ner_tags: O O O B-Software O O O O O O O O O O B-Software I-Software B-Software I-Software I-Software I-Software I-Software B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ObservationalTechniques I-ObservationalTechniques I-ObservationalTechniques B-ObservationalTechniques O O O O O O O O O O O O O O O B-ObservationalTechniques O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O B-ObservationalTechniques O O O O O O O O B-ObservationalTechniques O O O O O O O O O O O O O O O O O O B-ObservationalTechniques O O O O O O O O O O O O B-CelestialObject B-ObservationalTechniques O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ObservationalTechniques O O O O O O O B-ObservationalTechniques O O O O O O B-ObservationalTechniques O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O B-ObservationalTechniques O O O O O O O O O O O O O O O O O O O O O B-ObservationalTechniques O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O B-ObservationalTechniques O B-Formula I-Formula I-Formula O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O B-ObservationalTechniques O O O O O O O O O O O B-ObservationalTechniques B-Formula I-Formula I-Formula O O O B-Formula I-Formula I-Formula O B-Citation I-Citation I-Citation I-Citation O O O O O O B-ObservationalTechniques O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ObservationalTechniques O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O B-ObservationalTechniques I-ObservationalTechniques O O O O O O O O B-ObservationalTechniques O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O
tokens: standard packages in iraf . 1 Aperture photometry was carried out using the task daophot ii (Dominion Astrophysical Observatory Photometry software, Stetson 1987 ). Signal-to-noise ratio was found to peak for a photometric aperture radius nearly two times the seeing disc whose FWHM was determined by averaging the profiles of five fairly bright, unsaturated stars recorded in the same CCD frame. The measured instrumental magnitudes were then used to derive the ‘differential light curves’ (DLCs) of the target AGN relative to two (steady) comparison stars, as well as the star−star DLC for the session (see Goyal et al. 2013 , for details of the procedure). In Fig. 1 , we show the DLCs obtained from one of the 30 sessions; the DLCs for all 30 monitoring sessions are presented in the online Fig. 1. Figure 1. An example of the DLCs obtained in the present study. The target AGN is the BAL quasar J090552.41+025931.5. DLCs for all the 30 sessions are presented in online Fig. 1. The date and duration of monitoring and the telescope used are mentioned at the top. The profile in the upper panel presents the DLC of the chosen two comparison stars (‘star–star’ DLC). The two middle profiles display the DLCs of the BAL quasar relative to the two comparison stars, as mentioned in the labels on the right side. The profile in the bottom panel shows the variation of the seeing disc (full width at half-maximum, FWHM) through the monitoring session. 4 STATISTICAL ANALYSIS AND RESULTS Following Goyal et al. ( 2013 ), we applied the widely used F η test to the DLCs of the target AGN relative to the two comparison stars (s1, s2). The parameter F η computed for the two AGN DLCs is defined as Here Var( q − s j ) is the variance of the ‘AGN− j th comparison star’ DLC, where j = 1,2 and σ i ,err ( q − s j ) are the photometric error of the N individual data points in the DLC (as returned by the daophot routine). For each of the two DLCs, i = 1 to N and η = 1.5 (see Goyal et al. 2012 ). For each of the two DLCs of the target AGN, online Table 2 (Column 7) compares the computed values of F η with the critical value of F (=). The values of α are set at 0.05 and 0.01, corresponding to 95 per cent and 99 per cent confidence levels for INOV detection. If the computed F η for a DLC exceeds , the null hypothesis (i.e. no variability) is rejected at the corresponding confidence level. We thus classify a DLC as variable (‘V’) if its computed F η > F c (0.99); probably variable (‘PV’) if the F η lies between F c (0.95) and F c (0.99), and non-variable (‘NV’) in case F η F c (0.95) (Goyal et al. 2013 ). Column 10 in the online Table 2 lists the session averaged photometric accuracy of the measured differential magnitudes . It is estimated using the two AGN-star DLCs and is nearly always better than 3 per cent (median = 2.0 per cent). 5 DISCUSSION AND CONCLUSION The main result from this study (online Table 2) is the non-detection of INOV in any of the 30 sessions devoted to the