π 0 Detector
4.4 highland2
4.4
highland2
This analysis was completed utilising the bespoke software, highland2 (High Level Analysis at the ND280 Detector), which is currently the analysis framework of choice for T2K-ND280 analyses. It comprises a suite of connected classes, which help an ana- lyst to construct a new analysis or selection using a prescribed process and generally is designed to simplify the process of analysing data in particle detectors. It integrates the existing ROOT Data Analysis framework, and gives the analyst access to myriad fea- tures within the highland2 package which should make it easier to produce an analysis and make a physics measurement.
The highland2 analysis framework is split into two core packages : Propagation of Systematics and Characterization of Events (PSyChE) which performs event selection and propagation of systematics and HighLAND which adds in any necessary correc- tions, FlatTree creation, MiniTree creation and tools for drawing. In practice PSyChE is part of the HighLAND distribution, and this analysis did not require in-depth inves- tigation into the mechanisms of the underlying framework, only an understanding of implementing and validating an analysis.
FIGURE 4.9: An overview of the data flow when running an analysis using highland2 [123].
As inputs, an analysis built in highland2 can take 4 formats; an oaAnalysis file (which is the conventional choice of data structure for past analysts who did not use highland2), a FlatTree (a reduced data structure, optimised for analysis running effi- ciency (now obsolete)), a MiniTree (an updated version of the FlatTree which provides intermediate data reduction), and a list of any of the previous. This analysis has been performed using a list of MiniTrees as the inputs for both Data and Monte Carlo, which have proven to dramatically reduce the required running time for the analysis. The improvement in running time has been indispensable, especially throughout the anal- ysis development and optimisation, where changes must be made regularly and the analysis re-run. Proper parameters (contained in the highlandIO package) must be set when producing these MiniTrees, which were made in this instance using Condor [122], a batch processing system on the Lancaster University computing network, which al- lowed for fast final data quality checking and MiniTree creation.
At the core of the highland2 framework are the ‘Actions and Cuts’ which form the selection. These are applied on an event by event basis, and allow the analyst to pref- erentially select events of interest (signal) over events of no interest for this analysis (background). The entirety of this analysis was developed using only Monte Carlo (MC) data, with real data only being seen at the final stages, and used to make the plots which can be seen in Chapter6. Reconstructed variables used in the selection are ones to which the ND280 detector is sensitive, and therefore those that can be used when running on real data, and true variables can only be accessed in Monte Carlo (i.e does this event truly contain aπ0). True variables were used to optimise a selection, based on
reconstructed physical quantities. Objects in a given event, and their properties, e.g. a track and its number of hits, determined charge, momentum etc, are stored in the ‘toy- box’ which is highland2 specific. A graphical overview of the workings of highland2 is shown in Figure4.9.
There are detector systematic errors calculated as part of the analysis process. These are split into two types, ‘Weight’ and ‘Variation’ which both affect the number of events which will pass or fail a given selection for a given toy. The analyst determines which
4.4. highland2 121
systematics are required for a given analysis, for example an ECal object only analy- sis would not require systematics pertaining to the a TPC, and these are activated in parameters files. Additionally, a number of toys can be chosen (500 for this analysis), which represents the number of times the analysis is effectively re-run with changes to certain variables based on the active systematics. Systematic errors are discussed in more depth in Chapter5.
The output of the analysis is in the form of a ‘Microtree’ which contains some in- formation as standard, such as properties of certain tracks in the event (momentum, position, detector crossings etc), the events which pass each cut level and a header file containing information on the total Protons On Target (P.O.T) analysed and software versions used. The contents of this microtree are heavily expanded upon when creat- ing an analysis, such that very specific quantities are represented and can be plotted easily. Additional data classes were written as part of this analysis, to encapsulate data concerning the neutral pion candidates and their constituents, and specific quantities contained therein were added to the output microtree for analysis.
The ‘Drawing Tools’ of highland2 are possibly its most useful feature, offering a wide range of commands which once understood, can help an analyst to quickly and easily probe various details pertaining to a given analysis. The drawing tools are passed a data sample (Monte Carlo or Real Data Production), which can then be plot- ted in many useful forms, giving easy access to analysis variables, events passed for a given cut, truth information (often via the categories functionality) and selection ef- ficiency and purity. The categories mentioned here are exceptionally useful for repre- senting Monte Carlo data broken down into specific truth modes. For example, given a number of track objects being plotted against their momentum, the drawing categories could represent the true particle type of each object by colour (which would have been matched to a PDG particle code in the implementation).
Throughout this work, the drawing category used is called ‘reactionpi0’ is, an ex- ample of which can be found in Figure4.11. This drawing category stratifies events by
: the number ofπ0s produced in that event, whether or not that interaction was inclu- sive or exclusive, and other background options. A breakdown of the event properties which contribute to the drawing category are shown in Table4.3.
Event Category Description Pre-π0 Selection %
CC1π0 Charged-current event containing a µ−, a proton
and exactly oneπ0
5.1
CCNπ0 Charged-current event containing a µ−, a proton and more than oneπ0
0.9
CC1π0+ X Charged-current event containing a µ−, a proton and exactly oneπ0 + other particles
7.5
CCNπ0+ X Charged-current event containing a µ−, a proton and more than oneπ0+ other particles
2.0
CC0π A typically clean CCQE signal with no additional particles emitted
49.2
CCOther A Charged-current event producing a µ−, a proton + other particles (noπ0s)
25.5
Other Any interaction not falling into any of the above cat- egories (commonly neutral current interactions)
4.2
Out of FV An interaction taking place outside the defined fidu- cial volume of the detector
5.6
TABLE4.3: Definitions of the signals which define the subsets for the ‘re-
actionpi0’ drawing category, used throughout this analysis. All particles described are those present in the final state. ‘Other’ particles includes : charged-pions, neutral etas, neutral rhos, kaons, electrons, positrons and