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The large data volumes expected from the High Luminosity LHC (HL-LHC) present challenges to existing paradigms and facilities for end-user data analysis. Modern cyberinfrastructure tools provide a diverse set of services that can be composed into a system that provides physicists with powerful tools that give them straightforward access to large computing resources, with low barriers to entry. The Coffea-Casa analysis facility (AF) provides an environment for end users enabling the execution of increasingly complex analyses such as those demonstrated by the Analysis Grand Challenge (AGC) and capturing the features that physicists will need for the HL-LHC. We describe the development progress of the Coffea-Casa facility featuring its modularity while demonstrating the ability to port and customize the facility software stack to other locations. The facility also facilitates the support of batch systems while staying Kubernetes-native. We present the evolved architecture of the facility, such as the integration of advanced data delivery services (e.g. ServiceX) and making data caching services (e.g. XCache) available to end users of the facility. We also highlight the composability of modern cyberinfrastructure tools. To enable machine learning pipelines at coffee-casa analysis facilities, a set of industry ML solutions adopted for HEP columnar analysis were integrated on top of existing facility services. These services also feature transparent access for user workflows to GPUs available at a facility via inference servers while using Kubernetes as enabling technology. 

Data analysis in HEP has often relied on batch systems and event loops; users are given a non-interactive interface to computing resources and consider data event-by-event. The “Coffea-casa” prototype analysis facility is an effort to provide users with alternate mechanisms to access computing resources and enable new programming paradigms. Instead of the command-line interface and asynchronous batch access, a notebook-based web interface and interactive computing is provided. Instead of writing event loops, the columnbased Coffea library is used. In this paper, we describe the architectural components of the facility, the services offered to end users, and how it integrates into a larger ecosystem for data access and authentication. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.