{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:ZZ4X5GIMBFSG2OH4WWQHGDJDUJ","short_pith_number":"pith:ZZ4X5GIM","canonical_record":{"source":{"id":"1611.00363","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-11-01T20:00:01Z","cross_cats_sorted":[],"title_canon_sha256":"f399c2d1b277170d43efacba21a91d0c518d740f1af960e0e320f75a7295a95f","abstract_canon_sha256":"87c5e2b33bcd69eb2bf7b7bba57390e0d31001057d202d7a715bdaf13a5af62c"},"schema_version":"1.0"},"canonical_sha256":"ce797e990c09646d38fcb5a0730d23a26afe53d70ee78e2fbbb454b5dedd3749","source":{"kind":"arxiv","id":"1611.00363","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.00363","created_at":"2026-05-18T00:43:59Z"},{"alias_kind":"arxiv_version","alias_value":"1611.00363v1","created_at":"2026-05-18T00:43:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00363","created_at":"2026-05-18T00:43:59Z"},{"alias_kind":"pith_short_12","alias_value":"ZZ4X5GIMBFSG","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"ZZ4X5GIMBFSG2OH4","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"ZZ4X5GIM","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:ZZ4X5GIMBFSG2OH4WWQHGDJDUJ","target":"record","payload":{"canonical_record":{"source":{"id":"1611.00363","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-11-01T20:00:01Z","cross_cats_sorted":[],"title_canon_sha256":"f399c2d1b277170d43efacba21a91d0c518d740f1af960e0e320f75a7295a95f","abstract_canon_sha256":"87c5e2b33bcd69eb2bf7b7bba57390e0d31001057d202d7a715bdaf13a5af62c"},"schema_version":"1.0"},"canonical_sha256":"ce797e990c09646d38fcb5a0730d23a26afe53d70ee78e2fbbb454b5dedd3749","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:59.397268Z","signature_b64":"xUntQeQUC+mM77ypgb1C/mNQd0KK9Egb1iRikJ6hK4NzoNEOfibs+m03HHUc3G+uhkAY3SW8Ehiybz3zSGfmDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce797e990c09646d38fcb5a0730d23a26afe53d70ee78e2fbbb454b5dedd3749","last_reissued_at":"2026-05-18T00:43:59.396804Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:59.396804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.00363","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:43:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I4rzz4FaufuAvLEAHq+UfgnotjB+qoBi2kJcjgSll5KZr8EmFxZ1WLyj+ypY7JrbVjkHAr6PVm1UU66XkqH1Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T23:01:51.735892Z"},"content_sha256":"84bde603b530cc9ef0c11c4b7c9d145604803a135aede088f4129183d865439a","schema_version":"1.0","event_id":"sha256:84bde603b530cc9ef0c11c4b7c9d145604803a135aede088f4129183d865439a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:ZZ4X5GIMBFSG2OH4WWQHGDJDUJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations using an XD Gaussian Mixture Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Philip J. Marshall, Risa H. Wechsler, Thomas W.-S. Holoien","submitted_at":"2016-11-01T20:00:01Z","abstract_excerpt":"We describe two new open source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program for using Gaussian mixtures to do density estimation of noisy data using extreme deconvolution (XD) algorithms that has functionality not available in other XD tools. It allows the user to select between the AstroML (Vanderplas et al. 2012; Ivezic et al. 2015) and Bovy et al. (2011) fitting methods and is compatible with scikit-learn machine learning algorith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00363","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:43:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tAiR77c4NmOJpDsGhbM17EzacSliTS9gUzJEyXf15Fnig8F4mCAF+AiKXvGMtuub3CNCVB2OSn7DPkGi65sVDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T23:01:51.736640Z"},"content_sha256":"46593eac3d53267054a71ad271532cc3ce641d4a238f141668a5b3fa51ad1598","schema_version":"1.0","event_id":"sha256:46593eac3d53267054a71ad271532cc3ce641d4a238f141668a5b3fa51ad1598"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZZ4X5GIMBFSG2OH4WWQHGDJDUJ/bundle.json","state_url":"https://pith.science/pith/ZZ4X5GIMBFSG2OH4WWQHGDJDUJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZZ4X5GIMBFSG2OH4WWQHGDJDUJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T23:01:51Z","links":{"resolver":"https://pith.science/pith/ZZ4X5GIMBFSG2OH4WWQHGDJDUJ","bundle":"https://pith.science/pith/ZZ4X5GIMBFSG2OH4WWQHGDJDUJ/bundle.json","state":"https://pith.science/pith/ZZ4X5GIMBFSG2OH4WWQHGDJDUJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZZ4X5GIMBFSG2OH4WWQHGDJDUJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:ZZ4X5GIMBFSG2OH4WWQHGDJDUJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"87c5e2b33bcd69eb2bf7b7bba57390e0d31001057d202d7a715bdaf13a5af62c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-11-01T20:00:01Z","title_canon_sha256":"f399c2d1b277170d43efacba21a91d0c518d740f1af960e0e320f75a7295a95f"},"schema_version":"1.0","source":{"id":"1611.00363","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.00363","created_at":"2026-05-18T00:43:59Z"},{"alias_kind":"arxiv_version","alias_value":"1611.00363v1","created_at":"2026-05-18T00:43:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00363","created_at":"2026-05-18T00:43:59Z"},{"alias_kind":"pith_short_12","alias_value":"ZZ4X5GIMBFSG","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"ZZ4X5GIMBFSG2OH4","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"ZZ4X5GIM","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:46593eac3d53267054a71ad271532cc3ce641d4a238f141668a5b3fa51ad1598","target":"graph","created_at":"2026-05-18T00:43:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We describe two new open source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program for using Gaussian mixtures to do density estimation of noisy data using extreme deconvolution (XD) algorithms that has functionality not available in other XD tools. It allows the user to select between the AstroML (Vanderplas et al. 2012; Ivezic et al. 2015) and Bovy et al. (2011) fitting methods and is compatible with scikit-learn machine learning algorith","authors_text":"Philip J. Marshall, Risa H. Wechsler, Thomas W.-S. Holoien","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-11-01T20:00:01Z","title":"EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations using an XD Gaussian Mixture Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00363","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:84bde603b530cc9ef0c11c4b7c9d145604803a135aede088f4129183d865439a","target":"record","created_at":"2026-05-18T00:43:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"87c5e2b33bcd69eb2bf7b7bba57390e0d31001057d202d7a715bdaf13a5af62c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2016-11-01T20:00:01Z","title_canon_sha256":"f399c2d1b277170d43efacba21a91d0c518d740f1af960e0e320f75a7295a95f"},"schema_version":"1.0","source":{"id":"1611.00363","kind":"arxiv","version":1}},"canonical_sha256":"ce797e990c09646d38fcb5a0730d23a26afe53d70ee78e2fbbb454b5dedd3749","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce797e990c09646d38fcb5a0730d23a26afe53d70ee78e2fbbb454b5dedd3749","first_computed_at":"2026-05-18T00:43:59.396804Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:59.396804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xUntQeQUC+mM77ypgb1C/mNQd0KK9Egb1iRikJ6hK4NzoNEOfibs+m03HHUc3G+uhkAY3SW8Ehiybz3zSGfmDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:59.397268Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.00363","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84bde603b530cc9ef0c11c4b7c9d145604803a135aede088f4129183d865439a","sha256:46593eac3d53267054a71ad271532cc3ce641d4a238f141668a5b3fa51ad1598"],"state_sha256":"1e35ebfb27c5222797c4112232e4a92a4a07a0d9abc62ab11aad2cf91d95cbec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mzq30wgwdJDnOZUSIVagyu7hfiBch4cavt/i35Poy7nZ+719WL4CTAoXSJXZDmgdnjZEmJFeDJq3POE33YfvAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T23:01:51.740402Z","bundle_sha256":"f0502e6adcaa95f2b9192f5066980d07333faf6d5332baa0bbb5ea00336a5a0b"}}