Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network | Paper Citation Record

Observation 6adcea87-932c-4967-9309-225019001592 · outbound

This paper cites Zwicky, Helv.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Zwicky, Helv

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:54:18.734394Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 13a356d3-5419-4f90-819c-31a5d60c8c53 · outbound

This paper cites an unresolved cited work.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-05T04:54:18.725589Z

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Observation c637eaae-e9b2-465a-a4f3-2561801b1ac0 · outbound

This paper cites The Extended Rotation Curve and the Dark Matter Halo of M33.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The Extended Rotation Curve and the Dark Matter Halo of M33

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:11.933381Z

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Observation 42081276-a884-4947-8537-2991f10e49b0 · outbound

This paper cites A direct empirical proof of the existence of dark matter.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A direct empirical proof of the existence of dark matter

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.022471Z

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Observation bbb680c8-1b99-4bd2-b3e6-5d726f6a9f4c · outbound

This paper cites Planck 2018 results. VI. Cosmological parameters.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Planck 2018 results. VI. Cosmological parameters

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.109491Z

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Observation f37d298a-a9d3-4f60-9e9a-82bbfe011953 · outbound

This paper cites Particle Dark Matter: Evidence, Candidates and Constraints.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Particle Dark Matter: Evidence, Candidates and Constraints

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.197354Z

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Observation b60f57c6-7f3e-440b-aabf-4a06fe9e8016 · outbound

This paper cites Search for dark matter at colliders.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Search for dark matter at colliders

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.281916Z

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Observation 2675b926-654f-4752-849a-46049dae5470 · outbound

This paper cites Dunjko and H.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Dunjko and H

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:54:18.716659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 050793c2-b243-4f92-9296-e4e2b418b352 · outbound

This paper cites Krenn, M.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Krenn, M

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:54:18.707742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2a5a798d-6c20-48f3-b015-c5c2742bcfd7 · outbound

This paper cites Mutual Information, Neural Networks and the Renormalization Group.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Mutual Information, Neural Networks and the Renormalization Group

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.486577Z

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Observation aa9caab1-caac-46ae-bbfd-3fc3d0080305 · outbound

This paper cites A Living Review of Machine Learning for Particle Physics.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A Living Review of Machine Learning for Particle Physics

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.538820Z

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Observation a6c1607c-6a2a-406d-bd64-13a2607f9058 · outbound

This paper cites Collide and Conquer: Constraints on Simplified Dark Matter Models using Mono-X Collider Searches.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Collide and Conquer: Constraints on Simplified Dark Matter Models using Mono-X Collider Searches

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.616069Z

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Observation 7161142e-da56-492f-9697-0315ee4eb256 · outbound

This paper cites Mono-X Versus Direct Searches: Simplified Models for Dark Matter at the LHC.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Mono-X Versus Direct Searches: Simplified Models for Dark Matter at the LHC

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.622798Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fb508938-ee79-457e-a0de-d529fc9fc29c · outbound

This paper cites Actual Physics behind Mono-X.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Actual Physics behind Mono-X

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.610681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 24d9a2cb-bd07-4d62-9cda-0c4524e6db2b · outbound

This paper cites The frontier of simulation-based inference.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The frontier of simulation-based inference

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.862799Z

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Observation be5ae9ae-4c9a-4229-aae0-783928b379b9 · outbound

This paper cites Simulation-based inference methods for particle physics.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Simulation-based inference methods for particle physics

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.945575Z

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Observation 7d7d3d11-d67b-440f-a703-17053dc888dd · outbound

This paper cites Using Machine Learning to disentangle LHC signatures of Dark Matter candidates.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Using Machine Learning to disentangle LHC signatures of Dark Matter candidates

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.580645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 88d10208-d27e-4386-9dcf-2eec1f956b08 · outbound

This paper cites Towards a method to anticipate dark matter signals with deep learning at the LHC.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Towards a method to anticipate dark matter signals with deep learning at the LHC

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:13.084275Z

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Observation 09cb89ee-ee77-41c0-addc-96a7a036a961 · outbound

This paper cites Celik, Eur.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Celik, Eur

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T04:54:18.699368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2af45f02-17fb-444f-83de-b17d6294a297 · outbound

This paper cites Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.560393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5d6a368a-df38-4047-80d7-82912afdf1b1 · outbound

This paper cites Boosting mono-jet searches with model-agnostic machine learning.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Boosting mono-jet searches with model-agnostic machine learning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.547860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0e7cc49b-2734-4ea2-b4db-3623b10896c9 · outbound

This paper cites Physics potential and experimental challenges of the LHC luminosity upgrade.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Physics potential and experimental challenges of the LHC luminosity upgrade

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:13.398030Z

Source-reported events for the cited work

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Observation 74b1bbcd-6268-4003-960a-f4f00cfb7875 · outbound

This paper cites Two-Component Dark Matter.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Two-Component Dark Matter

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:13.460609Z

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Observation c18c6bb3-f6d8-4eec-8317-bc0b116da784 · outbound

This paper cites A minimal model for two-component dark matter.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A minimal model for two-component dark matter

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:13.525415Z

Source-reported events for the cited work

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Observation 14dbf398-84bf-40f6-b1af-652633469770 · outbound

This paper cites Implications of Two-component Dark Matter Induced by Forbidden Channels and Thermal Freeze-out.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Implications of Two-component Dark Matter Induced by Forbidden Channels and Thermal Freeze-out

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:13.564515Z

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Observation 996b5690-381c-4cc0-9ca0-d17c315a7dfe · outbound

This paper cites A Minimal Model For Two-Component FIMP Dark Matter: A Basic Search.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A Minimal Model For Two-Component FIMP Dark Matter: A Basic Search

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.502524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c8a438c-1150-45de-af24-b08dbfebb8f5 · outbound

This paper cites Two-component dark matter and a massless neutrino in a new B-L model.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Two-component dark matter and a massless neutrino in a new B-L model

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.490041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f4164e7-0f39-4224-9d72-2db320d87162 · outbound

This paper cites Minimal Two-component Scalar Doublet Dark Matter with Radiative Neutrino Mass.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Minimal Two-component Scalar Doublet Dark Matter with Radiative Neutrino Mass

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.477629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d12afce8-bcd2-4f39-ba62-bb2042a895eb · outbound

This paper cites The $Z_3$ symmetric I(2+1)HDM.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The $Z_3$ symmetric I(2+1)HDM

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:13.868244Z

Source-reported events for the cited work

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Observation 202e5f23-a9dd-4233-b695-8c9e30cd88c2 · outbound

This paper cites The $Z_5$ model of two-component dark matter.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The $Z_5$ model of two-component dark matter

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.456892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ff4ec12d-9bae-43f7-8be6-edae9f05594a · outbound

This paper cites Multi-component Dark Matter in a Simplified E$_6$SSM Model.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Multi-component Dark Matter in a Simplified E$_6$SSM Model

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.445264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e975d58e-af98-481c-ad4b-8ffe32cdf157 · outbound

This paper cites Two Component Doublet-Triplet Scalar Dark Matter stabilising the Electroweak vacuum.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Two Component Doublet-Triplet Scalar Dark Matter stabilising the Electroweak vacuum

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.433356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4381fa90-fce0-4db9-a78d-0dc702de0544 · outbound

This paper cites Fermion and scalar two-component dark matter from a $Z_4$ symmetry.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Fermion and scalar two-component dark matter from a $Z_4$ symmetry

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.421418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 17d6b9f9-e1a9-470c-acb3-ede4b1c0e16d · outbound

This paper cites The $Z_3$ soft breaking in the I(2+1)HDM and its probes at present and future colliders.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The $Z_3$ soft breaking in the I(2+1)HDM and its probes at present and future colliders

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.407919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa451f8f-7f37-4017-8e78-d0098c15b08c · outbound

This paper cites Complementary Probes of Two-component Dark Matter.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Complementary Probes of Two-component Dark Matter

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:14.308758Z

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Observation 5b664b75-595c-4c82-90c3-f88bf996d0aa · outbound

This paper cites Complementary collider and astrophysical probes of multi-component Dark Matter.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Complementary collider and astrophysical probes of multi-component Dark Matter

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:14.383383Z

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Observation 0e48a3a3-26ef-4e7e-81e2-0cfcdec3fefd · outbound

This paper cites A combined approach to the analysis of space and ground experimental data within a simplified E6SSM.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A combined approach to the analysis of space and ground experimental data within a simplified E6SSM

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.377788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0f94bbd1-82b2-4742-9365-52c004ff84e9 · outbound

This paper cites Multi-Component Dark Matter Systems and Their Observation Prospects.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Multi-Component Dark Matter Systems and Their Observation Prospects

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.327610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 527d2406-d89f-43fb-8d03-085115d947e9 · outbound

This paper cites Multi-component scalar dark matter from a $Z_N$ symmetry: a systematic analysis.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Multi-component scalar dark matter from a $Z_N$ symmetry: a systematic analysis

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:18.020861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2251bf5e-bbc5-4a34-aa57-6dd2c5878b3c · outbound

This paper cites The $Z_7$ model of three-component scalar dark matter.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The $Z_7$ model of three-component scalar dark matter

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:14.760960Z

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Observation 76b400b0-7f06-4f1f-ae6b-833d31fb7a28 · outbound

This paper cites Search for the Standard Model Higgs Boson at LEP.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Search for the Standard Model Higgs Boson at LEP

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:14.870814Z

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Observation 2e9b2cb2-9aef-4523-92a8-af8f405498ec · outbound

This paper cites Combined measurements of Higgs boson production and decay using up to $80$ fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=$ 13 TeV collected with the ATLAS experiment.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Combined measurements of Higgs boson production and decay using up to $80$ fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=$ 13 TeV collected with the ATLAS experiment

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:14.962715Z

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Observation 3bf387ed-69a1-43d1-b494-1b37da0aa119 · outbound

This paper cites A portrait of the Higgs boson by the CMS experiment ten years after the discovery.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A portrait of the Higgs boson by the CMS experiment ten years after the discovery

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.066452Z

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Observation 5bfc23d1-17d1-4dfb-be70-52295741a9f9 · outbound

This paper cites Dark Matter Spin Characterisation in Mono-$Z$ Channels.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Dark Matter Spin Characterisation in Mono-$Z$ Channels

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-05T04:54:17.771323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7f6f9431-7590-45d3-8e81-d352d6d618bf · outbound

This paper cites Sarah.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Sarah

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.132268Z

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Observation fc1a5877-367a-4ec9-870e-cd96e1570c91 · outbound

This paper cites SARAH 3.2: Dirac Gauginos, UFO output, and more.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network SARAH 3.2: Dirac Gauginos, UFO output, and more

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.232746Z

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Observation 41061f6d-a547-446c-a375-7cbb95ad3c89 · outbound

This paper cites SARAH 4: A tool for (not only SUSY) model builders.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network SARAH 4: A tool for (not only SUSY) model builders

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.374485Z

Source-reported events for the cited work

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Observation 6b59f245-f032-4ffe-9191-ff1f785e4f35 · outbound

This paper cites SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.584247Z

Source-reported events for the cited work

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Observation 6c07c477-fe32-4cdf-a523-c6d0d508b31f · outbound

This paper cites SPheno 3.1: extensions including flavour, CP-phases and models beyond the MSSM.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network SPheno 3.1: extensions including flavour, CP-phases and models beyond the MSSM

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.709770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f21634ee-486e-43c4-b196-a5b393d48f12 · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.814392Z

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Observation f51c55dd-fd5b-4aeb-89da-229d36ce2d52 · outbound

This paper cites A Brief Introduction to PYTHIA 8.1.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A Brief Introduction to PYTHIA 8.1

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:15.911656Z

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Observation 289628c4-d991-4358-bb5a-0718498f2c9b · outbound

This paper cites An Introduction to PYTHIA 8.2.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network An Introduction to PYTHIA 8.2

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.111686Z

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Observation 2720a525-e839-45c7-abbc-e681cf1ce16c · outbound

This paper cites DELPHES 3, A modular framework for fast simulation of a generic collider experiment.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network DELPHES 3, A modular framework for fast simulation of a generic collider experiment

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.210801Z

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Observation 9a66f150-4536-4107-ae34-e455194a15fa · outbound

This paper cites MadAnalysis 5, a user-friendly framework for collider phenomenology.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network MadAnalysis 5, a user-friendly framework for collider phenomenology

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.284140Z

Source-reported events for the cited work

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Observation 478295c1-ce19-45b0-b56b-4b20db9f3e02 · outbound

This paper cites Mining gold from implicit models to improve likelihood-free inference.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Mining gold from implicit models to improve likelihood-free inference

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.422478Z

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Observation edf9b6a8-3842-41be-9fc1-5e1c9a767e21 · outbound

This paper cites Classifying Anomalies THrough Outer Density Estimation (CATHODE).

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Classifying Anomalies THrough Outer Density Estimation (CATHODE)

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.592827Z

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Observation c32cf430-e5ac-4979-b408-f4bf49715450 · outbound

This paper cites E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.741091Z

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Observation 32562933-288f-47cd-b3ce-85ff6247ce40 · outbound

This paper cites Hierarchical Neural Simulation-Based Inference Over Event Ensembles.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Hierarchical Neural Simulation-Based Inference Over Event Ensembles

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.789476Z

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Observation 04e171f1-7fa3-4cad-93ba-86467de04c53 · outbound

This paper cites Classification without labels: Learning from mixed samples in high energy physics.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Classification without labels: Learning from mixed samples in high energy physics

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.949254Z

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Observation f6bb3b57-826f-4bfc-85fe-51af124988a7 · outbound

This paper cites Anomaly Detection with Density Estimation.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Anomaly Detection with Density Estimation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:17.044794Z

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Observation b98f03af-8cad-491c-9905-14c5b6904a00 · outbound

This paper cites Normalizing Flows for Probabilistic Modeling and Inference.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Normalizing Flows for Probabilistic Modeling and Inference

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:17.209853Z

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Observation fcbbc701-faab-41e9-9711-3c7732b07caf · outbound

This paper cites Masked Autoregressive Flow for Density Estimation.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network Masked Autoregressive Flow for Density Estimation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:17.326213Z

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