{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W7THZXMP65MV6RZNNF52RE77JB","short_pith_number":"pith:W7THZXMP","schema_version":"1.0","canonical_sha256":"b7e67cdd8ff7595f472d697ba893ff484fe2e74f77329f310b17c9045db4e5f7","source":{"kind":"arxiv","id":"2501.07469","version":2},"attestation_state":"computed","paper":{"title":"Deep Needlet: A CNN based full sky component separation method in Needlet space","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.CO","authors_text":"Debabrata Adak","submitted_at":"2025-01-13T16:35:55Z","abstract_excerpt":"One of the key steps in Cosmic Microwave Background (CMB) data analysis is component separation to recover the CMB signal from multi-frequency observations contaminated by foreground emissions. Needlet Internal Linear Combination (NILC) is one of the successful methods that applies the minimum variance estimation technique to a set of needlet-filtered frequency maps to recover CMB. In this work, we develop a deep convolutional neural network (CNN) model to recover CMB temperature map from needlet-filtered frequency maps over the full sky. The network operates on a multi-resolution representati"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.07469","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.CO","submitted_at":"2025-01-13T16:35:55Z","cross_cats_sorted":["astro-ph.GA"],"title_canon_sha256":"de522a236659946adc13102423904b0d23a18f569099682c01b281ec5996245f","abstract_canon_sha256":"1e94318e898bfdfe6d07544565182d35278ebd54fa6f5e7ad2cb8b2c2123605a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:23.699631Z","signature_b64":"lz3KJ3VgW8X6TX98TGcMLLiIwD2rUeKTfutHvSq4crvcIQkU+PzBULiR2Zk0u93R+BXc6h0tsRU1ZCc3gUdWAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7e67cdd8ff7595f472d697ba893ff484fe2e74f77329f310b17c9045db4e5f7","last_reissued_at":"2026-07-05T11:58:23.699162Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:23.699162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Needlet: A CNN based full sky component separation method in Needlet space","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.CO","authors_text":"Debabrata Adak","submitted_at":"2025-01-13T16:35:55Z","abstract_excerpt":"One of the key steps in Cosmic Microwave Background (CMB) data analysis is component separation to recover the CMB signal from multi-frequency observations contaminated by foreground emissions. Needlet Internal Linear Combination (NILC) is one of the successful methods that applies the minimum variance estimation technique to a set of needlet-filtered frequency maps to recover CMB. In this work, we develop a deep convolutional neural network (CNN) model to recover CMB temperature map from needlet-filtered frequency maps over the full sky. The network operates on a multi-resolution representati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.07469","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.07469/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.07469","created_at":"2026-07-05T11:58:23.699220+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.07469v2","created_at":"2026-07-05T11:58:23.699220+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.07469","created_at":"2026-07-05T11:58:23.699220+00:00"},{"alias_kind":"pith_short_12","alias_value":"W7THZXMP65MV","created_at":"2026-07-05T11:58:23.699220+00:00"},{"alias_kind":"pith_short_16","alias_value":"W7THZXMP65MV6RZN","created_at":"2026-07-05T11:58:23.699220+00:00"},{"alias_kind":"pith_short_8","alias_value":"W7THZXMP","created_at":"2026-07-05T11:58:23.699220+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB","json":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB.json","graph_json":"https://pith.science/api/pith-number/W7THZXMP65MV6RZNNF52RE77JB/graph.json","events_json":"https://pith.science/api/pith-number/W7THZXMP65MV6RZNNF52RE77JB/events.json","paper":"https://pith.science/paper/W7THZXMP"},"agent_actions":{"view_html":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB","download_json":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB.json","view_paper":"https://pith.science/paper/W7THZXMP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.07469&json=true","fetch_graph":"https://pith.science/api/pith-number/W7THZXMP65MV6RZNNF52RE77JB/graph.json","fetch_events":"https://pith.science/api/pith-number/W7THZXMP65MV6RZNNF52RE77JB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB/action/storage_attestation","attest_author":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB/action/author_attestation","sign_citation":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB/action/citation_signature","submit_replication":"https://pith.science/pith/W7THZXMP65MV6RZNNF52RE77JB/action/replication_record"}},"created_at":"2026-07-05T11:58:23.699220+00:00","updated_at":"2026-07-05T11:58:23.699220+00:00"}