Paper Citation Record · LEDGER

Deep learning detection of transients (ICRC-2019)

As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:1908.01615.

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

pith.paper-citation-record.v1
1908.01615 v1

Coverage vector

measured 32 of 32 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 8f663728-f9b7-42cd-9e40-de092f7bc541 · outbound

This paper cites ANNz2 - photometric redshift and probability distribution function estimation using machine learning.

Deep learning detection of transients (ICRC-2019) ANNz2 - photometric redshift and probability distribution function estimation using machine learning

Reference 1

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Observation ea4eb3d4-60ed-4cf1-b2d1-7975d63333ca · outbound

This paper cites Improved $\gamma$/hadron separation for the detection of faint gamma-ray sources using boosted decision trees.

Deep learning detection of transients (ICRC-2019) Improved $\gamma$/hadron separation for the detection of faint gamma-ray sources using boosted decision trees

Reference 2

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Observation 5e9b469b-f629-4cc9-a5e2-86ef58b4151b · outbound

This paper cites In: MNRAS 476 (May 2018), pp.

Deep learning detection of transients (ICRC-2019) In: MNRAS 476 (May 2018), pp

Reference 3

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Observation 0e89cc2e-6e22-4b95-a6fc-bd8f6ab65fca · outbound

This paper cites In: APS April Meeting Abstracts.

Deep learning detection of transients (ICRC-2019) In: APS April Meeting Abstracts

Reference 4

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Observation 4f404199-0c82-4d54-883b-20912147d8c6 · outbound

This paper cites Domínguez Sánchez et al.

Deep learning detection of transients (ICRC-2019) Domínguez Sánchez et al

Reference 5

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Observation 7bd75993-19c6-4066-9eba-3ea5bf8503ff · outbound

This paper cites Effective Image Differencing with ConvNets for Real-time Transient Hunting.

Deep learning detection of transients (ICRC-2019) Effective Image Differencing with ConvNets for Real-time Transient Hunting

Reference 6

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This paper cites an unresolved cited work.

Deep learning detection of transients (ICRC-2019) Unresolved cited work

Reference 7

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Observation d4b2f7de-fc2e-4a97-bb65-58f67c68d9c1 · outbound

This paper cites Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders.

Deep learning detection of transients (ICRC-2019) Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders

Reference 8

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This paper cites In: Nature 521 (May 2015), 436 EP –.

Deep learning detection of transients (ICRC-2019) In: Nature 521 (May 2015), 436 EP –

Reference 9

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Deep learning detection of transients (ICRC-2019) Unresolved cited work

Reference 10

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Observation 764bddb7-38d0-43c7-9d3b-7d04368647f1 · outbound

This paper cites Approximating Likelihood Ratios with Calibrated Discriminative Classifiers.

Deep learning detection of transients (ICRC-2019) Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

Reference 11

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Observation d3f42e85-3f99-4049-88e5-f3276ba1f30b · outbound

This paper cites Deep learning detection of transients.

Deep learning detection of transients (ICRC-2019) Deep learning detection of transients

Reference 12

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Observation e90dced5-2301-4686-8607-5564acee9a44 · outbound

This paper cites Low Luminosity Gamma-Ray Bursts as a Unique Population: Luminosity Function, Local Rate, and Beaming Factor.

Deep learning detection of transients (ICRC-2019) Low Luminosity Gamma-Ray Bursts as a Unique Population: Luminosity Function, Local Rate, and Beaming Factor

Reference 13

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Observation 11c8b06b-47c5-4159-89cc-347995269b66 · outbound

This paper cites Low-Luminosity Gamma-Ray Bursts as a Distinct GRB Population:A Firmer Case from Multiple Criteria Constraints.

Deep learning detection of transients (ICRC-2019) Low-Luminosity Gamma-Ray Bursts as a Distinct GRB Population:A Firmer Case from Multiple Criteria Constraints

Reference 14

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Observation 83b4bdf5-1b41-4d62-b1f9-b2d1c1714e9c · outbound

This paper cites The Observer's Guide to the Gamma-Ray Burst-Supernova Connection.

Deep learning detection of transients (ICRC-2019) The Observer's Guide to the Gamma-Ray Burst-Supernova Connection

Reference 15

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Observation 27411be2-818c-43b0-85d9-01f993382feb · outbound

This paper cites High-energy cosmic-ray nuclei from high- and low-luminosity gamma-ray bursts and implications for multi-messenger astronomy.

Deep learning detection of transients (ICRC-2019) High-energy cosmic-ray nuclei from high- and low-luminosity gamma-ray bursts and implications for multi-messenger astronomy

Reference 16

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Deep learning detection of transients (ICRC-2019) Unresolved cited work

Reference 17

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Observation e2ab891f-7195-4adb-9235-69fa59bfe7f1 · outbound

This paper cites On the common origin of cosmic rays across the ankle and diffuse neutrinos at the highest energies from low-luminosity Gamma-Ray Bursts.

Deep learning detection of transients (ICRC-2019) On the common origin of cosmic rays across the ankle and diffuse neutrinos at the highest energies from low-luminosity Gamma-Ray Bursts

Reference 18

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Observation 31aaae64-689f-41d8-b663-c5dc1d80f237 · outbound

This paper cites In: Astrophys.

Deep learning detection of transients (ICRC-2019) In: Astrophys

Reference 19

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Observation 0d3a8b61-31de-46b2-8484-8e2ddfdebf32 · outbound

This paper cites The second catalog of flaring gamma-ray sources from the Fermi All-sky Variability Analysis.

Deep learning detection of transients (ICRC-2019) The second catalog of flaring gamma-ray sources from the Fermi All-sky Variability Analysis

Reference 20

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Observation c37aeced-db70-4ba2-85a1-0a9089c55581 · outbound

This paper cites The First Fermi LAT Gamma-Ray Burst Catalog.

Deep learning detection of transients (ICRC-2019) The First Fermi LAT Gamma-Ray Burst Catalog

Reference 21

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Observation ae66ce17-7974-4a2d-8489-8227f33e8a7c · outbound

This paper cites Band et al.

Deep learning detection of transients (ICRC-2019) Band et al

Reference 22

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Observation a8818c34-e90d-4e81-879e-914bc93bddf4 · outbound

This paper cites GRB060218: A Relativistic Supernova Shock Breakout.

Deep learning detection of transients (ICRC-2019) GRB060218: A Relativistic Supernova Shock Breakout

Reference 23

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Observation 1082c5c8-a5a7-45b6-99be-e3defbad55dc · outbound

This paper cites A new population of ultra-long duration gamma-ray bursts.

Deep learning detection of transients (ICRC-2019) A new population of ultra-long duration gamma-ray bursts

Reference 24

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Observation 86ca3fdb-0197-4d05-b716-6fa1fbcdbaeb · outbound

This paper cites In: Astropart.

Deep learning detection of transients (ICRC-2019) In: Astropart

Reference 25

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Observation 3b1be247-29c4-49f4-88fa-316e47de82a0 · outbound

This paper cites GammaLib and ctools: A software framework for the analysis of astronomical gamma-ray data.

Deep learning detection of transients (ICRC-2019) GammaLib and ctools: A software framework for the analysis of astronomical gamma-ray data

Reference 26

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This paper cites Science with the Cherenkov Telescope Array.

Deep learning detection of transients (ICRC-2019) Science with the Cherenkov Telescope Array

Reference 27

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Deep learning detection of transients (ICRC-2019) Unresolved cited work

Reference 28

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Observation 1f87e2a2-5837-4aae-98c2-2b7e679b55e4 · outbound

This paper cites The extragalactic optical-infrared background radiations, their time evolution and the cosmic photon-photon opacity.

Deep learning detection of transients (ICRC-2019) The extragalactic optical-infrared background radiations, their time evolution and the cosmic photon-photon opacity

Reference 29

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This paper cites Dominguez et al.

Deep learning detection of transients (ICRC-2019) Dominguez et al

Reference 30

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Observation 8806030f-1e10-4435-bca8-bcc2f4e74b06 · outbound

This paper cites Gilmore et al.

Deep learning detection of transients (ICRC-2019) Gilmore et al

Reference 31

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Deep learning detection of transients (ICRC-2019) Unresolved cited work

Reference 32

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Pith citing papers

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