# MaLTPyNT documentation¶

The MaLTPyNT (Matteo’s Libraries and Tools in Python for NuSTAR Timing) suite is designed for the quick-look timing analysis of NuSTAR data. It treats properly orbital gaps (e.g., occultation, SAA passages) and performs the standard aperiodic timing analysis (power density spectrum, lags, etc.), plus the cospectrum, a proxy for the power density spectrum that uses the signals from two detectors instead of a single one (for an explanation of why this is important in NuSTAR, look at Bachetti et al., ApJ, 800, 109 -arXiv:1409.3248). The output of the analysis, be it a cospectrum, a power density spectrum, or a lag spectrum, can be fitted with Xspec, Isis or any other spectral fitting program.

Despite its main focus on NuSTAR, the software can be used to make standard spectral analysis on X-ray data from, in principle, any other satellite (for sure XMM-Newton and RXTE). Input files can be any event lists in FITS format, provided that they meet certain minimum standard. Also, light curves in FITS format or text format can be used. See the documentation of MPlcurve for more information.

## What’s new¶

Note

MaLTPyNT provisionally accepted as an Astropy affiliated package

In preparation for the 2.0 release, the API has received some visible changes. Names do not have the mp_ prefix anymore, as they were very redundant; the structure of the code base is now based on the AstroPy structure; tests have been moved and the documentation improved.

MPexposure is a new livetime correction script on sub-second timescales for NuSTAR. It will be able to replace nulccorr, and get results on shorter bin times, in observations done with a specific observing mode, where the observer has explicitly requested to telemeter all events (including rejected) and the user has run nupipeline with the CLEANCOLS = NO option. This tool is under testing.

MPfake is a new script to create fake observation files in FITS format, for testing. New functions to create fake data will be added to maltpynt.fake.

## Preliminary notes¶

### MaLTPyNT vs FTOOLS (and together with FTOOLS)¶

#### vs POWSPEC¶

MaLTPyNT does a better job than POWSPEC from several points of view:

• Good time intervals (GTIs) are completely avoided in the computation. No gaps dirtying up the power spectrum! (This is particularly important for NuSTAR, as orbital gaps are always present in typical observation timescales)
• The number of bins used in the power spectrum (or the cospectrum) need not be a power of two! No padding needed.

MaLTPyNT does not supersede nulccorr (yet). If one is only interested in frequencies below ~0.5 Hz, nulccorr treats robustly various dead time components and its use is recommended. Light curves produced by nulccorr can be converted to MaLTPyNT format using MPlcurve --fits-input <lcname>.fits, and used for the subsequent steps of the timing analysis.

Note

Improved livetime correction in progress!

In the upcoming release MaLTPyNT 2.0, MPexposure tries to push the livetime correction to timescales below 1 s, allowing livetime-corrected timing analysis above 1 Hz. The feature is under testing

### License and notes for the users¶

This software is released with a 3-clause BSD license. You can find license information in the LICENSE.rst file.

If you use this software in a publication, please refer to its Astrophysics Source Code Library identifier:

1. Bachetti, M. 2015, MaLTPyNT, Astrophysics Source Code Library, record ascl:1502.021.

In particular, if you use the cospectrum, please also refer to:

1. Bachetti et al. 2015, ApJ , 800, 109.

I listed a number of open issues in the Issues page. Feel free to comment on them and propose more. Please choose carefully the category: bugs, enhancements, etc.

### Acknowledgements¶

I would like to thank all the co-authors of the NuSTAR timing paper and the NuSTAR X-ray binaries working group. This software would not exist without the interesting discussions before and around that paper. In particular, I would like to thank Ivan Zolotukhin, Francesca Fornasini, Erin Kara, Felix Fürst, Poshak Gandhi, John Tomsick and Abdu Zoghbi for helping testing the code and giving various suggestions on how to improve it. Last but not least, I would like to thank Marco Buttu (by the way, check out his book if you speak Italian) for his priceless pointers on Python coding and code management techniques.