A Complete Bibliography of Publications in Computing and Software for Big Science

Nelson H. F. Beebe University of Utah

Department of Mathematics, 110 LCB

155 S 1400 E RM 233 Salt Lake City, UT 84112-0090

USA

Tel: +1 801 581 5254 FAX: +1 801 581 4148

E-mail: beebe@math.utah.edu, beebe@acm.org,
beebe@computer.org (Internet)
WWW URL: https://www.math.utah.edu/~beebe/

12 October 2023 Version 1.15

Title word cross-reference

b [39].

2020s [29, 20]. 2021 [78].

3 [69].

Accelerated [26]. Accelerating [72]. Access [93, 92]. Accurate [12]. Across [51]. ACTS [63]. additional [71]. Advances [78]. Advancing [40]. Adversarial [8, 17, 12, 83, 4]. Agents [5, 23]. AI [95]. Algorithm [30, 2]. Algorithms [77, 83]. Allen [36]. Amazon [1]. Analyses [90, 50]. Analysis [55, 81, 87, 56]. Analysis-Specific [55].

Analyzing [84]. Annealers [30]. Apache [11]. Application [93]. Applications [48, 45]. Applying [35]. Approach [58]. Approaches [95]. Archive [5, 23]. Artificial [59, 16]. Astroparticle [15]. ATLAS [27, 59, 75, 71, 89, 21]. AtlFast3 [75]. Aware [90, 4]. Axion [86].

B [67]. B2BII [13]. Background [61]. Based [60, 35, 32, 44]. batch [56]. Belle [41, 13, 2, 14]. Benchmarking [68]. Big [28, 37]. Binned [44]. Busy [27, 21].

Cache [90, 94]. Cache-Aware [90]. Caching [73]. Cait [87]. Calibration [96]. Calorimeter [17]. Calorimeters [59, 53]. Carlo [71, 38, 52]. Cascades [15]. Centers [47]. CephFS [49, 65]. CERN [47, 16].

Challenge [88]. Challenges [82, 28, 52]. Characteristics [9]. Charged [66]. Cherenkov [38]. Classification [35, 2, 25, 42]. Classifying [66]. Cloud [33, 6, 73]. Cloudscheduler [33]. Cluster [22, 90, 49]. Clusters [91]. CMS [23, 35, 45, 6, 1, 5, 16]. Code [72]. Collider [61, 82, 16]. Collision [35]. Columnar [90]. Commodity [65]. Common [76]. Comparison [69]. CompF4 [92]. Compression [10]. Computational [92, 82]. Computing [33, 78, 22, 79, 26, 29, 97, 6, 20, 68, 18, 40]. Concepts [74]. Concurrent [43]. Connectivity [64]. Consortium [74]. Constraints [81]. Containerized [49]. Continuum [67]. Convergent [95]. Conversion [13]. Core [14]. Correction [23, 27]. CORSIKA [38, 15]. Cost [65]. CPU [27, 69, 21]. Cryogenic [87]. CSBS [3]. CTA [38]. CUDA [72]. CVMFS [49]. Cylindrical [58].

Dask [90]. Data [93, 35, 10, 24, 41, 77, 47, 80, 90, 74, 13, 28, 51, 37, 85, 11, 70, 94, 73]. Decays [67]. Deep [35, 77, 55, 96, 86, 25, 70, 39]. Denoising [70]. Density [65]. Dependence [34]. Dependencies [9]. Deployment [22]. DESY [56]. Detector [35, 61, 8, 16]. Detectors [58, 12, 87]. Determination [58]. Developers [46]. Dielectric [86]. Direct [35]. Disk [65]. Distance [8]. Distributed [5, 23]. Distribution [47]. Dynamic [47, 22]. Dynamo [51].

Edge [66]. Edge-Classifying [66]. Editorial [3]. Education [46]. Efficiency [54]. Electromagnetic [17]. Emulating [71]. End [35]. End-to-End [35]. Energy [59, 78, 97, 96, 81, 37, 39, 50, 42]. Environments [22]. Errors [84]. Estimation [39]. Evaluating [65]. Evaluation [73]. Event [35, 19, 25, 42].

EventIndex [89]. Events [35, 71].

Example [4]. Experiment [69, 2, 5, 23, 16]. Experimental [18]. Experiments [10]. Explainability [95]. Exploit [27, 21]. Exploiting [64]. Extra [27, 21].

Facilities [1]. Fast [75, 55, 90, 12]. FastBDT [2]. Fidelity [53]. File [84]. Filter [60]. FITS [11]. Fitting [60]. FPGA [26]. FPGA-Accelerated [26]. FPGAs [59]. Framework [15]. Frontier [92]. Full [61, 19]. Fully [49]. Function [34]. Fundamental [77, 74, 40]. Future [79, 81].

GeantV [43]. Generalized [58].

Generating [8]. Generation [75, 38, 15]. Generative [17, 12, 4]. Generators [31]. Geneva [91]. Getting [53]. Gluon [7]. GPU [69, 60]. GPU-Based [60]. GPUs [36]. Granularity [53]. Graphcore [48]. Grid [27, 21]. Group [92].

Hadron [16]. Haloscopes [86]. Handling [51]. HEP [43, 29, 20, 1, 57, 95, 62, 94]. HEPCloud [1]. HEPiX [68]. High [36, 33, 78, 47, 53, 22, 97, 6, 18, 31, 81, 37, 65, 50, 42, 52]. High-Density [65]. High-Energy [42]. High-Level [36]. High-Luminosity [52]. High-Performance [18]. High-Quality [31]. High-Rate [47]. High-Throughput [33, 6, 18]. Home [27, 21, 6]. HPC [47, 49, 94, 91]. HPC/HTC [49]. HTC [49]. HTCondor [49, 64].

IceCube [72]. Identifying [9]. II

[41, 13, 2, 14]. Image [35]. Image-Based [35]. impact [71]. Implementation [63]. Implementations [69]. Improve [25]. Improving [83]. Inbound [64]. inelastic [71]. Inference [32, 26, 44]. infrastructure [45]. Initiative [74]. Input [9, 34]. Integrating [6]. Integration [94]. Intensive [73]. Interaction [66]. interactions [71]. Interactive [56]. Interpretation [19]. Investigation [1]. Invited [78]. IPUs [48].

Jet [96, 39, 83]. Jobs [49]. Julia [97, 50]. JupyterHub [64].

Kalman [60]. Kubeflow [96].

Language [97]. LAr [59]. Large [93, 10, 80, 16]. Large-Scale [93, 10, 80]. Learning [88, 35, 77, 32, 55, 84, 26, 96, 57, 37, 25, 70, 42, 4]. Learning-Based [32]. Level [36]. LHC [35, 55, 90, 5, 23, 25, 52]. LHCb [36, 69, 93]. Lightweight [94]. Likelihoods [44]. Location [4]. Location-Aware [4]. Longitudinal [58]. loop [82]. Luminosity [52].

Machine [88, 32, 84, 26, 57, 37, 42]. Machines [67]. MadMiner [32]. Management [24, 5, 23]. Media [51]. Meson [67]. Metadata [81]. MLaaS4HEP [57]. Modelling [80]. Modern [37]. Momentum [58]. Monitoring [16, 45]. Monte [71, 38, 52]. Multi [82]. Multi-loop [82]. Multivariate [2]. Muon [61]. Muonic [95].

Network [17, 64, 39, 9, 34, 44]. Networks [59, 54, 7, 66, 8, 86, 12, 16, 4]. Neural [59, 54, 7, 86, 16, 39, 9, 34, 44]. Next [75, 15]. notebooks [56]. Ntuple [93]. Nuclear [78, 79]. Nuisance [44]. Number [31].

Occupancy [61]. Offline [41]. Online [41]. Open [93, 35, 74, 85]. Operating [49]. Operations [41]. Opportunistic [94]. Optimal [44]. Optimization [91, 44]. Optimized [2]. Optimizing [38].

Papers [78]. Paradigm [1]. Parameterization [54]. Parameters [44].

Parametric [91]. Particle [43, 32, 22, 66, 26, 15, 8, 46, 48, 12, 87, 4]. Particles [95]. Pattern [30, 95]. Performance [22, 18, 65, 50]. Personal [85]. Perspective [85]. Phase [88]. Phenomenology [82]. Photon [72]. Photons [38]. Physics [35, 10, 77, 78, 32, 22, 79, 26, 90, 15, 74, 97, 81, 46, 48, 37, 40, 50, 42, 4]. Pipeline [96]. Pipelines [37]. Poisson [44]. Polynomial [10]. Potential [97, 48]. Precise [17]. presampling [71]. Presence [44]. Principle [86]. Processing [92, 47]. Programming [97]. Project [76]. Proof [86]. Proof-of-Principle [86]. Propagation [38, 72]. ProtoDUNE [70]. proton [71]. Prototype [43]. Provisioning [33]. PUNCH4NFDI [74]. Python [87].

Quality [31]. Quantum [30, 67, 40, 42]. Quark [7]. Quark/Gluon [7].

R&D [29, 20]. Random [31]. Rate [47]. Raw [70]. Real [59, 25]. Real-Time [59, 25]. Recognition [30, 95]. Reconstruction [59, 63]. Recursive [7]. Reducing [34]. Refining [8]. Relevant [9]. Remote [47]. Report [92]. Resolution [39]. Resource [92]. Resources [64, 68, 94, 56]. Response [9]. Results [43]. Review [31]. Roadmap [29, 20]. Robustness [83]. Rucio [24]. Run [69].

Scale [93, 10, 80, 90]. Science [28, 18, 73]. Scientific [24, 80, 51]. Selection [25]. Servers [65]. Service [33, 26, 57]. Services [1]. sets [71]. Shared [77]. Showers [17]. Simulation [75, 43, 61, 53, 55, 15, 17, 86, 12, 73, 71]. Simulations [38, 8]. Simultaneous [39]. Singularity [49]. Sites [51, 21, 27]. Snowmass [92]. Software [76, 29, 46, 14, 62, 52]. Solution [68, 5, 23]. Source [11]. Space [9, 34]. Spark [11].

Specific [55]. Speed [53, 2]. Speed-Optimized [2]. sPHENIX [63]. Statistical [44]. Storage [92, 51, 73]. Strategies [70]. Straw [58]. Studying [48]. Support [67]. Supporting [18]. Suppression [67]. Survey [74]. Synthesis [4]. System [41]. Systematic [34, 44]. Systems [81].

Tagging [7, 83]. Terabyte [90]. Terabyte-Scale [90]. Throughput [88, 33, 6, 18]. Time [59, 25]. Toolkit [87]. Tools [37]. Topical [92]. Topology [25]. Track [60, 63]. Tracking [76, 66, 88]. Training [46, 62, 83]. Transfer [84]. Transfers [80]. Transport [43]. Trends [79]. Trigger [36, 69]. Tube [58].

Uncertainties [34, 44]. Unleashing [64]. Unprecedented [61]. Usage [94]. Using [8, 17, 49, 64, 12, 21, 44, 27].

vCHEP [78]. Vector [43, 67]. via [66]. Virtualized [22]. VM [33]. Volunteer [6]. vs [65].

Wasserstein [8, 17]. Web [1]. Where [85]. Within [74]. Without [64]. Wizard [93]. WLCG [84, 68]. Workflow [5, 23]. Workflows [94].

References

Holzman:2017:HNP

[1] Burt Holzman, Lothar A. T. Bauerdick, and Eric W. Vaandering. HEPCloud, a new paradigm for HEP facilities: CMS Amazon Web Services investigation. Computing and Software for Big Science, 1(1):??, November 2017. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-017-0001-9.

Keck:2017:FSO

[2] Thomas Keck. FastBDT: a speedoptimized multivariate classification algorithm for the Belle II experiment. Computing and Software for Big Science, 1(1):??, November 2017. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-017-0002-8.

Beckmann:2017:CE

[3] Volker Beckmann, Markus Elsing, and Günter Quast. CSBS editorial. Computing and Software for Big Science, 1(1):??, November 2017. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-017-0003-7.

deOliveira:2017:LPP

[4] Luke de Oliveira, Michela Paganini, and Benjamin Nachman. Learning particle physics by example: Location-aware generative adversarial networks for physics synthesis. Computing and Software for Big Science, 1(1):??, November 2017. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-017-0004-6.

Kuznetsov:2018:ASD

[5] Valentin Kuznetsov, Nils Leif Fischer, and Yuyi Guo. The archive solution for distributed workflow management agents of the CMS experiment at LHC. Computing and Software for Big Science, 2(1):??, November 2018. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/

s41781-018-0005-0. See correction [23].

Field:2018:CIV

[6] L. Field, D. Spiga, and L. Cristella. CMS@home: Integrating the volunteer cloud and high-throughput computing. Computing and Software for Big Science, 2(1):??, November 2018. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-018-0006-z.

Cheng:2018:RNN

[7] Taoli Cheng. Recursive neural networks in quark/gluon tagging. Computing and Software for Big Science, 2(1):??, November 2018. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-018-0007-y.

Erdmann:2018:GRP

[8] Martin Erdmann, Lukas Geiger, and David Schmidt. Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks. Computing and Software for Big Science, 2(1):??, November 2018. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-018-0008-x.

Wunsch:2018:IRD

[9] Stefan Wunsch, Raphael Friese, and Günter Quast. Identifying the relevant dependencies of the neural network response on characteristics of the input space. Computing and Software for Big Science, 2(1):??, November 2018. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-018-0012-1.

Aubert:2018:PDC

[10] Pierre Aubert, Thomas Vuillaume, and Nahid Emad. Polynomial data compression for large-scale physics experiments. Computing and Software for Big Science, 2(1):??, November 2018. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-018-0010-3.

Peloton:2018:FDS

[11] Julien Peloton, Christian Arnault, and Stéphane Plaszczynski. FITS data source for Apache Spark. Computing and Software for Big Science, 2(1):??, November 2018. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

                                        https://link.springer.com/article/
                                        10.1007/s41781-018-0014-z.

Musella:2018:FAS

[12] Pasquale Musella and Francesco Pandolfi. Fast and accurate simulation of particle detectors using generative adversarial networks. Computing and Software for Big Science, 2(1):??, November 2018. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-018-0015-y.

Gelb:2018:BDC

[13] Moritz Gelb, Thomas Keck, and Anze Zupanc. B2BII: Data conversion from Belle to Belle II. Computing and Software for Big Science, 2(1):??, November 2018. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-018-0016-x.

Kuhr:2019:BIC

[14] T. Kuhr, C. Pulvermacher, and N. Braun.

The Belle II core software. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-018-0017-9.

Engel:2019:TNG

[15] Ralph Engel, Dieter Heck, and Darko Veberič. Towards a next generation of CORSIKA: a framework for the simulation of particle cascades in astroparticle physics. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-018-0013-0.

Pol:2019:DMA

[16] Adrian Alan Pol, Gianluca Cerminara, and Agrima Seth. Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-018-0020-1.

Erdmann:2019:PSE

[17] Martin Erdmann, Jonas Glombitza, and Thorben Quast. Precise simulation of electromagnetic calorimeter showers using a Wasserstein generative adversarial network. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-018-0019-7.

Huerta:2019:SHP

[18] E. A. Huerta, Roland Haas, and Daniel S. Katz. Supporting high-performance and high-throughput computing for experimental science. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-019-0022-7.

Keck:2019:FEI

[19] T. Keck, F. Abudinén, and A. Zupanc. The full event interpretation. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

                                        https://link.springer.com/article/
                                        10.1007/s41781-019-0021-8.

Foundation:2019:RHS

[20] The HEP Software Foundation, Johannes Albrecht, and Efe Yazgan. A roadmap for HEP software and computing R&D for the 2020s. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-018-0018-8.

Wu:2019:UAE

[21] Wenjing Wu, David Cameron, and Di Qing. Using ATLAS@Home to exploit extra CPU from busy grid sites. Computing and Software for Big Science, 3(1):??, December 2019. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/

s41781-019-0023-6. See correction [27].

Buhrer:2019:DVD

[22] Felix Bührer, Frank Fischer, and Bernd Wiebelt. Dynamic virtualized deployment of particle physics environments on a high performance computing cluster. Computing and Software for Big Science, 3(1):??, December 2019. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-019-0024-5.

Kuznetsov:2019:CAS

[23] Valentin Kuznetsov, Nils Leif Fischer, and Yuyi Guo. Correction to: The Archive Solution for Distributed Workflow Management Agents of the CMS Experiment at LHC. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-019-0025-4. See [5].

Barisits:2019:RSD

[24] Martin Barisits, Thomas Beermann, and Tobias Wegner. Rucio: Scientific data management. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-019-0026-3.

Nguyen:2019:TCD

[25] T. Q. Nguyen, D. Weitekamp III, and J-R. Vlimant. Topology classification with deep learning to improve real-time event selection at the LHC. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

                                        https://link.springer.com/article/
                                        10.1007/s41781-019-0028-1.

Duarte:2019:FAM

[26] Javier Duarte, Philip Harris, and Zhenbin Wu. FPGA-accelerated machine learning inference as a service for particle physics computing. Computing and Software for Big Science, 3(1):??, December 2019. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-019-0027-2.

Wu:2019:CUA

[27] Wenjing Wu, David Cameron, and Di Qing. Correction to: Using AT-LAS@Home to Exploit Extra CPU from Busy Grid Sites. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-019-0029-0. See [21].

Heiss:2019:BDC

[28] Andreas Heiss. Big data challenges in big science. Computing and Software for Big Science, 3(1):??, December 2019. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-019-0030-7.

Elsen:2019:RHS

[29] Eckhard Elsen. A roadmap for HEP software and computing R&D for the 2020s. Computing and Software for Big Science, 3(1):??, December 2019. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-019-0031-6.

Bapst:2020:PRA

[30] Frédéric Bapst, Wahid Bhimji, and Alex Smith. A pattern recognition algorithm for quantum annealers. Computing and Software for Big Science, 4(1):??, December 2020. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-019-0032-5.

James:2020:RHQ

[31] Frederick James and Lorenzo Moneta.

Review of high-quality random number generators. Computing and Software for Big Science, 4(1):2:1–2:12, December 2020. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-019-0034-3.

Brehmer:2020:MML

[32] Johann Brehmer, Felix Kling, and Kyle Cranmer. MadMiner: Machine learningbased inference for particle physics. Computing and Software for Big Science, 4(1):??, December 2020. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-0035-2.

Berghaus:2020:HTC

[33] F. Berghaus, K. Casteels, and J. Weldon. High-throughput cloud computing with the cloudscheduler VM provisioning service. Computing and Software for Big Science, 4(1):??, December 2020. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-0036-1.

Wunsch:2020:RDN

[34] Stefan Wunsch, Simon Jörger, and Günter Quast. Reducing the dependence of the neural network function to systematic uncertainties in the input space. Computing and Software for Big Science, 4(1):??, December 2020. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-020-00037-9.

Andrews:2020:EEP

[35] M. Andrews, M. Paulini, and B. Poczos. End-to-end physics event classification with CMS open data: Applying imagebased deep learning to detector data for the direct classification of collision events at the LHC. Computing and Software for Big Science, 4(1):??, December 2020. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-020-00038-8.

Aaij:2020:AHL

[36] R. Aaij, J. Albrecht, and M. Williams. Allen: a high-level trigger on GPUs for LHCb. Computing and Software for Big Science, 4(1):??, December 2020. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-020-00039-7.

Migliorini:2020:MLP

[37] M. Migliorini, R. Castellotti, and M. Zanetti. Machine learning pipelines with modern big data tools for high energy physics. Computing and Software for Big Science, 4(1):??, December 2020. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-00040-0.

Arrabito:2020:OCP

[38] Luisa Arrabito, Konrad Bernlöhr, and Guillaume Revy. Optimizing Cherenkov photons generation and propagation in CORSIKA for CTA Monte–Carlo simulations. Computing and Software for Big Science, 4(1):??, December 2020. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-00042-y.

Sirunyan:2020:DNN

[39] A. M. Sirunyan, A. Tumasyan, and CMS Collaboration. A deep neural network for simultaneous estimation of b jet energy and resolution. Computing and Software for Big Science, 4(1):??, December 2020. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-00041-z.

Spentzouris:2020:QCA

[40] Panagiotis Spentzouris. Quantum computing: Advancing fundamental physics. Computing and Software for Big Science, 4(1):??, December 2020. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-00043-x.

Barrett:2021:BIO

[41] Matthew Barrett, Takanori Hara, and Prashant Shingade. The Belle II online–offline data operations system. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-020-00045-9.

Terashi:2021:ECQ

[42] Koji Terashi, Michiru Kaneda, and Junichi Tanaka. Event classification with quantum machine learning in high-energy physics. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-020-00047-7.

Amadio:2021:GRP

[43] G. Amadio, A. Ananya, and Y. Zhang. GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-020-00048-6.

Wunsch:2021:OSI

[44] Stefan Wunsch, Simon Jörger, and Günter Quast. Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

                                        https://link.springer.com/article/
                                        10.1007/s41781-020-00049-5.

Ariza-Porras:2021:CMI

[45] Christian Ariza-Porras, Valentin Kuznetsov, and Federica Legger. The CMS monitoring infrastructure and applications.

Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-00051-x.

Kluth:2021:ETS

[46] Stefan Kluth. Education and training for software developers in particle physics. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-00046-8.

Boccali:2021:DDH

[47] T. Boccali, D. Cameron, and C. Wissing.

Dynamic distribution of high-rate data processing from CERN to remote HPC data centers. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-020-00052-w.

Maddrell-Mander:2021:SPG

[48] Samuel Maddrell-Mander, Lakshan Ram Madhan Mohan, and Alexander Titterton. Studying the potential of graphcore (R) IPUs for applications in particle physics. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00057-z.

Freyermuth:2021:OHH

[49] Oliver Freyermuth, Peter Wienemann, and Klaus Desch. Operating an HPC/ HTC cluster with fully containerized jobs using HTCondor, Singularity, CephFS and CVMFS. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-020-00050-y.

Stanitzki:2021:PJH

[50] Marcel Stanitzki and Jan Strube. Performance of Julia for high energy physics analyses. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00053-3.

Iiyama:2021:DHS

[51] Yutaro Iiyama, Benedikt Maier, and Christoph Paus. Dynamo: Handling scientific data across sites and storage media. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00054-2.

WG:2021:CMC

[52] The HSF Physics Event Generator WG, Andrea Valassi, and Graeme A. Stewart. Challenges in Monte Carlo event generator software for high-luminosity LHC. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00055-1.

Buhmann:2021:GHH

[53] Erik Buhmann, Sascha Diefenbacher, and Katja Krüger. Getting high: High fidelity simulation of high granularity calorimeters with high speed. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-021-00056-0.

Bello:2021:EPN

[54] Francesco Armando Di Bello, Jonathan Shlomi, and Marumi Kado. Efficiency parameterization with neural networks. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00059-x.

Chen:2021:ASF

[55] C. Chen, O. Cerri, and M. Pierini.

Analysis-specific fast simulation at the LHC with deep learning. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-021-00060-4.

Reppin:2021:IAN

[56] J. Reppin, C. Beyer, and Y. Kemp. Interactive analysis notebooks on DESY batch resources. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00058-y.

Kuznetsov:2021:MML

[57] Valentin Kuznetsov, Luca Giommi, and Daniele Bonacorsi. MLaaS4HEP: Machine learning as a service for HEP.

Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00061-3.

Andersson:2021:GAL

[58] Walter Ikegami Andersson, Adeel Akram, and Tobias Stockmanns. A generalized approach to longitudinal momentum determination in cylindrical straw tube detectors. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00064-0.

Aad:2021:ANN

[59] Georges Aad, Anne-Sophie Berthold, and Johann Christoph Voigt. Artificial neural networks on FPGAs for real-time energy reconstruction of the ATLAS LAr calorimeters. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00066-y.

Ai:2021:GBK

[60] Xiaocong Ai, Georgiana Mania, and Nicholas Styles. A GPU-based Kalman filter for track fitting. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-021-00065-z.

Bartosik:2021:FDS

[61] Nazar Bartosik, Paolo Andreetto, and Lorenzo Sestini. Full detector simulation with unprecedented background occupancy at a muon collider. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-021-00067-x.

Malik:2021:STH

[62] Sudhir Malik, Samuel Meehan, and Siqi Yuan. Software training in HEP. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00069-9.

Osborn:2021:IAS

[63] Joseph D. Osborn, Anthony D. Frawley, and Haiwang Yu. Implementation of ACTS into sPHENIX track reconstruction. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00068-w.

Freyermuth:2021:UJE

[64] Oliver Freyermuth, Katrin Kohl, and Peter Wienemann. Unleashing Jupyter-Hub: Exploiting resources without inbound network connectivity using HT-Condor. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00063-1.

Peters:2021:ECP

[65] Andreas J. Peters and Daniel C. van der Ster. Evaluating CephFS performance vs. cost on high-density commodity disk servers. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00071-1.

DeZoort:2021:CPT

[66] Gage DeZoort, Savannah Thais, and Peter Elmer. Charged particle tracking via edge-classifying interaction networks. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00073-z.

Heredge:2021:QSV

[67] Jamie Heredge, Charles Hill, and Martin Sevior. Quantum support vector machines for continuum suppression in B meson decays. Computing and Software for Big Science, 5(1):??, December 2021. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00075-x.

Giordano:2021:HBS

[68] Domenico Giordano, Manfred Alef, and Andrea Valassi. HEPiX benchmarking solution for WLCG computing resources. Computing and Software for Big Science, 5(1):??, December 2021. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00074-y.

Aaij:2022:CCG

[69] R. Aaij, M. Adinolfi, S. Aiola, S. Akar, J. Albrecht, M. Alexander, S. Amato, Y. Amhis, F. Archilli, M. Bala, G. Bassi, L. Bian, M. P. Blago, T. Boettcher, A. Boldyrev, S. Borghi, A. Brea Rodriguez, L. Calefice, M. Calvo Gomez, D. H. Cámpora Pérez, A. Cardini, M. Cattaneo, V. Chobanova, G. Ciezarek, X. Cid Vidal, J. L. Cobbledick, J. A. B. Coelho, T. Colombo, A. Contu, B. Couturier, D. C. Craik, R. Currie, P. d’Argent, M. De Cian, D. Derkach, F. Dordei, M. Dorigo, L. Dufour, P. Durante, A. Dziurda, A. Dzyuba, S. Easo, S. Esen, P. Fernandez Declara, S. Filippov, C. Fitzpatrick, M. Frank, P. Gandini, V. V. Gligorov, E. Golobardes, G. Graziani, L. Grillo, P. A. Günther, S. Hansmann-Menzemer, A. M. Hennequin, L. Henry, D. Hill, S. E. Hollitt, J. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, B. K. Jashal, C. R. Jones, S. Klaver, K. Klimaszewski, R. Kopecna, W. Krzemien, M. Kucharczyk, R. Lane, F. Lazzari, R. Le Gac, P. Li, J. H. Lopes, M. Lucio Martinez, A. Lupato, O. Lupton, X. Lyu, F. Machefert, O. Madejczyk, S. Malde, J. F. Marchand, S. Mariani, C. Marin Benito, D. Martinez Santos, F. Martinez Vidal, R. Matev, M. Mazurek, B. Mitreska, D. S. Mitzel, M. J. Morello, H. Mu, P. Muzzetto, P. Naik, M. Needham, N. Neri, N. Neufeld, N. S. Nolte, D. O’Hanlon, A. Oyanguren, M. Pepe Altarelli, S. Petrucci, M. Petruzzo, L. Pica, F. Pisani, A. Piucci, F. Polci, A. Poluektov, E. Polycarpo, C. Prouve, G. Punzi, R. Quagliani, R. I. Rabadan Trejo, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, F. Reiss, V. Renaudin, P. Robbe, A. Ryzhikov, M. Santimaria, M. Saur, M. Schiller, R. Schwemmer, B. Sciascia, A. Solomin, F. Suljik, N. Skidmore, M. D. Sokoloff, P. Spradlin, M. Stahl, S. Stahl, H. Stevens, L. Sun, A. Szabelski, T. Szumlak, M. Szymanski, D. Y. Tou, G. Tuci, A. Usachov, N. Valls Canudas, R. Vazquez Gomez, S. Vecchi, M. Vesterinen, X. Vilasis-Cardona, D. Vom Bruch, Z. Wang, T. Wojton, M. Whitehead, M. Williams, M. Witek, Y. Xie, A. Xu, H. Yin, M. Zdybal, O. Zenaiev, D. Zhang, L. Zhang, and X. Zhu. A comparison of CPU and GPU implementations for the LHCb Experiment Run 3 trigger. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

                                        https://link.springer.com/article/
                                        10.1007/s41781-021-00070-2.

Rossi:2022:DLS

[70] Marco Rossi and Sofia Vallecorsa. Deep learning strategies for ProtoDUNE raw data denoising. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00077-9.

Aad:2022:EIA

[71] G. Aad, B. Abbott, and ATLAS Collaboration. Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

                                        https://link.springer.com/article/
                                        10.1007/s41781-021-00062-2.

Schwanekamp:2022:AIP

[72] Hendrik Schwanekamp, Ramona Hohl, and Jakob van Santen. Accelerating Ice-Cube’s photon propagation code with CUDA. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-022-00080-8.

Wegner:2022:SEC

[73] Tobias Wegner, Mario Lassnig, and Christian Zeitnitz. Simulation and evaluation of cloud storage caching for data intensive science. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-021-00076-w.

Enke:2022:SOD

[74] Harry Enke, Andreas Haungs, and Stefan Typel. Survey of open data concepts within fundamental physics: an initiative of the PUNCH4NFDI consortium. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-022-00081-7.

Aad:2022:ANG

[75] G. Aad, B. Abbott, and ATLAS Collaboration. AtlFast3: The next generation of fast simulation in ATLAS. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-021-00079-7.

Ai:2022:CTS

[76] Xiaocong Ai, Corentin Allaire, and Jin Zhang. A common tracking software project. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-021-00078-8.

Benato:2022:SDA

[77] Lisa Benato, Erik Buhmann, and Kai Zhou. Shared data and algorithms for deep learning in fundamental physics. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-022-00082-6.

Bird:2022:ACH

[78] Ian Bird, Simone Campana, and Graeme A. Stewart. Advances in computing in high energy and nuclear physics — invited papers from vCHEP 2021. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-022-00083-5.

Diefenthaler:2022:FTN

[79] Markus Diefenthaler and Torre Wenaus. Future trends in nuclear physics computing. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-022-00085-3.

Bogado:2022:MLS

[80] Joaquin Bogado, Mario Lassnig, and Javier Díaz. Modelling large-scale scientific data transfers. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-022-00084-4.

Khoo:2022:CFA

[81] T. J. Khoo, A. Reinsvold Hall, and S. Wertz. Constraints on future analysis metadata systems in high energy physics. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-022-00086-2.

Cordero:2022:CCM

[82] Fernando Febres Cordero, Andreas von Manteuffel, and Tobias Neumann. Computational challenges for multi-loop collider phenomenology. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-022-00088-0.

Stein:2022:IRJ

[83] Annika Stein, Xavier Coubez, and Alexander Schmidt. Improving robustness of jet tagging algorithms with adversarial training. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-022-00087-1.

Clissa:2022:AWF

[84] Luca Clissa, Mario Lassnig, and Lorenzo Rinaldi. Analyzing WLCG file transfer errors through machine learning.

Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-022-00089-z.

Nachman:2022:WWH

[85] Benjamin Nachman. When, where, and how to open data: a personal perspective. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-022-00090-6.

Jung:2022:SDA

[86] Philipp Alexander Jung, Bernardo Ary dos Santos, Dominik Bergermann, Tim Graulich, Maximilian Lohmann, Andrzej Novák, Erdem Öz, Ali Riahinia, and Alexander Schmidt. Simulation of dielectric axion haloscopes with deep neural networks: a proof-of-principle. Computing and Software for Big Science, 6(1):??, December 2022. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-022-00091-5.

Wagner:2022:CAT

[87] Felix Wagner, Daniel Bartolot, Damir Rizvanovic, Florian Reindl, Jochen Schieck, and Wolfgang Waltenberger. Cait: Analysis toolkit for cryogenic particle detectors in Python. Computing and Software for Big Science, 6(1):??, December 2022. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-022-00092-4.

Amrouche:2023:TML

[88] Sabrina Amrouche, Laurent Basara, Paolo Calafiura, Dmitry Emeliyanov, Victor Estrade, Steven Farrell, Cécile Germain, Vladimir Vava Gligorov, Tobias Golling, Sergey Gorbunov, Heather Gray, Isabelle Guyon, Mikhail Hushchyn, Vincenzo Innocente, Moritz Kiehn, Marcel Kunze, Edward Moyse, David Rousseau, Andreas Salzburger, Andrey Ustyuzhanin, and Jean-Roch Vlimant. The Tracking Machine Learning Challenge: Throughput phase. Computing and Software for Big Science, 7(1):??, December 2023. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-023-00094-w.

Barberis:2023:AE

[89] Dario Barberis, Igor Alexandrov, Evgeny Alexandrov, Zbigniew Baranowski, Luca Canali, Elizaveta Cherepanova, Gancho Dimitrov, Andrea Favareto, Álvaro Fernández Casaní, Elizabeth J. Gallas, Carlos García Montoro, Santiago González de la Hoz, Julius Hřivnáč, Alexander Iakovlev, Andrei Kazymov, Mikhail Mineev, Fedor Prokoshin, Grigori Rybkin, José Salt, Javier Sánchez, Roman Sorokoletov, Rainer Többicke, Petya Vasileva, Miguel Villaplana Perez, and Ruijun Yuan. The ATLAS EventIndex. Computing and Software for Big Science, 7(1):??, December 2023. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-023-00096-8.

Eich:2023:FCP

[90] Niclas Eich, Martin Erdmann, Peter Fackeldey, Benjamin Fischer, Dennis Noll, and Yannik Rath. Fast columnar physics analyses of terabyte-scale LHC data on a cache-aware Dask cluster. Computing and Software for Big Science, 7(1):??, December 2023. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-023-00095-9.

Wessner:2023:POH

[91] Jonas Weßner, Rüdiger Berlich, Kilian Schwarz, and Matthias F. M. Lutz. Parametric optimization on HPC clusters with Geneva. Computing and Software for Big Science, 7(1):??, December 2023. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-023-00098-6.

Bhimji:2023:SCF

[92] W. Bhimji, D. Carder, E. Dart, J. Duarte, I. Fisk, R. Gardner, C. Guok, B. Jayatilaka, T. Lehman, M. Lin, C. Maltzahn, S. McKee, M. S. Neubauer, O. Rind, O. Shadura, N. V. Tran, P. van Gemmeren, G. Watts, B. A. Weaver, and F. Würthwein. Snowmass 2021 Computational Frontier CompF4 Topical Group report storage and processing resource access. Computing and Software for Big Science, 7(1):??, December 2023. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

                                        link.springer.com/article/10.1007/
                                        s41781-023-00097-7.

Aidala:2023:NWA

[93] Christine A. Aidala, Christopher Burr, Marco Cattaneo, Dillon S. Fitzgerald, Adam Morris, Sebastian Neubert, and Donijor Tropmann. Ntuple Wizard: an application to access large-scale open data from LHCb. Computing and Software for Big Science, 7(1):??, December 2023. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-023-00099-5.

Sammel:2023:LID

[94] Dirk Sammel, Michael Boehler, Anton J. Gamel, and Markus Schumacher. Lightweight integration of a data cache for opportunistic usage of HPC resources in HEP workflows. Computing and Software for Big Science, 7(1):??, December 2023. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

https://link.springer.com/article/
10.1007/s41781-023-00100-1.

Maglianella:2023:CAA

[95] Leandro Maglianella, Lorenzo Nicoletti, Stefano Giagu, Christian Napoli, and Simone Scardapane. Convergent approaches to AI explainability for HEP muonic particles pattern recognition. Computing and Software for Big Science, 7(1):??, December 2023. CO-DEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-023-00102-z.

Holmberg:2023:JEC

[96] Daniel Holmberg, Dejan Golubovic, and Henning Kirschenmann. Jet energy calibration with deep learning as a Kubeflow pipeline. Computing and Software for Big Science, 7(1):??, December 2023. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL https://

link.springer.com/article/10.1007/
s41781-023-00103-y.

Eschle:2023:PJP

[97] Jonas Eschle, Tamás Gál, Mosè Giordano, Philippe Gras, Benedikt Hegner, Lukas Heinrich, Uwe Hernandez Acosta, Stefan Kluth, Jerry Ling, Pere Mato, Mikhail Mikhasenko, Alexander Moreno Briceño, Jim Pivarski, Konstantinos Samaras-Tsakiris, Oliver Schulz, Graeme Andrew Stewart, Jan Strube, and Vassil Vassilev. Potential of the Julia programming language for high energy physics computing. Computing and Software for Big Science, 7(1):??, December 2023. CODEN ???? ISSN 2510-2036 (print), 2510-2044 (electronic). URL

                                        https://link.springer.com/article/
                                        10.1007/s41781-023-00104-x.