{
  "abstract": "The Tangent Galvanometer (TG) is a standard undergraduate laboratory experiment for estimating the horizontal component of Earth's magnetic field (BH) by measuring the angle of deflection of a magnetic needle corresponding to the current flowing through a circular coil. In this study, an artificial neural network (ANN) is used as a complementary data-driven model to predict the value of BH. A dataset comprising 225 observations obtained using 50-turn and 500-turn coils was used for developing the ANN model. After quality control, 223 observations were retained and divided into training (70%), validation (15%), and testing (15%) subsets. The model was optimized using a feed-forward neural network with Tanh activation. Three different models (Models A, B, and C) with different input variables were compared for optimum performance. Model C used five input variables: current, deflection angle, tan(theta), magnetic field produced by the coil, and number of turns. The addition of tan(theta) produced a substantial improvement in prediction performance, which was further improved by including the magnetic field produced by the coil. Model C gave the best test performance, with R2 = 0.99053, RMSE = 0.54076 microT, and MAE = 0.32940 microT. The experimental and ANN-predicted values of BH were also compared with an adopted local geomagnetic reference value of 39.0 microT. The mean experimental and ANN-predicted values were 37.38898 microT and 37.32161 microT, respectively. The results demonstrate the usefulness of ANN as a complementary tool for analyzing experimental variability and nonlinear relationships in an undergraduate physics laboratory experiment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.31930",
  "author_total": 4,
  "authors": [
    {
      "name": "Saralasrita Mohanty"
    },
    {
      "name": "Sudakshina Prusty"
    },
    {
      "name": "Anshuman Pal"
    },
    {
      "name": "Pradipta Kumar Mishra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1gswj4wng8sgx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.31930",
  "fulltext_url": null,
  "id": "1gswj4wng8sgx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.31930",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ed-ph"
    ],
    [
      "openalex",
      "1312|Geomagnetism and Paleomagnetism Studies"
    ]
  ],
  "openalex": "W7214785847",
  "pdf_url": "https://arxiv.org/pdf/2609.31930",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1gswj4wng8sgx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.31930",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.31930"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Geomagnetism and Paleomagnetism Studies",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Artificial Neural Network Assisted Modelling of Tangent Galvanometer Measurements for the Determination of Horizontal Component of Earth's Magnetic Field",
  "title_text": "Artificial Neural Network Assisted Modelling of Tangent Galvanometer Measurements for the Determination of Horizontal Component of Earth's Magnetic Field",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/1gswj4wng8sgx",
  "venue": "arXiv",
  "versions": []
}