| author | date | layout | slug | title |
|---|---|---|---|---|
herschellegacyproject |
2021-03-16 12:44:35 +0000 |
page |
faq |
FAQs |
The final tables are stored on HeDaM. All fields are available here. You need to click on the directory for a given field, then go in the data folder and download the fits file with name of the field. For example the final (5Gb) table for ELAIS-N1 is here:
Due to the size of these full field tables you might prefer to query the HELP VO; VOX.
Yes, this is available here. That is a link to what we call the 'A-list' that includes objects with two far infrared detections, a photometric redshift and SED fitting. You might also be interested in other samples. Perhaps you would rather query the virtual observatory to find everything detected in 250 micron SPIRE imaging for instance.
Blind catalogues are found directly from the far infrared images without using any other information. Because the Herschel maps are 'confused' these are not as deep as the main photometry whcih uses high resolution optical surveys to 'deblend' the far infrared maps and can effectively be deeper than the blind photometry. You could compare the numbers on the ELAIS-N1 field for instance. You would see that there are fewer objects and mainly brighter obejcts in the blind list here. While you would find many more in the main HELP catalogue here. This deblending is done with the XID+ code developed for HELP.
If you are just getting started you probably want to look at the final merged catalogues in what we call DMU32. These contain optical to far infrared photometry, photometric and spectroscopic redshifts and physical properties. Once you have found the main list you are interested in you might want to look at the larger data sets associated with your objects such as the full photometric redshift posterior distributions of individual SED fits. Please feel free to open a GitHub issue or email us and we will try to help you get the data that you want. The field overviews also provide a good entry point to begin to understand all the HELP data products.
The easiest way to conduct cross matches is using the virtual observatory. Here is an example notebook to query the VO using the Python PyVO tool. Alternatively you can use Topcat using the same query as for PyVO. The TAP service for conducting such queries is here:
Alternatively you can download the tables and cross match your own tables locally using Topcat, Astropy or your preferred tools.