Speaker
Description
In preparation for the XENON1T Dark Matter data acquisition, we have
prototyped and implemented a new computing model. The XENON signal and data processing
software is developed fully in Python 3, and makes extensive use of generic scientific data
analysis libraries, such as the SciPy stack. A certain tension between modern “Big Data”
solutions and existing HEP frameworks is typically experienced in smaller particle physics
experiments. ROOT is still the “standard” data format in our field, defined by large experiments
(ATLAS, CMS). To ease the transition, our computing model caters to both analysis paradigms,
leaving the choice of using ROOT-specific C++ libraries, or alternatively, Python and its data
analytics tools, as a front-end choice of developing physics algorithms. We present our path on
harmonizing these two ecosystems, which allowed us to use off-the-shelf software libraries (e.g.,
NumPy, SciPy, scikit-learn, matplotlib) and lower the cost of development and maintenance.
To analyse the data, our software allows researchers to easily create “mini-trees”; small, tabular
ROOT structures for Python analysis, which can be read directly into pandas DataFrame
structures. One of our goals was making ROOT available as a cross-platform binary for an
easy installation from the Anaconda Cloud (without going through the “dependency hell”). In
addition to helping us discover dark matter interactions, lowering this barrier helps shift the
particle physics toward non-domain-specific code.
| Primary Keyword (Mandatory) | Analysis tools and techniques |
|---|---|
| Secondary Keyword (Optional) | Career and diversity issues |
| Tertiary Keyword (Optional) | Data processing workflows and frameworks/pipelines |
