Padova-Asiago Supernova Group
SNOoPY: a package for supernova photometry.
SNOoPY (SuperNOva PhotometrY) is a package designed to facilitate the task of producing SN light curves from multi-band photometric observations obtained with a generic collection of telescopes.
This page is intended to provide an updated reference to the latest SNOoPY release.
The original package concept was developed almost 20 yr ago by Nando Patat (F. Patat 1996, PhD Thesis, Universita' di Padova). Unfortunately the code was not easily portable and with the upgrade of the operating system it could not be compiled anymore. For this reason at the end of the last millennium a new package was developed that comes as a collection of IRAF scripts. The software turned out quite successful in our group and among collaborators and indeed many of them are still using the latest version, dated 2000 (cf. Nicholl et al. 2013 Nature 502,346). Yet, the software had a number of limitations that were difficult to overcome remaining strictly confined to the IRAF environment
Eventually, the time was come for a step further. The main goal of this new attempt was to drastically reduce the time required to derive accurate SN photometry still maintaining the full control of the end user on all step of the analysis. Additional issues considered in developping the package were improving user interaction, software reliability and portability. The effort resulted in a new collection of scripts that are now written in python/pyraf. After extensive debugging, the last stable version of SNOoPY is now used by all member of our group in Padua along with some close external collaborators. Indeed, a few papers that make use of the updated SNOoPY were already published or are submitted (eg. Tomasella et al. 2013 MNRAS 434, 1636, Benetti et al. 2014 MNRAS 441, 298, Tartaglia et al. 2014 arXiv 1406.2120).
The new SNOoPY is a collection of python scripts calling standard IRAF tasks through pyraf and other specific analysis tools, in particular sextractor (Bertin,E. and Arnout,S. 1996 A&AS. 317,393) for source extraction and star/galaxy separation, daophot (Stetson, P.B. 1987 PASP 99,191) to measure the source magnitude via PSF fitting and hotpants (Becker A., 2015, ascl.soft, ascl:1504.004) for image difference with PSF match.
The source magnitudes are measured using the PSF-fitting technique, first subtracting the sky background estimated using a low order polynomial fit (typically a 2nd order polynomial) on the surrounding regions. The PSF is obtained by averaging the profiles of isolated field stars automatically selected in the observed frame. The fitted target is removed from the original frames, then a new estimate of the local background is derived and the fitting procedure is iterated. In typical runs the residuals are visually inspected to validate the fit.
Error estimates are obtained through artificial star experiment in which a fake star, of magnitude similar to that of the SN, is placed in the PSF-fit residual image in a position close to, but not coincident with that of the real source. The simulated image is processed through the PSF fitting procedure and the dispersion of measurements out of a number of experiments (with the fake star in slightly different positions), is taken as an estimate of the instrumental magnitude error. This is combined (in quadrature) with the PSF-fit error returned by daophot.
If a suitable template image is available, the user can choose to measure SN magnitude using template subtraction technique, after PSF matching using hotpants. Even in this case the residual sources is measured via PSF fitting because it was found to be less sensitive to the difference image noise.
A key feature is the ability to automatically match and compare the photometry of field stars in the SN field obtained in different nights (and typically with many different telescopes). This allows to calibrate a sequence of local stars and to adjust the photometric zero point of non photometric nights.
A few service modules are used to organise the observations, perform automatic astrometry, combine dithered images, automatically measure the seeing, measure zero points and color terms from standard star observations.
A (not updated) tutorial is in chapter 2 of L. Tartaglia (2016) PhD thesis.
New tutorial for version 2.0 (in preparation)
Portability and Distribution
The code is expected to work on any operating system where the required S/W can be installed. It was recently ported to the anaconda/astroconda python distribution.
At present THE CODE IS NOT PUBLICLY AVAILABLE.
The main reasons is that development is still in progress and that a complete user manual is not available. Therefore at the moment the code is distributed only among members of our group and very close collaborators.
Acknowledgment I want to thank all those who helped with suggestions and debugging in particular Andrea, Giacomo, Leonardo, Nancy and Stefano.
- 0.5 - [2014/01/01] First ALFOSC/AFOSC version
- 0.6 - [2016/10/01] Porting to astroconda/astropy
- 1.0 - [2019/04/01] Implemented as python package. Labelled ecsnoopy for gitlab distribution
- 2.0 - [2021/09/15] Revised management of parameter files and using private uparm for iraf
The following footnote is suggested for reference in the papers:
SNOoPy is a package for SN photometry using PSF fitting and/or template subtraction developped by E. Cappellaro. A package description can be found at http://sngroup.oapd.inaf.it/ecsnoopy.html.