{
  "_oai": {
    "updated": "2023-07-08T00:30:29Z", 
    "id": "oai:repo.scoap3.org:78797", 
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  "authors": [
    {
      "raw_name": "Jay Chan", 
      "affiliations": [
        {
          "country": "USA", 
          "value": "Department of Physics, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA"
        }, 
        {
          "country": "USA", 
          "value": "Physics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA"
        }
      ], 
      "surname": "Chan", 
      "given_names": "Jay", 
      "full_name": "Chan, Jay"
    }, 
    {
      "raw_name": "Benjamin Nachman", 
      "affiliations": [
        {
          "country": "USA", 
          "value": "Physics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA"
        }, 
        {
          "country": "USA", 
          "value": "Berkeley Institute for Data Science, University of California, Berkeley, California 94720, USA"
        }
      ], 
      "surname": "Nachman", 
      "given_names": "Benjamin", 
      "full_name": "Nachman, Benjamin"
    }
  ], 
  "titles": [
    {
      "source": "APS", 
      "title": "Unbinned profiled unfolding"
    }
  ], 
  "dois": [
    {
      "value": "10.1103/PhysRevD.108.016002"
    }
  ], 
  "publication_info": [
    {
      "journal_volume": "108", 
      "journal_title": "Physical Review D", 
      "material": "article", 
      "journal_issue": "1", 
      "year": 2023
    }
  ], 
  "$schema": "http://repo.scoap3.org/schemas/hep.json", 
  "acquisition_source": {
    "date": "2023-07-08T00:30:17.785995", 
    "source": "APS", 
    "method": "APS", 
    "submission_number": "929805d81d2611ee91ac6ee2827c7def"
  }, 
  "page_nr": [
    13
  ], 
  "license": [
    {
      "url": "https://creativecommons.org/licenses/by/4.0/", 
      "license": "CC-BY-4.0"
    }
  ], 
  "copyright": [
    {
      "statement": "Published by the American Physical Society", 
      "year": "2023"
    }
  ], 
  "control_number": "78797", 
  "record_creation_date": "2023-07-07T14:30:03.278684", 
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      "size": 878283
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  ], 
  "collections": [
    {
      "primary": "HEP"
    }, 
    {
      "primary": "Citeable"
    }, 
    {
      "primary": "Published"
    }
  ], 
  "arxiv_eprints": [
    {
      "categories": [
        "hep-ph", 
        "hep-ex", 
        "physics.data-an", 
        "stat.ML"
      ], 
      "value": "2302.05390"
    }
  ], 
  "abstracts": [
    {
      "source": "APS", 
      "value": "Unfolding is an important procedure in particle physics experiments that corrects for detector effects and provides differential cross section measurements that can be used for a number of downstream tasks, such as extracting fundamental physics parameters. Traditionally, unfolding is done by discretizing the target phase space into a finite number of bins and is limited in the number of unfolded variables. Recently, there have been a number of proposals to perform unbinned unfolding with machine learning. However, none of these methods (like most unfolding methods) allow for simultaneously constraining (profiling) nuisance parameters. We propose a new machine learning-based unfolding method that results in an unbinned differential cross section and can profile nuisance parameters. The machine learning loss function is the full likelihood function, based on binned inputs at detector level. We first demonstrate the method with simple Gaussian examples and then show the impact on a simulated Higgs boson cross section measurement."
    }
  ], 
  "imprints": [
    {
      "date": "2023-07-07", 
      "publisher": "APS"
    }
  ]
}