dorsal/arxiv
View SchemaElectron/Pion Identification with ALICE TRD Prototypes using a Neural Network Algorithm
| Authors | ALICE TRD Collaboration |
|---|---|
| Categories | |
| ArXiv ID | physics/0506202 |
| URL | https://arxiv.org/abs/physics/0506202 |
Abstract
We study the electron/pion identification performance of the ALICE Transition Radiation Detector (TRD) prototypes using a neural network (NN) algorithm. Measurements were carried out for particle momenta from 2 to 6 GeV/c. An improvement in pion rejection by about a factor of 3 is obtained with NN compared to standard likelihood methods.
{
"annotation_id": "f6bc1315-acf7-4d84-800d-fa91c7b5bca8",
"date_created": "2026-03-02T18:01:00.754000Z",
"date_modified": "2026-03-02T18:01:00.754000Z",
"file_hash": "d88cf867002f267ed789018b9616216fa4c71415dc6032c66230816caf696f47",
"private": false,
"record": {
"abstract": "We study the electron/pion identification performance of the ALICE Transition\nRadiation Detector (TRD) prototypes using a neural network (NN) algorithm.\nMeasurements were carried out for particle momenta from 2 to 6 GeV/c. An\nimprovement in pion rejection by about a factor of 3 is obtained with NN\ncompared to standard likelihood methods.",
"arxiv_id": "physics/0506202",
"authors": [
"ALICE TRD Collaboration"
],
"categories": [
"physics.ins-det"
],
"title": "Electron/Pion Identification with ALICE TRD Prototypes using a Neural Network Algorithm",
"url": "https://arxiv.org/abs/physics/0506202"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "ec9707be-5b0e-4845-aa91-f1fe6b062fcc",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}