View in EDS HTML Full Text PDF Full Text

A practical guide to unbinned unfolding.

Saved in:
Bibliographic Details
Title: A practical guide to unbinned unfolding.
Authors: Canelli, Florencia1 (AUTHOR), Cormier, Kyle1 (AUTHOR), Cudd, Andrew2 (AUTHOR), Gillberg, Dag3 (AUTHOR), Huang, Roger G.4 (AUTHOR), Jin, Weijie1 (AUTHOR), Lee, Sookhyun5 (AUTHOR), Mikuni, Vinicius6 (AUTHOR), Miller, Laura7 (AUTHOR), Nachman, Benjamin4,8,9 (AUTHOR), Pan, Jingjing4,10 (AUTHOR), Pani, Tanmay11 (AUTHOR), Pettee, Mariel12 (AUTHOR) mpettee@wisc.edu, Song, Youqi10 (AUTHOR), Acosta, Fernando Torales13 (AUTHOR)
Source: European Physical Journal C -- Particles & Fields. Feb2026, Vol. 86 Issue 2, p1-11. 11p.
Subjects: Deconvolution (Mathematics), Machine learning, Measurement errors, Data analysis, Acquisition of data, Particle physics
Abstract: Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data. [ABSTRACT FROM AUTHOR]
Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 192428936
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A practical guide to unbinned unfolding.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Canelli%2C+Florencia%22">Canelli, Florencia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cormier%2C+Kyle%22">Cormier, Kyle</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cudd%2C+Andrew%22">Cudd, Andrew</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gillberg%2C+Dag%22">Gillberg, Dag</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Roger+G%2E%22">Huang, Roger G.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Weijie%22">Jin, Weijie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Sookhyun%22">Lee, Sookhyun</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mikuni%2C+Vinicius%22">Mikuni, Vinicius</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miller%2C+Laura%22">Miller, Laura</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nachman%2C+Benjamin%22">Nachman, Benjamin</searchLink><relatesTo>4,8,9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Jingjing%22">Pan, Jingjing</searchLink><relatesTo>4,10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pani%2C+Tanmay%22">Pani, Tanmay</searchLink><relatesTo>11</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pettee%2C+Mariel%22">Pettee, Mariel</searchLink><relatesTo>12</relatesTo> (AUTHOR)<i> mpettee@wisc.edu</i><br /><searchLink fieldCode="AR" term="%22Song%2C+Youqi%22">Song, Youqi</searchLink><relatesTo>10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Acosta%2C+Fernando+Torales%22">Acosta, Fernando Torales</searchLink><relatesTo>13</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22European+Physical+Journal+C+--+Particles+%26+Fields%22">European Physical Journal C -- Particles & Fields</searchLink>. Feb2026, Vol. 86 Issue 2, p1-11. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Deconvolution+%28Mathematics%29%22">Deconvolution (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+errors%22">Measurement errors</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+physics%22">Particle physics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192428936
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1140/epjc/s10052-025-15265-9
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1
    Subjects:
      – SubjectFull: Deconvolution (Mathematics)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Measurement errors
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Acquisition of data
        Type: general
      – SubjectFull: Particle physics
        Type: general
    Titles:
      – TitleFull: A practical guide to unbinned unfolding.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Canelli, Florencia
      – PersonEntity:
          Name:
            NameFull: Cormier, Kyle
      – PersonEntity:
          Name:
            NameFull: Cudd, Andrew
      – PersonEntity:
          Name:
            NameFull: Gillberg, Dag
      – PersonEntity:
          Name:
            NameFull: Huang, Roger G.
      – PersonEntity:
          Name:
            NameFull: Jin, Weijie
      – PersonEntity:
          Name:
            NameFull: Lee, Sookhyun
      – PersonEntity:
          Name:
            NameFull: Mikuni, Vinicius
      – PersonEntity:
          Name:
            NameFull: Miller, Laura
      – PersonEntity:
          Name:
            NameFull: Nachman, Benjamin
      – PersonEntity:
          Name:
            NameFull: Pan, Jingjing
      – PersonEntity:
          Name:
            NameFull: Pani, Tanmay
      – PersonEntity:
          Name:
            NameFull: Pettee, Mariel
      – PersonEntity:
          Name:
            NameFull: Song, Youqi
      – PersonEntity:
          Name:
            NameFull: Acosta, Fernando Torales
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 14346044
          Numbering:
            – Type: volume
              Value: 86
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: European Physical Journal C -- Particles & Fields
              Type: main
ResultId 1