Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph

Overview

NIRPS-ETC

Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph

February 2022 - Before NIRPS on sky

Original NIRPS ETC code by Bruno L. Canto Martins 2018-2019

Additional edits by Nolan Grieves (University of Geneva) 2020-2022

Overview

  • The NIRPS ETC uses spectra from the NASA Infrared Telescope Facility (IRTF) as SEDs to get estimated flux values for different spectral types: http://irtfweb.ifa.hawaii.edu/~spex/IRTF_Spectral_Library/
  • The ETC calculates efficiency at different wavelengths using seeing, atmospheric efficiency from TAPAS (http://cds-espri.ipsl.fr/tapas/), and the measured global efficiency of the instrument
  • The signal to noise ratio (SNR) at each pixel or bin is calculated from the fiber diameter, sampling, readout noise, resolution, efficiency, and flux in the pixel or bin from the IRTF template (flux=(10.**(0.4*(Ho-H)))*flux_st)
  • RV precisions are calculated using, dRV=c/(Q*sqrt(Ne-)), equation 12 of Bouchy et al. (2001: https://ui.adsabs.harvard.edu/abs/2001A%26A...374..733B/abstract). The quality factors Q for spectra are calculated with ENIRIC from Phoenix simulated spectra or from spectral templates from the Spirou spectrograph
    • -> see: NIRPS-ETC/intermediate_preparation/update_RV_estimates/README_update_RV_estimates

Use

$ python NIRPS_ETC.py

  • change observing options within the code at the top
    • Observation Mode (HA/HE)
    • Seeing, in arcsec (range 0.7-1.2)
    • Airmass (range 1.0-2.0)
    • Object magnitude (H band)
    • Exposure time (in sec)
    • Spectral type (F0V/F5V/G0V/G5V/G8V/K0V/K3V/K7V/M0V/M1V/M2V/M3V/M4V/M5V/M6V/M7V/M8V/M9V/L1V/L2V/L3V/L4V/L5V/L6V/L8V/T2V)
    • bandpass ('CFHT' or 'Eniric') #YJH bandpasses that will affect the range of the spectra used to calculate RV precision
  • outputs mean SNR, in YJH, and each order, and RV precisisons for certain spectral types

OR use script version:

$ python NIRPS_ETC_script.py

  • change inputs for each target in a space separated text file with columns:
    • target st obs_mode seeing airmass H t_exp bandpass
  • change input and output text files within code to desired option
  • outputs to file the mean SNR, YJH SNRs, and RV precisions

Contents

  • inputs/
    • NIRPS_STAR_templates.txt
      • SEDs from IRTF (update with intermediate_preparation/update_effs/update_effs.py)
    • NIRPS_effs.txt
      • global efficiency of instrument (update with intermediate_preparation/update_effs/update_effs.py)
    • NIRPS_tapas.txt
      • atmospheric efficiency from TAPAS (update with intermediate_preparation/update_effs/update_effs.py)
    • NIRPS_wave_range.txt
      • wavelength range of echelle orders (update with intermediate_preparation/update_effs/update_effs.py)
    • phoenix_Q_conversions_CFHT-bandpass.txt
      • Q factor conversions for different resolutions in CFHT defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/phoenix_qfactor_resolution_conversion.py)
    • phoenix_Q_conversions_eniric-bandpass.txt
      • Q factor conversions for different resolutions in Eniric defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/phoenix_qfactor_resolution_conversion.py)
    • phoenix_eniric_Qfactors_CFHT-bandpass.csv
      • Q factors from Eniric in CFHT defined YJH bandpasses (update with eniric using command in intermediate_preparation/update_RV_estimates/README_update_RV_estimates)
    • phoenix_eniric_Qfactors_eniric-bandpass.csv
      • Q factors from Eniric in Eniric defined YJH bandpasses (update with eniric using command in intermediate_preparation/update_RV_estimates/README_update_RV_estimates)
    • spirou_fit_Qvalues_CFHT-bandpass.txt
      • Q factors from Spirou templates in CFHT defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/fit_spirou_qfactors.py)
    • spirou_fit_Qvalues_eniric-bandpass.txt.
      • Q factors from Spirou templates in Eniric defined YJH bandpasses (update with intermediate_preparation/update_RV_estimates/fit_spirou_qfactors.py)
  • intermediate_preparation/
    • ETC_v3.0_CantoMartins/
      • original ETC by Bruno Canto Martins
    • add_stellar_templates/
      • add and update stellar templates
    • update_RV_estimates/
      • update RV estimates and Q values
    • update_effs/
      • update efficiency files and resample wavelength grid for tapas, effs, and star_templates
  • outputs/
    • outputs SNR for each order and wavelength vs SNR plot from NIRPS_ETC.py
  • NIRPS_ETC.py
    • main ETC code for a single star
  • NIRPS_ETC_script.py
    • script that runs ETC for stars in etc_targets_input.txt and outputs to etc_targets_output.txt
  • etc_targets_input.txt
    • example input file for NIRPS_ETC_script.py
  • etc_targets_output.txt
    • example ouput file for NIRPS_ETC_script.py
  • nirps_etc_lib.py
    • definitions for fucntions in ETC code
Owner
Nolan Grieves
Postdoctoral Research Scientist [email protected]
Nolan Grieves
A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.

Awesome AutoDL A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awe

D-X-Y 2k Dec 30, 2022
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

EMI-Group 175 Dec 30, 2022
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21)

EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21) Citation If y

addisonwang 18 Nov 11, 2022
One-line your code easily but still with the fun of doing so!

One-liner-iser One-line your code easily but still with the fun of doing so! Have YOU ever wanted to write one-line Python code, but don't have the sa

5 May 04, 2022
deep learning for image processing including classification and object-detection etc.

深度学习在图像处理中的应用教程 前言 本教程是对本人研究生期间的研究内容进行整理总结,总结的同时也希望能够帮助更多的小伙伴。后期如果有学习到新的知识也会与大家一起分享。 本教程会以视频的方式进行分享,教学流程如下: 1)介绍网络的结构与创新点 2)使用Pytorch进行网络的搭建与训练 3)使用Te

WuZhe 13.6k Jan 04, 2023
This is my codes that can visualize the psnr image in testing videos.

CVPR2018-Baseline-PSNRplot This is my codes that can visualize the psnr image in testing videos. Future Frame Prediction for Anomaly Detection – A New

Wenhao Yang 12 May 29, 2021
Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

Location-Aware Generative Adversarial Networks (LAGAN) for Physics Synthesis This repository contains all the code used in L. de Oliveira (@lukedeo),

Deep Learning for HEP 57 Oct 22, 2022
dyld_shared_cache processing / Single-Image loading for BinaryNinja

Dyld Shared Cache Parser Author: cynder (kat) Dyld Shared Cache Support for BinaryNinja Without any of the fuss of requiring manually loading several

cynder 76 Dec 28, 2022
Framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample resolution

Sample-specific Bayesian Networks A framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample or per-patient re

Caleb Ellington 1 Sep 23, 2022
Pytorch tutorials for Neural Style transfert

PyTorch Tutorials This tutorial is no longer maintained. Please use the official version: https://pytorch.org/tutorials/advanced/neural_style_tutorial

Alexis David Jacq 135 Jun 26, 2022
Text completion with Hugging Face and TensorFlow.js running on Node.js

Katana ML Text Completion 🤗 Description Runs with with Hugging Face DistilBERT and TensorFlow.js on Node.js distilbert-model - converter from Hugging

Katana ML 2 Nov 04, 2022
Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

Drone Detection using Thermal Signature This repository highlights the work for night-time drone detection using a using an Optris PI Lightweight ther

Chong Yu Quan 6 Dec 31, 2022
RL agent to play μRTS with Stable-Baselines3

Gym-μRTS with Stable-Baselines3/PyTorch This repo contains an attempt to reproduce Gridnet PPO with invalid action masking algorithm to play μRTS usin

Oleksii Kachaiev 24 Nov 11, 2022
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our rece

Jaehyeon Kim 1.7k Jan 08, 2023
(CVPR 2021) PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds by Mutian Xu*, Runyu Ding*, Hengshuang Zhao, and Xiaojuan Qi. Int

CVMI Lab 228 Dec 25, 2022
Graph Attention Networks

GAT Graph Attention Networks (Veličković et al., ICLR 2018): https://arxiv.org/abs/1710.10903 GAT layer t-SNE + Attention coefficients on Cora Overvie

Petar Veličković 2.6k Jan 05, 2023
CMSC320 - Introduction to Data Science - Fall 2021

CMSC320 - Introduction to Data Science - Fall 2021 Instructors: Elias Jonatan Gonzalez and José Manuel Calderón Trilla Lectures: MW 3:30-4:45 & 5:00-6

Introduction to Data Science 6 Sep 12, 2022
Source code of D-HAN: Dynamic News Recommendation with Hierarchical Attention Network

D-HAN The source code of D-HAN This is the source code of D-HAN: Dynamic News Recommendation with Hierarchical Attention Network. However, only the co

30 Sep 22, 2022
A library for building and serving multi-node distributed faiss indices.

About Distributed faiss index service. A lightweight library that lets you work with FAISS indexes which don't fit into a single server memory. It fol

Meta Research 170 Dec 30, 2022
Pose estimation with MoveNet Lightning

Pose Estimation With MoveNet Lightning MoveNet is the TensorFlow pre-trained model that identifies 17 different key points of the human body. It is th

Yash Vora 2 Jan 04, 2022