Paper Citation Record · LEDGER

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments

As of 11 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2508.16518.

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

pith.paper-citation-record.v1
2508.16518 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:24:40.364545Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy27
unresolved6
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a78458d-c3bc-44d9-8921-e317576a26be · outbound

This paper cites Observation of a new particle in the search for the standard model higgs boson with the at- las detector at the lhc.Physics Letters B, 716(1):1–29, 2012.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Observation of a new particle in the search for the standard model higgs boson with the at- las detector at the lhc.Physics Letters B, 716(1):1–29, 2012

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.513560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aff580a2-89b5-4d7e-a0a1-b16dbf17e1fb · outbound

This paper cites Observation of a new boson at a mass of 125 gev with the cms experiment at the lhc.Physics Letters B, 716(1):30–61, 2012.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Observation of a new boson at a mass of 125 gev with the cms experiment at the lhc.Physics Letters B, 716(1):30–61, 2012

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.504445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec46852f-d1ea-45a5-bec5-1c0d071df0e3 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:24:41.494902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 197598b2-c931-4f9e-83ba-1113076508bf · outbound

This paper cites Expected tracking performance of the atlas inner trackeratthehigh-luminositylhc.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Expected tracking performance of the atlas inner trackeratthehigh-luminositylhc

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.485045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 63349705-bad2-4055-8d45-01006870af1c · outbound

This paper cites Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.474883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bfc62f65-fbd6-44d9-8ab8-6475ba00faae · outbound

This paper cites Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T17:24:38.148743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ded75340-d1e9-4582-9983-cffc89299f2b · outbound

This paper cites Towards a realistic track reconstruction algorithm based on graph neural networks for the hl-lhc.EPJ Web Conf., 251:03047, 2021.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Towards a realistic track reconstruction algorithm based on graph neural networks for the hl-lhc.EPJ Web Conf., 251:03047, 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.464955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4784d8af-5421-4d5a-9268-4162cd84204f · outbound

This paper cites ATLAS ITk Track Reconstruction with a GNN-based pipeline.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments ATLAS ITk Track Reconstruction with a GNN-based pipeline

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.454724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 127f7252-96d7-408f-a961-987c51281705 · outbound

This paper cites Acorn - a charged object reconstruc- tion network.https://gitlab.cern.ch/ gnn4itkteam/acorn/.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Acorn - a charged object reconstruc- tion network.https://gitlab.cern.ch/ gnn4itkteam/acorn/

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.445097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd011b34-42f1-429a-9b58-4db224a31d22 · outbound

This paper cites ATLAS Soft- ware and Computing HL-LHC Roadmap.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments ATLAS Soft- ware and Computing HL-LHC Roadmap

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.434948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 082ff794-0a8f-466b-a526-e41948b8ef75 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning, 2016.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Dropout as a bayesian approximation: Representing model uncertainty in deep learning, 2016

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T17:24:38.522580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20d1f645-aa3c-4f9b-ae26-e6e614653925 · outbound

This paper cites Trackml particle tracking challenge.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Trackml particle tracking challenge

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.419275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e81f798-1cff-4345-a61f-81f001d381ac · outbound

This paper cites Axiomatic attribution for deep networks, 2017.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Axiomatic attribution for deep networks, 2017

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T17:24:38.724008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3db4e02f-ae6b-401b-aeae-f3ec96de1a0b · outbound

This paper cites Fatal crash between a car operating with auto- mated control systems and a tractor- semitrailer truck.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Fatal crash between a car operating with auto- mated control systems and a tractor- semitrailer truck

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.402438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a784c3a2-32a1-472e-b52b-0dc87552479d · outbound

This paper cites Bayesian neural networks, 2020.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Bayesian neural networks, 2020

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.392744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 37fe19a0-3dbb-43ab-ab93-efd6dc674350 · outbound

This paper cites Maximizing overall di- versity for improved uncertainty estimates in deep ensembles, 2020.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Maximizing overall di- versity for improved uncertainty estimates in deep ensembles, 2020

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.373007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dcb6b2c9-9938-46ce-ae96-c29f4639ea46 · outbound

This paper cites Evidential deep learning to quantify classification uncertainty, 2018.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Evidential deep learning to quantify classification uncertainty, 2018

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.360030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1690e456-a5cc-49fa-9f2d-72e4265762ec · outbound

This paper cites Marius Zöllner.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Marius Zöllner

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.350682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 61f63428-c3d7-4067-a811-d7e2f8775379 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:24:41.341719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 927e8f51-ea25-4e5c-af9b-d202a545a66c · outbound

This paper cites An imple- mentation of neural simulation-based in- ference for parameter estimation in at- las.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments An imple- mentation of neural simulation-based in- ference for parameter estimation in at- las

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.332923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a3e0f66c-4bcb-4c21-b247-ea8508b89ad6 · outbound

This paper cites Deep neural network un- certainty quantification for lartpc recon- struction, 2023.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Deep neural network un- certainty quantification for lartpc recon- struction, 2023

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.323683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c14f3eac-8baa-4fde-a702-96a6377168b4 · outbound

This paper cites Evidential deep learning for uncertainty quantification and out-of- distribution detection in jet identification using deep neural networks.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Evidential deep learning for uncertainty quantification and out-of- distribution detection in jet identification using deep neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.314681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bf480816-69d7-4edd-a809-8f62b878da4f · outbound

This paper cites Chained machine learning model for predicting load capac- ity and ductility of steel fiber–reinforced 16 concrete beams.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Chained machine learning model for predicting load capac- ity and ductility of steel fiber–reinforced 16 concrete beams

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.305075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d1ca3c2e-06f3-4932-aa00-4b61944f2279 · outbound

This paper cites Multi- phase flow rate prediction using chained multi-output regression models.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Multi- phase flow rate prediction using chained multi-output regression models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.295072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bc60b95c-f4ea-498f-a4a5-a1483ea76374 · outbound

This paper cites Com- paring a composite model versus chained modelstolocateanearestvisualobject.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Com- paring a composite model versus chained modelstolocateanearestvisualobject

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.285654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f96a1b0-446d-49b5-beb7-5ce349adf316 · outbound

This paper cites Fast dropout training.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Fast dropout training

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.276000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e42e67f9-5b26-4f73-96bc-5af7ff4c6cc6 · outbound

This paper cites Un- certainty quantification via stable distribution propagation, 2024.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Un- certainty quantification via stable distribution propagation, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.266443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c6ce771-f80d-4407-8ae3-7701d5f10377 · outbound

This paper cites Uncertainty propagation within chained models for machine learning re- construction of neutrino-lar interactions, 2025.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Uncertainty propagation within chained models for machine learning re- construction of neutrino-lar interactions, 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.256348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f06b36f8-da74-45a1-8bcc-59b557675046 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:24:41.244966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d5d78294-e4ea-4241-85ef-cd785eeffd8d · outbound

This paper cites Acommontrackingsoftwareproject.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Acommontrackingsoftwareproject

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.234910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9ed48b2-e559-4f68-817f-847ef9c51108 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Unresolved cited work

Reference 31

Resolution: malformed identifier; no resolver link, observed 2026-08-05T17:24:39.996214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a673788e-3871-4710-99f4-275773a16fcb · outbound

This paper cites Uncertainty in Deep Learn- ing.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Uncertainty in Deep Learn- ing

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:41.024656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb6939d4-e445-4a21-81d4-df2bf100cd57 · outbound

This paper cites Wimmer, Y.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Wimmer, Y

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:40.847957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 37576be4-68f2-453c-a8dd-2854dbbf8fb4 · outbound

This paper cites Nixon, M.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Nixon, M

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:24:40.617941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.