{"id":3521,"date":"2023-08-31T11:12:58","date_gmt":"2023-08-31T11:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/star-type\/"},"modified":"2026-10-06T13:32:17","modified_gmt":"2026-10-06T11:32:17","slug":"star-type","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/","title":{"rendered":"Pulsar detection with machine learning"},"content":{"rendered":"<style>\n.nds{box-sizing:border-box;--nds-code-bg:#f3f7fa;--nds-code-fg:#12354b;width:100%;margin-left:0;margin-right:0;padding:22px 0 14px;background:#fff;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.nds *{box-sizing:border-box}.nds-wrap{width:min(calc(100% - 48px),1160px);margin:0 auto}.nds a{text-decoration:none;color:#2d799f}\n.nds-executive{margin:0 0 34px;padding:32px;border-radius:20px;}.nds-executive h2{margin:0 0 12px;font-size:30px}.nds-executive 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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"nds\" data-science-profile=\"physical-chemical\">\n<div class=\"nds-wrap\">\n<section class=\"nds-executive\">\n<h2>Identify pulsar candidates from signal summaries<\/h2>\n<p>Classify HTRU2 candidates from eight integrated pulse-profile and dispersion-measure statistics. The supplied network is a signal-screening benchmark evaluated on 3,579 held-out records.<\/p>\n<div class=\"nds-kpis\">\n<div class=\"nds-kpi\"><strong>17,898<\/strong><span>Source records<\/span><\/div>\n<div class=\"nds-kpi\"><strong>8<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"nds-kpi\"><strong>3,579<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"nds-kpi\"><strong>1<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"nds-actions\"><a class=\"aui aui-button\" href=\"#6-validation\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-pulsardetection-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"nds-toc\">\n<li><a href=\"#1-scientific-objective\">Scientific objective<\/a><\/li><li><a href=\"#2-data-provenance\">Data and provenance<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Selection<\/a><\/li><li><a href=\"#6-validation\">Validation<\/a><\/li><li><a href=\"#7-inference\">Inference<\/a><\/li><li><a href=\"#8-validity\">Validity<\/a><\/li>\n<\/ul>\n<section id=\"1-scientific-objective\" class=\"nds-card\"><h2>1. Scientific objective<\/h2><p>Radio-astronomy screening produces many candidates that need prioritization. The model learns the HTRU2 annotation from summary statistics, supporting candidate ranking and error inspection.<\/p><div class=\"nds-value-grid\"><div class=\"nds-value-card\"><h3>Signal screening<\/h3><p>Rank candidate patterns for scientific review.<\/p><\/div><div class=\"nds-value-card\"><h3>Rare-class errors<\/h3><p>Inspect missed candidates and false positives.<\/p><\/div><div class=\"nds-value-card\"><h3>Reproducibility<\/h3><p>Retain the saved split and signal-feature definitions.<\/p><\/div><\/div><div class=\"nds-audience\"><span>Radio astronomy<\/span><span>Signal analysis<\/span><span>Scientific computing<\/span><\/div><div class=\"nds-note\">Candidate classification within the HTRU2 benchmark; a model score does not confirm an astronomical discovery.<\/div><\/section>\n<section id=\"2-data-provenance\" class=\"nds-card\"><h2>2. Data and provenance<\/h2><p>The UCI HTRU2 dataset contains 17,898 records with eight numeric features and one binary class. The saved positive class is 1, representing a pulsar candidate.<\/p><p>Source: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/372\/htru2\">HTRU2<\/a>. Dataset license: CC-BY-4.0. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>candidate record<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>17,898<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>9<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>8<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>1<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>10740<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>3579<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>3579<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">profile_mean<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">profile_stdev<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">profile_skewness<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">profile_kurtosis<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">dm_mean<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">dm_stdev<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">dm_skewness<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">dm_kurtosis<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">class<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-pulsardetection-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-pulsardetection-t0-s2-20261006\" role=\"img\" aria-label=\"Target class distribution pie chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-pulsardetection-t0-s2-20261006.png\" alt=\"Target class distribution pie chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"0\",\"1\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[90.83999633789062,9.15999984741211]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Target class distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"height\":500}}<\/script><\/div><figcaption>Target class distribution pie chart. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"nds-note nds-note--provenance\">The saved project assigns rows to training, validation and testing as shown above. This is internal record-level evaluation; it does not demonstrate separation by subject, device, site or acquisition batch.<\/div><\/section>\n<section id=\"3-model\" class=\"nds-card\"><h2>3. Model<\/h2><p>The model has 8 encoded inputs and 1 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>8<\/td><td>8<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>8<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output values are uncalibrated model scores. For binary evaluation, the saved positive class is <strong>1<\/strong>.<\/p><figure class=\"nds-architecture-figure\" data-model-stage=\"initial\"><img decoding=\"async\" class=\"nds-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-pulsardetection-t3-s1-20261006.png\" alt=\"Pulsar detection with machine learning \u2014 initial network architecture\"><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"nds-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses QuasiNewton with WeightedSquaredError.<\/p><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>117<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:00<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.106<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.103<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-pulsardetection-t4-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-pulsardetection-t4-s2-20261006\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-pulsardetection-t4-s2-20261006.png\" alt=\"Quasi-Newton method error history\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116],\"y\":[0.6150000095367432,0.5740000009536743,0.3709999918937683,0.25099998712539673,0.22599999606609344,0.19699999690055847,0.16699999570846558,0.15600000321865082,0.14100000262260437,0.1340000033378601,0.12999999523162842,0.1289999932050705,0.12800000607967377,0.12399999797344208,0.12300000339746475,0.12099999934434891,0.11800000071525574,0.11400000005960464,0.1120000034570694,0.11100000143051147,0.11100000143051147,0.11100000143051147,0.10999999940395355,0.10899999737739563,0.10899999737739563,0.10899999737739563,0.1080000028014183,0.1080000028014183,0.1080000028014183,0.10700000077486038,0.10700000077486038,0.10599999874830246,0.10599999874830246,0.10599999874830246,0.10599999874830246,0.10599999874830246,0.10499999672174454,0.10499999672174454,0.10499999672174454,0.10499999672174454,0.10499999672174454,0.10499999672174454,0.10499999672174454,0.10499999672174454,0.10400000214576721,0.10400000214576721,0.10400000214576721,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10300000011920929,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10199999809265137,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10100000351667404,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.10000000149011612,0.09989999979734421,0.09989999979734421,0.09989999979734421]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Validation 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sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Training history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,116],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.7001000000000001],\"title\":{\"text\":\"Weighted squared error\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>Quasi-Newton method error history. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"nds-card\"><h2>5. Model selection and baseline<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><p>On this test subset a majority-class baseline would correctly classify 90.98% of the records. This baseline does not detect both classes and is not an optimized model.<\/p><\/section>\n<section id=\"6-validation\" class=\"nds-card\"><h2>6. Scientific validation<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 3579 source records.<\/p><p>ROC AUC describes ranking on this testing subset. Any optimal threshold shown in the saved ROC report was selected descriptively on that same subset; it is not an independently validated operating policy.<\/p><p>These operating-point metrics are calculated from the saved confusion counts at threshold <strong>0.5<\/strong>. The positive-label coding is stated in the model section.<\/p><h3>Confusion table<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>295 (8.2%)<\/td><td>28 (0.8%)<\/td><td>323 (9.0%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>97 (2.7%)<\/td><td>3159 (88.3%)<\/td><td>3256 (91.0%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>392 (11.0%)<\/td><td>3187 (89.0%)<\/td><td>3579 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing records<\/th><td>3579<\/td><\/tr><tr><th scope=\"row\">Positive cases<\/th><td>323<\/td><\/tr><tr><th scope=\"row\">Positive prevalence<\/th><td>9.02%<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>96.51%<\/td><\/tr><tr><th scope=\"row\">Sensitivity \/ recall<\/th><td>91.33%<\/td><\/tr><tr><th scope=\"row\">Specificity<\/th><td>97.02%<\/td><\/tr><tr><th scope=\"row\">Precision \/ PPV<\/th><td>75.26%<\/td><\/tr><tr><th scope=\"row\">F1 score<\/th><td>0.825<\/td><\/tr><\/tbody><\/table><\/div><h3>Area under curve<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Area under curve<\/th><td>0.975<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-pulsardetection-t5-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-pulsardetection-t5-s1-20261006\" role=\"img\" aria-label=\"ROC chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"nds-note\">Candidate classification within the HTRU2 benchmark; a model score does not confirm an astronomical discovery.<\/div><\/section>\n<section id=\"7-inference\" class=\"nds-card\"><h2>7. Inference and reproducibility<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"nds-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-pulsardetection-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-validity\" class=\"nds-card\"><h2>8. Validity, uncertainty and limitations<\/h2><p>Class prevalence affects precision. Internal row-level testing does not establish performance on another telescope, observing campaign or detection pipeline. Review candidates with independent observational evidence and check calibration before interpreting scores as probabilities.<\/p><\/section>\n<section id=\"references\" class=\"nds-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/372\/htru2\">HTRU2<\/a>. Robert Lyon. 10.24432\/C5DK6R<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. Full attribution and transformations are included in <code>LICENSES\/DATASET-LICENSE.txt<\/code>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-pulsardetection-licensed-20261006.zip\">Current Neural Designer project and saved task report<\/a>, snapshot 6 October 2026.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<style>\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle .nd-chart-fallback{display:block;margin:0 auto!important;width:100%;height:auto;max-width:100%}\n.nd-chart-bundle>script,.nd-chart-bundle>br,.nd-chart-bundle>p:empty{display:none!important}\n@media(max-width:600px){.nd-chart-bundle{margin:20px auto}.nd-chart-bundle .nd-chart-host{height:440px}}\n.nd-chart-bundle .nd-chart-host{min-width:0!important}.nd-chart-bundle{max-width:100%}html body .nd-comparison-grid>.nd-comparison-item:only-child{flex-basis:min(760px,100%)!important;width:min(760px,100%)!important}.ndb-card details,.nds-card details{margin:20px 0}.ndb-card summary,.nds-card summary{cursor:pointer;font-weight:600}<\/style><script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":13,"featured_media":1490,"template":"","categories":[29],"tags":[],"class_list":["post-3521","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pulsar detection with machine learning - Neural Designer<\/title>\n<meta name=\"description\" content=\"Classify HTRU2 candidates from eight integrated pulse-profile and dispersion-measure statistics. 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