SEDOBS: A tool to create simulated galaxy observations

Journal Pre-proof SEDOBS: A tool to create simulated galaxy observations R. Thomas

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S2213-1337(19)30004-6 https://doi.org/10.1016/j.ascom.2019.100354 ASCOM 100354

To appear in:

Astronomy and Computing

Received date : 14 January 2019 Accepted date : 14 November 2019 Please cite this article as: R. Thomas, SEDOBS: A tool to create simulated galaxy observations. Astronomy and Computing (2019), doi: https://doi.org/10.1016/j.ascom.2019.100354. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Elsevier B.V. All rights reserved.

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SEDOBS: A tool to create simulated galaxy observations Romain Thomas

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Abstract SEDOBS is a python software designed to produce large samples of simulated galaxy observations. It allows for the creation of several types of mock observation such as photometry, spectroscopy, multi-spectroscopy and full spectro-photometric combinations. It has been primarily created to test galaxy template fitting method against any configuration of data. It has been designed to be user-friendly easy to manipulate. SEDOBS is an open source software and it is published under the GNU general public license (v3). It is distributed from a github repository with an extensive documentation and can be installed from the python official repository (pip). Keywords: Galaxy, Simulation, Observations, Spectroscopy, Photometry

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Extragalactic studies rely on the ability of astronomers to measure physical quantities from observations taken from different space-based and/or ground based observatories. One of the widely used 30 techniques to measure galaxy physical properties is the template fitting method (e.g. Bolzonella et al. 2000; Ilbert et al. 2006; Michalowski et al. 2008; Robitaille et al. 2007; Walcher et al. 2011; de Barros et al. 2014; Thomas et al. 2017a, just to name a few). 35 It has been widely used to compute physical quantities such as stellar masses, star formation rates, ages, dust extinctions. It can be based on both photometric and spectroscopic data and applied to various objects like stellar objects(e.g. Robitaille et al. 2007; 40 Bayo et al. 2008), galaxies or AGNs (e.g. Arnouts et al. 1999; Hatziminaoglou et al. 2008; Franzetti et al. 2008; Calistro Rivera et al. 2016). This method consists of a comparison of the observed data to a set of synthetic spectra built from a large parameter 45 space. This comparison can be done through different algorithms like χ2 fitting or Monte Carlo Markov Chain (MCMC) (e.g. Acquaviva et al. 2012). The scientific results presented in these studies are strongly

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dependent on the ability of such methods to retrieve precise and accurate parameters as well as the knowledge of their limitations. The latter can arise from the theoretical models that carry degeneracies between parameters (e.g. the age-metallicity degeneracy, Worthey 1999). On the other hand, they can also come from the data themselves with the uncertainties on photometry and spectroscopic error budgets and the type of input data used for fitting. Different input can strongly influence the output of the fitting process. The stellar mass for example will strongly depend on the presence and sampling of near Infrared (NIR) data where the old stars are strongly emitting. On the other edge of the optical spectrum, the ultraviolet (UV) data will strongly influence the estimation of the unobscured SFR (Thomas et al., 2017b). Therefore any data combination that is used in template fitting should be properly tested in order to estimate the precision and accuracy of this method for that given combination. Efforts have already been made in the literature to test SED-fitting algorithms and their ability to retrieve galaxy parameters (e.g. Maraston 2005a; Bolzonella et al. 2010; Pforr et al. 2012, 2013) but they rarely include observational effect such as the sky emission. In the process of the development of the spectrophotometric fitting tool, SPARTAN1 , it became clear that we needed an independent tool to be able to test

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1. Introduction

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European Southern Observatory, Av. Alonso de C´ ordova 3107, Vitacura, Santiago, Chile Email address: [email protected] (Romain Thomas) URL: 1 https://astrom-tom.github.io/SEDSIM/build/html/index.html Spectroscopic And photometRic fitting Tool for Astronom(Romain Thomas) ical aNalysis, Thomas, in preparation. Preprint submitted to Astronomy and Computing

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2. SEDOBS in a nutshell

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For the first version of the software we included one of the most frequently used synthetic galaxy template in the literature; the models from (Bruzual & Charlot 2003, hereafter BC03). They have been used in the literature at both low and high redshifts (e.g. Bouwens et al. 2009; Moresco et al. 2012; Madau & Dickinson 2014; Brinchmann et al. 2004; Salim et al. 2016). BC03 templates are composed of three fundamental parameters: Metalicity (Z), initial mass function (IMF) and spectral resolution (R). They have been pre-convoluted with the two most used star formation histories in the literature, the declining exponential (Bell & de Jong, 2001; Smecker-Hane et al., 2002; Lee et al., 2007; Raichoor et al., 2012; Wu et al., 2018; McLure et al., 2018) and the delayed exponential (Gobat et al., 2008; Onodera et al., 2010; Dom´ınguez S´ anchez et al., 2016; Cassar` a et al., 2016; Ciesla et al., 2017). Each of these star formation histories are defined by an Star Formation History (SFH) timescale parameters (τ ). In case of an exponentially declining SFH, this parameter corresponds to the e-folding time of the SFH while in the case of an exponentially delayed SFH it corresponds to the time between the onset of star formation and the peak of maximum of the SFH. This parameter can take any value from 0.1 Gyr to 10.0 Gyr. The range of age of the synthetic templates is pre-defined and goes from 106 yr to 1.5 × 1010 yr. The user can use any value of age between these two limits. Emission lines can be included in the synthetic template. The recipe for including the lines is the one developed in Schaerer (2003) that creates the emission lines based on the UV continuum flux that is translated to Hβ flux. The other emission lines are then constructed using line ratio defined in Anders & Fritze-v. Alvensleben (2003) and are metallicity dependent. The Lyα line is treated in a particular way. As it can be both in emission and in absorption, the user is given the possibility to give the fraction of Lyα emitters that will be present in the final sample. The default value is 50% and can be changed to any required value.

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The software is divided into four mains parts that are executed consecutively. They are presented in Figure 1. The first part is the configuration defini-125 tion. SEDOBS requires the user to give a sample configuration. This configuration includes the final set of simulated magnitudes or spectra, the sky parameters, the parameter space of synthetic template (see section 3 for more details). Once done, SEDOBS130 starts to prepare the simulation run. This part checks the configuration that was given. A set of association rules has been defined in order to check if the configuration can be executed (for instance, if a spectrum has to be simulated, was a spectral resolution given? ).135 All these rules are checked and if one fails the software will inform the user what has to be corrected. Then SEDOBS enters the simulation part. This is where the actual simulated observations are created. During this process, templates are redshifted and at-140 tenuated by the extinctions (by dust and/or intergalactic medium) (cf. Sect. 4). Then simulations are created. For each object, several outputs are created describing the original synthetic template and the created simulation. They are also combined in 145 mock observed catalogues.

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3.1. Galaxy models

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its capabilities. This led to the development of SEDOBS that we present in this paper. It is a software that has been created to make the forward modelling of large galaxy observations samples which include100 noise and sky emission. As it has been designed to create multiple types of data configurations it is particularly useful for most template fitting studies but can also be used for other purposes like the preparation for telescope time proposals. We aim, in this105 paper, to present how SEDOBS is created and what it can produce. We leave technicalities about the implementation (for example input and output) to the online documentation. The paper is organized as follows. In section 2, an overview of the SEDOBS design110 is presented. Section 3 describes the various theoretical ingredients available to create simulations. In section 4 we detail how the simulations are created, both for photometry and spectroscopy. Finally we briefly present the user interface and the test runs115 that are included in the software.

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3.2. Extinctions The use of extinction is optional. SEDOBS includes multiple dust extinction curves: Calzetti’s law (Calzetti et al., 2000), the Large Magellanic Cloud extinction curve from Fitzpatrick (1986), the Milky

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Way extinction prescription from Allen (1976) and the prescription of Prevot et al. (1984) for the extinction of the Small Magellanic Cloud. They are applied to the synthetic template through using the total ex-165 ctinction in the V band (AV ) and the extinction ratio (RV = AV /E(B − V )) that are provided by the user. This first version of SEDOBS assumes that nebular and stellar extinction are the same and does not implement the reemission of the flux in the IR. It is also worth noting that SEDOBS includes the possibility 170 to add other extinction laws in a very easy way. SEDOBS includes as well the possibility to use intergalactic medium extinction. Four options are available. Two of them consist of constant prescription; Madau (Madau, 1995) and Meiksin (Meiksin, 2006). In this presciption a single extinction curve per red-175

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Figure 1: Workflow of SEDOBS. The simulation of a mock galaxy sample is divided in four major components. Once the configuration (in black) is complete, SEDOBS is validating it (blue). If it is accepted the simulation starts (red). Finally SEDOBS produces the outputs of the simulation (green).

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shift is available. Two varying prescriptions from Thomas et al. (2017a) have also been included where seven extinction curves per redshift are available. The latter allows for the simulation to create galaxies with very different lines of sight possibilities at a given redshift. 3.3. Sky emission The atmosphere of our planet has heavy consequences on the observation from ground based Telescope. Three main effects are dominant: (i) Attenuation: the global transmission of the atmosphere is wavelength dependant and will heavily affect the flux that arrives towards the observer from the science targets. This transmission can vary from 0% to 100% depending on the wavelength. It is also impor-

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Fattenuated = Fintrinsic × T rIGM +dust .

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with T rIGM is the IGM templates, k(λ) the attenuation coefficient and E(B-V) the color excess (see previous section). Then the library is redshifted at the redshift z. Optionally, SEDOBS removes all the templates that have an age higher than the age of the Universe at z. Finally, in order to make sure that no bias is introduced in the selection of the template to simulate the galaxy, a model is randomly selected in the leftover set of templates. Then the final observed magnitude of the simulation (msim ) is used to compute the normalization of the template. In case no sky emission is considered this magnitude is directly used. If the OH lines are added during the simulation this magnitude corresponds to a flux Fsim which is related to the flux of the sky and to the intrinsic flux of the galaxy attenuated by the extinctions by dust and IGM:

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tant to note that the airmass (the section of the atmosphere the light will travel through) will affect this215 attenuation. The higher the airmass, the higher the extinction. (ii) Emission: the atmosphere also emits radiation. This radiation is due to the OH emission lines created by the atmosphere (Osterbrock et al., 1996) and can greatly contaminate the observations.220 As the atmospheric attenuation, this emission will depend on airmass as when the observations are done at lower altitude the amount of atmosphere is thicker (cf Fig.2). (iii) Refraction: Due to the variation of density of the air as a function of elevation the atmosphere causes light to deviate (Filippenko, 1982). It is a well known effect and can be very important when, for example, doing slit spectroscopy. SEDOBS implements the sky emission only (and leave the refraction and attenuation for a later version). Three cases have been considered depending on the amount of airmass (see Fig.2): observations at low airmass (below 1.15), observations at intermediate airmass (between 1.15 and 1.4) and observations at high airmass (above 1.4). The user is able to choose between these three possibilities. The sky emission models have been created using the skycalc tool2 made available by the European Southern Observatory and are computed for the Very Large Telescope at Cerro Paranal, Chile. Skycalc implements scattered starlight, zodiacal light, molecular emission of lower atmosphere, Emission lines of Upper atmosphere and airglow residual.

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T rIGM +dust = T rIGM × 10−0.4×k(λ)×E(B−V )

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With T rIGM +dust the total transmission given by:

Figure 2: Sky emission as added to SEDOBS. They correspond to the sky above the Cerro Paranal observatory for three values of airmass: In black, airmass=1 (at the zenith, alt=90o ), in red, airmass=1.414 (alt = 45o )) and in blue airmass=3, alt=19.5o .

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Each object to be simulated is defined by a triplet [z, msim , signal-to-noise ratio (SNR)]. At first, all the extinctions are applied to the library. Both dust and IGM extinction are included in the template. The relation between the intrinsic flux of the galaxy template and the one after applying all extinction is given by:

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http://www.eso.org/observing/etc/bin/gen/ form?INS.MODE=swspectr+INS.NAME=SKYCALC

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Fsim = Fsky + Fattenuated

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which gives the intrinsic magnitude (sky-free) of the simulated galaxy:  msim +48.6 mattenuated = −2.5 log10 10− 2.5 −  (4) m +48.6 − sky2.5 10 − 48.6 Therefore the template will be normalized to Fattenuated and not Fsim . This normalization process is common for both spectroscopic and photometric simulations and diverge after the flux normalization: • Photometry: When simulating photometry the process is the following. The normalized template is convoluted with all the band-passes of the required filters. Of course, if a zero-point

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magfinal = magmeasured + offset. 225

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The simulated observed error is taken from the distribution of expected errors given in the configuration. A Gaussian distribution is created and SEDOBS will randomly choose an error in-250 side that distribution.

algorithms, preparation of proposals, etc. In this section we present an example of simulated sample created for the purpose of testing a SED-fitting process against mock data. The mock data are computed to be equivalent to the Ultra deep sample of the VLT VIMOS Deep Survey (VVDS, Le F`evre et al. 2013). We focus here on the simulations. The testing of the spectrophotometric fitting tool SPARTAN will be presented in a dedicated paper (Thomas in prep).

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offset was given, it will be applied to the proper filter and the final magnitude will be given by

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5.1. Sample configuration and inputs • Spectroscopy: The spectroscopic simulations For this mock survey we consider the following con(simulating 1D spectra only) require more steps figuration: to be performed. Once the template is normalized to the observed magnitude, the first step 255 • One optical spectrum from the VIMOS low resis to cut the spectrum region that defines the olution grism. The wavelength window ranges observed wavelengths window. Then the resolufrom 3500˚ A to 9500˚ A with a resolution of 240 tion has to be adjusted. This is done using a and a binning of 7.25˚ A (Le F`evre et al., 2003). Gaussian filter that passes through all the wavelength. The full width at half maximum (here• Multiple anciliary data are available for this surafter FWHM) of the Gaussian filter (FWHMf ilt )260 vey. We will simulate multi-wavelength photomis given by: etry using the ugriz bands from the Megacam q camera (Boulade, 1998), JHK bands fron the FWHMf ilt = FWHM2r − FWHM2t , (6) WIRCAM camera (Puget et al., 2004) and the two first channels of IRAC on the SPITZER telewhere FWHMr and FWHMt are the requested scope (Fazio et al., 2004). We will not apply any FWHM of the final template (the final resolu-265 zeropoint-offsets to the magnitude bands. tion) and the theoretical FWHM of the template,

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respectively. Finally the noise must be applied to match the requested SNR. This is done in two steps. First the noise region is isolated. Then the median of the flux (µ) inside that region is270 computed. This is used to estimate the expected σ of the flux using the relation; SN R =

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Then, a Gaussian noise is created using the estimated dispersion of the flux and added to the template. Finally, the noise spectrum is computed using a median absolute deviation filter on the noised spectrum.

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µ(f lux) . σ(f lux)

It is worth mentioning that photometric and spectro-280 scopic errors are completely decoupled and independent. 5. Example of a large sample of simulated 285 galaxies, combining space and ground based data As presented in the introduction, SEDOBS can be used for different purposes like testing SED-fitting 5

The VVDS Ultra Deep sample is composed of ∼ 1000 galaxies with a redshift range from 0 to 4. But we want to simulate only a fraction of it at 0.5 < z < 1.5. A very small mock sample (∼400 objects) would not be sufficient for assessing the SED fitting performance. So instead we will simulate 10000 objects with exactly the same redshift, magnitude (in the r -band) and SNR distributions. The input of the sample are given in the top and middle panel of Fig.3. 5.2. The simulated sample Besides the redshift and normalisation magnitude that are the same as hard definition of the sample the SNR is the result of a random Gaussian process that is applied to the synthetic template. Therefore we can compare the distributions on the data and on the simulated galaxies (Fig.3, bottom plot). The distributions are very close to each other. The mean difference between the expected SNR (equal to the one measured on the data) and the SNR actually measured on the simulation is 0.05±0.77, which means that the spectroscopic simulation is able to reproduce the SNR of the real sample very well. Examples of simulated data are shown in Fig.4 & 5. Figure

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The author wished to thank A. Solarz, B. Lemaux and A.

Razza for their useful help in the content of SEDOBS and their

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erees of A&C for they very useful comments. SEDOBS makes heavy use of the Numpy (Oliphant, 2006), Scipy (Jones et al., 2001–), h5py (Collette, 2013) python packages. All the plots have been made using the Photon tool (Thomas, 2019).

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Figure 3: Input sample from the VVDS-deep survey at 0.5 < z < 1, 5. Top: Observed redshift distribution, Middle: observed r -band magnitude (in AB) Bottom: distributions of SNR as measured in the real data and in the simulated data.

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4 compares spectra from the simulated sample and real spectra at equivalent redshift and magnitude. It clearly shows the ability of SEDOBS to simulate con-340 vincing spectroscopic data. Figure 5 shows photometric simulation of various objects in the mock sample at different redshift. Different types of galaxies have been chosen to show the influence of emission lines345 in the photometric measurements. These plots show the clear ability of SEDOBS to create simulated photometric observations.

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reading of the manuscript. The author thanks as well the ref-

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SEDOBS remains in active development. A second version of the software is in preparation and will include new features like a full library of pre-defined instruments configurations (both for spectrographs and imagers). This will allow the user to load internally pre-defined instrument configurations and have a faster software configuration. We are also gathering more synthetic model prescriptions to be added in SEDOBS (e.g. Maraston 2005b; Maraston & Str¨ omb¨ ack 2011) to give a broader choice of theoretical prescriptions to the end user. We will also expand the choice of SFH to multi-burst prescriptions. Additionally, we wish to open SEDOBS to different type of astronomical sources. Discussions are on-going to add AGN modeling and stellar synthetic templates.

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rAB = 24.77 zs = 0.706

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3.8 Xlabel

3.9

3.7 3.8 Wavelength [log

10 Å

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3.7 3.8 Wavelength [log

10 Å

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3.9

]

rAB = 23.63 zs = 0.6828

4 2 0

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3.7 3.8 Wavelength [log10Å]

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rAB = 24.57 zs = 1.0389

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3.6

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rAB = 24.57 zs = 1.0389

[OII]

3.7 3.8 Wavelength [log10Å]

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rAB = 23.69 zs = 1.3607

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8 6

10 Å

4

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rAB = 23.69 zs = 1.3607

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zs = 0.706

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Figure 4: Example of spectroscopic mock observations produced by SEDOBS and comparison with equivalent data from the VVDS survey. The left column shows simulated spectra by SEDOBS. In each left panel we show the final spectrum in black, the original theoretical template in red and the sky contribution to the simulated spectrum in blue. The right hand side column shows real data from the VVDS Ultra Deep sample. Redshift (zs ), Observed r-band magnitude (rAB ) and theoretical positions of the most common emission lines are given for each plot.

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Original Template Simulated photometry

rAB = 24.53 zs = 0.5119

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Figure 5: Example of simulated photometric observations at various redshifts from the sample of mock galaxies created by the SEDOBS software. The simulated bands are ugriz from the Megacam camera, JHK from the Wircam and the two first channel of IRAC from the Spitzer Telescope. For each plot we show the magnitudes with their errors (pink) and the original template from which they have been simulated.

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Declaration of interests Ö The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: