Cluster Cosmology

h1. Summer final results

Stage 1: Creating Colored JPGs of Panstarrs Data for Planck's Unconfirmed Clusters

All information is located in /data1/users/hhead/CLUSTER_CANDIDATE_JPEGS/ 

Included in this directory is the main_script.sh that makes the jpegs, an automator_script.sh which takes in a list of cluster IDs to mass-produce jpegs, a chart with information on each cluster (ex. if a cluster is visible or if photometric issues are present within the image), three directories named for the types of classification (NOT_CLUSTERS, POTENTIAL_CLUSTERS, and UNCERTAIN_CANDIDATES), and a README.txt to explain how to work the codes. 

Stage 2: Source Extraction Code

The code for making the SExtractor catalog files can be found at /home/moon/hhead/nextstage.sh

This code goes through the whole process of finding the images of the cluster, unpacking them, running the SExtractor script, and putting these files into a new directory. 

Stage 3: Data Ingestion

Before the unfortunate crash of the database, the next step before SLR could be run was using a data ingestion process. This was done using /home/moon/hhead/ingestclusterdata.sh. 

Stage 4: SLR

Part 1:

Originally, SLR was completed using the script at /home/moon/hhead/PHOT_CAL/runslr.sh 

A slight mistype in one of the options meant that many had to be redone, but shortly after beginning this process, the database crashed. Therefore, a new method had to be found to continue with the SLR process. 

Part 2:

A Python code was eventually found for the purposes of this project. The code is located at /home/moon/hhead/PHOT_CAL/SDSS_SLR/big-macs-calibrate/fit_locus.py. And example of how to run this code can be found in example 3 of the README file within this same directory. In order to run this code, we had to make a columns file, a filters file (available at /home/moon/hhead/PHOT_CAL/SDSS_SLR/big-macs-calibrate/FILTERS/SDSS-?.res) , and test the boostrap option to get the best results. 

Example of the command used for running our code: "python fit_locus.py --file catalog_101.fits --columns SDSS.columns --extension 1 --bootstrap 2 -l -r RA -d DEC -j 

Before this code can be run though, the script /home/moon/hhead/PHOT_CAL/SDSS_SLR/big-macs-calibrate/SDSS_makingplotinfo.sh must be run on the cluster. And after the SLR code is successful, the script in the same directory called createinputforredsequencer.sh must also be run for the next step. 

Stage 5: Red-sequencer code

This code was developed by Christina Hennig and Jiayi Liu. This code is a two-part process. The first part is the checkphotoz, located at /home/moon/hhead/package/. It intakes the R500, BCG_RA, BCG_DEC, and an output file name to make a rough estimate of the redshift for a cluster. Next, the Python code plotZ.py (also located in the same directory) takes in a file name and outputs both a graph from which to determine the best likelihood color combination and sends to the screen the uncertainties on the peak likelihood in each color combination. In total for the unconfirmed clusters, the process goes as follows: 

First, training is necessary to know what kind of general redshift range the cluster will be in. 

(WIP. WILL CONTINUE LATER)

(WIP. WILL FINISH SOON.)