Kinga Anna Woźniak

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than 1$\mu\mathrm{s}$ on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the $\mathtt{hls4ml}$ library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.

We propose a new strategy for anomaly detection at the LHC based on unsupervised quantum machine learning algorithms. To accommodate the constraints on the problem size dictated by the limitations of current quantum hardware we develop a classical convolutional autoencoder. The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder. The performance of the quantum algorithms is benchmarked against classical counterparts on different new-physics scenarios and its dependence on the dimensionality of the latent space and the size of the training dataset is studied. For kernel-based anomaly detection, we identify a regime where the quantum model significantly outperforms its classical counterpart. An instance of the kernel machine is implemented on a quantum computer to verify its suitability for available hardware. We demonstrate that the observed consistent performance advantage is related to the inherent quantum properties of the circuit used.

Abstract In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments ATLAS and CMS reduce this overwhelming data volume to a sustainable level, by deciding in real-time whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events that emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised new physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at this https URL. Code to reproduce the analysis is provided at this https URL.

We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset.

The PHOBOS experiment at RHIC has measured the total multiplicity of primary charged particles as a function of collision centrality in Au+Au collisions at sqrt(s_NN) = 19.6, 130 and 200 GeV. Above sqrt(s_NN) ~ 20 GeV, the total multiplicity per participating nucleon pair ( / ) in central events scales with sqrt(s) in the same way as in e+e- data. This is suggestive of a universal mechanism of particle production in strongly-interacting systems, controlled mainly by the amount of energy available for particle production (per participant pair for heavy ion collisions). The same effect has been observed in pp/pbar-p data after correcting for the energy taken away by leading particles. An approximate independence of / on the number of participating nucleons is also observed, reminiscent of ``wounded nucleon'' scaling ( proportional to ), but with the constant of proportionality set by the multiplicity measured in e+e- data rather than by pp/pbar-p data.

The hash join operator is one of the most important relational operators in database applications and a prominent research topic in the domain of parallel processing. However, up to date, no consistent algorithm design guidelines for high-performance implementations on parallel platforms have been derived from the available experimental results. In this work we define a taxonomy of the parallel hash join operator landscape and categorize state of the art research accordingly. Moreover, we implement and benchmark three taxonomy types: A sequential implementation on the CPU, a hybrid CPU-GPU implementation as well as a fully parallel version on the GPU. The results show that (1) the hybrid CPU- GPU type outperforms the other two, showcasing the benefits of a good fit between algorithm type and hardware platform choice, (2) the poor end-to-end performance of the GPU-only type highlights the impact of GPU specific synchronization and contention issues that appear with an unfit design choice, (3) parallelization improves runtime by a factor of 2.2X in the end-to-end algorithm, a factor of 83X in the join phase and shows good scaling behavior with increasing number of threads. This proves that the GPU is a valuable co-processor option for computation offloading in database applications. We anticipate this classification framework to be a starting-point for design decisions for parallel big data hash join operators on other heterogeneous systems.

Unsupervised New Physics detection at 40 MHz data challenge Training dataset, consisting of a cocktail of Standard Model collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Abstract We investigate supervised and unsupervised quantum machine learning algorithms in the context of typical data analyses at the LHC. To accommodate the constraints on the problem size, dictated by limitations on the quantum hardware, we concatenate the quantum algorithms to the encoder of a classical convolutional autoencoder, used for dimensionality reduction. We present results for a quantum classifier and a quantum anomaly detection algorithm, comparing performance to corresponding classical algorithms.

Dataset used for https://arxiv.org/abs/2301.10780. The initial dataset is compressed to a low-dimensional latent space using a deep autoencoder. Files with compressed data are provided here in HDF5 format. Different sets of files are given, for different choices of dimensionality for the latent space. A description of the dataset is provided in https://arxiv.org/abs/2301.10780

Dataset used for https://arxiv.org/abs/2301.10780. The initial dataset is compressed to a low-dimensional latent space using a deep autoencoder. Files with compressed data are provided here in HDF5 format. Different sets of files are given, for different choices of dimensionality for the latent space. A description of the dataset is provided in https://arxiv.org/abs/2301.10780

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of Leptoquarks -> b tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

In the Southern Zechstein Basin there is an exploration risk related with high nitrogen content in the Main Dolomite (Ca2) reservoir. Existing theory of nitrogen-rich gases origin in Ca2 reservoirs (the Sulecin isolated platform) states, that natural gas was generated from type III kerogen at high-temperature stage of thermogenic processes and probably migrated from the Carboniferous/Rotliegend deposits, sourced by the post-mature Carboniferous shales. A comparison of isotopic composition of gases from Ca2 and the Rotliegend reservoirs from Wedrzyn-1well does not confirm this theory. δ15N values from these reservoirs are significantly different, around +1,3‰ for the Rotliegend and +9,8‰ for Ca2 what excludes their common source. The comparison of isotopic composition of methane indicates that both gases are thermogenic. The methane from Ca2 is isotopically lighter, what suggest it contains biogenic components or was generated from organic matter of lower maturity or different composition. Nitrogen content in Ca2 gas can result from the specific organic matter type. Sedimentary conditions during deposition allowed strong development of microorganisms. Their activity during and after sedimentation might lead to nitrogen formation through biochemical reactions starting from atmospheric N2 fixation through ammonification, nitrification, ending on denitrification and sulfate reduction.

United States Department of Energy (grants DE-AC02-98CH10886, DE-FG02-93ER40802, DEFG02- 94ER40818, DE-FG02-94ER40865, DE-FG02- 99ER41099, and DE-AC02-06CH11357)

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of A -> 4 leptons decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of h^0 -> tau tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of h+ -> tau nu decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Distorting faces by changing the distances between internal features alters perceived configuration and induces strong aftereffects in the appearance of the original face (Webster and MacLin, Psychonomic Bulletin, 1999). We compared these local distortion aftereffects for images that were also globally distorted by stretching the entire picture. This change in aspect ratio also alters feature distances yet has comparatively little effect on perceived identity (Hole et al., Perception, 2002). Faces were shown with the full head or cropped to show only the internal face or specific features, so that the source of the distortion (local vs. global) was increasingly ambiguous. After adapting to different combinations of local or global changes, a staircase was used to vary the local distortions to null any aftereffect. For most observers, aftereffects were similar for the full face images regardless of the relative aspect ratios of the images. Thus contracted faces induced expanded aftereffects whether the picture as a whole was contracted or expanded. The transfer of the aftereffects across global distortions suggests that the adaptation is adjusting at least in part to high-level image properties like perceived identity.

Author(s): Wosiek, B; Alver, B; Back, BB; Baker, MD; Ballintijn, M; Barton, DS; Betts, RR; Bickley, AA; Bindel, R; Busza, W; Carroll, A; Chai, Z; Chetluru, V; Decowski, MP; Garcia, E; Gburek, T; George, N; Gulbrandsen, K; Halliwell, C; Hamblen, J; Harnarine, I; Hauer, M; Henderson, C; Hofman, DJ; Hollis, RS; Holynski, R; Holzman, B; Iordanova, A; Johnson, E; Kane, JL; Khan, N; Kulinich, P; Kuo, CM; Li, W; Lin, WT; Loizides, C; Manly, S; Mignerey, AC; Nouicer, R; Olszewski, A; Pak, R; Reed, C; Richardson, E; Roland, C; Roland, G; Sagerer, J; Seals, H; Sedykh, I; Smith, CE; Stankiewicz, MA; Steinberg, P; Stephans, GSF; Sukhanov, A; Szostak, A; Tonjes, MB; Trzupek, A; Vale, C; van Nieuwenhuizen, GJ; Vaurynovich, SS; Verdier, R; Veres, GI; Walters, P; Wenger, E; Willhelm, D; Wolfs, FLH; Woźniak, K; Wyngaardt, S; Wyslouch, B | Abstract: Recently PHOBOS has focused on the study of fluctuations and correlations in particle production in heavy-ion collisions at the highest energies delivered by the Relativistic Heavy Ion Collider (RHIC). In this report, we present results on event-by-event elliptic flow fluctuations in Au + Au collisions at √sNN =200 GeV. A data-driven method was used to estimate the dominant contribution from non-flow correlations. Over the broad range of collision centralities, the observed large elliptic flow fluctuations are in agreement with the fluctuations in the initial source eccentricity.

over a wide range of pseudorapidity, using the PHOBOS detector. Making a global comparison of Cu + Cu and Au + Au results, we nd that the total number of produced charged particles and the rough shape (height and width) of the pseudorapidity distributions are determined by the number of nucleon participants. More detailed studies reveal that a more precise matching of the shape of the Cu + Cu and Au + Au pseudorapidity distributions over the full range of pseudorapidity occurs for the same Npart/2A value rather than the same Npart value. In other words, it is the collision geometry rather than just the number of nucleon participants that drives the detailed shape of the pseudorapidity distribution and its centrality dependence at RHIC energies. PACS numbers: 25.75.-q, 25.75.Dw The advent of Cu + Cu collisions from the Relativistic Heavy Ion Collider (RHIC) at energies similar to those of the earlier Au + Au collisions presents a new opportunity to measure the system size dependence of important observables using dieren t collision geometries. The Cu + Cu results are expected to provide critical tests of the parametric dependence of the pseudorapidity density of charged particles, dNch=d , observed previously in Au + Au collisions [1{3]. They signican tly extend the range of measurements with respect to the number of participant nucleons, Npart, compared to Au + Au and also allow for a direct comparison at the same Npart. The observed dNch=d is a conceptually well-dened quantity that reects most eects that contribute to particle production in heavy-ion collisions. It is sensitive to the initial conditions of the system, i.e. parton shadowing, and also to the eects of rescattering and hadronic nal-state interactions. In short, the full distribution of dNch=d represents a time-integral of particle production throughout the entire heavy-ion collision.

The PHOBOS experiment has measured the properties of particle production in heavy ion collisions between sqrt(s_NN) of 20 and 200 GeV. The dependencies of charged particle yield on energy, system size, and both longitudinal and transverse momentum have been determined over close to the full kinematic range. Identified charged particles emitted near mid-rapidity have been studied over about 2 orders of magnitude in transverse momentum. This broad data set was found to be characterized by a small number of simple scalings which factorize to a surprising degree. This study has recently been extended by the addition of new data for Cu+Cu as well as new analyses of Au+Au data, including more peripheral collisions. In addition, the exploration of global properties has been expanded with the use of new techniques, including two-particle correlations, more sensitive searches for rare events, and more detailed studies of particles emitted at very forward rapidity. The characteristics of particle production which are revealed by this extensive data set will be described along with the implications for future data from the LHC.

Deep learning plays a significant role in jet tagging. Interaction network / message passing network are parameter efficient. The proposed network out-performs some other deep learning approaches. There is promising direction for future taggers and other problems.

Abstract 4D fluorescence microscopy allows the study of spatiotemporal cell dynamics in embryonic development in unprecedented detail, yet the uneven scattering of light within developing embryos presents challenges in discerning fine details. We present a tool which pre-processes large in vivo 4D microscopy datasets and can then track the movements and lineage histories of rapidly dividing embryonic stem cells. This solution offers a robust, simple segmentation technique to segment high intensity fluorescent features in highly scattered datasets, such as from deep within a developing zebrafish embryo. This tool then offers lineage tracing functionality by tracking rapidly dividing nuclei and their progeny, while also accounting for the jumps rapidly moving features make between frames in low frame rate data. Based on the user having prior knowledge of their imaging subject, this tool has applications in datasets with limitations with signal to noise, and frame rate. It can provide information regarding the position and movement of rapidly dividing nuclei. We demonstrate this pipeline in developing early zebrafish embryos.

We have measured transverse momentum distributions of charged hadrons produced in d+Au collisions at SNN = 200 GeV, in the range 0.25 < pT < 6.0 GeV/c. With increasing collision centrality, the yield at high transverse momenta increases more rapidly than the overall particle density, leading to a strong modification of the spectral shape. This change in spectral shape is qualitatively different from observations in Au+Au collisions at the same energy. The results provide important information for discriminating between different models for the suppression of high‐pT hadrons observed in Au+Au collisions.

We discuss several observables measured by PHOBOS that show common scaling features in Cu+Cu and Au+Au collisions at RHIC energies. In particular, we examine the centrality and energy dependence of the charged particle multiplicity, as well as the centrality dependence of the elliptic flow at mid-rapidity. The discrepancy between Cu+Cu and Au+Au of the final state azimuthal asymmetry (elliptic flow), relative to the initial state geometry of the collision, can be resolved by accounting for fluctuations in the description of the initial geometry.

We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.

We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.

In particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data rate is reduced to a sustainable level by a real-time event filter processing system which decides whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events which emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, by publishing this dataset we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised New Physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of the signal+background Black Box datasets, containing collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Training dataset, consisting of a cocktail of Standard Model collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of A -&gt; 4 leptons decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Training dataset, consisting of a cocktail of Standard Model collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of Leptoquarks -&gt; b tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of Leptoquarks -&gt; b tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of h^0 -&gt; tau tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of h+ -&gt; tau nu decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of h^0 -&gt; tau tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of h+ -&gt; tau nu decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of the signal+background Black Box datasets, containing collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of A -&gt; 4 leptons decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

Unsupervised New Physics detection at 40 MHz data challenge Signal Benchmark Dataset consisting of the signal+background Black Box datasets, containing collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/

The first measurements of charged particle pseudorapidity distributions obtained from Au + Au collisions at the maximum RHIC energy sqrt(s_{NN}) = 200 GeV) using the PHOBOS detector are presented. A comparison of the pseudorapidity distributions at energies 130 and 200 GeV for different centrality bins is made, including an estimate of the total number of charged particles. Away from the mid-rapidity region, a comparison between Pb + Pb at SPS energy sqrt(s_{NN}) = 17.3 GeV and Au + Au at RHIC energy sqrt(s_{NN}) = 130 GeV indicates that the extent of the limiting fragmentation region grows by about 1.5 units of eta - y_{beam} over this energy range. We also observe that the extent of the limiting fragmentation region is independent of centrality at the same energy, but that the particle production per participant in the limiting fragmentation region grows at high eta - y_{beam} &gt;= -1.5 for more peripheral collisions. In combination with results from lower energies and from bar{p} + p collisions, these data permit a systematic analysis of particle production mechanisms in nucleus-nucleus collisions.

A correction to the TPC efficiency calculations based on discriminator response data is shown to have an average effect of 20% on the absolute magnitude of the earlier results. Consistency between runs with the NA36 magnet in different polarities is demonstrated. Comparisons are made with NA35 S+Ag data. The absolute flux of Λ particles is approximately a factor of two in disagreement between the two experiments. A dependence of the rapidity spectrum for lambda’s on centrality is demonstrated.

Author(s): Andersen, E.; Barnes, P.D.; Blaes, R.; Braun, H.; Brom, J.M.; Cherney, M.; Cruz, B. de la; Diebold, G.E.; Dulny, B.; Fernandez, C.; Franklin, G.; Garabatos, C.; Barzon, J.A.; Geist, W.M.; Greiner, D.E.; Gruhn, C.R.; Hafidouni, M.; Hrubec, J.; Jones, P.G.; Judd, E.G.; Kuipers, J.P.M.; Ladrem, M.; Guevara, P. Ladron de; Lovhoiden, G.; MacNaughton, J.; Michalon, A.; Michalon-Mentzer, M.E.; Mosquera, J.; Natkaniec, Z.; Nelson, J.M.; Neuhofer, G.; Ogle, W.C.; Heros, C. Perez de los; Plo, M.; Porth, P.; Powell, B.; Quinn, B.; Ramil, A.; Riester, J.L.; Rohringer, H.; Sakrejda, I.; Thorsteinsen, T.J.; Traxler, J.; Voltolini, C.; Wozniak, K.; Yanez, A.; Lee, Y.; Zybert, R.