Paper Citation Record · LEDGER

SymbolFit: Automatic Parametric Modeling with Symbolic Regression

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 5 inbound Pith citation observations for arXiv:2411.09851.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.09851 v4

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:27:34.636830Z

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A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T03:06:28.991682Z

Reference resolution

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External citation measurements

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Outbound references

Observation 0f29005b-a529-4f4c-b1ad-99b4e355142f · outbound

This paper cites an unresolved cited work.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Unresolved cited work

Reference 1

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Observation aa721993-a68c-4808-98aa-57bf080524df · outbound

This paper cites & Tegmark, M.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression & Tegmark, M

Reference 2

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Observation 010fdf37-cc5e-40c7-a02c-a218bd0539b3 · outbound

This paper cites S., Liberzon, A.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression S., Liberzon, A

Reference 3

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Observation 1cbd9ef7-a159-4cd2-8d5e-d8b2c2c1c640 · outbound

This paper cites Contemporary Symbolic Regression Methods and their Relative Performance.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Contemporary Symbolic Regression Methods and their Relative Performance

Reference 4

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Observation 6546a447-789f-43cb-901f-ccd3849f768d · outbound

This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 5

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Observation 610f01b7-27ac-41a4-98a8-8f038b801420 · outbound

This paper cites SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression

Reference 6

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Unresolved cited work

Reference 7

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Unresolved cited work

Reference 8

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Unresolved cited work

Reference 9

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Observation 0528e5fe-a6d3-44cb-a47e-f5da65340e7d · outbound

This paper cites Rediscovering orbital mechanics with machine learning.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Rediscovering orbital mechanics with machine learning

Reference 10

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Observation 3b4d9bf9-5e5c-4f55-bf84-48675c8732c3 · outbound

This paper cites Modeling the galaxy-halo connection with machine learning.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Modeling the galaxy-halo connection with machine learning

Reference 11

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Observation b9dfca2f-4cf4-48ec-8e31-475353a78841 · outbound

This paper cites Symbolic Regression on FPGAs for Fast Machine Learning Inference.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Symbolic Regression on FPGAs for Fast Machine Learning Inference

Reference 12

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Observation e2cd8cd9-b79a-4262-b0de-413d87e13fc4 · outbound

This paper cites Back to the Formula -- LHC Edition.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Back to the Formula -- LHC Edition

Reference 13

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This paper cites Finding universal relations in subhalo properties with artificial intelligence.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Finding universal relations in subhalo properties with artificial intelligence

Reference 14

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This paper cites & Lipson, H.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression & Lipson, H

Reference 15

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This paper cites Genetic programming in Python, with a scikit-learn inspired API: gplearn (2016).

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Genetic programming in Python, with a scikit-learn inspired API: gplearn (2016)

Reference 16

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This paper cites & Kommenda, M.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression & Kommenda, M

Reference 17

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression & Bosman, P

Reference 18

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This paper cites Genetic programming as a means for programming computers by natural selection.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Genetic programming as a means for programming computers by natural selection

Reference 19

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This paper cites Search for narrow trijet resonances in proton-proton collisions at $\sqrt{s}$ = 13 TeV.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for narrow trijet resonances in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Reference 20

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Unresolved cited work

Reference 21

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This paper cites Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

Reference 22

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This paper cites Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC

Reference 23

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This paper cites Observation of a new boson with mass near 125 GeV in pp collisions at sqrt(s) = 7 and 8 TeV.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Observation of a new boson with mass near 125 GeV in pp collisions at sqrt(s) = 7 and 8 TeV

Reference 24

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s} =$ 13 TeV

Reference 25

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for resonant and nonresonant production of pairs of dijet resonances in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Reference 26

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for new physics in high-mass diphoton events from proton-proton collisions at $\sqrt{s}$ = 13 TeV

Reference 27

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for a high-mass dimuon resonance produced in association with b quark jets at $\sqrt{s}$ = 13 TeV

Reference 28

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV

Reference 29

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Reference 30

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Performance of reconstruction and identification of $\tau$ leptons decaying to hadrons and $\nu_\tau$ in pp collisions at $\sqrt{s}=$ 13 TeV

Reference 31

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s} =$ 13 TeV

Reference 32

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes

Reference 33

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This paper cites Model selection and signal extraction using Gaussian Process regression.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Model selection and signal extraction using Gaussian Process regression

Reference 34

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression A Measurement of Boosted Dibosons with Gaussian Process Background Modeling at the ATLAS Detector (2024)

Reference 35

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression P., Gulian, M., Frankel, A

Reference 36

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression The CMS statistical analysis and combination tool: COMBINE

Reference 38

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression & Stark, G

Reference 39

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression & Cranmer, K

Reference 40

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Handling uncertainties in background shapes: the discrete profiling method

Reference 41

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at √s = 13 TeV

Reference 42

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for new physics in high-mass diphoton events from proton-proton collisions at √s = 13 TeV

Reference 43

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Observation 8a7c103a-1b3c-40ca-81d2-9735bf053b3d · outbound

This paper cites Search for narrow trijet resonances in proton-proton collisions at √s = 13 TeV.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for narrow trijet resonances in proton-proton collisions at √s = 13 TeV

Reference 44

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Observation de3db0b2-ca13-4640-ba41-ecfed7f4f556 · outbound

This paper cites Search for resonant and nonresonant production of pairs of dijet resonances in proton-proton collisions at √s = 13 TeV.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for resonant and nonresonant production of pairs of dijet resonances in proton-proton collisions at √s = 13 TeV

Reference 45

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SymbolFit: Automatic Parametric Modeling with Symbolic Regression Search for a high-mass dimuon resonance produced in association with b quark jets at √s=13 TeV

Reference 46

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Pith citing papers

Observation 7130f7c9-f2ac-48b0-bb70-4f45eca7bc04 · inbound

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes cites this paper.

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes SymbolFit: Automatic Parametric Modeling with Symbolic Regression

Reference 34

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Observation ab002030-c398-4894-8f0c-f8e693e78d11 · inbound

$\mathcal{CP}$-Analyses with Symbolic Regression cites this paper.

$\mathcal{CP}$-Analyses with Symbolic Regression SymbolFit: Automatic Parametric Modeling with Symbolic Regression

Reference 27

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Toward Supporting Narrative-Driven Data Exploration: Barriers and Design Opportunities cites this paper.

Toward Supporting Narrative-Driven Data Exploration: Barriers and Design Opportunities SymbolFit: Automatic Parametric Modeling with Symbolic Regression

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Modeling Falling Backgrounds with Exponential Mixtures cites this paper.

Modeling Falling Backgrounds with Exponential Mixtures SymbolFit: Automatic Parametric Modeling with Symbolic Regression

Reference 4

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Observation 08e46b4a-42f0-465e-aac6-c93adb538310 · inbound

Machine Can Automatically Discover Parametric Functions to Model HEP Data cites this paper.

Machine Can Automatically Discover Parametric Functions to Model HEP Data SymbolFit: Automatic Parametric Modeling with Symbolic Regression

Reference 12

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