Computing and Software for Big Science

Abstract This paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine . However, the online documentation referenced within this paper provides an up-to-date and complete user guide.

A. Hayrapetyan

A. Tumasyan

W. Adam

J. W. Andrejkovic

T. Bergauer

S. Chatterjee

K. Damanakis

M. Dragicevic

P. S. Hussain

M. Jeitler

N. Krammer

A. Li

D. Liko

I. Mikulec

J. Schieck

R. Schöfbeck

D. Schwarz

M. Sonawane

S. Templ

W. Waltenberger

C.-E. Wulz

M. R. Darwish

T. Janssen

P. Van Mechelen

N. Breugelmans

J. D’Hondt

S. Dansana

A. De Moor

M. Delcourt

F. Heyen

S. Lowette

I. Makarenko

D. Müller

S. Tavernier

M. Tytgat

G. P. Van Onsem

S. Van Putte

D. Vannerom

B. Clerbaux

A. K. Das

G. De Lentdecker

H. Evard

L. Favart

P. Gianneios

D. Hohov

J. Jaramillo

A. Khalilzadeh

F. A. Khan

K. Lee

M. Mahdavikhorrami

A. Malara

S. Paredes

M. A. Shahzad

L. Thomas

M. Vanden Bemden

C. Vander Velde

P. Vanlaer

M. De Coen

D. Dobur

G. Gokbulut

Y. Hong

J. Knolle

L. Lambrecht

D. Marckx

G. Mestdach

K. Mota Amarilo

A. Samalan

K. Skovpen

N. Van Den Bossche

J. van der Linden

L. Wezenbeek

A. Benecke

A. Bethani

G. Bruno

C. Caputo

J. De Favereau De Jeneret

C. Delaere

I. S. Donertas

A. Giammanco

A. O. Guzel

Sa Jain

V. Lemaitre

J. Lidrych

P. Mastrapasqua

T. T. Tran

S. Wertz

G. A. Alves

M. Alves Gallo Pereira

E. Coelho

G. Correia Silva

C. Hensel

T. Menezes De Oliveira

A. Moraes

P. Rebello Teles

M. Soeiro

A. Vilela Pereira

W. L. Aldá Júnior

M. Barroso Ferreira Filho

H. Brandao Malbouisson

W. Carvalho

The CMS Statistical Analysis and Combination Tool: Combine

CMS统计分析与组合工具：Combine

A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further analysis. Typically, these workflows use distributed storage and computing resources. A straightforward setup of storage media would be low-cost tape storage and higher-cost disk storage. The large, infrequently accessed input data are stored on tape storage. The smaller, frequently accessed derived data is stored on disk storage. In a best-case scenario, the large input data is only accessed very infrequently and in a well-planned pattern. However, practice shows that often the data has to be processed continuously and unpredictably. This can significantly reduce tape storage performance. A common approach to counter this is storing copies of the large input data on disk storage. This contribution evaluates an approach that uses cloud storage resources to serve as a flexible cache or buffer, depending on the computational workflow. The proposed model is explored for the case of continuously processed data. For the evaluation, a simulation tool was developed, which can be used to analyse models related to storage and network resources. We show that using commercial cloud storage can reduce on-premises disk storage requirements, while maintaining an equal throughput of jobs. Moreover, the key metrics of the model are discussed, and an approach is described, which uses the simulation to assist with the decision process of using commercial cloud storage. The goal is to investigate approaches and propose new evaluation methods to overcome future data challenges.

Tobias Wegner

Mario Lassnig

Peer Ueberholz

Christian Zeitnitz

Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science

数据密集型科学中的云存储缓存模拟与评估

Abstract In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. The reconstruction workchain consists of converting digital detector signals into physical high-level quantities. We address the first step in reconstruction, namely raw data denoising, leveraging deep learning algorithms. We design two architectures based on graph neural networks, aiming to enhance the receptive field of basic convolutional neural networks. We benchmark this approach against traditional algorithms implemented by the DUNE collaboration. We test the capabilities of graph neural network hardware accelerator setups to speed up training and inference processes.

Marco Rossi

Sofia Vallecorsa

Deep Learning Strategies for ProtoDUNE Raw Data Denoising

用于ProtoDUNE原始数据去噪的深度学习策略

Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.

G. Aad

B. Abbott

D. C. Abbott

A. Abed Abud

K. Abeling

D. K. Abhayasinghe

S. H. Abidi

O. S. AbouZeid

N. L. Abraham

H. Abramowicz

H. Abreu

Y. Abulaiti

A. C. Abusleme Hoffman

B. S. Acharya

B. Achkar

L. Adam

C. Adam Bourdarios

L. Adamczyk

L. Adamek

J. Adelman

A. Adiguzel

S. Adorni

T. Adye

A. A. Affolder

Y. Afik

C. Agapopoulou

M. N. Agaras

A. Aggarwal

C. Agheorghiesei

J. A. Aguilar-Saavedra

A. Ahmad

F. Ahmadov

W. S. Ahmed

X. Ai

G. Aielli

S. Akatsuka

M. Akbiyik

T. P. A. Åkesson

E. Akilli

A. V. Akimov

K. Al Khoury

G. L. Alberghi

J. Albert

M. J. Alconada Verzini

S. Alderweireldt

M. Aleksa

I. N. Aleksandrov

C. Alexa

T. Alexopoulos

A. Alfonsi

F. Alfonsi

M. Alhroob

B. Ali

S. Ali

M. Aliev

G. Alimonti

C. Allaire

B. M. M. Allbrooke

P. P. Allport

A. Aloisio

F. Alonso

C. Alpigiani

E. Alunno Camelia

M. Alvarez Estevez

M. G. Alviggi

Y. Amaral Coutinho

A. Ambler

L. Ambroz

C. Amelung

D. Amidei

S. P. Amor Dos Santos

S. Amoroso

C. S. Amrouche

C. Anastopoulos

N. Andari

T. Andeen

J. K. Anders

S. Y. Andrean

A. Andreazza

V. Andrei

C. R. Anelli

S. Angelidakis

A. Angerami

A. V. Anisenkov

Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

通过预采样非弹性蒙特卡罗事件集模拟ATLAS模拟中额外质子-质子相互作用的影响

ISSN	2510-2036, 2510-2044
h-index	20

出版信息	出版商: Springer Nature，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2017，原创研究文献占比: ，自引率:， Gold OA占比:

Computing and Software for Big Science

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The CMS Statistical Analysis and Combination Tool: Combine

Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science

Deep Learning Strategies for ProtoDUNE Raw Data Denoising

Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

Accelerating IceCube’s Photon Propagation Code with CUDA

The CMS Statistical Analysis and Combination Tool: Combine

4D Track Reconstruction on Free-Streaming Data with PANDA at FAIR

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

Quantum-Annealing-Inspired Algorithms for Track Reconstruction at High-Energy Colliders

One Flow to Correct Them all: Improving Simulations in High-Energy Physics with a Single Normalising Flow and a Switch