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I am currently a postdoctoral fellow at CFT, Poland.
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Priyanka Jalan, Maciej Bilicki, Wojciech A. Hellwing, Angus H. Wright...
We present a method to refine photometric redshift galaxy catalogs by comparing their color-space matching with overlapping spectroscopic calibration data. We focus on cases where photometric redshifts (photo-z) are estimated empirically. Identifying galaxies that are poorly represented in spectroscopic data is crucial, as their photo-z may be unreliable due to extrapolation beyond the training sample. Our approach uses a self-organizing map (SOM) to project a multi-dimensional parameter space of magnitudes and colors onto a 2-D manifold, allowing us to analyze the resulting patterns as a function of various galaxy properties. Using SOM, we compare the Kilo-Degree Survey bright galaxy sample (KiDS-Bright), limited to r<20 mag, with various spectroscopic samples, including the Galaxy And Mass Assembly (GAMA). Our analysis reveals that GAMA under-represents KiDS-Bright at its faintest (r>19.5) and highest-redshift (z>0.4) ranges, however no strong trends in color or stellar mass. By incorporating additional spectroscopic data from the SDSS, 2dF, and early DESI, we identify SOM cells where photo-z are estimated suboptimally. We derive a set of SOM-based criteria to refine the photometric sample and improve photo-z statistics. For the KiDS-Bright sample, this improvement is modest: exclusion of the least represented 20 % of the sample reduces photo-z scatter by less than 10%.We conclude that GAMA, used for KiDS-Bright photo-z training, is sufficiently representative for reliable redshift estimation across most of the color space. Future spectroscopic data from surveys such as DESI should be better suited for exploiting the full improvement potential of our method.
Priyanka Jalan, Vikram Khaire, Vivek M. and Prakash Gaikwad
We introduce FLAME, a machine learning algorithm designed to fit Voigt profiles to HI Lyman-alpha absorption lines using deep convolutional neural networks. FLAME integrates two algorithms: the first determines the number of components required to fit absorption lines, and the second calculates the Doppler parameter b, the HI column density NHI, and the velocity separation of individual components. For the current version of FLAME, we trained it on low-redshift forests observed with the Far Ultraviolet gratings of the Cosmic Origin Spectrograph (COS) aboard the Hubble Space Telescope (HST). Drawing on this data, we trained FLAME on ~1M simulated Voigt profiles, forward-modeled to mimic absorption lines observed with HST-COS, to classify lines as either single or double components and then determine Voigt profile fitting parameters. FLAME shows impressive accuracy on the simulated data by identifying more than 98% (90%) of single (double) component lines. It determines b values within 15 (18) km/s and log NHI cm2 values within 0.3 (0.8) for 90% of the single (double) component lines. However, when applied to real data, FLAME's component classification accuracy drops by ~10%. Despite this, there is a reasonable agreement between the b and NHI distributions obtained from traditional Voigt profile fitting methods and FLAME's predictions. Our mock HST-COS data analysis, designed to emulate real data parameters, demonstrated that FLAME could achieve consistent accuracy comparable to its performance with simulated data. This finding suggests that the drop in FLAME's accuracy when used on real data primarily arises from the difficulty of replicating the full complexity of real data in the training sample. Nevertheless, FLAME's performance validates the use of machine learning for Voigt profile fitting, underscoring the significant potential of machine learning for detailed analysis of absorption lines.
Priyanka Jalan, Suvendu Rakshit, Jong-Jak Woo, Jari Kotilainen, and C. S. Stalin
Measurement of black hole mass for low-z (z<0.8) Active Galactic Nuclei (AGNs) is difficult due to the strong contribution from host galaxy stellar light, necessitating detailed spectral decomposition to estimate the AGN luminosity. Here, we present an empirical relation to estimate host galaxy stellar luminosity from the optical spectra of AGNs at z≤0.8. The spectral data were selected from the fourteenth data release of the Sloan Digital Sky Survey (SDSS-DR14) quasar catalog having a signal-to-noise ratio at 5100 \AA (SNR5100) >10 containing 11415 quasars. The median total luminosity (log (Ltotal/[erg s−1])), stellar luminosity (log (Lstar/[erg s−1])), and AGN continuum luminosity (log (Lcont/[erg s−1])) in our sample are 44.52, 44.06, and 44.30, respectively. We fit the AGN power-law continuum, host galaxy, and iron blend contribution, simultaneously over the entire available spectrum. We found the host galaxy fraction to anti-correlate with continuum luminosity and can be well-represented by a polynomial function, which can be used to correct the stellar light contribution from AGN spectra. We also found anti-correlation between host galaxy fraction and iron strength, Eddington ratio, and redshift. The empirical relation gives comparable results of host-fraction with the image decomposition method.
Priyanka Jalan, Hum Chand, and Raghunathan Srianand
We present a detailed study of the longitudinal proximity effect using a sample of 85 quasars spanning an emission redshift range of 3.5≤zem≤4.5 and Lyman continuum luminosity (L912) ranging from 1.06×1031 to 2.24×1032 erg s−1 Hz−1. We use the high-quality spectra of these quasars obtained at a spectral resolution of R∼ 5100 and S/N ∼ 30 using X-SHOOTER spectrograph mounted on the Very Large Telescope (VLT). In our analysis, we compared the transmitted flux and pixel optical depth of the Lyα absorption originating from the vicinity of quasars to those from the general intergalactic medium by using a redshift matched control sample. The longitudinal proximity effect is found up to r≤12 Mpc (proper) from quasars. By appropriately scaling up the pixel optical depth in the vicinity of quasars to account for the excess ionization by quasars, we constrain the ratio of median HI optical depth in the vicinity of the quasar to that of the IGM (Rτ(r)). The Rτ(r) is found to be significantly higher than unity up to 6 Mpc from the quasar with a typical radial profile of the form Rτ(r)=1+A×exp(−r/r0) with A=9.16±0.68 and r0=1.27±0.08 Mpc. The integrated value of the scaled pixel optical depth over the radial bin of 0-6 Mpc is found to be higher by a factor of 2.55±0.17 than the corresponding integrated value of the median pixel optical depth of the IGM. We also found Rτ(r) to be luminosity dependent.
Priyanka Jalan, Hum Chand, and Raghunathan Srianand
We have used spectra of 181 projected quasar pairs at separations ≤1.5 arcmins from the Sloan Digital Sky-Survey Data Release 12 in the redshift range of 2.5 to 3.5 to probe the proximity regions of the foreground quasars. We study the proximity effect both in the longitudinal as well as in the transverse directions, by carrying out a comparison of the \lya absorption lines originating from the vicinity of quasars to those originating from the general inter-galactic medium at the same redshift. We found an enhancement in the transmitted flux within 4 Mpc to the quasar in the longitudinal direction. However, the trend is found to be reversed in the transverse direction. In the longitudinal direction, we derived an excess overdensity profile showing an excess up to r≤5 Mpc after correcting for the quasar's ionization, taking into account the effect of low spectral resolution. This excess overdensity profile matches with the average overdensity profile in the transverse direction without applying any correction for the effect of the quasar's ionization. Among various possible interpretations, we found that the anisotropic obscuration of the quasar's ionization seems to be the most probable explanation. This is also supported by the fact that all of our foreground quasars happen to be Type-I AGNs. Finally, we constrain the average quasar's illumination along the transverse direction as compared to that along the longitudinal direction to be ≤27\% (3σconfidence level).
Contact pjalan@cft.edu.pl to get more information about these projects.