SAME = N1 A +B 1 ; (1) RP T2 OPPOSITE = N2 A +B 2 ;. (2) N1 and N2 are the numbers of physics protons generated by MC in the two subsamples. The simulation with µ = 8.3 is tuned in order to take into account the elastic and inelastic cross sections measured by TOTEM as well as the diffractive rates and the probability of each side of T2 to be active. More details on the tuning parameters are reported in Appendix A. The average proton acceptance used in Eqs. 2 is estimated to be ( 13-14%) from MC simulations. The background probability of each horizontal RP is estimated from B 1 for different values of the tracks multiplicities in the RP: Prob(0) = 85.5% Prob(1) = 12.5% Prob(> 1) = 2% (3) Several hypothesis have been tested in order to properly rescale the background measured in this biased subsample (quantified by eq. 3) to the background expected in the bunch crossing triggered sample. Each hypothesis has been tested with the simulation and the predicted multiplicity has been compared to the data. It has been found that a good agreement is obtained assuming that the background of the RP T2 SAME sample has to be rescaled by a factor 2. This value is close to the ratio of the average number of interactions (including the elastic) expected in the sample without any selection bias and in the RP T2 SAME configuration. MC simulation predicts indeed a value µ inelastic+elastic /µ RPT 2SAME 11.1/5.2. Other hypothesis have been tested: the one predicting a rescaling factor=1, which implicitly assumes the independence of the event background from the physics interactions occurred at the IP (pure beam related background) has been discarded, as it underestimates the multiplicities. The hypothesis where the measured background is supposed to be associated to each inelastic vertex producing forward energy towards the RP has been discarded because it overestimates the measured multiplicities. The hypothesis assuming that the background probability has to be associated to each generated protons (diffractive or not) also doesn t describe the data. The comparison of the multi-track multiplicity predicted by simulation and measured in the data is shown, for µ = 8.3, in Fig. 6. In the simulation, a single (multi) primary-track reconstruction efficiency of 98% (90%) is deduced from the difference between the strip cluster multiplicity and the number of reconstructed tracks. The same study has been repeated by measuring the RP T2 SAME and RP T2 OPPOSITE rates using the cluster multiplicities. Indeed cluster reconstruction is expected to be fully efficient. The following background probability has been estimated 8 : Prob(0) = 82% Prob(1) = 14% Prob(> 1) = 4% (4) Such probabilities should then be scaled in the same manner as for the multi-track case, in order to obtain the background cluster multiplicity of the unbiased sample. Unbiased method The background probability per bunch-crossing has also been estimated directly in the unbiased sample. A MC simulation estimates the multiplicity of the primary tracks reconstructed in 8 The high multiplicity tail (cluster multiplicity N i >2) is parametrized with the following distribution: f(n i )=2+exp(-Ni/4). This parametrization is introduced uniquely to describe the high multiplicity events seen in the data, a GEANT4 simulations of the shower productions is not available.

11 Performance studies of the Roman Pot timing detectors 9 Fig. 6: Track multiplicity in the horizontal RPs measured in data (left) and in the simulation (right), at µ = 8.3, β =0.6m and 6σ approach. The asymmetry of the multiplicity with respect to the sectors (left), due to different RP acceptance and background conditions has not been reproduced; the highest background rates and acceptances are instead used for the simulation. the horizontal pots. By comparing the primary multiplicity with the inclusive cluster multiplicity from the data it is then possible to extract the probability of the background multiplicity per bunch-crossing. The estimate background probabilities are 9 : Prob(0) = 76% Prob(1) = 16% Prob(2) = 4% Prob(> 2) = 4% (5) The MC-data comparison of the cluster multiplicity per bunch crossing, obtained at µ = 8.3 is reported in Fig. 7. The corresponding probabilities at µ = 50 are: Prob(0) = 19% Prob(1) = 24% Prob(2) = 19% Prob(> 2) = 38% (6) In summary: the biased method can describe only part of the background in the RPs but it is nevertheless fundamental to set the lower limit of the background with a small dependence on the leading proton signal simulation. Moreover, the required extrapolation proportional to the number of interactions, is consistent with expected behaviour of the beam-beam background. The results obtained with the direct unbiased method are compatible with the results of the biased method. The comparison of the methods using tracks or clusters allows a more precise determination of the rate of the background (clusters) and its position on the detector (tracks) (fundamental for the study in Sec.4). The unbiased method estimate will then be used for the studies in Secs. 4.1 and The high multiplicity tail (cluster multiplicity N i >2) is parametrized with the following distribution: f(n i )=2+exp(-Ni/10). This parametrization is introduced uniquely to describe the high multiplicity events seen in the data, a GEANT4 simulations of the shower productions is not available.

12 10 M. Berretti(CERN) Fig. 7: Cluster multiplicity in the horizontal RPs measured in data (left) and in the simulation (right), at µ = 8.3, β =0.6m and 6σ approach. 3.2 High β optics The background estimation in the vertical RPs at the high-β scenario has been done using the DSH dataset. In this configuration the component of the background due to the beam-halo is expected to be the main contribution with respect to the beam-beam background described in 3.1. This optics configuration is optimized for the detection of elastic events in the vertical RPs. For any background estimate it is then important to exclude any elastic signature in the selected events. The beam halo contribution was calculated from zero bias triggered events, as the probability to have a proton track reconstructed in the vertical RPs when both T2 arms are empty and no elastic signature is present (ie no protons on the other arm). The estimation is conservative and probably overestimate the beam-halo, as the selection includes also the contributions due to low mass SD (which gives no signal in T2 but can have the single proton in the RPs acceptance) and a small fraction of elastic events with a proton on one arm escaping the detection (due to edge and smering effects). This background, assumed to scale with µ, has a probability of about 2-3% per BX in each vertical RP at µ = The beam-beam background has been estimated by selecting events with tracks in both arms of T2: in this subsample the probability to have at least a cluster in the RP for events without elastic candidates was found to be 1.5%. In this estimation the contribution of the high-mass diffraction is already subctracted (about 0.5%). Conservatively, the possible overestimation of the background that can arise because of common background events in the two categories was neither estimated nor subtracted. This background, assumed to scale with µ, has a probability of about 0.75% per BX in each vertical RP at µ =0.5. The comparison between the inclusive cluster multiplicity obtained in the simulation and measured on data is shown, for µ = 0.05, in Fig. 8. In conclusion, the background probability estimated for a scenario with high-β and µ =0.5 is about 3% per BX, including beam-halo and beam-beam effects.

13 Performance studies of the Roman Pot timing detectors 11 Fig. 8: Cluster multiplicity in the vertical RPs measured in data (left) and in the simulation (right) with β =90m, µ = 0.05, 9.5σ approach. 4 Timing detectors performance The goal of this study is to assess the requirements on the readout geometry of the timing detectors. Two geometries have been taken into account. A conservative approach considers a segmentation of the readout based on equal size cells with dimension 3x3mm. A second option considers cells of variable size, optimized according to the occupancy expected in the detectors. This option could be realized if diamond sensors can be used for timing measurement. To simplify the electronics, only a limited number of readout channels have been considered: 16 and 10 channels respectively for each horizontal and vertical RP. Moreover it is assumed that due to the large rising time of the signal, double hits in the same cell cannot be resolved. In this case, the protons arriving in a cell hit by more than one particle are considered lost. The readout geometry optimisation is therefore done taking into account both the geometry constraints explained above and the double hit probability. The simulated events include the physics protons signals (DPE/Phojet, pileup/pythia8-4c) and the background as estimated in the previous sections. The background rates are simulated according to the cluster multiplicity found in the data (to minimize underestimation due to multi-track inefficiencies) and with positions according to the reconstructed background tracks. 4.1 Low β optics The readout geometry is optimized such that each cell has a constant loss due to double hit. This new geometry is visible in Fig. 9(left), with 16 readout pads of 5 mm size in Y and sizes in X ranging between 0.3 mm and 5 mm 10. In the same figure is shown the comparison in terms of occupancy (signal+background and only background) for the optimized geometry and for a geometry with 25 squared cells with 3mm edges. The X coordinate of the distributions reported in this section has not been corrected to the distance between the active edge of the silicon and the beam center: the minimum X value of these distributions correspond to a distance between the active silicon edge and the beam center of about 1.2 mm, i.e. 9σ (see sec. 2). Table 2 shows the inefficiency of the reconstruction of a DPE event (one proton on each arm is required) due to double hits in the horizontal RP for both geometries. In the fixed cells geometry the main contri- 10 Rectangular diamond crystal devices with minimum edge of 5 mm are currently available on the market.

14 12 M. Berretti(CERN) Fig. 9: Cell occupancy and background rate for the optimised horizontal RP read-out geometry (left plots) and for a geometry with equal size pads (right plots). β =0.6m, 6σ approach and µ =50. Top: inclusive cell occupancy per BX. Bottom: background occupancy per BX. bution to the inefficiency is given by the cell close to the edge, while, by construction, the variable cell geometry has a constant loss of 0.5-2% per cell in case signal+pileup or signal+pileup+background is considered (see Fig 10). The conclusion is that by optimising the detector geometry better performances can be obtained with respect to a fixed square shape, even with less readout channels. For trigger purposes anyway only a limited number of cells can be included in the combinatory calculation. By selecting only events having 0 < N Time cells < 3 the inefficiency in the reconstruction can be estimated at 19% for the variable cell geometry and 43% for the fixed cell geometry. Fig. 11 shows the expected occupancy if a cut on the number of active cells is required.

15 Performance studies of the Roman Pot timing detectors 13 Table 2: Inefficiency of DPE event selection due to double hits in the horizontal RP, assuming the same condition of the DSL data sample (β =0.6m, 6σ) with µ = 30 and µ = 50. Optimized cells Fixed cells µ =30 21% 38% µ =50 32% 53% µ =50 & 0 < N Time cells < 3 19% 43% x cell <4.5mm elsewhere x cell <4.5mm elsewhere y cell <2.5mm y cell <1.5mm µ =50 (signal+pileup) 3.5% 5% 15% 5% µ =50 (signal+pileup+background) 11.5% 20% 33.7% 19.5% Fig. 10: Inefficiency in DPE events reconstruction in different region of the detector for optimized (left) and equal cell size (right) configurations, when signal+pileup or signal+pileup+background is considered. Fig. 11: Inclusive cell occupancy per BX for the optimised horizontal RP read-out geometry (left plot) and for a geometry with equal size pads (right plot) with the condition 0 < N Time cells < 3 applied. The results are obtained from a simulation with β =0.6m, 6σ approach, µ =50.

16 14 M. Berretti(CERN) 4.2 High β optics The optimised detector geometry obtained in the high-β optics configuration at µ = 0.5 is shown in Fig. 12, with 10 readout channels. The pitch size around X= 0 ranges between 0.8 mm and 6 mm. In this configuration, also the signal due to the elastic interactions, which is the dominant source of the detector occupancy, has been taken into account. The estimated inefficiency of the DPE event reconstruction due to double hits in the vertical RPs is 0.6% (µ =0.5) and 3% (µ =1). Even at µ = 1 which is an extreme pile-up condition for the β =90 m optics, the efficiency is about 97%. The study has been repeated requiring a multiplicity cut in the events, where events with more than 2 trigger roads in the vertical RPs in the two arms have been discarded. As the occupancy in this scenario is very low, this cut doesn t affect the inefficiency. The equal cell size geometry was not investigated for this optics. Indeed this geometry is typical for a Cherenkov timing detector based on quartz bars which doesn t fit into the vertical RPs. Fig. 12: Optimised read-out geometry and cell occupancy per BX for the top-vertical RP in high-β runs (β =90m, µ = 0.5, 9.5σ approach). 5 Physics performances 5.1 Low β DPE trigger strategy The aim of this section is to develop a trigger strategy allowing to select the DPE events with optimised efficiency and purity, by using a common CMS/TOTEM trigger algorithm in a low-β optics scenario. Having established the incidence of the background in the horizontal RPs (Sec. 3.1) and the effect of the detector inefficiencies (Sec. 4.1), a simulation with µ = 30 or 50 and beam σ Z =10 cm has been performed with the tuning of the cross-sections (see Appendix A). The cluster multiplicity at µ=50 is shown in Fig. 13. The basic idea of the trigger strategy is to use the timing detectors, read by a multihit TDC, for triggering events with at least a DPE vertex, by measuring the time of flight of the protons. This information has

17 Performance studies of the Roman Pot timing detectors 15 Fig. 13: Cluster multiplicity per bunch crossing at µ = 50, β =0.6m, 6σ approach. to be mixed in the CMS L1 algo with the RP trigger road multiplicity and CMS central information in order to select the good events at the HLT stage. The study has been done considering the trigger roads available in the TOTEM trigger system. Although at high luminosity runs it is not clear if these information will be available, it will be equivalent to use the number of the timing detector cells which are ON. At the end of this sections the results with both assumptions are shown. In the following text the numerical values describing the selection performance are reported for µ=30. A summary table describing the selection performance for both µ=30 and 50 is at the end of this section. Protons arriving at the RP have a time that is the sum of the generated vertex time, the travelling time to the detector (t d ) and an additional smearing due to the detector resolution. For background particles attributed to secondary interactions a small delay is added. This is computed considering the time spent by the particles to travel an extra-path length of 3cm before hitting some material (of any aperture limitation in the LHC lattice). For each pair of protons detected in opposite arms, the relevant observables are the sum and the difference of the arrival times. To avoid a large number of combinations when a shower develops just in front of the RP, it has been decided to limit the search on events in which the number of trigger roads reconstructed on each side is not greater than 2. This cut has an efficiency of about 0.3. Having reduced the multiplicity to 2 2, the trigger can compute in one clock cycle the four combinations of the sum and difference of the arrival times. The sum, t = t 1+t 2 2 t d,nominal 11, gives a hint of the generation time of the proton collision while the difference of the arrival time ( t) is proportional to the longitudinal position of the collision vertex (z pp = c t 2 ). The resolution of the timing detectors can be between 10 ps (hence σ(z pp ) 3mm/ 2 = 2.1mm) and 25 ps (σ(z pp ) 5.3 mm), to be compared with the rms vertex spread of rms bunch / 2 = 7.1cm. The worst detectors resolution has been considered in this study. The trigger strategy should be optimised for the selection of the DPE events whose vertexes, reconstructed by the central detector, are enough separated (at least 1 cm) from all the others. In this way the reconstructed z pp can be associated without any ambiguity to the vertex of the central event in CMS (z central ). 11 t d,nominal is the constant traveling time for a proton generated at (X,Y,Z)=(0,0,0).

18 16 M. Berretti(CERN) Because of the considerations above, the sample of events with at least a DPE event associated to an isolated vertexes reconstructed by CMS and at most 2 2 trigger roads in RP is considered the golden sample on which efficiency of the trigger algorithm and the purity of the selected sample are calculated. With the introduction of the multiplicity cut (efficiency 100%) the purity is found to be From simulation it appears clear that colliding bunches tend to accumulate the vertexes near z = 0 and isolated vertexes tends to be seen in the tails of the distribution (see Fig 14). This effect is larger at higher pile-up probability. Therefore a clean way to increase the purity of the sample is to cut the central vertexes and Fig. 14: Longitudinal distribution of vertexes for µ =30 (left) and µ =10 (right) for 100K bunch crossing triggers. Blue and red curves show respectively the cumulative distribution of the vertexes and the distributions of the vertexes separated by at least 1 cm from all the others. select the events with vertexes on the tails, that are more likely to have at least one isolated vertex to be associated to one of the CMS list. This can be achieved requiring t > 800 ps ( z > 12 cm). To further suppress the combination of tracks generated by secondaries which are coming with a longer path and tend to have later recording times, it is required in addition t < 200 ps. With these additional cuts the purity of the sample increases up to 0.12 and the efficiency on the golden sample is reduced to 9%. The rate at this stage is estimated to be 47 khz and with harder cuts even a better purity of the signal can be obtained. Supposing that the full rate is sent to the CMS HLT (not realistic indeed), the next cut that can be performed is the matching of timing vertexes with the z central position list of the primary vertexes recognized by CMS tracker. We search for a precise match of the 4 vertexes measured by the timing detectors, out of which only one or none is a true vertex, according to the list of the vertexes reconstructed by CMS 12. The event is triggered only if a timing vertex is closer than 10 mm to one of the CMS tracker vertexes, and if the recognized vertex is more than 10 mm apart from the other vertexes. This cut is highly efficient on our golden sample, and it removes most of the events in which mainly SD protons from other interactions are mimicking a DPE event due to the large pileup. The final rate is 11 khz with an efficiency of 8% and a purity of The trigger rates, the trigger cut efficiencies and the selection purity on the golden sample of DPE events are summarized in table 3 for µ =30 and 50. It is worth to note that the final number of isolated reconstructed DPE vertexes collected by using the above trigger algorithm constitutes about (µ =30) and (µ =50) of the total DPE interactions. The trigger efficiency study has been repeated by using a L1 trigger multiplicity cut based on the timing 12 The CMS vertex reconstruction efficiency is assumed to be 1, as the masses under study are quite large M = sξ 1 ξ 2, with the 50% ξ -acceptance (ξ = p/p) starting at about for this optics.

19 Performance studies of the Roman Pot timing detectors 17 Table 3: Trigger rate (per bunch crossing), selection efficiency and purity for DPE process at µ =30 (top) and µ =50 (bottom) for runs with β =0.6m and an equivalent 6σ approach to the beam. N-Cut is related to trigger roads. µ =30 N Cut N Cut and timing cut N Cut, timing cut, Z matching Purity Efficiency Rate (khz) µ =50 N Cut N Cut and timing cut N Cut, timing cut, Z matching Purity Efficiency Rate (khz) Table 4: Trigger rate (per bunch crossing), selection efficiency and purity for DPE process at µ =30 (top) and µ =50 (bottom) for runs with β =0.6m and an equivalent 6σ approach to the beam. N-Cut is related to timing detectors cells. µ =30 N Cut N Cut and timing cut N Cut, timing cut, Z matching Purity Efficiency Rate (khz) µ =50 N Cut N Cut and timing cut N Cut, timing cut, Z matching Purity Efficiency Rate (khz) detector cell multiplicity (instead of using the trigger roads provided by the silicon strip detector in the RPs). The results are summarized in table 4. With this selection, the final number of isolated reconstructed DPE vertexes are about (µ =30) and (µ =50) of the total DPE interactions. In both cases, considering the luminosity foreseen at LHC and assuming a cross section of inclusive DPE of 1 mb, we can conclude that these selections allow to trigger 100 of millions of events per day. The possibility to have a coincidence at L1 with two jets at relatively low energy (25 GeV in the past) or leptons, together with a direct link of the jets or muons to the CD vertex at HLT, would allow to get a higher trigger efficiency to rare hard diffractive physics processes. 5.2 High β DPE background reduction The aim of this section is to prove the benefits that an upgrade with timing detector in the vertical RPs would give for the background reduction of the DPE analysis. The track multiplicity expected in the vertical RPs for the sector 45 and 56 of the LHC, is shown in Fig. 15 for µ = 0.5 and β =90 m. Both multiplicities of the top and bottom RPs are added. The main background of the DPE analysis is due to events where the DPE signature is reproduced by two opposite protons where at least one of them is related to beam halo background, to single diffractive or elastic events. The purity and selection efficiency of the DPE sample is here reported for two cases, based on a selection strategy where the possibility to add timing information is allowed ( NEW selection ) or not ( OLD selection ). The results are based on simulations with µ = 0.5 and beam σ Z =10 cm. The background simulation has been described in sec. 3.2 while the cross section values are reported in

20 18 M. Berretti(CERN) Fig. 15: Cluster multiplicity (signal + background) per bunch crossing in the vertical RPs, in the case of µ = 0.5; data (left) and simulation (right) are compared. Table 5: DPE purity and efficiency selection in β =90m runs (assuming an equivalent RP approach of 9.5σ), for a configuration including (NEW) or not (OLD) the timing detectors in the vertical RPs. 500K bunch crossing events have been simulated at µ =0.5. µ =0.5 Num. True DPE Num. Fake DPE Num. DPE in acceptance OLD NEW appendix. The resolution of the timing detectors is expected to be between 25 ps (σ(z pp ) 7mm/ 2 = 5.3mm) and 50 ps (σ(z pp ) 10.6mm), to be compared with the rms of the vertex Z-position of RMS bunch / 2 = 7.1cm. The following study is made considering the time resolution for the worst detectors, the geometry of the timing detector is the one described in sec. 4. In order to remove large part of the elastic protons, in these analyses only the DPE events having both protons in the top RPs or the DPE events with both protons in the bottom RPs are considered. The road multiplicity cut described in the previous section (N-Cut) is also applied. Tab. 5 shows the number of DPE events correctly selected (first column) and wrongly selected (second column) in a sample of 500K bunch crossing events with µ = 0.5. These numbers have to be compared with the total number of DPE generated with both protons in the RP acceptance (third column). At µ = 0.5 the trigger probability per BX is expected to be 7% once the N-Cut is applied. By requiring also a distance of the reconstructed vertex to the CMS one less than 3 cm the rate decreases to 0.2%. The selection efficiency with the OLD detector configuration suffers by the condition required on the maximum number of vertex reconstructed by CMS (no more than 1 vertex has to be reconstructed by CMS for an unambiguous CMS/TOTEM event matching) and by the impossibility to reconstruct more than 1 track per RP (because of the inability to resolve the ghosts tracks by using only silicon detectors). As shown in table 5, these limitations reduce the efficiency of the DPE selection in the OLD configuration to about 60% (to be compared with the 98% efficiency in the upgraded configuration). Moreover, with

21 Performance studies of the Roman Pot timing detectors 19 an upgraded detector configuration including timing detectors the contribution of the misidentified DPE candidates can be reduced further by asking the CMS-TOTEM vertex matching. In the OLD configuration the fraction of misidentified DPE events is 21% while in the NEW one is reduced to 12%. Purity can be furthermore increased by using a more strict cut on the Z matching of the CMS-TOTEM vertices, but with a reduction of the selection efficiency. 6 Conclusions This work has demonstrated that timing detector in the RPs, even with a small number of channels, would allow the study of the central diffractive processes together with CMS in runs with a high pile up probability. Moreover it would contribute significantly to the reduction of the background in the low-mass DPE analysis, to be performed in low-luminosity runs. The background rates, evaluated directly from data, have been included in the simulations with different running conditions and the optimal configurations of the vertical and horizontal read-out geometry of the timing detectors have been established. An efficiency around 80% is expected to be reached for the horizontal RPs with only 16 read out channels and assuming a running scenario with µ = 30, β =0.6 m and a distance of the detector from the beam axis of 6σ. An efficiency of about 99% is found for the vertical detectors, assuming a read out geometry with only 10 channels and a running scenario with µ = 0.5, β =90 m and with the detector placed at a distance of 9.5σ from the beam axis. A trigger strategy for the DPE selection at high luminosity has been developed, based on the horizontal RP timing detectors. It has been shown that, even with a poor time resolution such as 25 ps, a trigger level purity of 23% can be achieved with a final HLT rate of 10 khz. Timing detectors in the vertical RPs would also significantly improve the DPE analysis to be done with a high β optics, allowing a sensible enhancement of the selection efficiency and of the sample purity. A Cross section values used in the simulation The values of the cross sections assumed by the simulation used in this work are reported in table A.1. These values refers to the 7 TeV measurements already published by TOTEM or to internal analysis results that are under approval. Only the ratio of the different cross sections are important in this work as the average number of interactions is decided in advance according to the pile-up probability of the run. It is therefore assumed that these ratios will not change too much with the center of mass energy, going from 7 to 14 TeV. Table A.1: Cross section values used in the simulation. σ TOT 98 mb [5] σ EL 25.1 mb [6] σ INEL V ISIBLE 70.3 mb [7] σ INEL INV ISIBLE 2.6 mb [7] σ SD, 3.4<M<7 GeV 1.8 mb σ SD, 7<M<350 GeV 3.3 mb σ SD, 0.35<M<1.1 TeV 1.4 mb σ SD, M>1.1 TeV 1 mb 1 mb σ CD

22 20 M. Berretti(CERN) The inelastic cross section is also subdivided in order to distinguish events with only one arm of T2 ON (26%) from events with tracks in both T2 sides (74%). Diffractive events with masses M<1.1 TeV are simulated when the inelastic events have only one arm of the T2 ON. Larger diffractive masses are instead simulated within the inelastic events having tracks in both sides of T2. The elastic scattering is simulated with an exponential t-distribution, as measured by TOTEM in [6]. References [1] G. Anelli et al. (TOTEM Collaboration), CERN-LHCC ; LHCC-P-007, [2] The CMS and TOTEM Collaboration, CERN/LHC /G-124, [3] G. Anelli et al. (TOTEM Collaboration), JINST, 3, S08007, [4] P. Aspell et al., VFAT2: a front-end system on chip providing fast trigger information, digitized data storage and formatting for the charge sensitive readout of multi-channel silicon and gas particle detectors, Proceedings of TWEPP-07, Topical Workshop on Electronics for Particle Physics, Prague Czech Republic (2007), [5] G. Antchev et al. (TOTEM Collaboration), EPL , [6] G. Antchev et al. (TOTEM Collaboration), EPL , [7] G. Antchev et al. (TOTEM Collaboration), EPL , 2013.