Welcome on the Transient Discovery Science Case page

Scope of the following document is to define the feasibility study to provide in DAME a scientific workflow to implement a set of tools for the real time detection and classification of photometric transients in multiband, multi-epoch surveys. Classification (in terms of probability density function) needs to be performed in real time in order to trigger possible follow-ups.

The DAME team directly involved in this project is composed by:

• Marianna Annunziatella (M.Sc. in Astrophysics)
• Massimo Brescia (Project Manager)
• Stefano Cavuoti (PhD in Physics)
• George S. Djorgovski (Science Consultant)
• Giuseppe Longo (Principal Investigator)
• Ashish Mahabal (Science Consultant)

 

The Strategy

We plan to implement a simulated realistic workflow to be used for studying the optimal classifier.

Basic requirements are:

1. Interface with VO needed to provide multi-wavelength constraints to be fed as input to the classifiers (as additional constraints);
2. GRID (SCOPE) needed to perform intensive simulation and image segmentation;
3. DAME Cloud infrastructure needed for classifiers as well as to expose the simulation package to the wide community (this also imposes the largest possible flexibility and generality of the GUI).

Summary of steps:

1. Check for existing stuff;
2. Define set of rules for most significant variable object models;
3. Search for algorithms able to work on transients;
4. Define a strategy to evaluate their performances in a realistic framework (i.e. simulations);
5. Implement within DAME the simulation package;
6. Evaluate and test classification algorithms on simulated data;
7. Implement selected algorithms on real pipeline;

This project is directly connected with the KDD-IG, the Interest Group for data mining established within the IVOA Consortium.

We plan to massively involve some scientists and software engineers coming from the Kerala school of astronomy and mathematics, South India, to implement most of the variable object models and, if possible – to implement specific classifiers.

Finally, the participation is intended to be extended to externals collaborators, to improve science expertise and background on variable object modeling and to gain the possibility to test the workflow on real new generation survey pipelines.

The evaluation of classifiers requires benchmarks, hence reference templates. At the moment, these templates are not available. The usage of existing data (cf. PQ survey, etc.) does not solve all problems due to: high threshold for detection (which rules out many variable objects), incomplete classification of most variable objects.

In our opinion, the best way to obtain these templates is to provide a realistic sky simulation framework. These realistic simulations should take into account as many as possible relevant factors (except the random presence of artifacts which, however, could be inserted at a later stage). In particular:

• Instrumental realistic setup (telescope scale and mounting, S/N ratio, band, instrument features, optical design and optimization, residual aberrations, wavefront sensing, site environment and seeing parameters, etc…);
• Survey strategy (how many bands, how deep).  A good guide on what are the relevant parameters could be derived from [9];
• Sampling mode (even, uneven, realistic, i.e. taken from existing surveys) and rate (how frequently is the same region of the sky observed in each band);
• Realistic distribution of pre-modeled variable objects (from the list of objects defined in the following);

By having this simulation framework available, a set of synthetic multi-band and multi-epoch sky images can be obtained.

The second step of the workflow should hence be based on the analysis and implementation of data mining algorithms, deployed on the DAME Cloud/GRID platforms already available, that could perform detection and classification of such variable objects on the simulated images.

The final step should foresee the test of the best DM algorithms on real cases, i.e. next generation telescope and focal plane instrument survey pipelines, in order to evaluate the selected DM models in a realistic and operational environment.

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The Simulation Environment

As mentioned before, there is available a software package for simulations of sky patches and instrumentation FOV response.
We started to analyze in details the packages Stuff and SkyMaker. In both cases, their internal mechanism is quiet simple. They require to configure specific setup files in order to specify the correct sky, instrument and observation site features for the current simulation.

In particular, in Stuff, after adding star objects, the main specification to simulate a variable object is to introduce a realistic (i.e. obtained through the set of rules discussed below) change in magnitude.

Hence, one of the main tasks of the variable object modeling is to identify a pseudo-analytical function (i.e. a look-up table) of the variation of magnitude with time, for each kind of objects: by giving as input a magnitude, a phase and a time, the pseudo-analytical function should return the changed magnitude (the beginning of the phenomena could be randomly generated). It can be also taken into account an overlap effect, induced by Stuff that works on centroids, together with transient effects on bulge and disk axes.

We verified also that it is possible to generate extended objects with SkyMaker (see sample in the appendix).

By analyzing several releases of Stuff and SkyMaker, slight variations are present, that , however, do not seem to affect our final result. A special effort should be spent on the right integration of optical aberrations into the original software, depending on the specific telescope and instrument modeled in the simulation..

By executing in sequence Stuff and SkyMaker, configured with setup files reported in the previous sections of this appendix, the output image, representing a simulated sky patch in the g band, obtained is showed in fig. 1 below. All scientific details about the image are addressed in the setup files.

Fig. 1 – sky2.fits, the sky patch obtained with SkyMaker, by using our setup file

 

Sky simulation for 5 variable objects (3 cepheids + 2 random objects) in the B,V,I bands. The instrument parameters are referred to the VLT Survey telescope (VST):

Mag: 18 - 26.0

Pixel Size: 0.213 arcsec

Exp Time: 1500.0(s)

Seeing: 0.6 - 1.1

Image Size: 1024x1024

 

 

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The Classification algorithms

This is the section related with the DM methods and algorithms to be explored in order to perform the required detection and classification of variable objects. We are currently at the very beginning of this phase. It requires a preliminary review of all existing algorithms which could be relevant to the classification in real time of transients. For instance

• Bayesian Networks;
• Wavelets;
• Supervised classifiers trained on simulations;
• Etc…

The application of such methods could also arise from the analysis of algorithms and applications already available in the DAME Cloud framework. At the moment, these are listed in the following table.

Tab. 1 – data mining models currently available in DAME

Further methods, after the decision to be employed in this project, will be implemented in the DAME package.

Items to discuss

• Sampling provides important a priori constrains to be fed to classification algorithms (reverse engineering of paper by Madore 1985) (objects which can and cannot be detected).

Various indicators for variable objects (uniformity index, etc…) need also to be explored.

 

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Preliminary Results

work in progress

 

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Bibliography & References

1. Cavuoti s., Annunziatella M., Brescia M., Longo G., Variable Sky Objects Simulation and Detection Workflow, internal DAME Technical Documentation, transientDiscovery_DAME-SPE-NA-0011-Rel0.5;
2. Simulating the LSST Survey;
3. Stuff 1.19;
4. Skymaker 3.3.3;
5. Ontology of Astronomical Object Types, Version 1.3, IVOA Technical Note 17 January 2010 available here;
6. Astromatic: pipeline software available here;

 

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DAME is a collaboration between INAF OACN, Dip. of Physics Science of University Federico II and Caltech Institute aimed at designing and developing instruments and tools for scientific data mining, based on information and comunication technology, funded by the Italian Ministry of Foreign Affairs as well as by the European project VOTECH (Virtual Observatory Technological Infrastructures) and by the Italian PON-S.Co.P.E.