ampel-hu-astro 0.10.0a4

Astronomy units for the Ampel system from HU-Berlin

AMPEL-HU-astro

Contributed Ampel units from HU/DESY group

Installing

1. Install poetry. If you install poetry with conda, be sure to install it in its own environment, e.g. conda create -n poetry.
2. git clone https://github.com/AmpelProject/Ampel-HU-astro.git; cd Ampel-HU-astro
3. Check your virtualenv setup with poetry env info (or conda run -n poetry poetry env info if using conda). The output should include:

   Virtualenv
   Python:         3.10.x
   

   If not, point poetry at an installation of Python 3.10 with (conda run -n poetry) poetry env use PATH_TO_PYTHON_310
4. (conda run -n poetry) poetry install -E "ztf sncosmo extcats notebook"
5. cd notebooks
6. (conda run -n poetry) poetry run jupyter notebook

This will allow a number of Demo / access / development notebooks to be run. Note that most of them requires an access token if data is to be retrieved.

Provided units

T0 units (alert filters):

• LensedTransientFilter
• PredetectionFilter: Filter derived from the DecentFilter.
• RandFilter
• RcfFilter: Filter for the ZTF Redshift Completeness Factor program..
• RedshiftCatalogFilter: Filter derived from DecentFilter designed to only accept transients located close to a galaxy in a catalog, and within redshift bounds.
• SimpleDecentFilter: General-purpose filter devloped alongside DecentFilter but without use of external catalogs.
• StellarFilter: a.k.a. as the IndecentFilter, i.e. an inversion of the DecentFilter mainly used for finding extragalactic objects.
• TransientInClusterFilter: Filter derived from the DecentFilter, in addition selecting candidates with position compatible with that of nearby galaxy clusters..
• XShooterFilter: Filter derived from the DecentFilter, in addition selecting very new transients which are visible from the South.

T2 units (augment):

• T2BayesianBlocks: T2 unit for running a bayesian block search algorithm to highlight excess regions.
• T2BrightSNProb: Derive a number of simple metrics describing the rise, peak and decline of a lc.
• T2CatalogMatchLocal: Cross matches the position of a transient to those of sources in a set of catalogs.
• T2DemoLightcurveFitter: Demonstration class showing how methods of T2BaseLightcurveFitter can be used develop a specific classifier.
• T2DigestRedshifts: Compare potential matches from different T2 units providing redshifts.
• T2DustEchoEval
• T2ElasticcRedshiftSampler: Parse the elasticc diaSource host information and returns a list of redshifts and weights.
• T2ElasticcReport: Parse a series of T2 results from T2RunParsnip and T2XgbClassifier, and create combined classifications according to the taxonomy of https://github.com/LSSTDESC/elasticc/blob/main/taxonomy/taxonomy.ipynb.
• T2FastDecliner: Determine decline rate in two last obs.
• T2GetLensSNParameters
• T2InfantCatalogEval: Evaluate whether a transient fulfills criteria for being a potentially infant (extragalactic) transient.
• T2KilonovaEval: Evaluate whether a transient fulfills criteria for being a potential kilonova-like event.
• T2KilonovaStats: Evaluate kilonovaness stats for transient given map distance and number of detections..
• T2LCQuality: determine the 'quality' of the light curve by computing ratios between the number of detection and that of upper limits.
• T2LSPhotoZTap: Query the NOIR DataLab service for photometric redshifts from the Legacy Survey.
• T2LoadRedshift: Add redshifts from external .csv.
• T2MatchBTS: Add information from the BTS explorer page.
• T2MultiXgbClassifier: For a range of xgboost classifier models, find a classification.
• T2NedSNCosmo: Fits lightcurves using SNCOSMO (using SALT2 defaultwise) with redshift constrained by catalog matching results.
• T2NedTap: See also: https://ned.ipac.caltech.edu/tap/sync?QUERY=SELECT+*+FROM+TAP_SCHEMA.tables&REQUEST=doQuery&LANG=ADQL&FORMAT=text Export all NED: https://ned.ipac.caltech.edu/tap/sync?QUERY=SELECT+*+FROM+NEDTAP.objdir&REQUEST=doQuery&LANG=ADQL&FORMAT=text.
• T2RunParsnip: Gathers information and runs the parsnip model and classifier.
• T2RunParsnipRiseDecline
• T2RunPossis: Load a POSSIS kilnova model and fit to a LightCurve object as process is called.
• T2RunSncosmo: Gathers information and runs Sncosmo.
• T2RunSnoopy: Gathers information and runs snoopy.
• T2RunTDE: Create a TDE model and fit to a LightCurve object as process is called.
• T2TNSEval: Evalute whether a transient fulfills criteria for submission to TNS.
• T2XgbClassifier: Load a series of xgboost classifier models (distinguished by number of detections) and return a classification.

T3 units (react):

• AstroColibriPublisher: Publish results to AstroColibri.
• ChannelSummaryPublisher: Create a json file with summary statistics for the channel.
• CostCounter: Derive metrics for the total cost, as parsed by the provided documents.
• ElasticcClassTablePrinter: Unit which will print Elasticc unit classifications to STDOUT and/or an output csv file.
• HealpixCorrPlotter: Compare healpix coordinate P-value with output from T2RunSncosmo..
• PlotLightcurveSample: Unit plots results from lightcurve fitters (RunSncosmo, RunParsnip).
• PlotTransientLightcurves: Create a (pdf) plot summarizing lightcurves of candidates provided to the unit.
• RapidBase: Trigger rapid reactions.
• RapidLco: Submit LCO triggers for candidates passing criteria..
• RapidSedm: Select transients for rapid reactions.
• ScoreSingleObject: Calculate score based on how early a specific SN is detected.
• ScoreTNSObjects: Calculate score based on detection time reported to TNS, if any..
• SlackSummaryPublisher
• SubmitTNS: Submit candidates to TNS (unless already submitted).
• TransientInfoPrinter
• TransientTablePublisher: Construct a table based on selected T2 output values.
• TransientViewDumper

T4 units (control):

• HealpixTokenGenerator: Based on a URL to a Healpix map.
• RandomMapGenerator: Generate smoothed circular healpix probability values around a random coordinate..
• StreamTokenGenerator: Stream based token generator for.