{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:S5OKTSMTF6JIBDAUQFMDXYICGZ","short_pith_number":"pith:S5OKTSMT","schema_version":"1.0","canonical_sha256":"975ca9c9932f92808c1481583be102365cd8d1eea8185ef62b14d84fe8252466","source":{"kind":"arxiv","id":"2405.11738","version":1},"attestation_state":"computed","paper":{"title":"Diffusion Models for Generating Ballistic Spacecraft Trajectories","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Agnimitra Dasgupta, Assad Oberai, Daniel Erwin, Tyler Presser","submitted_at":"2024-05-20T02:38:38Z","abstract_excerpt":"Generative modeling has drawn much attention in creative and scientific data generation tasks. Score-based Diffusion Models, a type of generative model that iteratively learns to denoise data, have shown state-of-the-art results on tasks such as image generation, multivariate time series forecasting, and robotic trajectory planning. Using score-based diffusion models, this work implements a novel generative framework to generate ballistic transfers from Earth to Mars. We further analyze the model's ability to learn the characteristics of the original dataset and its ability to produce transfer"},"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":"2405.11738","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-20T02:38:38Z","cross_cats_sorted":[],"title_canon_sha256":"fad177c97a74a99de982acd745429a358880e4cc21de67cd133136cf63a5cead","abstract_canon_sha256":"cd63632a81f26acb8d8c0f7fbeed94fa52a3345d9e41a0159e93653ad8ca58f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:43.811287Z","signature_b64":"lojc3fR1/bk6iB65aC/PtmZzMptRRnQvLzHoq3icOa1BThcCYbfRLBRr61kApvceV6N2HVFBZc4GmZ6Ubdr3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"975ca9c9932f92808c1481583be102365cd8d1eea8185ef62b14d84fe8252466","last_reissued_at":"2026-07-05T08:20:43.810794Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:43.810794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusion Models for Generating Ballistic Spacecraft Trajectories","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Agnimitra Dasgupta, Assad Oberai, Daniel Erwin, Tyler Presser","submitted_at":"2024-05-20T02:38:38Z","abstract_excerpt":"Generative modeling has drawn much attention in creative and scientific data generation tasks. Score-based Diffusion Models, a type of generative model that iteratively learns to denoise data, have shown state-of-the-art results on tasks such as image generation, multivariate time series forecasting, and robotic trajectory planning. Using score-based diffusion models, this work implements a novel generative framework to generate ballistic transfers from Earth to Mars. We further analyze the model's ability to learn the characteristics of the original dataset and its ability to produce transfer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.11738","kind":"arxiv","version":1},"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/2405.11738/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":"2405.11738","created_at":"2026-07-05T08:20:43.810853+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.11738v1","created_at":"2026-07-05T08:20:43.810853+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.11738","created_at":"2026-07-05T08:20:43.810853+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5OKTSMTF6JI","created_at":"2026-07-05T08:20:43.810853+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5OKTSMTF6JIBDAU","created_at":"2026-07-05T08:20:43.810853+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5OKTSMT","created_at":"2026-07-05T08:20:43.810853+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28207","citing_title":"Three-Body Earth-Moon Transfers with Different Departure/Arrival Orbital Altitudes: New Phenomenon and Diffusion Model-Augmented Construction","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ","json":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ.json","graph_json":"https://pith.science/api/pith-number/S5OKTSMTF6JIBDAUQFMDXYICGZ/graph.json","events_json":"https://pith.science/api/pith-number/S5OKTSMTF6JIBDAUQFMDXYICGZ/events.json","paper":"https://pith.science/paper/S5OKTSMT"},"agent_actions":{"view_html":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ","download_json":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ.json","view_paper":"https://pith.science/paper/S5OKTSMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.11738&json=true","fetch_graph":"https://pith.science/api/pith-number/S5OKTSMTF6JIBDAUQFMDXYICGZ/graph.json","fetch_events":"https://pith.science/api/pith-number/S5OKTSMTF6JIBDAUQFMDXYICGZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ/action/storage_attestation","attest_author":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ/action/author_attestation","sign_citation":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ/action/citation_signature","submit_replication":"https://pith.science/pith/S5OKTSMTF6JIBDAUQFMDXYICGZ/action/replication_record"}},"created_at":"2026-07-05T08:20:43.810853+00:00","updated_at":"2026-07-05T08:20:43.810853+00:00"}