No Evolution in the IR-Radio Relation for IR-Luminous Galaxies at z
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J. Healy,
1;2?S-L. Blyth,1E. Elson,1;3W. van Driel,4;5Z. Butcher,6S. Schneider,6
M.D. Lehnert,
7and R. Minchin8;9
1 Department of Astronomy, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa2Kapteyn Astronomical Institute, University of Groningen, Landleven 12, 9747 AV Groningen, The Netherlands
3Department of Physics and Astronomy, University of the Western Cape, Robert Sobukwe Road, Bellville, 7535, South Africa
4GEPI, Observatoire de Paris, PSL Research University, CNRS, 5 place Jules Janssen, 92190 Meudon, France
5Station de Radioastronomie de Nancay, Observatoire de Paris, CNRS/INSU USR 704, Universite d'Orleans OSUC, route de Souesmes,
18330 Nancay, France
6University of Massachusetts, Astronomy Program, 619E LGRT-B, Amherst, MA 01003, U.S.A.
7Sorbonne Universite, CNRS UMR 7095, Institut d'Astrophysique de Paris, 98bis bd Arago, 75014 Paris, France
8Arecibo Observatory, National Astronomy and Ionosphere Center, Arecibo, PR 00612, USA
9SOFIA-USRA, NASA Ames Research Center, MS 232-12, Moett Field, CA 94035, USA
Accepted XXX. Received YYY; in original form ZZZ
ABSTRACT
Histacking has proven to be a highly effective tool to statistically analyse average Hi properties for samples of galaxies which may or may not be directly detected. With the plethora of Hidata expected from the various upcoming Hisurveys with the SKA Precursor and Pathfinder telescopes, it will be helpful to standardize the way in which stacking analyses are conducted. In this work we present a new python-based package, HISS, designed to stack Hi(emission and absorption) spectra in a consistent and reliable manner. As an example, we use HISS to study the Hicontent in various galaxy sub-samples from the NIBLES survey of SDSS galaxies which were selected to represent their entire range in total stellar mass without a prior colour selection. This allowed us to compare the galaxy colour to average Hicontent in both detected and non-detected galaxies. Our sample, with a stellar mass range of108Y orket a l.
2 000 and ultraviolet (Galaxy Evolution Explorer,B ianchi&
GALEX Team
2 000 )su rveys.H owever,co mparatively little is known about the evolution of the gas in galaxies. Understanding how the cold gas evolves is important as it is the raw fuel for the formation of stars and thus galaxies. Neutral atomic hydrogen (Hi) forms the most signi- cant reservoir of neutral gas in galaxies. Studies have shown that blue, star-forming galaxies have a higher fraction of E-mail: julia@ast.uct.ac.zaHigas compared to red, quiescent galaxies (e.g.Ro berts&Haynes
1 994Ga vazziet a l.
19 96M cGaugh& d eB lok
1 997Cortese et al.
2 011F abelloet a l.
2 011B rowne ta l.
20 15 which suggests that Hiplays an important role in star formation. Hiis dicult to detect in most galaxies beyond the local universe (z0:06) with existing radio telescopes due to the weak nature of the emission. Researchers have had to exploit dierent techniques in order to measure very weak Hisignals from galaxies. One such technique is Histacking; with this technique, an average Himass per galaxy can be estimated by co-adding the Hispectra of a sample of galaxies in which Hiis not necessarily directly detected.The idea of co-adding the undetected Hispectra in
studies of the gas content in galaxies was rst presented byZw aanet a l.
2001)an d
Ch engaluret a l.
2001).B oth groups were studying the Hiwithin galaxies located in and
©2017 The Authors??X??¬∞???????∞??∞←←??∫????⎷??GA?←←∞←A?}←??∞?
2J. Healy et al.
around clusters. With low detection counts in their samples, both groups independently co-added the non-detections in an eort to obtain a statistically meaningful averaged detection for their samples. Stacking analyses have become commonplace in the last15+years. The technique has been applied in various areas:
Hicontent of galaxies in dense environments and how the gas content relates to other observables (Chengalur et al.
2001V erheijenet a l.
2 007La het a l.
2 009Ja eet a l.
2 016 gas content of active galaxies (Gerebet al .
2 013 2 015 measurement of at low to intermediate (z<0:4) redshiftsLahet a l.
2 007Del haizeet a l.
2 013Rh eee ta l.
2 013 2016);a ndu singst ackingto st udyth erel ationsb etweenH i and various stellar mass/star formation indicators (
Fabello
et al. 2 011 2 012B rownet a l.
2 015 20 17G erebet a l.
2 015 The gas scaling relations for galaxies are average trends which relate the Himass (MHi) or Himass to stellar mass (M ?) ratio (gas fraction,fHi) to various other galaxy properties. Stacking a sample of5000 galaxies with M ?>1010Mthat had both ultraviolet and optical imaging,F abelloet a l.
2011)fo undu singH istacking that the gas fraction correlates better with NUVrcolour than stellar mass. This result was later conrmed and extended to a sample with M ?>109MbyB rownet a l.( 2015). To date, studies of the Higas scaling relations have been limited to samples with M ?>109M, a mass which has been identied as a turning point around which the M Hi vs M ?slope changes (Huang et al.2 012;M addoxet a l.2 015). Directly detecting Hiwith current radio telescopes beyondz0:1is challenging due to the long observing times required (e.g. HIGHz (
Catinella & Cortese
2 015 ) on Arecibo using>300hours and CHILES (Fernandeze ta l. 2016He sset a l.
2 018 )o nth eJV LArequ iring1000hours of observing time). The upcoming surveys with the SKAPrecursors and Pathnders (e.g. MeerKAT,
B oothet a l.
2009;A SKAP,
Jo hnstonet al .
2 008 ;A PERTIF,V erheijen
et al. 2 008 )wi llg reatlye xtendt here dshiftra ngeo ver which Hiin galaxies is studied, either directly or indirectly. Techniques such as Histacking will have an important role to play in order to study the average Hiproperties of dierent galaxy samples, particularly at higher redshifts (z&0:6). Deep SKA Precursor surveys such as LADUMA1Holwerdaet a l.
2 011 )o nM eerKATh aveid entiedst acking as an important tool to probe higher redshifts, and push to lower Himass limits.As we rapidly approach the start of the Precursor
Surveys, work is under-way on developing a data analysis toolkit that will enable consistent and comparable studies of the survey data. Already available is the versatile source nder,SoFiA(Serraet a l.2 015).Wi tht hei mportant role that stacking will play in the analysis of high redshift and low mass samples, it is imperative that a tool capable of reliably and consistently stacking Hispectra is developed.In this work, we present our new software pack-
1Looking At the Distant Universe with the MeerKAT Arrayage, HISS, that has been designed for the astron-
omy community in response to the need for a stack- ing software package. HISS can be downloaded from https://github.com/healytwin1/HISS. We use HISS to revisit the gas scaling relations with a sample of 1000 galaxies from the Nancay Interstellar Baryon Legacy Extra- galactic Survey (NIBLES v anDriel et a l. 2 016 ).N IBLES is an SDSS-selected targeted Hisurvey with the Nancay Radio Telescope which aims to study the Hiproperties of galaxies as a function stellar mass, covering a representable M ?range of the ensemble of galaxies in the nearby Universe. The outline for this paper is as follows: the rst half of the paper (Section 2) describes the design of the HiStacking Software (HISS). In the second half of the paper (Section 3 and Section 4), we give an introduction to the NIBLES Sur- vey and the ancillary data we use in our stacking analysis and describe the classication of Hinon-detections. We use the NIBLES sample to explore the well-known Himass to stellar mass scaling relations in Section 4, and nally, sum- marise our ndings in Section 5.2 THE HISTACKING SOFTWARE (HISS)
The stacking method that the software needs to implement can be summarized by the following steps: (i)I ngest1 D21cm Hispectra (radio data) and list of
associated redshifts for the sample of galaxies to be stacked; (ii) S pectrallysh ifta ndre- scaleth eH ispectra to align the expected line emission at a common frequency (usually the Hirest frequency:1420:4058MHz); (iii) W eightt hesp ectra( accordingt oa p referredw eight- ing scheme) and co-add the spectra. We have designed and created the HiStacking Software (HISS) package, after discussion with colleagues and with the following guiding principles in mind: be freely available in most operating systems to be open source easy to modify (extensible) stack hundreds or thousands of galaxy spectra in an ecient and reliable manner.The Python
2programming language was chosen for
development of HISS because it is freely available, can be used on any operating system, and is also one of the most commonly used languages within the astronomy community. Where appropriate, HISS makes use of the publicly available Python modules (such as AstroPy, NumPy, SciPy, etc.) which have been optimized for data input, manipulation, and display.Fig. 1
sh owsa owd iagramo fh owt hep rocessesre- quired to create a stacked spectrum are incorporated in the dierent modules of HISS. In the sections that follow we will illustrate how each of the processes highlighted inF ig.1
a re implemented. Basic instructions on how to run HISS can be found in Appendix A 1 2 Developed in Python 2.7, but compatible with Python 3MNRAS000,1 {42( 2017)
NIBLES III: Stacking3Start
Input Module (x2.2)Cong File
(x2.2)CatalogueFile (x2.2)User input point 1
BinCatalogue?
(x2.2)Bin ModuleStack Module (x2.3)
1.S electc atalogueo fH ispectra (x2.3.1)
2.C onvertsp ectrato res t-frame( x2.3.2)
3.C o-addsp ectra( x2.3.4)Spectra
Analysis Module (x2.4)
Calculate prole detection statistics (x2.4.1)
Manipulate stacked spectrum (x2.4.2)
Characterise stacked prole (x2.4.3)
Calculate uncertainty (x2.4.4)
Save results (x2.5)Uncertainty Module (x2.4.4)StackingOutputDisplay
Data? (x2.6)User input point 2Display Module (x2.6)Stopnoyes
noyesFigure 1:This
ow diagram shows how the individual spectra and user information are taken by HISS to produce a stacked
spectrum from which average galaxy properties (such as total Himass, MHi, and the Hi-to-stellar mass ratio,fHi) may be
extracted. The orange rectangles show the six modules of the package, the blue parallelograms show the points of input or
output, and the green diamonds show where the user may choose to incorporate the optional modules.MNRAS000,1 {42( 2017)
4J. Healy et al.
2.1 Simulated Data
When developing new data analysis tools, one of the most important steps is to quantify the accuracy and reliability of any generated results. For this purpose, simulated spec- tra are used instead of real spectra because properties of the input data are then well known. In this work, we use a data set of 1000 simulated Hiproles of galaxies to il- lustrate and test the capabilities of HISS. The simulated Hispectra are created using the formulation outlined inObreschkow et al.
2009c,A ppendixA ),a nda reb asedo n the evaluated properties extracted from the S
3-SAX cata-
logue (Obreschkow et al.
20 09a
b c )fo rga laxiesi na red shift range of0:12.2 Catalogue of Hispectra
When initiated, HISS requires some information about the sample of Hispectra { seeF ig.2 fo ra no verview.Th eI nput Module requires two input les: a conguration le and a catalogue le. There are two ways to provide HISS with the required information: the user can provide a text le in JSON3format or use the graphical user interface shown
inFi g.2
t og eneratea c onguration le( alsoin JS ON format). The JSON format is used for the conguration le as it is easy to read/write, and when imported into Python, the information is dumped into an easy - to - access Python dictionary. The conguration le includes options such as dierent weighting functions for the stacking procedure, and options to bin the stacking sample based on additional data provided in the catalogue le.The catalogue le is a user-created text le in CSV
4 format that contains at least the following columns,Object ID
Spectrum lename
Redshift
Redshift uncertainty
Other data (optional)
for each spectrum that the user would like to include in the stack. Although the catalogue le may contain any number of columns, the non-optional columns mentioned above are those that are required to run HISS. Additional columns containing numerical information such as stellar mass or optical colour may be used to rene the catalogue into a number of dierent sub-samples to be stacked separately.This rening is done through the Bin Module (
Fig. 1
).I n this rst version of HISS, bins can only be created using one quantity, e.g. stellar mass or colour, and the stacked results are stored for each sub-sample 5. During the processing of the conguration le, the code 3 JSON stands for JavaScript Object Notation, a JSON format- ted le has the extension.json4comma separated values
5In the current implementation this process is sequential i.e. each
binned sub-sample of spectra is stacked one after the other. Thisprocess will be parallelised in future versions.checks that the catalogue le and location of the spectra
exist. If these checks are passed, HISS will continue.2.3 Stacking the spectra
The stacking procedure is the heart of HISS, and is con- trolled by the Stack Module which is responsible for read- ing in and preparing each spectrum for stacking, as well as maintaining the stacked spectrum and associated informa- tion (e.g.,number of objects in the stack, stacked noise). One of the features of this module is the option that allows the user to watch the progress of the stacking in a window such as the one inF ig.A 1
.T hep rogressw indowsh owsea cho f the spectra in the observed frame as they are read in, as well as the individual spectra as they are converted to the rest-frame and then added to the total stacked spectrum.2.3.1 Reading in the spectra
Regardless of how the Hispectra were created, HISS requires that the spectra be in a text le type format containing a column for the spectral axis (either frequency or velocity) and another for the ux density (in units ofJy, mJy, Jy or
ux density/beam).Fig. A2
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