Visfo.r in A.ctronm~~ Vol. 40, No. I, pp. 57-61, I996

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AMETHODOFPROBINGTHETORUS GEOMETRY?

CHRIS SIMPSON Space Telescope Science Institute, Baltimore, U.S.A.

Abstract- I propose a method of inferring the geometry of the obscur- ing material in radio-loud active galaxies by examining the relationship between the inferred nuclear extinction and the core-to-lobe radio flux ratio. I use Monte Carlo simulations to estimate the quality of data ex- pected from such a project and examine its usefulness in discriminating between alternative structures. Copyright @ 1996 Elsevier Science Ltd.

1. INTRODUCTION

The current AGN paradigm is one of orientation-dependent obscuration of the BLR and non-stellar continuum source. This obscuration can be very large, only starting to transmit radiation at near-infrared wavelengths. The detection of quasar-like features in “narrow- line” objects at these wavelengths has occurred in the form of broad wings to the hydrogen recombination lines (e.g. Blanc0 et al., 1990) and nuclear near-infrared excesses, believed to be transmission of the thermal radiation from the hot dust surrounding the central source. These were first detected photometrically by Lilly et al. (1985), but subsequent imaging observations have supported this theory (e.g. Cygnus A, Djorgovski er al., 1991). As technology improves, the detection of these excesses is becoming easier, and here I shall determine what can be gleaned from a near-infrared imaging survey of a sample of narrow- line radio galaxies.

2. METHODOLOGY

To determine the geometry of the obscuring material, and hence determine whether it is toroidal, requires analysis of how the nuclear extinction varies as a function of viewing angle. A similar project has been suggested by Goodrich et al. (1994). but their proposed use of X-ray cut-offs and detections of broad recombination lines limits them to a heterogeneous sample of objects, or only those with very low obscuring columns.

I therefore propose an infrared imaging technique to detect the directly-transmitted quasar continuum. This has been proven to be able to detect sources through much greater column densities than spectroscopy: the extinction in IC 5063 is Av = 85 2 20 (Simp- son et al. 1994) and re-analysis of Djorgovski et d’s data has suggested that Av = 150

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58 C. Simpson

(Simpson, 1994a; see also Ueno et al., 1994). By making observations at A4 as well as JKL, the probability of detecting the nuclear source at two wavelengths increases-in these cases, comparison of the observed nuclear colour with that of a quasar will yield the red- dening. In addition, according to Laing et al. (1994), the [0 III] h5007 line can be used as an isotropic indicator of intrinsic quasar luminosity in high-ionization radio galaxies- although this disagrees with the findings of Jackson and Browne (1990), this earlier study made use of lower-quality data and contained a number of misclassified objects in addi- tion to uncertain selection effects in its matched non-3CRR quasars. Using this line as an additional continuum point allows the extinction to be inferred if a detection is made at only one wavelength, or to tighten estimates made from a colour (see Simpson et al., 1994 for an application of this method).

Selecting radio-loud objects only allows the core-to-lobe ratio, R, to be used as an orientation-dependent measurement, under the beaming model of Orr and Browne (1982). The observed distribution of R for 3CRR radio galaxies (Laing et (II., 1983) can be re- produced if they are randomly distributed at angles 0 > 40” to the line-of-sight and the unbeamed core-to-lobe ratio, RT. is drawn from a parent Normal population with mean 10e2 5 and an intrinsic scatter of 0.5 in the logarithm. This is consistent with the result of Orr and Browne after accounting for their assumption that the orientation of quasars to the line-of-sight is random.

3. SAMPLE SELECTION AND THE ARTIFICIAL DATASET

In order to determine the likely success of a project, it is essential to produce an artificial dataset which accurately reflects the distribution of properties of the real sample of objects. The most obvious sample to use is 3CRR (otherwise known as LRL, Laing et al., 1983) as spectrophotometry exists for most of the low-z sources (Rawlings et al., 1989). I apply a redshift cutoff of z < 0.5 to remove significant evolutionary effects, and this leaves a sample of 28 objects with strong high-ionization emission lines (I use the criteria of Laing et al.,

1994 to classify the ionization class). My artificial dataset therefore consists of 28 objects, whose [0 III] fluxes and redshifts are taken to be those of the actual galaxies themselves, and which are assumed to be randomly-oriented with viewing angles 0 > 40”.

Two models have been assumed for the optical depth of the “torus’“-the first (Model A) has Av randomly distributed with a mean of 50 mag and a standard deviation of 15 mag. The second (Model B) has Av varying with viewing angle 8 as

Av = 100 I- - i

cos 8 1 coso,

where 0, = 40” is the torus half-opening angle; a scatter of 15 mag has also been added to this model. These two models may be likened to a warped disk and a torus, for example.

The observing strategy for the proposed project consists of taking short (- 15 min) exposures at JK and deep (- 30 min) exposures at LM. Although no detectable transmission is expected shorter than L, these images will allow the detection of, and correction for, foreground dust (again, see Simpson et al., 1994). The level of the quasar continuum is calculated from the [0 III] h5007 line and a typical quasar SED, and the host galaxy magnitude from the K-band Hubble diagram (the scatter in both of these relationships is carried through). The IRCAM3 sensitivity figures are used to estimate the significance of a detection and noise is added on the basis of this. The nuclear contribution at the

Probing the Torus Geometry.? 59

0 20 x

Fig. 1. Results of statistical analysis on the 1000 simulated datasets. (a) Correlation

analysis: the solid lines show the number of times the null hypothesis could be rejected at

a given confidence level. The diagonal dashed line is the limit reached by the orientation- independent model after many runs and the difference between this and the solid line

indicates the random element still present in the data. (b) Regression analysis: the solid

lines show the number of runs in which the gradient of the least-squares regression line

was below a given value.

Fig. 2. Simulated observational data for near-infrared imaging of low-redshift 3CRR radio

galaxies. In each case, the 500th run (after sorting in order of P($s)) has been used to

represent the set of 1000 runs. (a) Model A: P(3&) = 47.2’%(b) Model B: P(Sfs) = 98.7%.

longer wavelengths is separated from the host galaxy by extrapolating the starlight from the shorter wavelengths assuming normal elliptical galaxy colours. In practice, modelling would be used which would probably improve the separation of components by removing possible systematic errors (Simpson, 1994b).

60 C. Simpson

4. DATA ANALYSIS

The observing run and data reduction will furnish LIS with a set of (R, Av) pairs for all 28 objects. The signal being looked for is an anti-correlation between these two variables, which Model B will produce at some level, but Model A will not. Survival analysis techniques (Isobe et al., 1986) are used to look for this signal, as they account for the presence of lower limits to Av caused by non-detections of the nuclear radiation. Both correlation and regression methods are used, to determine the significance at which the null hypothesis (that R and Av are uncorrelated) can be rejected, and the results from 1000 simulated datasets are presented in Fig. 1. It is seen that the null hypothesis can be rejected at 95%) confidence if Model A is correct in only 5% of the datasets, by 80”/;, of the time for Model B. This makes the detection of a signal truly significant, but non-detection is less conclusive for only 28 objects. The addition of a similar sample of southern radio galaxies to increase the numbers may provide an answer. For the present sample, indicative datasets are shown in Fig. 2, to show the quality of data to be expected.

Given that a scatter of only 15magnitudes has been applied to the torus extinction, it is clear that much of the noise in Fig. 2 is caused by the much greater random element in R or, more specifically, RT. The factor of - 3 scatter in this variable manages to swamp much of the true signal produced by a factor of - 10 increase in R as the viewing angle changes from 90” to 40”. It is this lack of a good orientation-dependent quantity which will ultimately limit the success of a project of the kind described here.

5. SUMMARY

I have shown that the detection of near-infrared excesses in the vast majority of bright radio galaxies is feasible with current detectors. This now makes it possible to attempt to probe the geometry of the obscuring material and discriminate between alternative models. I have presented simulated data of the quality to be expected from such a project, and shown that the signal of a toroidal structure can be detected, although it has been assumed that all “tori” are drawn from a single population, whereas their thicknesses may be related to the quasar luminosity. A project such as that outlined here will be able to tell us something about the scatter in “torus” properties, even if it does not achieve its primary goal.

References

Blanc0 P.R., Ward M.J. and Wright G.S. (1990) h4.NR.A.S. 242,4P. Djorgovski S., Weir N., Matthews K. and Graham J.R. (1991) Ap. J 372, L67. Goodrich R.W., Veilleux S. and Hill G.J. (1994) Ap. J 422, 521. Isobe T., Feigelson E.D. and Nelson PI. (1986) Ap. J 306,490. Jackson N. and Browne I.W.A. (1990) Nature 343,43. Laing R.A., Riley J.M. and Longair M.S. (1983) M.NR.A.S. 204, 151. Laing R.A., Jenkins C.R., Wall J.V. and Unger S.W. (1994) ASP Conference Series 54:

The First Stromlo Symposium: The Physics of Active Galaxies (Edited by Bicknell G.V., Dopita M.A. and Quinn P.J.), p. 201. ASP, San Francisco.

Lilly S.J., Longair MS. and Miller L. (1985) M.NR.A.S. 214, 109. Orr M.J.L. and Browne I.W.A. (1982) M.NR.A.S. 200, 1067.

Probing the Torus Geometry? 6

Rawlings S., Saunders R., Eales S.A. and Mackay CD. ( 1989) M. N R. A. S. 240, 70 I. Simpson C.J. (1994a) DPhil. thesis, University of Oxford. Simpson C. (1994b) h4.NR.A.S. 271, 247. Simpson C., Ward M. and Kotilainen J. (1994) M.iVR.A.S. 271, 250. Ueno S., Koyama K., Nishida M., Yamauchi S. and Ward M.J. (1994) Ap. J 431, Ll.