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Selecting interesting objects using CIGALE physical properties


As covered in our introduction to using the HELP virtual observatory server (VOX), we can easily search the HELP database for catalogues in images. In this example, we show how you can do similar searches, but using CIGALE properties rather than positions. One of the advantages of HELP is that it covers a large area, and with a larger area, you can get larger statistical samples of the rarer objects.

In this notebook, we will search the entire HELP database for some galaxies with extreme starformation and get the corresponding SPIRE cutouts.

In [1]:
!pip install pyvo
Collecting pyvo
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In [3]:
from astropy.io import ascii, fits
import numpy as np
from astropy.table import Table
import pylab as plt
import seaborn as sns
%matplotlib inline
from astropy import wcs
import pyvo as vo

First, create the TAP service using PyVO

In [5]:
service = vo.dal.TAPService("https://herschel-vos.phys.sussex.ac.uk/__system__/tap/run/tap")

Lets select the top 1000 galaxies which have high starformation rate (i.e. SFR>1000 $M_{\odot}/yr$)

As before, we need to create a query, so:

  • use the SELECT operational statement and ask for the TOP 1000 results
  • we specify what columns we want (i.e. redshift, starformation rate and stellar mass), rather than returning all (as before)
  • Tell the VO to get the results FROM herschelhelp.main, i.e. the main HELP masterlist. You can find the other tables you could use here.
  • specify our condition
In [8]:
resultset= service.search("SELECT TOP 100 ra,dec,redshift,cigale_mstar,cigale_sfr FROM herschelhelp.main WHERE cigale_sfr>1000")
In [9]:
masterlist=resultset.to_table()
In [24]:
masterlist.show_in_notebook()
Out[24]:
Table length=100
idxradecredshiftcigale_mstarcigale_sfr
degdegMsunMsun / yr
034.6715089475648-5.646773158297615.5069189901422274.7421497.14628391181
134.7214719475648-5.657020158297615.1541439717232811.7252592.40844298865
234.8559762522776-5.693541124773234.1005449923925549.1771588.63468495625
335.0222152722776-5.698050324773234.2907799333947897.975045.7118616998
434.9233084422776-5.609765204773235.6961135131688898.3565462.43158380247
535.0984050822776-5.512915234773235.1875633300196639.8461725.04141562314
634.6088506122776-5.461798504773234.15591809691425872.294797.29912286647
734.7898939475648-5.420532158297611.94071633228166190.241571.92375035589
834.8067338722776-5.357144724773232.9219262978332262.5991540.89465885952
934.8919648322776-5.349492974773232.88031364555930395.423619.57801552597
1034.9350279475648-5.235795158297615.587839502900281.58831087.58841628607
1135.2255119475648-5.802906158297623.7632406959308136.1671103.98605142256
1235.1835389475648-5.739588158297614.5042593236497030.5351602.42365491299
1335.3909650422776-5.811900114773231.8772174825041572.362097.83103916779
1435.2058528730314-5.545750285374234.2979733028434993.2431957.64582775986
1535.6529233022776-5.647810264773232.8518175000659329.1271109.76505721406
1635.5107039475648-5.564112158297612.7811174837658438.8321114.41373064963
1735.5920774685735-5.56647869029122.2201144383633793.2471310.37760246103
1835.1453549475648-5.344941158297624.7254248881447432.8751326.45569815482
1935.4081347722776-5.443442904773235.74462366149415441.076267.24256980812
2035.3188729475648-5.346463158297615.0969427536638253.52112357.2772583364
2135.1233584522776-5.172980784773232.658407490714991.2951060.7713303599
2235.5321425322776-5.310743184773232.7153237283875750.2961650.73939180245
2335.6350796822776-5.378617744773233.1984241024752834.5311192.27002757693
2435.6156722322776-5.130769634773231.0448107872729745.1131770.10404893357
2534.6320449475648-5.104770158297624.157511058748815.4041631.15205728139
2634.5398173122776-5.033423524773232.8353188435484765.0291272.10449442896
2734.5343513922776-4.979256294773232.82761130620469548.152981.44407316261
2835.0087127422776-5.017244004773231.7836130544935192.4871178.48403138048
2934.8470674622776-4.863568704773232.61342156313508146.112081.60922436172
3034.8593275953778-4.870179328968232.0086380047213552.9961446.19152001234
3134.6716713522776-4.743539434773234.19661659172763657.324388.57513284079
3234.8210799475648-4.662229158297611.961431726346315.8861180.27336165523
3335.0459769475648-4.576807158297625.439843069469197.19191732.91174083607
3435.0597432722776-4.488107374773232.3713738926053578.8751240.20201782855
3534.9913136522776-4.480968464773234.0335223226950110.7093790.45155527935
3635.0725659475648-4.478379158297625.740171980839213.57282911.59188388255
3735.1126787522776-5.067233814773234.0084215299221733.8061503.75407866323
3835.1993039475648-4.931032158297624.796867306776109.56491067.50475265492
3935.3034509475648-4.858180158297615.4182516930444764.8131410.20116267768
4035.3437946222776-4.782987354773232.6282697832431779.7171001.39657979695
4135.4100166822776-4.581763144773235.70971100782300736.83397.05890650607
4235.3986569475648-4.531852158297624.9121549188376595.571530.85478372981
4335.3665699475648-4.469453158297615.1317967330337272.6526574.87574209613
4435.3829471122776-4.504348754773235.6973396564451601.5510507.3852549397
4535.3637209475648-4.438325158297624.8074922040330241.2082465.22681594516
4635.5560172722776-4.694896264773233.2863245644401107.0841032.67657367729
4735.4152109475648-4.584186158297615.298175920551391.9141104.88476324426
4835.5466289475648-4.414047158297623.515486547153529.72781178.90293826913
4935.6139789475648-4.380140158297613.8538607827120555.6891617.39514188156
5036.0979485222776-5.763250334773233.4034188106563792.7551120.77840971595
5136.2102444822776-5.675515844773231.01853255706377043.441920.72815887178
5235.7069823922776-5.248112514773231.95881402346615034.021351.51725035982
5335.7156899722776-5.120149724773234.06461375482767484.953640.17659626632
5435.9558417215293-5.081236556844012.3927292148719758.5661728.19322887365
5536.2055862369884-5.346762204015014.6843927085429952.3692473.71860380698
5636.2323905222776-5.366648214773232.7901296664111033.8891131.27636639451
5736.3505779475648-5.474616158297613.7484582577694281.7891565.6044057002
5836.5147909475648-5.179557158297611.7127127317669358.5941041.47581013654
5936.4796089475648-5.087752158297622.58781134991405321.311095.46183902282
6036.6752489475648-5.333795158297625.7714432258357395.3111187.85391142476
6136.0797872822776-5.011465554773231.66382539040423698.192447.05803634788
6236.2182438563488-4.95826449288324.919934860166563.60051283.26531165882
6336.1363428563488-4.69622349288324.8449181441794701.7131053.62219420884
6436.1849008563488-4.72643949288322.110789988800528.96491076.1570203295
6536.2263017522776-4.719974444773234.2404465282016792.0392505.3326753472
6635.7583499475648-4.643195158297623.6662190130950166.1631167.69113957868
6735.8901949475648-4.665600158297612.273326605991190.3151035.17172426637
6835.7154351707378-4.40294520579862.0026283111120546.351333.63498274499
6935.799661809384-4.384291135288522.7166389712769363.9291032.22170944533
7036.1784708522776-4.596429734773233.4073131316878777.9151277.07255083826
7136.4017967222776-5.003313574773230.61864471631892546.511292.18018141735
7236.3099753122776-4.783416614773231.8894382538251352.1291613.88982482428
7336.8094048563488-4.92879749288325.17832233484517578.695927.14655026521
7436.81473108181-4.930937082983492.5745223036499146.1851149.05772914591
7536.6234348922776-4.820713654773233.4236525455414268.461636.39977949769
7636.6267778563488-4.77157949288325.0673871297489556.7184639.13391835221
7736.8464058563488-4.81504849288324.6405189519721627.4721262.38199014912
7836.8061359475648-4.691953158297614.806530754904554.70291168.71031733041
7936.6503224522776-4.574743364773232.7309238012956443.0171594.59579047057
8036.5969715122776-4.481510014773233.7854961992627171.4722547.57023305508
8136.6027918563488-4.38658049288322.3687206288255781.9411039.54895968725
8236.6452738563488-4.32684049288322.8103101786249465.1911012.89513206236
8336.6667580422776-4.302609184773231.8706128160062182.4021078.41589039397
8434.7453059022776-4.399000604773232.8043314699519653.9391391.81738541518
8534.7760575722776-4.326136414773233.2416250345855822.6131979.99411443603
8634.5889688822776-4.180459034773232.8536508391759141.4041362.51463338931
8734.5408193322776-4.135815164773233.7948373909098919.5832310.70290351374
8834.6282772122776-4.051627964773233.0815459697147914.4691225.31450004264
8934.7390319475648-4.114823158297615.10921802990206500.364798.3517605931
9034.8780879475648-4.354902158297613.6427443879348217.081466.75402273708
9134.9103013550186-4.368803929641024.0738398059999419.4831092.08617444129
9234.5447664622776-4.033644274773235.7821358145873841.9369107.79626997041
9335.0128507522776-3.879825424773232.7428849021139324.3182246.37475295397
9435.2901749475648-4.326565158297625.534243104195145.7533839.67359349602
9535.3461714822776-4.328383254773232.842345195627794.30271765.84734661283
9635.1544219475648-4.153350158297615.0421705470691338.3531895.69249168985
9735.3975229475648-4.033837158297623.490835412478559.3361263.75716608771
9835.4759145822776-4.249605774773232.5175320818810830.811876.23522781576
9935.6446593622776-4.071594274773232.57123376545743.8331116.57448298443

Lets look at the marginalised distributions

In [33]:
g=sns.PairGrid(masterlist[['redshift','cigale_sfr','cigale_mstar']].to_pandas())
g.map_diag(plt.hist)
g.map_upper(plt.scatter)
g.map_lower(plt.scatter)

g.axes[0,1].set_xscale('log')
g.axes[0,2].set_xscale('log')
g.axes[1,0].set_yscale('log')
g.axes[2,0].set_yscale('log')

Lets look at the object that has an SFR of over 12000 $M_{\odot}/yr$

Authors: Peter Hurley

The Herschel Extragalactic Legacy Project, (HELP), is a European Commission Research Executive Agency funded project under the SP1-Cooperation, Collaborative project, Small or medium-scale focused research project, FP7-SPACE-2013-1 scheme, Grant Agreement Number 607254.