Victor Perevoztchikov

CCD Base Line Subtraction Algorithms

High statistics astronomical surveys require photometric accuracy on a few percent level. The accuracy of sensor calibration procedures should match this goal. The first step in calibration procedures is the base line subtraction. The accuracy and robustness of different base line subtraction techniques used for Charge Coupled Device (CCD) sensors are discussed.

Overview of the inner silicon detector alignment procedure and techniques in the RHIC/STAR experiment

The STAR experiment was primarily designed to detect signals of a possible phase transition in nuclear matter. Its layout, typical for a collider experiment, contains a large Time Projection Chamber (TPC) in a solenoid magnet, a set of four layers of combined silicon strip and silicon drift detectors for secondary vertex reconstruction, plus other detectors. In this presentation, we will report on recent global and individual detector element alignment as well as drift velocity calibration work performed on this STAR inner silicon tracking system. We will show how attention to details positively impacts the physics capabilities of STAR and explain the iterative procedure conducted to reach such results in low, medium and high track density and detector occupancy.

The STAR offline framework

The Solenoidal Tracker At RHIC (STAR) is a-large acceptance collider detector, commissioned at Brookhaven National Laboratory in 1999. STAR has developed a software framework supporting simulation, reconstruction and analysis in offline production, interactive physics analysis and online monitoring environments that is well matched both to STARs present status of transition between Fortran and C++ based software and to STARs evolution to a fully OO software base. This paper presents the results of two years effort developing a modular C++ framework based on the ROOT package that encompasses both wrapped Fortran components (legacy simulation and reconstruction code) served by IDL-defined data structures, and fully OO components (all physics analysis code) served by a recently developed object model for event data. The framework supports chained components, which can themselves be composite subchains, with components (makers) managing data sets they have created and are responsible for. An St-DataSet class from which data sets and makers inherit allows the construction of hierarchical organizations of components and data, and centralizes almost all system tasks such as data set navigation, I/O, database access, and inter-component communication. This paper will present an overview of this system, now deployed and well exercised in production environments with real and simulated data, and in an active physics analysis development program.

Vertex Reconstruction at STAR: Overview and Performance Evaluation

The STAR experiment at the Relativistic Heavy Ion Collider (RHIC) has a rich physics program ranging from studies of the Quark Gluon Plasma to the exploration of the spin structure of the proton. Many measurements carried out by the STAR collaboration rely on the efficient reconstruction and precise knowledge of the position of the primary-interaction vertex. Throughout the years two main vertex finders have been predominantly utilized in event reconstruction by the experiment: MinutVF and PPV with their application domains focusing on heavy ion and proton-proton events respectively. In this work we give a brief overview and discuss recent improvements to the vertex finding algorithms implemented in the STAR software library. In our studies we focus on the finding efficiency and the quality of the reconstructed primary vertex. We examine the effect of an additional constraint, imposed by an independent measurement of the beam line position, when it is applied during the fit. We evaluate the significance of the improved primary vertex resolution on identification of the secondary decay vertices occurring inside the beam pipe. Finally, we present a method and its software implementation developed to measure the performance of the primary vertex reconstruction algorithms.

Developments in tracking with STAR's heavy flavor tracker

A primary goal of the high luminosity era at RHIC will be the study of heavy quark behavior in Quark Gluon Plasma. The integration of high precision silicon-based tracking in the form of the Heavy Flavor Tracker for the STAR Experiment should enable the reconstruction and identification of charmed hadron decays, working in concert with STAR’s Time Projection Chamber to determine momenta and displacement of decay daughters from the primary collision vertex. To reach the precision demands, the new detectors must be calibrated and sufficiently accounted in tracking to observe charmed hadrons with high signal-to-noise. In this paper we will review the STAR Collaboration’s developments and achievements in this critical effort.

The STAR "plug and play" event generator framework

The STAR experiment pursues a broad range of physics topics in pp,pA and AA collisions produced by the Relativistic Heavy Ion Collider (RHIC). Such a diverse experimental program demands a simulation framework capable of supporting an equally diverse set of event generators, and a flexible event record capable of storing the (common) particle-wise and (varied) event-wise information provided by the external generators. With planning underway for the next round of upgrades to exploit ep and eA collisions from the electron-ion collider (or eRHIC), these demands on the simulation infrastructure will only increase and requires a versatile framework. STAR has developed a new event-generator framework based on the best practices in the community (a survey of existing approach had been made and the best of all worlds kept in mind in our design). It provides a common set of base classes which establish the interface between event generators and the simulation and handles most of the bookkeeping associated with a simulation run. This streamlines the process of integrating and configuring an event generator within our software chain. Developers implement two classes: the interface for their event generator, and their event record. They only need to loop over all particles in their event and push them out into the event record. The framework is responsible for vertex assignment, stacking the particles out for simulation, and event persistency. Events from multiple generators can be merged together seamlessly, with an event record which is capable of tracing each particle back to its parent generator. We present our work and approach in detail and illustrate its usefulness by providing examples of event generators implemented within the STAR framework covering for very diverse physics topics. We will also discuss support for event filtering, allowing users to prune the event record of particles which are outside of our acceptance, and/or abort events prior to the more computationally expensive digitization and reconstruction phases. Event filtering has been supported in the previous framework and showed to save enormous amount of resources – the approach within the new framework is a generalization of filtering.

The abstract geometry modeling language (AgML): experience and road map toward eRHIC

The STAR experiment has adopted an Abstract Geometry Modeling Language (AgML) as the primary description of our geometry model. AgML establishes a level of abstraction, decoupling the definition of the detector from the software libraries used to create the concrete geometry model. Thus, AgML allows us to support both our legacy GEANT 3 simulation application and our ROOT/TGeo based reconstruction software from a single source, which is demonstrably self- consistent. While AgML was developed primarily as a tool to migrate away from our legacy FORTRAN-era geometry codes, it also provides a rich syntax geared towards the rapid development of detector models. AgML has been successfully employed by users to quickly develop and integrate the descriptions of several new detectors in the RHIC/STAR experiment including the Forward GEM Tracker (FGT) and Heavy Flavor Tracker (HFT) upgrades installed in STAR for the 2012 and 2013 runs. AgML has furthermore been heavily utilized to study future upgrades to the STAR detector as it prepares for the eRHIC era. With its track record of practical use in a live experiment in mind, we present the status, lessons learned and future of the AgML language as well as our experience in bringing the code into our production and development environments. We will discuss the path toward eRHIC and pushing the current model to accommodate for detector miss-alignment and high precision physics.

Planning for Evolution in a Production Environment: Migration from a Legacy Geometry Code to an Abstract Geometry Modeling Language in STAR

Increasingly detailed descriptions of complex detector geometries are required for the simulation and analysis of todays high-energy and nuclear physics experiments. As new tools for the representation of geometry models become available during the course of an experiment, a fundamental challenge arises: how best to migrate from legacy geometry codes developed over many runs to the new technologies, such as the ROOT/TGeo [1] framework, without losing touch with years of development, tuning and validation. One approach, which has been discussed within the community for a number of years, is to represent the geometry model in a higher-level language independent of the concrete implementation of the geometry. The STAR experiment has used this approach to successfully migrate its legacy GEANT 3-era geometry to an Abstract geometry Modelling Language (AgML), which allows us to create both native GEANT 3 and ROOT/TGeo implementations. The language is supported by parsers and a C++ class library which enables the automated conversion of the original source code to AgML, supports export back to the original AgSTAR[5] representation, and creates the concrete ROOT/TGeo geometry implementation used by our track reconstruction software. In this paper we present our approach, design and experience and will demonstrate physical consistency between the original AgSTAR and new AgML geometry representations.

StarBASE: Fighting and Tracing Geometry Changes by Applying Differential Studies

The STAR experiment has evolved significantly since it first began operation. Detector subsystems have been added, removed, and/or significantly modified between (and on occasion within) the 10 RHIC runs. Mistakes, oversimplifications and bugs in the geometry model have been discovered and addressed as simulations are confronted with ever-more-precise data. We therefore maintain over 30 distinct versions of the geometry in order to support simulation needs related to ongoing analysis, upgrade studies and historical reference. In order to help us understand the impact of geometry changes on detector response in our various simulation productions we have developed the StarBASE application within the VMC framework. StarBASE provides the capability to perform detailed comparisons of the material and medium properties between any version of our geometry and a baseline version. This allows us to perform regression tests between library releases, to ensure that changes to one part of the geometry do not have unintended consequences in another part of the geometry, and to help to quantify the impact of an evolving geometry on different physics measurements.

CCD base line subtraction algorithms

High statistics astronomical surveys require higher accuracy of sensor calibration procedures. The first step in calibration procedures is the base line subtraction. The accuracy and robustness of different base line subtraction techniques used for Charge Coupled Device (CCD) sensors are discussed. The specialized algorithm of the base line subtraction for CCD images containing sparse signals was developed and is discussed in section 3. This algorithm does not require taking additional bias exposures and uses data from the same image. Statistical properties of the algorithm are discussed in section 3. The algorithm performance on 55Fe data and comparison with bias exposures approach is presented in section 4. Details of the bias exposure analysis are presented in section 2.